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eynollah/sbb_newspapers_org_image/eynollah.py

12907 lines
579 KiB
Python

4 years ago
#! /usr/bin/env python3
__version__ = '1.0'
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sys import getsizeof
import random
from tqdm import tqdm
from keras.models import model_from_json
from keras.models import load_model
import math
from shapely import geometry
from sklearn.cluster import KMeans
import gc
from keras import backend as K
import tensorflow as tf
tf.get_logger().setLevel('ERROR')
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
import xml.etree.ElementTree as ET
import warnings
import click
import time
from multiprocessing import Process, Queue, cpu_count
from matplotlib import pyplot, transforms
import matplotlib.patches as mpatches
import imutils
warnings.filterwarnings('ignore')
#
__doc__ = \
"""
tool to extract table form data from alto xml data
"""
class eynollah:
def __init__(self, image_dir, f_name,dir_out, dir_models,dir_of_cropped_images=None,dir_of_layout=None,dir_of_deskewed=None,dir_of_all=None,allow_enhancement='false',curved_line='false',full_layout='false',allow_scaling='false',headers_off='false'):
self.image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file
self.dir_out = dir_out
self.f_name = f_name
self.dir_of_cropped_images=dir_of_cropped_images
self.allow_enhancement=allow_enhancement
self.curved_line=curved_line
self.full_layout=full_layout
self.allow_scaling=allow_scaling
self.dir_of_layout=dir_of_layout
self.headers_off=headers_off
self.dir_of_deskewed=dir_of_deskewed
self.dir_of_all=dir_of_all
if self.f_name is None:
try:
self.f_name = image_dir.split('/')[len(image_dir.split('/')) - 1]
self.f_name = self.f_name.split('.')[0]
except:
self.f_name = self.f_name.split('.')[0]
self.dir_models = dir_models
self.kernel = np.ones((5, 5), np.uint8)
self.model_dir_of_enhancemnet=dir_models +'/model_enhancement.h5'
self.model_dir_of_col_classifier=dir_models +'/model_scale_classifier.h5'
self.model_region_dir_p = dir_models +'/model_main_covid19_lr5-5_scale_1_1_great.h5'#dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
#self.model_region_dir_p_ens = dir_models +'/model_ensemble_s.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
self.model_region_dir_p2 = dir_models +'/model_main_home_corona3_rot.h5'
self.model_region_dir_fully_np = dir_models + '/model_no_patches_class0_30eopch.h5'
self.model_region_dir_fully = dir_models + '/model_3up_new_good_no_augmentation.h5'#"model_3col_p_soft_10_less_aug_binarization_only.h5"
self.model_page_dir = dir_models + '/model_page_mixed_best.h5'
self.model_region_dir_p_ens =dir_models +'/model_ensemble_s.h5'#dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5' #dir_models +'/model_ensemble_s.h5'
###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
self.model_textline_dir = dir_models + '/model_textline_newspapers.h5'#'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
def filter_contours_area_of_image_tables(self,image,contours,hirarchy,max_area,min_area):
found_polygons_early = list()
jv=0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
#area = cv2.contourArea(c)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
#print(hirarchy[0][jv][3],hirarchy )
if area >=min_area*np.prod(image.shape[:2]) and area <=max_area*np.prod(image.shape[:2]):#and hirarchy[0][jv][3]==-1 :
#print(c[0][0][1])
found_polygons_early.append(
np.array( [ [point] for point in polygon.exterior.coords] , dtype=np.int32) )
jv+=1
return found_polygons_early
def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
found_polygons_early = list()
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
# Check that polygon has area greater than minimal area
if area >= median_area * scaler_down and area <= median_area * scaler_up:
found_polygons_early.append(
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(
image.shape[:2]) and hirarchy[0][jv][3] == -1 : # and hirarchy[0][jv][3]==-1 :
found_polygons_early.append(
np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and \
hirarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def resize_image(self, img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def resize_ann(self, seg_in, input_height, input_width):
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def rotatedRectWithMaxArea(self,w, h, angle):
if w <= 0 or h <= 0:
return 0,0
width_is_longer = w >= h
side_long, side_short = (w,h) if width_is_longer else (h,w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.*sin_a*cos_a*side_long or abs(sin_a-cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a*cos_a - sin_a*sin_a
wr,hr = (w*cos_a - h*sin_a)/cos_2a, (h*cos_a - w*sin_a)/cos_2a
return wr,hr
def get_one_hot(self, seg, input_height, input_width, n_classes):
seg = seg[:, :, 0]
seg_f = np.zeros((input_height, input_width, n_classes))
for j in range(n_classes):
seg_f[:, :, j] = (seg == j).astype(int)
return seg_f
def color_images(self, seg, n_classes):
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
seg = seg[:, :, 0]
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8)
colors = sns.color_palette("hls", n_classes)
for c in ann_u:
c = int(c)
segl = (seg == c)
seg_img[:, :, 0] = segl * c
seg_img[:, :, 1] = segl * c
seg_img[:, :, 2] = segl * c
return seg_img
def color_images_diva(self, seg, n_classes):
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
seg = seg[:, :, 0]
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
# colors=sns.color_palette("hls", n_classes)
colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]]
for c in ann_u:
c = int(c)
segl = (seg == c)
seg_img[:, :, 0][seg == c] = colors[c][0] # segl*(colors[c][0])
seg_img[:, :, 1][seg == c] = colors[c][1] # seg_img[:,:,1]=segl*(colors[c][1])
seg_img[:, :, 2][seg == c] = colors[c][2] # seg_img[:,:,2]=segl*(colors[c][2])
return seg_img
def rotate_image(self, img_patch, slope):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, slope, 1.0)
return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
def rotyate_image_different(self,img,slope):
#img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]
rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), slope, 1)
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows) )
return img_rotation
def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
# Smooth
if sigma > 0.:
return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
elif sigma == 0.:
return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
else: # Negative sigma, do not do anything
return probs
def crop_image_inside_box(self, box, img_org_copy):
image_box = img_org_copy[box[1]:box[1] + box[3], box[0]:box[0] + box[2]]
return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
def otsu_copy(self, img):
img_r = np.zeros(img.shape)
img1 = img[:, :, 0]
img2 = img[:, :, 1]
img3 = img[:, :, 2]
# print(img.min())
# print(img[:,:,0].min())
# blur = cv2.GaussianBlur(img,(5,5))
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:, :, 0] = threshold1
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
return img_r
def otsu_copy_binary(self,img):
img_r=np.zeros((img.shape[0],img.shape[1],3))
img1=img[:,:,0]
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1
img_r[:,:,1]=threshold1
img_r[:,:,2]=threshold1
img_r=img_r/float(np.max(img_r))*255
return img_r
def predict_enhancement(self,img):
model_enhancement,session_enhancemnet=self.start_new_session_and_model(self.model_dir_of_enhancemnet)
img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
#n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3]
if img.shape[0]<img_height_model:
img=cv2.resize( img, ( img.shape[1],img_width_model) ,interpolation=cv2.INTER_NEAREST)
if img.shape[1]<img_width_model:
img=cv2.resize( img, ( img_height_model,img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
margin=True
if margin:
kernel = np.ones((5,5),np.uint8)
margin = int(0 * img_width_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg =label_p_pred[0,:,:,:]
seg=seg*255
if i==0 and j==0:
seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg
elif i==nxf-1 and j==nyf-1:
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
:] = seg
elif i==0 and j==nyf-1:
seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
:] = seg
elif i==nxf-1 and j==0:
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg
elif i==0 and j!=0 and j!=nyf-1:
seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg
elif i!=0 and i!=nxf-1 and j==0:
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
:] = seg
else:
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg
prediction_true = prediction_true.astype(int)
del model_enhancement
del session_enhancemnet
return prediction_true
def check_dpi(self):
self.dpi=os.popen('identify -format "%x " '+self.image_dir).read()
self.dpi=int(float(self.dpi))
def resize_image_with_column_classifier(self,is_image_enhanced):
self.check_dpi()
img=cv2.imread(self.image_dir)
img=img.astype(np.uint8)
_,page_coord=self.early_page_for_num_of_column_classification()
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
img_1ch=cv2.imread(self.image_dir,0)
width_early=img_1ch.shape[1]
img_1ch=img_1ch[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]
#plt.imshow(img_1ch)
#plt.show()
img_1ch=img_1ch/255.
img_1ch=cv2.resize( img_1ch, ( 448,448) ,interpolation=cv2.INTER_NEAREST)
img_in=np.zeros((1,img_1ch.shape[0],img_1ch.shape[1],3))
img_in[0,:,:,0]=img_1ch[:,:]
img_in[0,:,:,1]=img_1ch[:,:]
img_in[0,:,:,2]=img_1ch[:,:]
label_p_pred=model_num_classifier.predict(img_in)
num_col=np.argmax(label_p_pred[0])+1
print(num_col,label_p_pred,'num_col_classifier')
session_col_classifier.close()
del model_num_classifier
del session_col_classifier
K.clear_session()
gc.collect()
#sys.exit()
if num_col==1 and width_early<1100:
img_w_new=2000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
elif num_col==1 and width_early>=2500:
img_w_new=2000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
elif num_col==1 and width_early>=1100 and width_early<2500:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==2 and width_early<2000:
img_w_new=2400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)
elif num_col==2 and width_early>=3500:
img_w_new=2400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)
elif num_col==2 and width_early>=2000 and width_early<3500:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==3 and width_early<2000:
img_w_new=3000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)
elif num_col==3 and width_early>=4000:
img_w_new=3000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)
elif num_col==3 and width_early>=2000 and width_early<4000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==4 and width_early<2500:
img_w_new=4000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)
elif num_col==4 and width_early>=5000:
img_w_new=4000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)
elif num_col==4 and width_early>=2500 and width_early<5000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==5 and width_early<3700:
img_w_new=5000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)
elif num_col==5 and width_early>=7000:
img_w_new=5000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)
elif num_col==5 and width_early>=3700 and width_early<7000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==6 and width_early<4500:
img_w_new=6500#5400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 6500)
else:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
if label_p_pred[0][int(num_col-1)]<0.9 and img_w_new<width_early:
img_new=np.copy(img)
num_column_is_classified=False
else:
img_new=self.resize_image(img,img_h_new,img_w_new)
num_column_is_classified=True
if img_new.shape[1]>img.shape[1]:
img_new=self.predict_enhancement(img_new)
is_image_enhanced=True
return img,img_new,is_image_enhanced
def resize_and_enhance_image_with_column_classifier(self,is_image_enhanced):
self.check_dpi()
img=cv2.imread(self.image_dir)
img=img.astype(np.uint8)
_,page_coord=self.early_page_for_num_of_column_classification()
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
img_1ch=cv2.imread(self.image_dir,0)
img_1ch=img_1ch.astype(np.uint8)
width_early=img_1ch.shape[1]
img_1ch=img_1ch[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]
#plt.imshow(img_1ch)
#plt.show()
img_1ch=img_1ch/255.
img_1ch=cv2.resize( img_1ch, ( 448,448) ,interpolation=cv2.INTER_NEAREST)
img_in=np.zeros((1,img_1ch.shape[0],img_1ch.shape[1],3))
img_in[0,:,:,0]=img_1ch[:,:]
img_in[0,:,:,1]=img_1ch[:,:]
img_in[0,:,:,2]=img_1ch[:,:]
#plt.imshow(img_in[0,:,:,:])
#plt.show()
label_p_pred=model_num_classifier.predict(img_in)
num_col=np.argmax(label_p_pred[0])+1
print(num_col,label_p_pred,'num_col_classifier')
session_col_classifier.close()
del model_num_classifier
del session_col_classifier
del img_in
del img_1ch
del page_coord
K.clear_session()
gc.collect()
print(self.dpi)
if self.dpi<298:
#sys.exit()
if num_col==1 and width_early<1100:
img_w_new=2000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
elif num_col==1 and width_early>=2500:
img_w_new=2000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2000)
elif num_col==1 and width_early>=1100 and width_early<2500:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==2 and width_early<2000:
img_w_new=2400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)
elif num_col==2 and width_early>=3500:
img_w_new=2400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 2400)
elif num_col==2 and width_early>=2000 and width_early<3500:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==3 and width_early<2000:
img_w_new=3000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)
elif num_col==3 and width_early>=4000:
img_w_new=3000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 3000)
elif num_col==3 and width_early>=2000 and width_early<4000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==4 and width_early<2500:
img_w_new=4000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)
elif num_col==4 and width_early>=5000:
img_w_new=4000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 4000)
elif num_col==4 and width_early>=2500 and width_early<5000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==5 and width_early<3700:
img_w_new=5000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)
elif num_col==5 and width_early>=7000:
img_w_new=5000
img_h_new=int(img.shape[0]/float(img.shape[1]) * 5000)
elif num_col==5 and width_early>=3700 and width_early<7000:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
elif num_col==6 and width_early<4500:
img_w_new=6500#5400
img_h_new=int(img.shape[0]/float(img.shape[1]) * 6500)
else:
img_w_new=width_early
img_h_new=int(img.shape[0]/float(img.shape[1]) * width_early)
if label_p_pred[0][int(num_col-1)]<0.9 and img_w_new<width_early:
img_new=np.copy(img)
num_column_is_classified=False
else:
img_new=self.resize_image(img,img_h_new,img_w_new)
num_column_is_classified=True
#img_new=self.resize_image(img,img_h_new,img_w_new)
image_res=self.predict_enhancement(img_new)
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16)
#self.scale_x=1
#self.scale_y=1
#self.height_org = self.image.shape[0]
#self.width_org = self.image.shape[1]
is_image_enhanced=True
else:
"""
if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]:
img_h_new=3000
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
img_new=self.resize_image(img,img_h_new,img_w_new)
image_res=self.predict_enhancement(img_new)
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16)
##self.scale_x=1
##self.scale_y=1
##self.height_org = self.image.shape[0]
##self.width_org = self.image.shape[1]
is_image_enhanced=True
else:
is_image_enhanced=False
image_res=np.copy(img)
"""
is_image_enhanced=False
num_column_is_classified=True
image_res=np.copy(img)
return is_image_enhanced,img,image_res,num_col,num_column_is_classified
def resize_and_enhance_image(self,is_image_enhanced):
self.check_dpi()
img=cv2.imread(self.image_dir)
img=img.astype(np.uint8)
#sys.exit()
print(self.dpi)
if self.dpi<298:
if img.shape[0]<1000:
img_h_new=int(img.shape[0]*3)
img_w_new=int(img.shape[1]*3)
if img_h_new<2800:
img_h_new=3000
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
elif img.shape[0]>=1000 and img.shape[0]<2000:
img_h_new=int(img.shape[0]*2)
img_w_new=int(img.shape[1]*2)
if img_h_new<2800:
img_h_new=3000
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
else:
img_h_new=int(img.shape[0]*1.5)
img_w_new=int(img.shape[1]*1.5)
img_new=self.resize_image(img,img_h_new,img_w_new)
image_res=self.predict_enhancement(img_new)
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16)
#self.scale_x=1
#self.scale_y=1
#self.height_org = self.image.shape[0]
#self.width_org = self.image.shape[1]
is_image_enhanced=True
else:
is_image_enhanced=False
image_res=np.copy(img)
return is_image_enhanced,img,image_res
def resize_and_enhance_image_new(self,is_image_enhanced):
#self.check_dpi()
img=cv2.imread(self.image_dir)
img=img.astype(np.uint8)
#sys.exit()
image_res=np.copy(img)
return is_image_enhanced,img,image_res
def get_image_and_scales(self,img_org,img_res,scale):
self.image = np.copy(img_res)
self.image_org=np.copy(img_org)
self.height_org = self.image.shape[0]
self.width_org = self.image.shape[1]
self.img_hight_int =int(self.image.shape[0]*scale)
self.img_width_int = int(self.image.shape[1]*scale)
self.scale_y = self.img_hight_int / float(self.image.shape[0])
self.scale_x = self.img_width_int / float(self.image.shape[1])
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
del img_res
del img_org
def get_image_and_scales_after_enhancing(self,img_org,img_res):
#self.image = cv2.imread(self.image_dir)
self.image=np.copy(img_res)
self.image=self.image.astype(np.uint8)
self.image_org=np.copy(img_org)
self.height_org = self.image_org.shape[0]
self.width_org = self.image_org.shape[1]
self.scale_y = img_res.shape[0] / float(self.image_org.shape[0])
self.scale_x = img_res.shape[1]/ float(self.image_org.shape[1])
del img_org
del img_res
def get_image_and_scales_deskewd(self,img_deskewd):
self.image = img_deskewd
self.image_org=np.copy(self.image)
self.height_org = self.image.shape[0]
self.width_org = self.image.shape[1]
self.img_hight_int =int(self.image.shape[0]*1)
self.img_width_int = int(self.image.shape[1]*1)
self.scale_y = self.img_hight_int / float(self.image.shape[0])
self.scale_x = self.img_width_int / float(self.image.shape[1])
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
def start_new_session_and_model(self, model_dir):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.InteractiveSession()
model = load_model(model_dir, compile=False)
return model, session
def return_bonding_box_of_contours(self,cnts):
boxes_tot=[]
for i in range(len(cnts)):
x,y,w,h = cv2.boundingRect(cnts[i])
box=[x,y,w,h]
boxes_tot.append(box)
return boxes_tot
def find_features_of_lines(self,contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
slope_lines=[]
for kk in range(len(contours_main)):
[vx,vy,x,y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2,0,0.01,0.01)
slope_lines.append( ( (vy/vx)/np.pi*180 )[0] )
slope_lines_org=slope_lines
slope_lines=np.array(slope_lines)
slope_lines[(slope_lines<10) & (slope_lines>-10)]=0
slope_lines[(slope_lines<-200) | (slope_lines>200)]=1
slope_lines[ (slope_lines!=0) & (slope_lines!=1)]=2
dis_x=np.abs(x_max_main-x_min_main)
return slope_lines,dis_x, x_min_main ,x_max_main ,np.array(cy_main),np.array(slope_lines_org),y_min_main ,y_max_main,np.array(cx_main)
def return_parent_contours(self,contours,hierarchy):
contours_parent=[ contours[i] for i in range(len(contours) ) if hierarchy[0][i][3]==-1 ]
return contours_parent
def isNaN(self,num):
return num != num
def early_deskewing_slope_calculation_based_on_lines(self,region_pre_p):
# lines are labels by 6 in this model
seperators_closeup=( (region_pre_p[:,:,:]==6))*1
seperators_closeup=seperators_closeup.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_lines)
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]/4.0
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x/2.0]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2]
slope_mean_hor=np.mean(slope_lines_org_hor)
if np.abs(slope_mean_hor)>1.2:
slope_mean_hor=0
#deskewed_new=rotate_image(image_regions_eraly_p[:,:,:],slope_mean_hor)
args_ver=args[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
dist_y_ver=y_max_main_ver-y_min_main_ver
len_y=seperators_closeup.shape[0]/3.0
return slope_mean_hor,cx_main_ver,dist_y_ver
def return_contours_of_interested_region(self,region_pre_p,pixel,min_area=0.0002):
# pixels of images are identified by 5
if len(region_pre_p.shape)==3:
cnts_images=(region_pre_p[:,:,0]==pixel)*1
else:
cnts_images=(region_pre_p[:,:]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=min_area)
return contours_imgs
def return_contours_of_interested_region_by_min_size(self,region_pre_p,pixel,min_size=0.00003):
# pixels of images are identified by 5
if len(region_pre_p.shape)==3:
cnts_images=(region_pre_p[:,:,0]==pixel)*1
else:
cnts_images=(region_pre_p[:,:]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=min_size)
return contours_imgs
def return_contours_of_interested_textline(self,region_pre_p,pixel):
# pixels of images are identified by 5
if len(region_pre_p.shape)==3:
cnts_images=(region_pre_p[:,:,0]==pixel)*1
else:
cnts_images=(region_pre_p[:,:]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.000000003)
return contours_imgs
def find_images_contours_and_replace_table_and_graphic_pixels_by_image(self,region_pre_p):
# pixels of images are identified by 5
cnts_images=(region_pre_p[:,:,0]==5)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
#print(len(contours_imgs),'contours_imgs')
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.0003)
#print(len(contours_imgs),'contours_imgs')
boxes_imgs=self.return_bonding_box_of_contours(contours_imgs)
for i in range(len(boxes_imgs)):
x1=int(boxes_imgs[i][0] )
x2=int(boxes_imgs[i][0]+ boxes_imgs[i][2])
y1=int(boxes_imgs[i][1] )
y2=int(boxes_imgs[i][1]+ boxes_imgs[i][3])
region_pre_p[y1:y2,x1:x2,0][region_pre_p[y1:y2,x1:x2,0]==8]=5
region_pre_p[y1:y2,x1:x2,0][region_pre_p[y1:y2,x1:x2,0]==7]=5
return region_pre_p
def do_prediction(self,patches,img,model,marginal_of_patch_percent=0.1):
img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
n_classes = model.layers[len(model.layers) - 1].output_shape[3]
if patches:
if img.shape[0]<img_height_model:
img=self.resize_image(img,img_height_model,img.shape[1])
if img.shape[1]<img_width_model:
img=self.resize_image(img,img.shape[0],img_width_model)
#print(img_height_model,img_width_model)
#margin = int(0.2 * img_width_model)
margin = int(marginal_of_patch_percent * img_height_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
#print(sys.getsizeof(img))
#print(np.max(img))
img=img.astype(np.float16)
#print(sys.getsizeof(img))
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i==0 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i==0 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i==0 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
:] = seg_color
else:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
prediction_true = prediction_true.astype(np.uint8)
del img
del mask_true
del seg_color
del seg
del img_patch
if not patches:
img_h_page=img.shape[0]
img_w_page=img.shape[1]
img = img /float( 255.0)
img = self.resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(
img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
del img
del seg_color
del label_p_pred
del seg
del model
gc.collect()
return prediction_true
def early_page_for_num_of_column_classification(self):
img=cv2.imread(self.image_dir)
img=img.astype(np.uint8)
patches=False
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
###img = self.otsu_copy(self.image)
for ii in range(1):
img = cv2.GaussianBlur(img, (5, 5), 0)
img_page_prediction=self.do_prediction(patches,img,model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
cnt = contours[np.argmax(cnt_size)]
x, y, w, h = cv2.boundingRect(cnt)
box = [x, y, w, h]
croped_page, page_coord = self.crop_image_inside_box(box, img)
session_page.close()
del model_page
del session_page
del contours
del thresh
del img
del cnt_size
del cnt
del box
del x
del y
del w
del h
del imgray
del img_page_prediction
gc.collect()
return croped_page, page_coord
def extract_page(self):
patches=False
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
###img = self.otsu_copy(self.image)
for ii in range(1):
img = cv2.GaussianBlur(self.image, (5, 5), 0)
img_page_prediction=self.do_prediction(patches,img,model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
cnt = contours[np.argmax(cnt_size)]
x, y, w, h = cv2.boundingRect(cnt)
if x<=30:
w=w+x
x=0
if (self.image.shape[1]-(x+w) )<=30:
w=w+(self.image.shape[1]-(x+w) )
if y<=30:
h=h+y
y=0
if (self.image.shape[0]-(y+h) )<=30:
h=h+(self.image.shape[0]-(y+h) )
box = [x, y, w, h]
croped_page, page_coord = self.crop_image_inside_box(box, self.image)
self.cont_page=[]
self.cont_page.append( np.array( [ [ page_coord[2] , page_coord[0] ] ,
[ page_coord[3] , page_coord[0] ] ,
[ page_coord[3] , page_coord[1] ] ,
[ page_coord[2] , page_coord[1] ]] ) )
session_page.close()
del model_page
del session_page
del contours
del thresh
del img
del imgray
gc.collect()
return croped_page, page_coord
def extract_drop_capital_13(self, img,patches,cols):
img_height_h=img.shape[0]
img_width_h=img.shape[1]
patches=False
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
img_1=img[:int(img.shape[0]/3.),:,:]
img_2=img[int(img.shape[0]/3.):int(2*img.shape[0]/3.),:,:]
img_3=img[int(2*img.shape[0]/3.):,:,:]
#img_1 = self.otsu_copy_binary(img_1)#self.otsu_copy(img)
#img_1 = img_1.astype(np.uint16)
plt.imshow(img_1)
plt.show()
#img_2 = self.otsu_copy_binary(img_2)#self.otsu_copy(img)
#img_2 = img_2.astype(np.uint16)
plt.imshow(img_2)
plt.show()
#img_3 = self.otsu_copy_binary(img_3)#self.otsu_copy(img)
#img_3 = img_3.astype(np.uint16)
plt.imshow(img_3)
plt.show()
prediction_regions_1=self.do_prediction(patches,img_1,model_region)
plt.imshow(prediction_regions_1)
plt.show()
prediction_regions_2=self.do_prediction(patches,img_2,model_region)
plt.imshow(prediction_regions_2)
plt.show()
prediction_regions_3=self.do_prediction(patches,img_3,model_region)
plt.imshow(prediction_regions_3)
plt.show()
prediction_regions=np.zeros((img_height_h,img_width_h))
prediction_regions[:int(img.shape[0]/3.),:]=prediction_regions_1[:,:,0]
prediction_regions[int(img.shape[0]/3.):int(2*img.shape[0]/3.),:]=prediction_regions_2[:,:,0]
prediction_regions[int(2*img.shape[0]/3.):,:]=prediction_regions_3[:,:,0]
session_region.close()
del img_1
del img_2
del img_3
del prediction_regions_1
del prediction_regions_2
del prediction_regions_3
del model_region
del session_region
del img
gc.collect()
return prediction_regions
def extract_text_regions(self, img,patches,cols):
img_height_h=img.shape[0]
img_width_h=img.shape[1]
###if patches and cols>=3 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
###if not patches:
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
###if patches and cols==2 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
###if patches and cols==1 :
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
###if patches and cols>=2:
###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
###img = img.astype(np.uint8)
###if patches and cols==1:
###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
###img = img.astype(np.uint8)
###img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )
if patches :
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
if not patches:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
if patches and cols==1:
img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
img2 = img2.astype(np.uint8)
img2= self.resize_image(img2, int(img_height_h*0.7), int(img_width_h*0.7) )
marginal_of_patch_percent=0.1
prediction_regions2=self.do_prediction(patches,img2,model_region,marginal_of_patch_percent)
prediction_regions2=self.resize_image(prediction_regions2, img_height_h, img_width_h )
if patches and cols==2:
img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
img2 = img2.astype(np.uint8)
img2= self.resize_image(img2, int(img_height_h*0.4), int(img_width_h*0.4) )
marginal_of_patch_percent=0.1
prediction_regions2=self.do_prediction(patches,img2,model_region,marginal_of_patch_percent)
prediction_regions2=self.resize_image(prediction_regions2, img_height_h, img_width_h )
elif patches and cols>2:
img2 = self.otsu_copy_binary(img)#self.otsu_copy(img)
img2 = img2.astype(np.uint8)
img2= self.resize_image(img2, int(img_height_h*0.3), int(img_width_h*0.3) )
marginal_of_patch_percent=0.1
prediction_regions2=self.do_prediction(patches,img2,model_region,marginal_of_patch_percent)
prediction_regions2=self.resize_image(prediction_regions2, img_height_h, img_width_h )
if patches and cols==2:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
if img_width_h>=2000:
img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
else:
pass#img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )
img = img.astype(np.uint8)
if patches and cols==1:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
img= self.resize_image(img, int(img_height_h*0.5), int(img_width_h*0.5) )
img = img.astype(np.uint8)
if patches and cols==3:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols==4:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols>=5:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if not patches:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
prediction_regions2=None
marginal_of_patch_percent=0.1
prediction_regions=self.do_prediction(patches,img,model_region,marginal_of_patch_percent)
prediction_regions=self.resize_image(prediction_regions, img_height_h, img_width_h )
session_region.close()
del model_region
del session_region
del img
gc.collect()
return prediction_regions,prediction_regions2
def extract_only_text_regions(self, img,patches):
model_region, session_region = self.start_new_session_and_model(self.model_only_text)
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8)
img_org=np.copy(img)
img_h=img_org.shape[0]
img_w=img_org.shape[1]
img= self.resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
prediction_regions1=self.do_prediction(patches,img,model_region)
prediction_regions1= self.resize_image(prediction_regions1, img_h, img_w)
#prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
#prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
#prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)
img= self.resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
prediction_regions2=self.do_prediction(patches,img,model_region)
prediction_regions2= self.resize_image(prediction_regions2, img_h, img_w)
#prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2)
prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
#prediction_regions=( (prediction_regions2[:,:,0]==1) & (prediction_regions1[:,:,0]==1) )
#prediction_regions=(prediction_regions1[:,:,0]==1)
session_region.close()
del model_region
del session_region
gc.collect()
return prediction_regions1[:,:,0]
def extract_binarization(self, img,patches):
model_bin, session_bin = self.start_new_session_and_model(self.model_binafrization)
img_h=img.shape[0]
img_w=img.shape[1]
img= self.resize_image(img, int(img.shape[0]*1), int(img.shape[1]*1))
prediction_regions=self.do_prediction(patches,img,model_bin)
res=(prediction_regions[:,:,0]!=0)*1
img_fin=np.zeros((res.shape[0],res.shape[1],3) )
res[:,:][res[:,:]==0]=2
res=res-1
res=res*255
img_fin[:,:,0]=res
img_fin[:,:,1]=res
img_fin[:,:,2]=res
session_bin.close()
del model_bin
del session_bin
gc.collect()
#plt.imshow(img_fin[:,:,0])
#plt.show()
return img_fin
def get_slopes_and_deskew_new(self, contours,contours_par,textline_mask_tot,image_page_rotated,boxes,slope_deskew):
num_cores =cpu_count()
queue_of_all_params = Queue()
processes = []
nh=np.linspace(0, len(boxes), num_cores+1)
indexes_by_text_con=np.array(range(len(contours_par)))
for i in range(num_cores):
boxes_per_process=boxes[int(nh[i]):int(nh[i+1])]
contours_per_process=contours[int(nh[i]):int(nh[i+1])]
contours_par_per_process=contours_par[int(nh[i]):int(nh[i+1])]
indexes_text_con_per_process=indexes_by_text_con[int(nh[i]):int(nh[i+1])]
processes.append(Process(target=self.do_work_of_slopes_new, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process,contours_par_per_process,indexes_text_con_per_process,image_page_rotated,slope_deskew)))
for i in range(num_cores):
processes[i].start()
slopes = []
all_found_texline_polygons=[]
all_found_text_regions=[]
all_found_text_regions_par=[]
boxes=[]
all_box_coord=[]
all_index_text_con=[]
for i in range(num_cores):
list_all_par=queue_of_all_params.get(True)
slopes_for_sub_process=list_all_par[0]
polys_for_sub_process=list_all_par[1]
boxes_for_sub_process=list_all_par[2]
contours_for_subprocess=list_all_par[3]
contours_par_for_subprocess=list_all_par[4]
boxes_coord_for_subprocess=list_all_par[5]
indexes_for_subprocess=list_all_par[6]
for j in range(len(slopes_for_sub_process)):
slopes.append(slopes_for_sub_process[j])
all_found_texline_polygons.append(polys_for_sub_process[j])
boxes.append(boxes_for_sub_process[j])
all_found_text_regions.append(contours_for_subprocess[j])
all_found_text_regions_par.append(contours_par_for_subprocess[j])
all_box_coord.append(boxes_coord_for_subprocess[j])
all_index_text_con.append(indexes_for_subprocess[j])
for i in range(num_cores):
processes[i].join()
#print(slopes,'slopes')
return slopes,all_found_texline_polygons,boxes,all_found_text_regions,all_found_text_regions_par,all_box_coord,all_index_text_con
def get_slopes_and_deskew_new_curved(self, contours,contours_par,textline_mask_tot,image_page_rotated,boxes,mask_texts_only,num_col,scale_par,slope_deskew):
num_cores =cpu_count()
queue_of_all_params = Queue()
processes = []
nh=np.linspace(0, len(boxes), num_cores+1)
indexes_by_text_con=np.array(range(len(contours_par)))
for i in range(num_cores):
boxes_per_process=boxes[int(nh[i]):int(nh[i+1])]
contours_per_process=contours[int(nh[i]):int(nh[i+1])]
contours_par_per_process=contours_par[int(nh[i]):int(nh[i+1])]
indexes_text_con_per_process=indexes_by_text_con[int(nh[i]):int(nh[i+1])]
processes.append(Process(target=self.do_work_of_slopes_new_curved, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process,contours_par_per_process,image_page_rotated,mask_texts_only,num_col,scale_par,indexes_text_con_per_process,slope_deskew)))
for i in range(num_cores):
processes[i].start()
slopes = []
all_found_texline_polygons=[]
all_found_text_regions=[]
all_found_text_regions_par=[]
boxes=[]
all_box_coord=[]
all_index_text_con=[]
for i in range(num_cores):
list_all_par=queue_of_all_params.get(True)
polys_for_sub_process=list_all_par[0]
boxes_for_sub_process=list_all_par[1]
contours_for_subprocess=list_all_par[2]
contours_par_for_subprocess=list_all_par[3]
boxes_coord_for_subprocess=list_all_par[4]
indexes_for_subprocess=list_all_par[5]
slopes_for_sub_process=list_all_par[6]
for j in range(len(polys_for_sub_process)):
slopes.append(slopes_for_sub_process[j])
all_found_texline_polygons.append(polys_for_sub_process[j])
boxes.append(boxes_for_sub_process[j])
all_found_text_regions.append(contours_for_subprocess[j])
all_found_text_regions_par.append(contours_par_for_subprocess[j])
all_box_coord.append(boxes_coord_for_subprocess[j])
all_index_text_con.append(indexes_for_subprocess[j])
for i in range(num_cores):
processes[i].join()
#print(slopes,'slopes')
return all_found_texline_polygons,boxes,all_found_text_regions,all_found_text_regions_par,all_box_coord,all_index_text_con,slopes
def do_work_of_slopes_new_curved(self,queue_of_all_params,boxes_text,textline_mask_tot_ea,contours_per_process,contours_par_per_process,image_page_rotated,mask_texts_only,num_col,scale_par,indexes_r_con_per_pro,slope_deskew):
slopes_per_each_subprocess = []
bounding_box_of_textregion_per_each_subprocess=[]
textlines_rectangles_per_each_subprocess=[]
contours_textregion_per_each_subprocess=[]
contours_textregion_par_per_each_subprocess=[]
all_box_coord_per_process=[]
index_by_text_region_contours=[]
slope_biggest=0
textline_cnt_seperated=np.zeros(textline_mask_tot_ea.shape)
for mv in range(len(boxes_text)):
all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
all_text_region_raw=all_text_region_raw.astype(np.uint8)
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
##img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
#plt.imshow(img_int_p)
#plt.show()
if img_int_p.shape[0]/img_int_p.shape[1]<0.1:
slopes_per_each_subprocess.append(0)
slope_first=0
slope_for_all=[slope_deskew][0]
else:
try:
textline_con,hierachy=self.return_contours_of_image(img_int_p)
textline_con_fil=self.filter_contours_area_of_image(img_int_p,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)
sigma_des=int( y_diff_mean * (4./40.0) )
if sigma_des<1:
sigma_des=1
img_int_p[img_int_p>0]=1
#slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
if abs(slope_for_all)<0.5:
slope_for_all=[slope_deskew][0]
#old method
#slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
#text_patch_processed=textline_contours_postprocessing(gada)
except:
slope_for_all=999
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
if slope_for_all==999:
slope_for_all=[slope_deskew][0]
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
##elif slope_for_all==999:
##slope_for_all=slope_biggest
slopes_per_each_subprocess.append(slope_for_all)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
if abs(slope_for_all)<45:
#all_box_coord.append(crop_coor)
textline_region_in_image=np.zeros(textline_mask_tot_ea.shape)
cnt_o_t_max=contours_par_per_process[mv]
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
mask_biggest=np.zeros(mask_texts_only.shape)
mask_biggest=cv2.fillPoly(mask_biggest, pts =[cnt_o_t_max], color=(1,1,1))
mask_region_in_patch_region=mask_biggest[y:y+h,x:x+w]
textline_biggest_region=mask_biggest*textline_mask_tot_ea
#print(slope_for_all,'slope_for_all')
textline_rotated_seperated=self.seperate_lines_new2(textline_biggest_region[y:y+h,x:x+w],0,num_col,slope_for_all)
#new line added
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
textline_rotated_seperated[mask_region_in_patch_region[:,:]!=1]=0
#till here
textline_cnt_seperated[y:y+h,x:x+w]=textline_rotated_seperated
textline_region_in_image[y:y+h,x:x+w]=textline_rotated_seperated
#plt.imshow(textline_region_in_image)
#plt.show()
#plt.imshow(textline_cnt_seperated)
#plt.show()
pixel_img=1
cnt_textlines_in_image=self.return_contours_of_interested_textline(textline_region_in_image,pixel_img)
textlines_cnt_per_region=[]
for jjjj in range(len(cnt_textlines_in_image)):
mask_biggest2=np.zeros(mask_texts_only.shape)
mask_biggest2=cv2.fillPoly(mask_biggest2, pts =[cnt_textlines_in_image[jjjj]], color=(1,1,1))
if num_col+1==1:
mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=5)
else:
mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=4)
pixel_img=1
mask_biggest2=self.resize_image(mask_biggest2,int(mask_biggest2.shape[0]*scale_par) ,int(mask_biggest2.shape[1]*scale_par) )
cnt_textlines_in_image_ind=self.return_contours_of_interested_textline(mask_biggest2,pixel_img)
try:
#textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except:
pass
else:
slope_first=0
add_boxes_coor_into_textlines=True
textlines_cnt_per_region=self.textline_contours_postprocessing(all_text_region_raw,slope_for_all,contours_par_per_process[mv],boxes_text[mv],slope_first,add_boxes_coor_into_textlines)
add_boxes_coor_into_textlines=False
#print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
#textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
#index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
#all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
all_box_coord_per_process.append(crop_coor)
queue_of_all_params.put([textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess,contours_textregion_par_per_each_subprocess,all_box_coord_per_process,index_by_text_region_contours,slopes_per_each_subprocess])
def do_work_of_slopes_new(self,queue_of_all_params,boxes_text,textline_mask_tot_ea,contours_per_process,contours_par_per_process,indexes_r_con_per_pro,image_page_rotated,slope_deskew):
slopes_per_each_subprocess = []
bounding_box_of_textregion_per_each_subprocess=[]
textlines_rectangles_per_each_subprocess=[]
contours_textregion_per_each_subprocess=[]
contours_textregion_par_per_each_subprocess=[]
all_box_coord_per_process=[]
index_by_text_region_contours=[]
slope_biggest=0
for mv in range(len(boxes_text)):
crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
#all_box_coord.append(crop_coor)
mask_textline=np.zeros((textline_mask_tot_ea.shape))
mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
4 years ago
denoised=None
all_text_region_raw=(textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
4 years ago
all_text_region_raw=all_text_region_raw.astype(np.uint8)
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
if img_int_p.shape[0]/img_int_p.shape[1]<0.1:
slopes_per_each_subprocess.append(0)
slope_for_all=[slope_deskew][0]
all_text_region_raw=textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slope_for_all,contours_par_per_process[mv],boxes_text[mv],0)
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
#all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )
else:
try:
textline_con,hierachy=self.return_contours_of_image(img_int_p)
textline_con_fil=self.filter_contours_area_of_image(img_int_p,textline_con,hierachy,max_area=1,min_area=0.00008)
y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)
sigma_des=int( y_diff_mean * (4./40.0) )
if sigma_des<1:
sigma_des=1
img_int_p[img_int_p>0]=1
#slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
if abs(slope_for_all)<=0.5:
slope_for_all=[slope_deskew][0]
except:
slope_for_all=999
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
if slope_for_all==999:
slope_for_all=[slope_deskew][0]
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
##slope_for_all=slope_biggest
##elif slope_for_all==999:
##slope_for_all=slope_biggest
slopes_per_each_subprocess.append(slope_for_all)
slope_first=0
mask_only_con_region=np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region=cv2.fillPoly(mask_only_con_region,pts=[contours_par_per_process[mv]],color=(1,1,1))
#plt.imshow(mask_only_con_region)
#plt.show()
all_text_region_raw=np.copy(textline_mask_tot_ea[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ])
mask_only_con_region=mask_only_con_region[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
##plt.imshow(textline_mask_tot_ea)
##plt.show()
##plt.imshow(all_text_region_raw)
##plt.show()
##plt.imshow(mask_only_con_region)
##plt.show()
all_text_region_raw[mask_only_con_region==0]=0
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slope_for_all,contours_par_per_process[mv],boxes_text[mv],slope_first)
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
#all_found_texline_polygons.append(cnt_clean_rot)
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv] )
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
all_box_coord_per_process.append(crop_coor)
queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess,contours_textregion_par_per_each_subprocess,all_box_coord_per_process,index_by_text_region_contours])
def get_text_region_contours_and_boxes(self, image):
rgb_class_of_texts = (1, 1, 1)
mask_texts = np.all(image == rgb_class_of_texts, axis=-1)
image = np.repeat(mask_texts[:, :, np.newaxis], 3, axis=2) * 255
image = image.astype(np.uint8)
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, self.kernel)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, self.kernel)
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
self.boxes = []
for jj in range(len(main_contours)):
x, y, w, h = cv2.boundingRect(main_contours[jj])
self.boxes.append([x, y, w, h])
return main_contours
def boosting_headers_by_longshot_region_segmentation(self,textregion_pre_p,textregion_pre_np,img_only_text):
textregion_pre_p_org=np.copy(textregion_pre_p)
# 4 is drop capitals
headers_in_longshot= (textregion_pre_np[:,:,0]==2)*1
#headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
textregion_pre_p[:,:,0][(headers_in_longshot[:,:] ==1) & (textregion_pre_p[:,:,0] !=4)]=2
textregion_pre_p[:,:,0][textregion_pre_p[:,:,0]==1]=0
#textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=4) & (textregion_pre_p[:,:,0]!=2)]=1
return textregion_pre_p
def boosting_text_only_regions_by_header(self,textregion_pre_np,img_only_text):
result= (( img_only_text[:,:]==1) | (textregion_pre_np[:,:,0]==2) ) *1
return result
def get_all_image_patches_coordination(self, image_page):
self.all_box_coord=[]
for jk in range(len(self.boxes)):
_,crop_coor=self.crop_image_inside_box(self.boxes[jk],image_page)
self.all_box_coord.append(crop_coor)
def textline_contours(self, img,patches,scaler_h,scaler_w):
if patches:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
if not patches:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)
##img = self.otsu_copy(img)
img = img.astype(np.uint8)
img_org=np.copy(img)
img_h=img_org.shape[0]
img_w=img_org.shape[1]
img= self.resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
prediction_textline=self.do_prediction(patches,img,model_textline)
prediction_textline= self.resize_image(prediction_textline, img_h, img_w)
patches=False
prediction_textline_longshot=self.do_prediction(patches,img,model_textline)
prediction_textline_longshot_true_size= self.resize_image(prediction_textline_longshot, img_h, img_w)
#scaler_w=1.5
#scaler_h=1.5
#patches=True
#img= self.resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
#prediction_textline_streched=self.do_prediction(patches,img,model_textline)
#prediction_textline_streched= self.resize_image(prediction_textline_streched, img_h, img_w)
##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show()
#sys.exit()
session_textline.close()
del model_textline
del session_textline
del img
del img_org
gc.collect()
return prediction_textline[:,:,0],prediction_textline_longshot_true_size[:,:,0]
def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
self.area_of_cropped = []
self.all_text_region_raw = []
for jk in range(len(boxes)):
crop_img, crop_coor = self.crop_image_inside_box(boxes[jk],
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
crop_img=crop_img.astype(np.uint8)
self.all_text_region_raw.append(crop_img[:, :, 0])
self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
def seperate_lines_new_inside_teils2(self, img_patch, thetha):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
#contour_text_interest_copy = contour_text_interest.copy()
#x_cont = contour_text_interest[:, 0, 0]
#y_cont = contour_text_interest[:, 0, 1]
#x_cont = x_cont - np.min(x_cont)
#y_cont = y_cont - np.min(y_cont)
x_min_cont = 0
x_max_cont = img_patch.shape[1]
y_min_cont = 0
y_max_cont = img_patch.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
textline_patch_sum_along_width = img_patch.sum(axis=1)
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
y_padded = np.zeros(len(y) + 40)
y_padded[20:len(y) + 20] = y
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if 1>0:
try:
y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
y_padded_up_to_down_e=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
peaks_new=peaks_e[:]
peaks_neg_new=peaks_neg_e[:]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
else:
peaks_new_tot=peaks_e[:]
textline_con,hierachy=self.return_contours_of_image(img_patch)
textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus=int( y_diff_mean * (7./40.0) )
#print(sigma_gaus,'sigma_gaus')
except:
sigma_gaus=12
if sigma_gaus<3:
sigma_gaus=3
#print(sigma_gaus,'sigma')
y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
y_padded_up_to_down=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
peaks, _ = find_peaks(y_padded_smoothed, height=0)
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
peaks_new=peaks[:]
peaks_neg_new=peaks_neg[:]
try:
neg_peaks_max=np.max(y_padded_smoothed[peaks])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.24 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
if len(arg_neg_must_be_deleted)==1:
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
#plt.plot(y_padded_up_to_down_padded)
#plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
#plt.show()
#plt.plot(y_padded_up_to_down_padded)
#plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
#plt.show()
#plt.plot(y_padded_smoothed)
#plt.plot(peaks,y_padded_smoothed[peaks],'*')
#plt.show()
#plt.plot(y_padded_smoothed)
#plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
#plt.show()
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
except:
pass
else:
peaks_new_tot=peaks[:]
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
std_value_of_peaks=np.std(y_padded_smoothed[peaks])
peaks_values=y_padded_smoothed[peaks]
###peaks_neg = peaks_neg - 20 - 20
###peaks = peaks - 20
peaks_neg_true=peaks_neg[:]
peaks_pos_true=peaks[:]
if len(peaks_neg_true)>0:
peaks_neg_true=np.array(peaks_neg_true)
peaks_neg_true = peaks_neg_true - 20 - 20
#print(peaks_neg_true)
for i in range(len(peaks_neg_true)):
img_patch[peaks_neg_true[i]-6:peaks_neg_true[i]+6,:]=0
else:
pass
if len(peaks_pos_true)>0:
peaks_pos_true=np.array(peaks_pos_true)
peaks_pos_true = peaks_pos_true - 20
for i in range(len(peaks_pos_true)):
##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
img_patch[peaks_pos_true[i]-6:peaks_pos_true[i]+6,:]=1
else:
pass
kernel = np.ones((5,5),np.uint8)
#img_patch = cv2.erode(img_patch,kernel,iterations = 3)
#######################img_patch = cv2.erode(img_patch,kernel,iterations = 2)
img_patch = cv2.erode(img_patch,kernel,iterations = 1)
return img_patch
def seperate_lines_new_inside_teils(self, img_path, thetha):
(h, w) = img_path.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
x_min_cont = 0
x_max_cont = img_path.shape[1]
y_min_cont = 0
y_max_cont = img_path.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
mada_n = img_path.sum(axis=1)
##plt.plot(mada_n)
##plt.show()
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = mada_n[:] # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 40)
y_help[20:len(y) + 20] = y
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if len(peaks_real)<=2 and len(peaks_real)>1:
sigma_gaus=10
else:
sigma_gaus=5
z= gaussian_filter1d(y_help, sigma_gaus)
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
for nn in range(len(peaks_neg)):
if peaks_neg[nn]>len(z)-1:
peaks_neg[nn]=len(z)-1
if peaks_neg[nn]<0:
peaks_neg[nn]=0
diff_peaks=np.abs( np.diff(peaks_neg) )
cut_off=20
peaks_neg_true=[]
forest=[]
for i in range(len(peaks_neg)):
if i==0:
forest.append(peaks_neg[i])
if i<(len(peaks_neg)-1):
if diff_peaks[i]<=cut_off:
forest.append(peaks_neg[i+1])
if diff_peaks[i]>cut_off:
#print(forest[np.argmin(z[forest]) ] )
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
forest=[]
forest.append(peaks_neg[i+1])
if i==(len(peaks_neg)-1):
#print(print(forest[np.argmin(z[forest]) ] ))
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
diff_peaks_pos=np.abs( np.diff(peaks) )
cut_off=20
peaks_pos_true=[]
forest=[]
for i in range(len(peaks)):
if i==0:
forest.append(peaks[i])
if i<(len(peaks)-1):
if diff_peaks_pos[i]<=cut_off:
forest.append(peaks[i+1])
if diff_peaks_pos[i]>cut_off:
#print(forest[np.argmin(z[forest]) ] )
if not self.isNaN(forest[np.argmax(z[forest]) ]):
peaks_pos_true.append(forest[np.argmax(z[forest]) ])
forest=[]
forest.append(peaks[i+1])
if i==(len(peaks)-1):
#print(print(forest[np.argmin(z[forest]) ] ))
if not self.isNaN(forest[np.argmax(z[forest]) ]):
peaks_pos_true.append(forest[np.argmax(z[forest]) ])
#print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss')
if len(peaks_neg_true)>0:
peaks_neg_true=np.array(peaks_neg_true)
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
peaks_neg_true = peaks_neg_true - 20 - 20
#print(peaks_neg_true)
for i in range(len(peaks_neg_true)):
img_path[peaks_neg_true[i]-6:peaks_neg_true[i]+6,:]=0
else:
pass
if len(peaks_pos_true)>0:
peaks_pos_true=np.array(peaks_pos_true)
peaks_pos_true = peaks_pos_true - 20
for i in range(len(peaks_pos_true)):
img_path[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
else:
pass
kernel = np.ones((5,5),np.uint8)
#img_path = cv2.erode(img_path,kernel,iterations = 3)
img_path = cv2.erode(img_path,kernel,iterations = 2)
return img_path
def seperate_lines_new(self, img_path, thetha,num_col):
if num_col==1:
num_patches=int(img_path.shape[1]/200.)
else:
num_patches=int(img_path.shape[1]/100.)
#num_patches=int(img_path.shape[1]/200.)
if num_patches==0:
num_patches=1
(h, w) = img_path.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
x_min_cont = 0
x_max_cont = img_path.shape[1]
y_min_cont = 0
y_max_cont = img_path.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
mada_n = img_path.sum(axis=1)
##plt.plot(mada_n)
##plt.show()
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = mada_n[:] # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 40)
y_help[20:len(y) + 20] = y
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if len(peaks_real)<=2 and len(peaks_real)>1:
sigma_gaus=10
else:
sigma_gaus=6
z= gaussian_filter1d(y_help, sigma_gaus)
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
for nn in range(len(peaks_neg)):
if peaks_neg[nn]>len(z)-1:
peaks_neg[nn]=len(z)-1
if peaks_neg[nn]<0:
peaks_neg[nn]=0
diff_peaks=np.abs( np.diff(peaks_neg) )
cut_off=20
peaks_neg_true=[]
forest=[]
for i in range(len(peaks_neg)):
if i==0:
forest.append(peaks_neg[i])
if i<(len(peaks_neg)-1):
if diff_peaks[i]<=cut_off:
forest.append(peaks_neg[i+1])
if diff_peaks[i]>cut_off:
#print(forest[np.argmin(z[forest]) ] )
if not self.isNaN(forest[np.argmin(z[forest]) ]):
#print(len(z),forest)
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
forest=[]
forest.append(peaks_neg[i+1])
if i==(len(peaks_neg)-1):
#print(print(forest[np.argmin(z[forest]) ] ))
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
peaks_neg_true=np.array(peaks_neg_true)
"""
#plt.figure(figsize=(40,40))
#plt.subplot(1,2,1)
#plt.title('Textline segmentation von Textregion')
#plt.imshow(img_path)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.subplot(1,2,2)
#plt.title('Dichte entlang X')
#base = pyplot.gca().transData
#rot = transforms.Affine2D().rotate_deg(90)
#plt.plot(zneg,np.array(range(len(zneg))))
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
#plt.gca().invert_yaxis()
#plt.xlabel('Dichte')
#plt.ylabel('Y')
##plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
"""
peaks_neg_true = peaks_neg_true - 20 - 20
peaks = peaks - 20
#dis_up=peaks_neg_true[14]-peaks_neg_true[0]
#dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
##plt.imshow(img_patch_ineterst)
##plt.show()
length_x=int(img_path.shape[1]/float(num_patches))
margin = int(0.04 * length_x)
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise=[]
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
#img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline=img_patch_ineterst[:,index_x_d:index_x_u]
sigma=2
try:
slope_xline=self.return_deskew_slop(img_xline,sigma)
except:
slope_xline=0
slopes_tile_wise.append(slope_xline)
#print(slope_xline,'xlineeee')
img_line_rotated=self.rotate_image(img_xline,slope_xline)
img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1
"""
xline=np.linspace(0,img_path.shape[1],nx)
slopes_tile_wise=[]
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
##plt.imshow(img_xline)
##plt.show()
sigma=3
try:
slope_xline=self.return_deskew_slop(img_xline,sigma)
except:
slope_xline=0
slopes_tile_wise.append(slope_xline)
print(slope_xline,'xlineeee')
img_line_rotated=self.rotate_image(img_xline,slope_xline)
##plt.imshow(img_line_rotated)
##plt.show()
"""
#dis_up=peaks_neg_true[14]-peaks_neg_true[0]
#dis_down=peaks_neg_true[18]-peaks_neg_true[14]
img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised=np.zeros(img_patch_ineterst.shape)
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline=img_patch_ineterst[:,index_x_d:index_x_u]
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[i])
img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1
img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
##plt.imshow(img_patch_seperated)
##plt.show()
img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[i])
img_patch_seperated_returned[:,:][img_patch_seperated_returned[:,:]!=0]=1
img_patch_seperated_returned_true_size=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin:length_x - margin]
img_patch_ineterst_revised[:,index_x_d + margin:index_x_u - margin]=img_patch_seperated_returned_true_size
"""
for ui in range( nx-1 ):
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
##plt.imshow(img_xline)
##plt.show()
img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[ui])
#img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
##plt.imshow(img_patch_seperated)
##plt.show()
print(img_patch_seperated_returned.shape)
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
#plt.show()
img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
"""
#print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
##plt.imshow(img_patch_ineterst_revised)
##plt.show()
return img_patch_ineterst_revised
def seperate_lines_new2(self, img_path, thetha,num_col,slope_region):
if num_col==1:
num_patches=int(img_path.shape[1]/200.)
else:
num_patches=int(img_path.shape[1]/140.)
#num_patches=int(img_path.shape[1]/200.)
if num_patches==0:
num_patches=1
img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
#plt.imshow(img_patch_ineterst)
#plt.show()
length_x=int(img_path.shape[1]/float(num_patches))
#margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
margin = int(0.04 * length_x)
#print(margin,'margin')
#if margin<=4:
#margin = int(0.08 * length_x)
#margin=0
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
slopes_tile_wise=[]
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
#img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline=img_patch_ineterst[:,index_x_d:index_x_u]
sigma=2
try:
slope_xline=self.return_deskew_slop(img_xline,sigma)
except:
slope_xline=0
if abs(slope_region)<25 and abs(slope_xline)>25:
slope_xline=[slope_region][0]
#if abs(slope_region)>70 and abs(slope_xline)<25:
#slope_xline=[slope_region][0]
slopes_tile_wise.append(slope_xline)
#print(slope_xline,'xlineeee')
img_line_rotated=self.rotate_image(img_xline,slope_xline)
img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1
#print(slopes_tile_wise,'slopes_tile_wise')
img_patch_ineterst=img_path[:,:]#[peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised=np.zeros(img_patch_ineterst.shape)
for i in range(nxf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
img_xline=img_patch_ineterst[:,index_x_d:index_x_u]
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
#plt.imshow(img_xline)
#plt.show()
img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[i])
img_line_rotated[:,:][img_line_rotated[:,:]!=0]=1
img_patch_seperated=self.seperate_lines_new_inside_teils2(img_line_rotated,0)
img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[i])
img_patch_seperated_returned[:,:][img_patch_seperated_returned[:,:]!=0]=1
img_patch_seperated_returned_true_size=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin:length_x - margin]
img_patch_ineterst_revised[:,index_x_d + margin:index_x_u - margin]=img_patch_seperated_returned_true_size
#plt.imshow(img_patch_ineterst_revised)
#plt.show()
return img_patch_ineterst_revised
def seperate_lines(self, img_patch, contour_text_interest, thetha,x_help,y_help):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
contour_text_interest_copy = contour_text_interest.copy()
x_cont = contour_text_interest[:, 0, 0]
y_cont = contour_text_interest[:, 0, 1]
x_cont = x_cont - np.min(x_cont)
y_cont = y_cont - np.min(y_cont)
x_min_cont = 0
x_max_cont = img_patch.shape[1]
y_min_cont = 0
y_max_cont = img_patch.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
textline_patch_sum_along_width = img_patch.sum(axis=1)
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
y_padded = np.zeros(len(y) + 40)
y_padded[20:len(y) + 20] = y
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if 1>0:
try:
y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
y_padded_up_to_down_e=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
peaks_new=peaks_e[:]
peaks_neg_new=peaks_neg_e[:]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
else:
peaks_new_tot=peaks_e[:]
textline_con,hierachy=self.return_contours_of_image(img_patch)
textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus=int( y_diff_mean * (7./40.0) )
#print(sigma_gaus,'sigma_gaus')
except:
sigma_gaus=12
if sigma_gaus<3:
sigma_gaus=3
#print(sigma_gaus,'sigma')
y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
y_padded_up_to_down=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
peaks, _ = find_peaks(y_padded_smoothed, height=0)
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
try:
neg_peaks_max=np.max(y_padded_smoothed[peaks])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
except:
arg_neg_must_be_deleted=[]
arg_diff_cluster=[]
try:
peaks_new=peaks[:]
peaks_neg_new=peaks_neg[:]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
if len(arg_neg_must_be_deleted)==1:
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
##plt.plot(y_padded_up_to_down_padded)
##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
##plt.show()
##plt.plot(y_padded_up_to_down_padded)
##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
##plt.show()
##plt.plot(y_padded_smoothed)
##plt.plot(peaks,y_padded_smoothed[peaks],'*')
##plt.show()
##plt.plot(y_padded_smoothed)
##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
##plt.show()
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
else:
peaks_new_tot=peaks[:]
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
except:
pass
mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
std_value_of_peaks=np.std(y_padded_smoothed[peaks])
peaks_values=y_padded_smoothed[peaks]
peaks_neg = peaks_neg - 20 - 20
peaks = peaks - 20
for jj in range(len(peaks_neg)):
if peaks_neg[jj] > len(x) - 1:
peaks_neg[jj] = len(x) - 1
for jj in range(len(peaks)):
if peaks[jj] > len(x) - 1:
peaks[jj] = len(x) - 1
textline_boxes = []
textline_boxes_rot = []
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
for jj in range(len(peaks)):
if jj==(len(peaks)-1):
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
else:
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
if point_down_narrow >= img_patch.shape[0]:
point_down_narrow = img_patch.shape[0] - 2
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
x_min_rot1=x_min_rot1-x_help
x_max_rot2=x_max_rot2-x_help
x_max_rot3=x_max_rot3-x_help
x_min_rot4=x_min_rot4-x_help
point_up_rot1=point_up_rot1-y_help
point_up_rot2=point_up_rot2-y_help
point_down_rot3=point_down_rot3-y_help
point_down_rot4=point_down_rot4-y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
elif len(peaks) < 1:
pass
elif len(peaks) == 1:
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
#x_min = x_min_cont
#x_max = x_max_cont
y_min = y_min_cont
y_max = y_max_cont
p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
x_min_rot1=x_min_rot1-x_help
x_max_rot2=x_max_rot2-x_help
x_max_rot3=x_max_rot3-x_help
x_min_rot4=x_min_rot4-x_help
point_up_rot1=point_up_rot1-y_help
point_up_rot2=point_up_rot2-y_help
point_down_rot3=point_down_rot3-y_help
point_down_rot4=point_down_rot4-y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(y_min)],
[int(x_max), int(y_min)],
[int(x_max), int(y_max)],
[int(x_min), int(y_max)]]))
elif len(peaks) == 2:
dis_to_next = np.abs(peaks[1] - peaks[0])
for jj in range(len(peaks)):
if jj == 0:
point_up = 0#peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
if point_up < 0:
point_up = 1
point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
elif jj == 1:
point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
if point_down >= img_patch.shape[0]:
point_down = img_patch.shape[0] - 2
point_up = peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next)
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside)
x_max = np.max(xvinside)
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
x_min_rot1=x_min_rot1-x_help
x_max_rot2=x_max_rot2-x_help
x_max_rot3=x_max_rot3-x_help
x_min_rot4=x_min_rot4-x_help
point_up_rot1=point_up_rot1-y_help
point_up_rot2=point_up_rot2-y_help
point_down_rot3=point_down_rot3-y_help
point_down_rot4=point_down_rot4-y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
else:
for jj in range(len(peaks)):
if jj == 0:
dis_to_next = peaks[jj + 1] - peaks[jj]
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
if point_up < 0:
point_up = 1
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next)
elif jj == len(peaks) - 1:
dis_to_next = peaks[jj] - peaks[jj - 1]
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
point_down = peaks[jj] + first_nonzero + int(1. / 1.7 * dis_to_next)
if point_down >= img_patch.shape[0]:
point_down = img_patch.shape[0] - 2
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
else:
dis_to_next_down = peaks[jj + 1] - peaks[jj]
dis_to_next_up = peaks[jj] - peaks[jj - 1]
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next_up)
point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down)
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
x_min_rot1=x_min_rot1-x_help
x_max_rot2=x_max_rot2-x_help
x_max_rot3=x_max_rot3-x_help
x_min_rot4=x_min_rot4-x_help
point_up_rot1=point_up_rot1-y_help
point_up_rot2=point_up_rot2-y_help
point_down_rot3=point_down_rot3-y_help
point_down_rot4=point_down_rot4-y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
return peaks, textline_boxes_rot
def return_rotated_contours(self,slope,img_patch):
dst = self.rotate_image(img_patch, slope)
dst = dst.astype(np.uint8)
dst = dst[:, :, 0]
dst[dst != 0] = 1
imgray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return contours
def seperate_lines_vertical_cont(self, img_patch, contour_text_interest, thetha,box_ind,add_boxes_coor_into_textlines):
kernel = np.ones((5, 5), np.uint8)
pixel=255
min_area=0
max_area=1
if len(img_patch.shape)==3:
cnts_images=(img_patch[:,:,0]==pixel)*1
else:
cnts_images=(img_patch[:,:]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=max_area,min_area=min_area)
cont_final=[]
###print(add_boxes_coor_into_textlines,'ikki')
for i in range(len(contours_imgs)):
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
img_contour = img_contour.astype(np.uint8)
img_contour = cv2.dilate(img_contour, kernel, iterations=4)
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
##0]
##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
##if add_boxes_coor_into_textlines:
##print(np.shape(contours_text_rot[0]),'sjppo')
##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
cont_final.append(contours_text_rot[0])
##print(cont_final,'nadizzzz')
return None,cont_final
def seperate_lines_vertical(self, img_patch, contour_text_interest, thetha):
thetha=thetha+90
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
contour_text_interest_copy = contour_text_interest.copy()
x_cont = contour_text_interest[:, 0, 0]
y_cont = contour_text_interest[:, 0, 1]
x_cont = x_cont - np.min(x_cont)
y_cont = y_cont - np.min(y_cont)
x_min_cont = 0
x_max_cont = img_patch.shape[1]
y_min_cont = 0
y_max_cont = img_patch.shape[0]
xv = np.linspace(x_min_cont, x_max_cont, 1000)
textline_patch_sum_along_width = img_patch.sum(axis=0)
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
y_padded = np.zeros(len(y) + 40)
y_padded[20:len(y) + 20] = y
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if 1>0:
try:
y_padded_smoothed_e= gaussian_filter1d(y_padded, 2)
y_padded_up_to_down_e=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40)
y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e
y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
peaks_new=peaks_e[:]
peaks_neg_new=peaks_neg_e[:]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
else:
peaks_new_tot=peaks_e[:]
textline_con,hierachy=self.return_contours_of_image(img_patch)
textline_con_fil=self.filter_contours_area_of_image(img_patch,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus=int( y_diff_mean * (7./40.0) )
#print(sigma_gaus,'sigma_gaus')
except:
sigma_gaus=12
if sigma_gaus<3:
sigma_gaus=3
#print(sigma_gaus,'sigma')
y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus)
y_padded_up_to_down=-y_padded+np.max(y_padded)
y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40)
y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down
y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
peaks, _ = find_peaks(y_padded_smoothed, height=0)
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
#plt.plot(y_padded_up_to_down_padded)
#plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
#plt.title('negs')
#plt.show()
#plt.plot(y_padded_smoothed)
#plt.plot(peaks,y_padded_smoothed[peaks],'*')
#plt.title('poss')
#plt.show()
neg_peaks_max=np.max(y_padded_up_to_down_padded[peaks_neg])
arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42 ]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1]
peaks_new=peaks[:]
peaks_neg_new=peaks_neg[:]
clusters_to_be_deleted=[]
if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1])
for i in range(len(arg_diff_cluster)-1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1:arg_diff_cluster[i+1]+1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:])
elif len(arg_neg_must_be_deleted)>=2 and len(arg_diff_cluster)==0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
if len(arg_neg_must_be_deleted)==1:
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
if len(clusters_to_be_deleted)>0:
peaks_new_extra=[]
for m in range(len(clusters_to_be_deleted)):
min_cluster=np.min(peaks[clusters_to_be_deleted[m]])
max_cluster=np.max(peaks[clusters_to_be_deleted[m]])
peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) )
for m1 in range(len(clusters_to_be_deleted[m])):
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]]
peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]]
peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[clusters_to_be_deleted[m][m1]]]
peaks_new_tot=[]
for i1 in peaks_new:
peaks_new_tot.append(i1)
for i1 in peaks_new_extra:
peaks_new_tot.append(i1)
peaks_new_tot=np.sort(peaks_new_tot)
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
else:
peaks_new_tot=peaks[:]
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
mean_value_of_peaks=np.mean(y_padded_smoothed[peaks])
std_value_of_peaks=np.std(y_padded_smoothed[peaks])
peaks_values=y_padded_smoothed[peaks]
peaks_neg = peaks_neg - 20 - 20
peaks = peaks - 20
for jj in range(len(peaks_neg)):
if peaks_neg[jj] > len(x) - 1:
peaks_neg[jj] = len(x) - 1
for jj in range(len(peaks)):
if peaks[jj] > len(x) - 1:
peaks[jj] = len(x) - 1
textline_boxes = []
textline_boxes_rot = []
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
#print('11')
for jj in range(len(peaks)):
if jj==(len(peaks)-1):
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =x_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =x_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
else:
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
if point_down_narrow >= img_patch.shape[0]:
point_down_narrow = img_patch.shape[0] - 2
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
elif len(peaks) < 1:
pass
elif len(peaks) == 1:
x_min = x_min_cont
x_max = x_max_cont
y_min = y_min_cont
y_max = y_max_cont
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(y_min)],
[int(x_max), int(y_min)],
[int(x_max), int(y_max)],
[int(x_min), int(y_max)]]))
elif len(peaks) == 2:
dis_to_next = np.abs(peaks[1] - peaks[0])
for jj in range(len(peaks)):
if jj == 0:
point_up = 0#peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
if point_up < 0:
point_up = 1
point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
elif jj == 1:
point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
if point_down >= img_patch.shape[0]:
point_down = img_patch.shape[0] - 2
point_up = peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next)
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside)
x_max = np.max(xvinside)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
else:
for jj in range(len(peaks)):
if jj == 0:
dis_to_next = peaks[jj + 1] - peaks[jj]
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
if point_up < 0:
point_up = 1
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next)
elif jj == len(peaks) - 1:
dis_to_next = peaks[jj] - peaks[jj - 1]
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
point_down = peaks[jj] + first_nonzero + int(1. / 1.7 * dis_to_next)
if point_down >= img_patch.shape[0]:
point_down = img_patch.shape[0] - 2
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next)
else:
dis_to_next_down = peaks[jj + 1] - peaks[jj]
dis_to_next_up = peaks[jj] - peaks[jj - 1]
point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next_up)
point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down)
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
if x_min_rot1<0:
x_min_rot1=0
if x_min_rot4<0:
x_min_rot4=0
if point_up_rot1<0:
point_up_rot1=0
if point_up_rot2<0:
point_up_rot2=0
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)],
[int(x_max_rot2), int(point_up_rot2)],
[int(x_max_rot3), int(point_down_rot3)],
[int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)],
[int(x_max), int(point_up)],
[int(x_max), int(point_down)],
[int(x_min), int(point_down)]]))
return peaks, textline_boxes_rot
def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind,slope_first,add_boxes_coor_into_textlines=False):
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8)
kernel = np.ones((5, 5), np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel)
textline_mask = cv2.erode(textline_mask, kernel, iterations=2)
#textline_mask = cv2.erode(textline_mask, kernel, iterations=1)
#print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz')
try:
#if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
#plt.imshow(textline_mask)
#plt.show()
#if abs(slope)>1:
#x_help=30
#y_help=2
#else:
#x_help=2
#y_help=2
x_help=30
y_help=2
textline_mask_help=np.zeros((textline_mask.shape[0]+int(2*y_help),textline_mask.shape[1]+int(2*x_help),3))
textline_mask_help[y_help:y_help+textline_mask.shape[0],x_help:x_help+textline_mask.shape[1],:]=np.copy(textline_mask[:,:,:])
dst = self.rotate_image(textline_mask_help, slope)
dst = dst[:, :, 0]
dst[dst != 0] = 1
#if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
#plt.imshow(dst)
#plt.show()
contour_text_copy = contour_text_interest.copy()
contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
0]
contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
img_contour = np.zeros((box_ind[3], box_ind[2], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255))
#if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
#plt.imshow(img_contour)
#plt.show()
img_contour_help=np.zeros((img_contour.shape[0]+int(2*y_help),img_contour.shape[1]+int(2*x_help),3))
img_contour_help[y_help:y_help+img_contour.shape[0],x_help:x_help+img_contour.shape[1],:]=np.copy(img_contour[:,:,:])
img_contour_rot = self.rotate_image(img_contour_help, slope)
#plt.imshow(img_contour_rot_help)
#plt.show()
#plt.imshow(dst_help)
#plt.show()
#if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
#plt.imshow(img_contour_rot_help)
#plt.show()
#plt.imshow(dst_help)
#plt.show()
img_contour_rot = img_contour_rot.astype(np.uint8)
#dst_help = dst_help.astype(np.uint8)
imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))]
ind_big_con = np.argmax(len_con_text_rot)
#print('juzaa')
if abs(slope)>45:
#print(add_boxes_coor_into_textlines,'avval')
_, contours_rotated_clean = self.seperate_lines_vertical_cont(textline_mask, contours_text_rot[ind_big_con], box_ind,slope,add_boxes_coor_into_textlines=add_boxes_coor_into_textlines)
else:
_, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope,x_help,y_help)
except:
contours_rotated_clean = []
return contours_rotated_clean
def textline_contours_to_get_slope_correctly(self, textline_mask, img_patch, contour_interest):
slope_new = 0 # deskew_images(img_patch)
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, self.kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, self.kernel)
textline_mask = cv2.erode(textline_mask, self.kernel, iterations=1)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, self.kernel)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)
textline_maskt = textline_mask[:, :, 0]
textline_maskt[textline_maskt != 0] = 1
peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new)
mean_dis = np.mean(np.diff(peaks_point))
len_x = thresh.shape[1]
slope_lines = []
contours_slope_new = []
for kk in range(len(main_contours)):
if len(main_contours[kk].shape)==2:
xminh=np.min(main_contours[kk][:,0])
xmaxh=np.max(main_contours[kk][:,0])
yminh=np.min(main_contours[kk][:,1])
ymaxh=np.max(main_contours[kk][:,1])
elif len(main_contours[kk].shape)==3:
xminh=np.min(main_contours[kk][:,0,0])
xmaxh=np.max(main_contours[kk][:,0,0])
yminh=np.min(main_contours[kk][:,0,1])
ymaxh=np.max(main_contours[kk][:,0,1])
if ymaxh - yminh <= mean_dis and (
xmaxh - xminh) >= 0.3 * len_x: # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
contours_slope_new.append(main_contours[kk])
rows, cols = thresh.shape[:2]
[vx, vy, x, y] = cv2.fitLine(main_contours[kk], cv2.DIST_L2, 0, 0.01, 0.01)
slope_lines.append((vy / vx) / np.pi * 180)
if len(slope_lines) >= 2:
slope = np.mean(slope_lines) # slope_true/np.pi*180
else:
slope = 999
else:
slope = 0
return slope
def return_contours_of_image(self,image):
if len(image.shape)==2:
image=np.repeat(image[:, :, np.newaxis], 3, axis=2)
image=image.astype(np.uint8)
else:
image=image.astype(np.uint8)
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours,hierachy
def find_contours_mean_y_diff(self,contours_main):
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
return np.mean( np.diff( np.sort( np.array(cy_main) ) ) )
def isNaN(self,num):
return num != num
def find_num_col_olddd(self,regions_without_seperators,sigma_,multiplier=3.8 ):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)
meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
y_help=np.zeros(len(y)+20)
y_help[10:len(y)+10]=y
x=np.array( range(len(y)) )
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+20)
zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
zneg= gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0)
peaks_neg=peaks_neg-10-10
last_nonzero=last_nonzero-0#100
first_nonzero=first_nonzero+0#+100
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
interest_pos=z[peaks]
interest_pos=interest_pos[interest_pos>10]
interest_neg=z[peaks_neg]
if interest_neg[0]<0.1:
interest_neg=interest_neg[1:]
if interest_neg[len(interest_neg)-1]<0.1:
interest_neg=interest_neg[:len(interest_neg)-1]
min_peaks_pos=np.min(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
interest_neg_fin=interest_neg#[(interest_neg<grenze)]
peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
interest_neg_fin=interest_neg#[(interest_neg<grenze)]
num_col=(len(interest_neg_fin))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_g_l=int(len(y)/3.)
p_g_u=len(y)-int(len(y)/3.)
diff_peaks=np.abs( np.diff(peaks_neg_fin) )
diff_peaks_annormal=diff_peaks[diff_peaks<30]
return interest_neg_fin
def find_num_col_deskew(self,regions_without_seperators,sigma_,multiplier=3.8 ):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)
##meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
4 years ago
##first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
##last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
4 years ago
##last_nonzero=len(regions_without_seperators_0)-last_nonzero
4 years ago
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
##y_help=np.zeros(len(y)+20)
4 years ago
##y_help[10:len(y)+10]=y
4 years ago
##x=np.array( range(len(y)) )
4 years ago
##zneg_rev=-y_help+np.max(y_help)
4 years ago
##zneg=np.zeros(len(zneg_rev)+20)
4 years ago
##zneg[10:len(zneg_rev)+10]=zneg_rev
4 years ago
z=gaussian_filter1d(y, sigma_)
###zneg= gaussian_filter1d(zneg, sigma_)
4 years ago
###peaks_neg, _ = find_peaks(zneg, height=0)
###peaks, _ = find_peaks(z, height=0)
4 years ago
###peaks_neg=peaks_neg-10-10
4 years ago
####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
4 years ago
#####plt.plot(z)
#####plt.show()
4 years ago
#####plt.imshow(regions_without_seperators)
#####plt.show()
###"""
###last_nonzero=last_nonzero-0#100
###first_nonzero=first_nonzero+0#+100
4 years ago
###peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
4 years ago
###peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
###"""
###interest_pos=z[peaks]
4 years ago
###interest_pos=interest_pos[interest_pos>10]
4 years ago
###interest_neg=z[peaks_neg]
4 years ago
###min_peaks_pos=np.mean(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
4 years ago
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
####print(interest_pos)
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
4 years ago
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
###peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
4 years ago
###"""
###if interest_neg[0]<0.1:
###interest_neg=interest_neg[1:]
###if interest_neg[len(interest_neg)-1]<0.1:
###interest_neg=interest_neg[:len(interest_neg)-1]
4 years ago
###min_peaks_pos=np.min(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
4 years ago
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
###"""
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
####peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
4 years ago
###num_col=(len(interest_neg_fin))+1
4 years ago
###p_l=0
###p_u=len(y)-1
###p_m=int(len(y)/2.)
###p_g_l=int(len(y)/3.)
###p_g_u=len(y)-int(len(y)/3.)
4 years ago
###diff_peaks=np.abs( np.diff(peaks_neg_fin) )
###diff_peaks_annormal=diff_peaks[diff_peaks<30]
4 years ago
#print(len(interest_neg_fin),np.mean(interest_neg_fin))
return np.std(z)#interest_neg_fin,np.std(z)
4 years ago
def return_deskew_slop(self,img_patch_org,sigma_des,main_page=False):
if main_page and self.dir_of_all is not None:
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_density_of_textline.png'))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
#img_patch_org=self.resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
max_shape=np.max(img_int.shape)
img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
4 years ago
#img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))
4 years ago
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
4 years ago
#print(img_resized.shape,'img_resizedshape')
#plt.imshow(img_resized)
#plt.show()
4 years ago
if main_page and img_patch_org.shape[1]>img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
#res=[]
#num_of_peaks=[]
#index_cor=[]
4 years ago
var_res=[]
#indexer=0
4 years ago
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
##print(rot,var_spectrum,'var_spectrum')
#res_me=np.mean(neg_peaks)
#if res_me==0:
#res_me=1000000000000000000000
#else:
#pass
#res_num=len(neg_peaks)
4 years ago
except:
#res_me=1000000000000000000000
#res_num=0
4 years ago
var_spectrum=0
#if self.isNaN(res_me):
#pass
#else:
#res.append( res_me )
#var_res.append(var_spectrum)
#num_of_peaks.append( res_num )
#index_cor.append(indexer)
#indexer=indexer+1
var_res.append(var_spectrum)
#index_cor.append(indexer)
#indexer=indexer+1
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
#res=[]
#num_of_peaks=[]
#index_cor=[]
4 years ago
var_res=[]
4 years ago
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif main_page and img_patch_org.shape[1]<=img_patch_org.shape[0]:
#plt.imshow(img_resized)
#plt.show()
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
4 years ago
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
if self.dir_of_all is not None:
#print('galdi?')
4 years ago
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_rotation_angle.png'))
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
early_slope_edge=11
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-12,100)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,12,100)
4 years ago
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
else:
angels=np.linspace(-25,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
#plt.imshow(img_rot)
#plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum')
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
#plt.plot(var_res)
#plt.show()
##plt.plot(mom3_res)
##plt.show()
#print(ang_int,'ang_int111')
4 years ago
early_slope_edge=22
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-25,60)
var_res=[]
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,25,60)
var_res=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
##plt.imshow(img_rot)
##plt.show()
img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try:
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
4 years ago
#print(indexer,'indexer')
except:
var_spectrum=0
var_res.append(var_spectrum)
4 years ago
try:
var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
return ang_int
def return_deskew_slope_new(self,img_patch,sigma_des):
max_x_y=max(img_patch.shape[0],img_patch.shape[1])
##img_patch=self.resize_image(img_patch,max_x_y,max_x_y)
img_patch_copy=np.zeros((img_patch.shape[0],img_patch.shape[1]))
img_patch_copy[:,:]=img_patch[:,:]#img_patch_org[:,:,0]
img_patch_padded=np.zeros((int( max_x_y*(1.4) ) , int( max_x_y*(1.4) ) ))
img_patch_padded_center_p=int(img_patch_padded.shape[0]/2.)
len_x_org_patch_half=int(img_patch_copy.shape[1]/2.)
len_y_org_patch_half=int(img_patch_copy.shape[0]/2.)
img_patch_padded[img_patch_padded_center_p-len_y_org_patch_half:img_patch_padded_center_p-len_y_org_patch_half+img_patch_copy.shape[0],img_patch_padded_center_p-len_x_org_patch_half:img_patch_padded_center_p-len_x_org_patch_half+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
#img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
angles=np.linspace(-25,25,80)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
#plt.imshow(img_patch)
#plt.show()
indexer=0
for rot in angles:
#print(rot,'rot')
img_rotated=self.rotate_image(img_patch_padded,rot)
img_rotated[img_rotated!=0]=1
#plt.imshow(img_rotated)
#plt.show()
try:
neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3 )
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
except:
res_me=1000000000000000000000
res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
try:
var_res=np.array(var_res)
#print(var_res)
ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
if abs(ang_int)>15:
angles=np.linspace(-90,-50,30)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
#plt.imshow(img_patch)
#plt.show()
indexer=0
for rot in angles:
#print(rot,'rot')
img_rotated=self.rotate_image(img_patch_padded,rot)
img_rotated[img_rotated!=0]=1
#plt.imshow(img_rotated)
#plt.show()
try:
neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3 )
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
except:
res_me=1000000000000000000000
res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
try:
var_res=np.array(var_res)
#print(var_res)
ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
return ang_int
def do_work_of_slopes(self,q,poly,box_sub,boxes_per_process,textline_mask_tot,contours_per_process):
slope_biggest=0
slopes_sub = []
boxes_sub_new=[]
poly_sub=[]
for mv in range(len(boxes_per_process)):
crop_img, _ = self.crop_image_inside_box(boxes_per_process[mv],
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
crop_img=crop_img[:,:,0]
crop_img=cv2.erode(crop_img,self.kernel,iterations = 2)
try:
textline_con,hierachy=self.return_contours_of_image(crop_img)
textline_con_fil=self.filter_contours_area_of_image(crop_img,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)
sigma_des=int( y_diff_mean * (4./40.0) )
if sigma_des<1:
sigma_des=1
crop_img[crop_img>0]=1
slope_corresponding_textregion=self.return_deskew_slop(crop_img,sigma_des)
except:
slope_corresponding_textregion=999
if slope_corresponding_textregion==999:
slope_corresponding_textregion=slope_biggest
##if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
##slope_corresponding_textregion=slope_biggest
##elif slope_corresponding_textregion==999:
##slope_corresponding_textregion=slope_biggest
slopes_sub.append(slope_corresponding_textregion)
cnt_clean_rot = self.textline_contours_postprocessing(crop_img
, slope_corresponding_textregion,
contours_per_process[mv], boxes_per_process[mv])
poly_sub.append(cnt_clean_rot)
boxes_sub_new.append(boxes_per_process[mv] )
q.put(slopes_sub)
poly.put(poly_sub)
box_sub.put(boxes_sub_new )
def get_slopes_and_deskew(self, contours,textline_mask_tot):
slope_biggest=0#self.return_deskew_slop(img_int_p,sigma_des)
num_cores = cpu_count()
q = Queue()
poly=Queue()
box_sub=Queue()
processes = []
nh=np.linspace(0, len(self.boxes), num_cores+1)
for i in range(num_cores):
boxes_per_process=self.boxes[int(nh[i]):int(nh[i+1])]
contours_per_process=contours[int(nh[i]):int(nh[i+1])]
processes.append(Process(target=self.do_work_of_slopes, args=(q,poly,box_sub, boxes_per_process, textline_mask_tot, contours_per_process)))
for i in range(num_cores):
processes[i].start()
self.slopes = []
self.all_found_texline_polygons=[]
self.boxes=[]
for i in range(num_cores):
slopes_for_sub_process=q.get(True)
boxes_for_sub_process=box_sub.get(True)
polys_for_sub_process=poly.get(True)
for j in range(len(slopes_for_sub_process)):
self.slopes.append(slopes_for_sub_process[j])
self.all_found_texline_polygons.append(polys_for_sub_process[j])
self.boxes.append(boxes_for_sub_process[j])
for i in range(num_cores):
processes[i].join()
def order_of_regions_old(self, textline_mask,contours_main):
mada_n=textline_mask.sum(axis=1)
y=mada_n[:]
y_help=np.zeros(len(y)+40)
y_help[20:len(y)+20]=y
x=np.array( range(len(y)) )
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
sigma_gaus=8
z= gaussian_filter1d(y_help, sigma_gaus)
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks_neg=peaks_neg-20-20
peaks=peaks-20
if contours_main!=None:
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
if contours_main!=None:
indexer_main=np.array(range(len(contours_main)))
if contours_main!=None:
len_main=len(contours_main)
else:
len_main=0
matrix_of_orders=np.zeros((len_main,5))
matrix_of_orders[:,0]=np.array( range( len_main ) )
matrix_of_orders[:len_main,1]=1
matrix_of_orders[len_main:,1]=2
matrix_of_orders[:len_main,2]=cx_main
matrix_of_orders[:len_main,3]=cy_main
matrix_of_orders[:len_main,4]=np.array( range( len_main ) )
peaks_neg_new=[]
peaks_neg_new.append(0)
for iii in range(len(peaks_neg)):
peaks_neg_new.append(peaks_neg[iii])
peaks_neg_new.append(textline_mask.shape[0])
final_indexers_sorted=[]
for i in range(len(peaks_neg_new)-1):
top=peaks_neg_new[i]
down=peaks_neg_new[i+1]
indexes_in=matrix_of_orders[:,0][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
cxs_in=matrix_of_orders[:,2][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
sorted_inside=np.argsort(cxs_in)
ind_in_int=indexes_in[sorted_inside]
for j in range(len(ind_in_int)):
final_indexers_sorted.append(int(ind_in_int[j]) )
return final_indexers_sorted, matrix_of_orders
def order_and_id_of_texts_old(self, found_polygons_text_region ,matrix_of_orders ,indexes_sorted ):
id_of_texts=[]
order_of_texts=[]
index_b=0
for mm in range(len(found_polygons_text_region)):
id_of_texts.append('r'+str(index_b) )
index_matrix=matrix_of_orders[:,0][( matrix_of_orders[:,1]==1 ) & ( matrix_of_orders[:,4]==mm ) ]
order_of_texts.append(np.where(indexes_sorted == index_matrix)[0][0])
index_b+=1
order_of_texts
return order_of_texts, id_of_texts
def write_into_page_xml_only_textlines(self,contours,page_coord ,all_found_texline_polygons,all_box_coord,dir_of_image):
found_polygons_text_region=contours
# create the file structure
data = ET.Element('PcGts')
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
metadata=ET.SubElement(data,'Metadata')
author=ET.SubElement(metadata, 'Creator')
author.text = 'SBB_QURATOR'
created=ET.SubElement(metadata, 'Created')
created.text = '2019-06-17T18:15:12'
changetime=ET.SubElement(metadata, 'LastChange')
changetime.text = '2019-06-17T18:15:12'
page=ET.SubElement(data,'Page')
page.set('imageFilename', self.image_dir)
page.set('imageHeight',str(self.height_org) )
page.set('imageWidth',str(self.width_org) )
page.set('type',"content")
page.set('readingDirection',"left-to-right")
page.set('textLineOrder',"top-to-bottom" )
page_print_sub=ET.SubElement(page, 'PrintSpace')
coord_page = ET.SubElement(page_print_sub, 'Coords')
points_page_print=''
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm])==2:
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
else:
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )
if lmm<(len(self.cont_page[0])-1):
points_page_print=points_page_print+' '
coord_page.set('points',points_page_print)
if len(contours)>0:
id_indexer=0
id_indexer_l=0
for mm in range(len(found_polygons_text_region)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
textregion.set('type','paragraph')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_text_region[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons[mm][j])):
#point = ET.SubElement(coord, 'Point')
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1] +page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1] +page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
#print(dir_of_image)
print(self.f_name)
#print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
def write_into_page_xml_full(self,contours,contours_h,page_coord,dir_of_image,order_of_texts , id_of_texts,all_found_texline_polygons,all_found_texline_polygons_h,all_box_coord,all_box_coord_h,found_polygons_text_region_img,found_polygons_tables,found_polygons_drop_capitals,found_polygons_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,slopes,slopes_marginals):
found_polygons_text_region=contours
found_polygons_text_region_h=contours_h
# create the file structure
data = ET.Element('PcGts')
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
metadata=ET.SubElement(data,'Metadata')
author=ET.SubElement(metadata, 'Creator')
author.text = 'SBB_QURATOR'
created=ET.SubElement(metadata, 'Created')
created.text = '2019-06-17T18:15:12'
changetime=ET.SubElement(metadata, 'LastChange')
changetime.text = '2019-06-17T18:15:12'
page=ET.SubElement(data,'Page')
page.set('imageFilename', self.image_dir)
page.set('imageHeight',str(self.height_org) )
page.set('imageWidth',str(self.width_org) )
page.set('type',"content")
page.set('readingDirection',"left-to-right")
page.set('textLineOrder',"top-to-bottom" )
page_print_sub=ET.SubElement(page, 'PrintSpace')
coord_page = ET.SubElement(page_print_sub, 'Coords')
points_page_print=''
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm])==2:
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
else:
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )
if lmm<(len(self.cont_page[0])-1):
points_page_print=points_page_print+' '
coord_page.set('points',points_page_print)
if len(contours)>0:
region_order=ET.SubElement(page, 'ReadingOrder')
region_order_sub = ET.SubElement(region_order, 'OrderedGroup')
region_order_sub.set('id',"ro357564684568544579089")
#args_sort=order_of_texts
for vj in order_of_texts:
name="coord_text_"+str(vj)
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index',str(order_of_texts[vj]) )
name.set('regionRef',id_of_texts[vj])
id_of_marginalia=[]
indexer_region=len(contours)+len(contours_h)
for vm in range(len(found_polygons_marginals)):
id_of_marginalia.append('r'+str(indexer_region))
name="coord_text_"+str(indexer_region)
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index',str(indexer_region) )
name.set('regionRef','r'+str(indexer_region))
indexer_region+=1
id_indexer=0
id_indexer_l=0
for mm in range(len(found_polygons_text_region)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
textregion.set('type','paragraph')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_text_region[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons[mm][j])):
#point = ET.SubElement(coord, 'Point')
if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
if (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and np.abs(slopes[mm])<=45 :
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+page_coord[0])/self.scale_y) )
elif (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and np.abs(slopes[mm])>45 :
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
print(len(contours_h))
if len(contours_h)>0:
for mm in range(len(found_polygons_text_region_h)):
textregion=ET.SubElement(page, 'TextRegion')
try:
id_indexer=id_indexer
id_indexer_l=id_indexer_l
except:
id_indexer=0
id_indexer_l=0
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
textregion.set('type','header')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region_h[mm])):
if len(found_polygons_text_region_h[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_text_region_h[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons_h[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons_h[mm][j])):
#point = ET.SubElement(coord, 'Point')
if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_h[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
if len(all_found_texline_polygons_h[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons_h[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
if len(found_polygons_drop_capitals)>0:
id_indexer=len(contours_h)+len(contours)+len(found_polygons_marginals)
for mm in range(len(found_polygons_drop_capitals)):
textregion=ET.SubElement(page, 'TextRegion')
#id_indexer_l=id_indexer_l
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
textregion.set('type','drop-capital')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_drop_capitals[mm])):
if len(found_polygons_drop_capitals[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_drop_capitals[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
##for j in range(len(all_found_texline_polygons_h[mm])):
##textline=ET.SubElement(textregion, 'TextLine')
##textline.set('id','l'+str(id_indexer_l))
##id_indexer_l+=1
##coord = ET.SubElement(textline, 'Coords')
##texteq=ET.SubElement(textline, 'TextEquiv')
##uni=ET.SubElement(texteq, 'Unicode')
##uni.text = ' '
###points = ET.SubElement(coord, 'Points')
##points_co=''
##for l in range(len(all_found_texline_polygons_h[mm][j])):
###point = ET.SubElement(coord, 'Point')
##if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
###point.set('x',str(found_polygons[j][l][0]))
###point.set('y',str(found_polygons[j][l][1]))
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x ) )
##points_co=points_co+','
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
##if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
##if len(all_found_texline_polygons_h[mm][j][l])==2:
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
##+page_coord[2])/self.scale_x) )
##points_co=points_co+','
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
##+page_coord[0])/self.scale_y) )
##else:
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
##+page_coord[2])/self.scale_x ) )
##points_co=points_co+','
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
##+page_coord[0])/self.scale_y) )
##if l<(len(all_found_texline_polygons_h[mm][j])-1):
##points_co=points_co+' '
###print(points_co)
####coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
try:
try:
###id_indexer=id_indexer
id_indexer_l=id_indexer_l
except:
###id_indexer=0
id_indexer_l=0
for mm in range(len(found_polygons_marginals)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id',id_of_marginalia[mm])
textregion.set('type','marginalia')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_marginals[mm])):
if len(found_polygons_marginals[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_marginals[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
#point = ET.SubElement(coord, 'Point')
if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
+all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
+all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
+all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
+all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons_marginals[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
except:
pass
try:
id_indexer=len(contours_h)+len(contours)+len(found_polygons_marginals)+len(found_polygons_drop_capitals)
for mm in range(len(found_polygons_text_region_img)):
textregion=ET.SubElement(page, 'ImageRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region_img[mm])):
if len(found_polygons_text_region_img[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_text_region_img[mm])-1):
points_co=points_co+' '
coord_text.set('points',points_co)
except:
pass
try:
for mm in range(len(found_polygons_tables)):
textregion=ET.SubElement(page, 'TableRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_tables[mm])):
if len(found_polygons_tables[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_tables[mm])-1):
points_co=points_co+' '
coord_text.set('points',points_co)
except:
pass
print(dir_of_image)
print(self.f_name)
print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
def write_into_page_xml(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts,all_found_texline_polygons,all_box_coord,found_polygons_text_region_img,found_polygons_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,curved_line,slopes,slopes_marginals):
found_polygons_text_region=contours
##found_polygons_text_region_h=contours_h
# create the file structure
data = ET.Element('PcGts')
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
metadata=ET.SubElement(data,'Metadata')
author=ET.SubElement(metadata, 'Creator')
author.text = 'SBB_QURATOR'
created=ET.SubElement(metadata, 'Created')
created.text = '2019-06-17T18:15:12'
changetime=ET.SubElement(metadata, 'LastChange')
changetime.text = '2019-06-17T18:15:12'
page=ET.SubElement(data,'Page')
page.set('imageFilename', self.image_dir)
page.set('imageHeight',str(self.height_org) )
page.set('imageWidth',str(self.width_org) )
page.set('type',"content")
page.set('readingDirection',"left-to-right")
page.set('textLineOrder',"top-to-bottom" )
page_print_sub=ET.SubElement(page, 'PrintSpace')
coord_page = ET.SubElement(page_print_sub, 'Coords')
points_page_print=''
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm])==2:
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
else:
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )
if lmm<(len(self.cont_page[0])-1):
points_page_print=points_page_print+' '
coord_page.set('points',points_page_print)
if len(contours)>0:
region_order=ET.SubElement(page, 'ReadingOrder')
region_order_sub = ET.SubElement(region_order, 'OrderedGroup')
region_order_sub.set('id',"ro357564684568544579089")
indexer_region=0
for vj in order_of_texts:
name="coord_text_"+str(vj)
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index',str(indexer_region) )
name.set('regionRef',id_of_texts[vj])
indexer_region+=1
id_of_marginalia=[]
for vm in range(len(found_polygons_marginals)):
id_of_marginalia.append('r'+str(indexer_region))
name="coord_text_"+str(indexer_region)
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index',str(indexer_region) )
name.set('regionRef','r'+str(indexer_region))
indexer_region+=1
id_indexer=0
id_indexer_l=0
for mm in range(len(found_polygons_text_region)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
textregion.set('type','paragraph')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_text_region[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_text_region[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons[mm][j])):
#point = ET.SubElement(coord, 'Point')
if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
if (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and abs(slopes[mm])<=45 :
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+page_coord[0])/self.scale_y) )
elif (curved_line=='true' or curved_line=='True' or curved_line=='TRUE') and abs(slopes[mm])>45:
if len(all_found_texline_polygons[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons[mm][j][l][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][0]
+all_box_coord[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons[mm][j][l][0][1]
+all_box_coord[mm][0]+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
texteqreg=ET.SubElement(textregion, 'TextEquiv')
unireg=ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
###print(len(contours_h))
###if len(contours_h)>0:
###for mm in range(len(found_polygons_text_region_h)):
###textregion=ET.SubElement(page, 'TextRegion')
###try:
###id_indexer=id_indexer
###id_indexer_l=id_indexer_l
###except:
###id_indexer=0
###id_indexer_l=0
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###textregion.set('type','header')
####if mm==0:
#### textregion.set('type','header')
####else:
#### textregion.set('type','paragraph')
###coord_text = ET.SubElement(textregion, 'Coords')
###points_co=''
###for lmm in range(len(found_polygons_text_region_h[mm])):
###if len(found_polygons_text_region_h[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
###if lmm<(len(found_polygons_text_region_h[mm])-1):
###points_co=points_co+' '
####print(points_co)
###coord_text.set('points',points_co)
###for j in range(len(all_found_texline_polygons_h[mm])):
###textline=ET.SubElement(textregion, 'TextLine')
###textline.set('id','l'+str(id_indexer_l))
###id_indexer_l+=1
###coord = ET.SubElement(textline, 'Coords')
###texteq=ET.SubElement(textline, 'TextEquiv')
###uni=ET.SubElement(texteq, 'Unicode')
###uni.text = ' '
####points = ET.SubElement(coord, 'Points')
###points_co=''
###for l in range(len(all_found_texline_polygons_h[mm][j])):
####point = ET.SubElement(coord, 'Point')
####point.set('x',str(found_polygons[j][l][0]))
####point.set('y',str(found_polygons[j][l][1]))
###if len(all_found_texline_polygons_h[mm][j][l])==2:
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###else:
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0] +page_coord[2]
###+all_box_coord_h[mm][2])/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1] +page_coord[0]
###+all_box_coord_h[mm][0])/self.scale_y) )
###if l<(len(all_found_texline_polygons_h[mm][j])-1):
###points_co=points_co+' '
####print(points_co)
###coord.set('points',points_co)
###texteqreg=ET.SubElement(textregion, 'TextEquiv')
###unireg=ET.SubElement(texteqreg, 'Unicode')
###unireg.text = ' '
try:
#id_indexer_l=0
try:
###id_indexer=id_indexer
id_indexer_l=id_indexer_l
except:
###id_indexer=0
id_indexer_l=0
for mm in range(len(found_polygons_marginals)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id',id_of_marginalia[mm])
textregion.set('type','marginalia')
#if mm==0:
# textregion.set('type','header')
#else:
# textregion.set('type','paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_marginals[mm])):
if len(found_polygons_marginals[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_marginals[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_marginals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_marginals[mm])-1):
points_co=points_co+' '
#print(points_co)
coord_text.set('points',points_co)
for j in range(len(all_found_texline_polygons_marginals[mm])):
textline=ET.SubElement(textregion, 'TextLine')
textline.set('id','l'+str(id_indexer_l))
id_indexer_l+=1
coord = ET.SubElement(textline, 'Coords')
texteq=ET.SubElement(textline, 'TextEquiv')
uni=ET.SubElement(texteq, 'Unicode')
uni.text = ' '
#points = ET.SubElement(coord, 'Points')
points_co=''
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
#point = ET.SubElement(coord, 'Point')
if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
#point.set('x',str(found_polygons[j][l][0]))
#point.set('y',str(found_polygons[j][l][1]))
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
+all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
+all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
+all_box_coord_marginals[mm][2]+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
+all_box_coord_marginals[mm][0]+page_coord[0])/self.scale_y) )
if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
if len(all_found_texline_polygons_marginals[mm][j][l])==2:
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][0]
+page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int( (all_found_texline_polygons_marginals[mm][j][l][1]
+page_coord[0])/self.scale_y) )
else:
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][0]
+page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( ( all_found_texline_polygons_marginals[mm][j][l][0][1]
+page_coord[0])/self.scale_y) )
if l<(len(all_found_texline_polygons_marginals[mm][j])-1):
points_co=points_co+' '
#print(points_co)
coord.set('points',points_co)
except:
pass
try:
for mm in range(len(found_polygons_text_region_img)):
textregion=ET.SubElement(page, 'ImageRegion')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_text_region_img[mm])):
points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm,0,0]+page_coord[2] )/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm,0,1]+page_coord[0] )/self.scale_y ) )
if lmm<(len(found_polygons_text_region_img[mm])-1):
points_co=points_co+' '
coord_text.set('points',points_co)
###for mm in range(len(found_polygons_text_region_img)):
###textregion=ET.SubElement(page, 'ImageRegion')
###textregion.set('id','r'+str(id_indexer))
###id_indexer+=1
###coord_text = ET.SubElement(textregion, 'Coords')
###print(found_polygons_text_region_img[mm])
###points_co=''
###for lmm in range(len(found_polygons_text_region_img[mm])):
###print(len(found_polygons_text_region_img[mm][lmm]))
###if len(found_polygons_text_region_img[mm][lmm])==2:
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
###points_co=points_co+','
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
###else:
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
###points_co=points_co+','
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1]+page_coord[0] )/self.scale_y) )
###if lmm<(len(found_polygons_text_region_img[mm])-1):
###points_co=points_co+' '
###coord_text.set('points',points_co)
except:
pass
####try:
####for mm in range(len(found_polygons_tables)):
####textregion=ET.SubElement(page, 'TableRegion')
####textregion.set('id','r'+str(id_indexer))
####id_indexer+=1
####coord_text = ET.SubElement(textregion, 'Coords')
####points_co=''
####for lmm in range(len(found_polygons_tables[mm])):
####if len(found_polygons_tables[mm][lmm])==2:
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
####points_co=points_co+','
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
####else:
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
####points_co=points_co+','
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
####if lmm<(len(found_polygons_tables[mm])-1):
####points_co=points_co+' '
####coord_text.set('points',points_co)
####except:
####pass
"""
try:
for mm in range(len(found_polygons_drop_capitals)):
textregion=ET.SubElement(page, 'DropCapitals')
textregion.set('id','r'+str(id_indexer))
id_indexer+=1
coord_text = ET.SubElement(textregion, 'Coords')
points_co=''
for lmm in range(len(found_polygons_drop_capitals[mm])):
if len(found_polygons_drop_capitals[mm][lmm])==2:
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
points_co=points_co+','
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
else:
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
points_co=points_co+','
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
if lmm<(len(found_polygons_drop_capitals[mm])-1):
points_co=points_co+' '
coord_text.set('points',points_co)
except:
pass
"""
#print(dir_of_image)
print(self.f_name)
#print(os.path.join(dir_of_image, self.f_name) + ".xml")
tree = ET.ElementTree(data)
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
#cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
def deskew_region_prediction(self,regions_prediction, slope):
image_regions_deskewd=np.zeros(regions_prediction[:,:].shape)
for ind in np.unique(regions_prediction[:,:]):
interest_reg=(regions_prediction[:,:]==ind)*1
interest_reg=interest_reg.astype(np.uint8)
deskewed_new=self.rotate_image(interest_reg,slope)
deskewed_new=deskewed_new[:,:]
deskewed_new[deskewed_new!=0]=ind
image_regions_deskewd=image_regions_deskewd+deskewed_new
return image_regions_deskewd
def deskew_erarly(self,textline_mask):
textline_mask_org=np.copy(textline_mask)
#print(textline_mask.shape,np.unique(textline_mask),'hizzzzz')
#slope_new=0#deskew_images(img_patch)
textline_mask=np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)*255
textline_mask=textline_mask.astype(np.uint8)
kernel = np.ones((5,5),np.uint8)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(hirarchy)
commenst_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=0.01,min_area=0.003)
main_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=1,min_area=0.003)
interior_contours=self.filter_contours_area_of_image_interiors(thresh,contours,hirarchy,max_area=1,min_area=0)
img_comm=np.zeros(thresh.shape)
img_comm_in=cv2.fillPoly(img_comm, pts =main_contours, color=(255,255,255))
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
img_comm_in=np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
img_comm_in=img_comm_in.astype(np.uint8)
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
##imgray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
##mask = cv2.inRange(imgray, lower_blue, upper_blue)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
#print(np.unique(mask))
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
##plt.imshow(thresh)
##plt.show()
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
areas=[cv2.contourArea(contours[jj]) for jj in range(len(contours))]
median_area=np.mean(areas)
contours_slope=contours#self.find_polugons_size_filter(contours,median_area=median_area,scaler_up=100,scaler_down=0.5)
if len(contours_slope)>0:
for jv in range(len(contours_slope)):
new_poly=list(contours_slope[jv])
if jv==0:
merged_all=new_poly
else:
merged_all=merged_all+new_poly
merge=np.array(merged_all)
img_in=np.zeros(textline_mask.shape)
img_p_in=cv2.fillPoly(img_in, pts =[merge], color=(255,255,255))
##plt.imshow(img_p_in)
##plt.show()
rect = cv2.minAreaRect(merge)
box = cv2.boxPoints(rect)
box = np.int0(box)
indexes=[0,1,2,3]
x_list=box[:,0]
y_list=box[:,1]
index_y_sort=np.argsort(y_list)
index_upper_left=index_y_sort[np.argmin(x_list[index_y_sort[0:2]])]
index_upper_right=index_y_sort[np.argmax(x_list[index_y_sort[0:2]])]
index_lower_left=index_y_sort[np.argmin(x_list[index_y_sort[2:]]) +2]
index_lower_right=index_y_sort[np.argmax(x_list[index_y_sort[2:]])+2]
alpha1=float(box[index_upper_right][1]-
box[index_upper_left][1])/(float(box[index_upper_right][0]-box[index_upper_left][0]))
alpha2=float(box[index_lower_right][1]-
box[index_lower_left][1])/(float(box[index_lower_right][0]-box[index_lower_left][0]))
slope_true=(alpha1+alpha2)/2.0
#slope=0#slope_true/np.pi*180
#if abs(slope)>=1:
#slope=0
#dst=self.rotate_image(textline_mask,slope_true)
#dst=dst[:,:,0]
#dst[dst!=0]=1
image_regions_deskewd=np.zeros(textline_mask_org[:,:].shape)
for ind in np.unique(textline_mask_org[:,:]):
interest_reg=(textline_mask_org[:,:]==ind)*1
interest_reg=interest_reg.astype(np.uint8)
deskewed_new=self.rotate_image(interest_reg,slope_true)
deskewed_new=deskewed_new[:,:]
deskewed_new[deskewed_new!=0]=ind
image_regions_deskewd=image_regions_deskewd+deskewed_new
return image_regions_deskewd,slope_true
def return_regions_without_seperators(self,regions_pre):
kernel = np.ones((5,5),np.uint8)
regions_without_seperators=( (regions_pre[:,:]!=6) & (regions_pre[:,:]!=0) )*1
#regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
regions_without_seperators=regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators,kernel,iterations = 6)
return regions_without_seperators
def return_regions_without_seperators_new(self,regions_pre,regions_only_text):
kernel = np.ones((5,5),np.uint8)
regions_without_seperators=( (regions_pre[:,:]!=6) & (regions_pre[:,:]!=0) & (regions_pre[:,:]!=1) & (regions_pre[:,:]!=2))*1
#plt.imshow(regions_without_seperators)
#plt.show()
regions_without_seperators_n=( (regions_without_seperators[:,:]==1) | (regions_only_text[:,:]==1) )*1
#regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
regions_without_seperators_n=regions_without_seperators_n.astype(np.uint8)
regions_without_seperators_n = cv2.erode(regions_without_seperators_n,kernel,iterations = 6)
return regions_without_seperators_n
def image_change_background_pixels_to_zero(self,image_page):
image_back_zero=np.zeros((image_page.shape[0],image_page.shape[1]))
image_back_zero[:,:]=image_page[:,:,0]
image_back_zero[:,:][image_back_zero[:,:]==0]=-255
image_back_zero[:,:][image_back_zero[:,:]==255]=0
image_back_zero[:,:][image_back_zero[:,:]==-255]=255
return image_back_zero
def find_num_col_only_image(self,regions_without_seperators,multiplier=3.8):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_=15
meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
y_help=np.zeros(len(y)+20)
y_help[10:len(y)+10]=y
x=np.array( range(len(y)) )
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+20)
zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
zneg= gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0)
peaks_neg=peaks_neg-10-10
peaks_neg_org=np.copy(peaks_neg)
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
peaks=peaks[(peaks>.09*regions_without_seperators.shape[1]) & (peaks<0.91*regions_without_seperators.shape[1])]
peaks_neg=peaks_neg[ (peaks_neg>500) & (peaks_neg< (regions_without_seperators.shape[1]-500) ) ]
#print(peaks)
interest_pos=z[peaks]
interest_pos=interest_pos[interest_pos>10]
interest_neg=z[peaks_neg]
min_peaks_pos=np.mean(interest_pos)#np.min(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
#$print(min_peaks_pos)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
#print(interest_pos)
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
interest_neg_fin=interest_neg[(interest_neg<grenze)]
peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
num_col=(len(interest_neg_fin))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_g_l=int(len(y)/3.)
p_g_u=len(y)-int(len(y)/3.)
if num_col==3:
if (peaks_neg_fin[0]>p_g_u and peaks_neg_fin[1]>p_g_u) or (peaks_neg_fin[0]<p_g_l and peaks_neg_fin[1]<p_g_l ) or (peaks_neg_fin[0]<p_m and peaks_neg_fin[1]<p_m ) or (peaks_neg_fin[0]>p_m and peaks_neg_fin[1]>p_m ):
num_col=1
else:
pass
if num_col==2:
if (peaks_neg_fin[0]>p_g_u) or (peaks_neg_fin[0]<p_g_l):
num_col=1
else:
pass
diff_peaks=np.abs( np.diff(peaks_neg_fin) )
cut_off=400
peaks_neg_true=[]
forest=[]
for i in range(len(peaks_neg_fin)):
if i==0:
forest.append(peaks_neg_fin[i])
if i<(len(peaks_neg_fin)-1):
if diff_peaks[i]<=cut_off:
forest.append(peaks_neg_fin[i+1])
if diff_peaks[i]>cut_off:
#print(forest[np.argmin(z[forest]) ] )
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
forest=[]
forest.append(peaks_neg_fin[i+1])
if i==(len(peaks_neg_fin)-1):
#print(print(forest[np.argmin(z[forest]) ] ))
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
num_col=(len(peaks_neg_true))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_quarter=int(len(y)/4.)
p_g_l=int(len(y)/3.)
p_g_u=len(y)-int(len(y)/3.)
p_u_quarter=len(y)-p_quarter
if num_col==3:
if (peaks_neg_true[0]>p_g_u and peaks_neg_true[1]>p_g_u) or (peaks_neg_true[0]<p_g_l and peaks_neg_true[1]<p_g_l ) or (peaks_neg_true[0]<p_m and peaks_neg_true[1]<p_m ) or (peaks_neg_true[0]>p_m and peaks_neg_true[1]>p_m ):
num_col=1
peaks_neg_true=[]
elif (peaks_neg_true[0]<p_g_u and peaks_neg_true[0]>p_g_l) and (peaks_neg_true[1]>p_u_quarter):
peaks_neg_true=[ peaks_neg_true[0] ]
elif (peaks_neg_true[1]<p_g_u and peaks_neg_true[1]>p_g_l) and (peaks_neg_true[0]<p_quarter):
peaks_neg_true=[ peaks_neg_true[1] ]
else:
pass
if num_col==2:
if (peaks_neg_true[0]>p_g_u) or (peaks_neg_true[0]<p_g_l):
num_col=1
peaks_neg_true=[]
if num_col==4:
if len(np.array(peaks_neg_true)[np.array(peaks_neg_true)<p_g_l])==2 or len( np.array(peaks_neg_true)[np.array(peaks_neg_true)>(len(y)-p_g_l)] )==2:
num_col=1
peaks_neg_true=[]
else:
pass
#no deeper hill around found hills
peaks_fin_true=[]
for i in range(len(peaks_neg_true)):
hill_main=peaks_neg_true[i]
#deep_depth=z[peaks_neg]
hills_around=peaks_neg_org[( (peaks_neg_org>hill_main) & (peaks_neg_org<=hill_main+400) ) | ( (peaks_neg_org<hill_main) & (peaks_neg_org>=hill_main-400) )]
deep_depth_around=z[hills_around]
#print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
try:
if np.min(deep_depth_around)<z[hill_main]:
pass
else:
peaks_fin_true.append(hill_main)
except:
pass
diff_peaks_annormal=diff_peaks[diff_peaks<360]
if len(diff_peaks_annormal)>0:
arg_help=np.array(range(len(diff_peaks)))
arg_help_ann=arg_help[diff_peaks<360]
peaks_neg_fin_new=[]
for ii in range(len(peaks_neg_fin)):
if ii in arg_help_ann:
arg_min=np.argmin([interest_neg_fin[ii],interest_neg_fin[ii+1] ] )
if arg_min==0:
peaks_neg_fin_new.append( peaks_neg_fin[ii])
else:
peaks_neg_fin_new.append( peaks_neg_fin[ii+1])
elif (ii-1) in arg_help_ann:
pass
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii] )
else:
peaks_neg_fin_new=peaks_neg_fin
# sometime pages with one columns gives also some negative peaks. delete those peaks
param=z[peaks_neg_true]/float(min_peaks_pos)*100
if len(param[param<=41])==0:
peaks_neg_true=[]
return len(peaks_fin_true), peaks_fin_true
def return_hor_spliter_by_index_for_without_verticals(self,peaks_neg_fin_t,x_min_hor_some,x_max_hor_some):
#print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
arg_min_hor_sort=np.argsort(x_min_hor_some)
x_min_hor_some_sort=np.sort(x_min_hor_some)
x_max_hor_some_sort=x_max_hor_some[arg_min_hor_sort]
arg_minmax=np.array(range(len(peaks_neg_fin_t)))
indexer_lines=[]
indexes_to_delete=[]
indexer_lines_deletions_len=[]
indexr_uniq_ind=[]
for i in range(len(x_min_hor_some_sort)):
min_h=peaks_neg_fin_t-x_min_hor_some_sort[i]
max_h=peaks_neg_fin_t-x_max_hor_some_sort[i]
min_h[0]=min_h[0]#+20
max_h[len(max_h)-1]=max_h[len(max_h)-1]-20
min_h_neg=arg_minmax[(min_h<0)]
min_h_neg_n=min_h[min_h<0]
try:
min_h_neg=[ min_h_neg[np.argmax(min_h_neg_n)] ]
except:
min_h_neg=[]
max_h_neg=arg_minmax[(max_h>0)]
max_h_neg_n=max_h[max_h>0]
if len(max_h_neg_n)>0:
max_h_neg=[ max_h_neg[np.argmin(max_h_neg_n)] ]
else:
max_h_neg=[]
if len(min_h_neg)>0 and len(max_h_neg)>0:
deletions=list(range(min_h_neg[0]+1,max_h_neg[0]))
unique_delets_int=[]
#print(deletions,len(deletions),'delii')
if len(deletions)>0:
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
#print(deletions,indexes_to_delete,'badiii')
unique_delets=np.unique(indexes_to_delete)
#print(min_h_neg[0],unique_delets)
unique_delets_int=unique_delets[unique_delets<min_h_neg[0]]
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true=min_h_neg[0]-len(unique_delets_int)
#print(index_line_true)
if index_line_true>0 and min_h_neg[0]>=2:
index_line_true=index_line_true
else:
index_line_true=min_h_neg[0]
indexer_lines.append(index_line_true)
if len(unique_delets_int)>0:
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true=[]
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines,peaks_true,arg_min_hor_sort,indexer_lines_deletions_len,indexr_uniq_ind
def find_num_col_by_vertical_lines(self,regions_without_seperators,multiplier=3.8):
regions_without_seperators_0=regions_without_seperators[:,:,0].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_=35#70#35
z=gaussian_filter1d(regions_without_seperators_0, sigma_)
peaks, _ = find_peaks(z, height=0)
#print(peaks,'peaksnew')
return peaks
def find_num_col(self,regions_without_seperators,multiplier=3.8):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_=35#70#35
meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
#print(last_nonzero)
#print(isNaN(last_nonzero))
#last_nonzero=0#halalikh
last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
y_help=np.zeros(len(y)+20)
y_help[10:len(y)+10]=y
x=np.array( range(len(y)) )
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+20)
zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
zneg= gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0)
peaks_neg=peaks_neg-10-10
last_nonzero=last_nonzero-100
first_nonzero=first_nonzero+200
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
peaks_neg=peaks_neg[ (peaks_neg>370) & (peaks_neg< (regions_without_seperators.shape[1]-370) ) ]
#print(peaks)
interest_pos=z[peaks]
interest_pos=interest_pos[interest_pos>10]
#plt.plot(z)
#plt.show()
interest_neg=z[peaks_neg]
min_peaks_pos=np.min(interest_pos)
max_peaks_pos=np.max(interest_pos)
if max_peaks_pos/min_peaks_pos>=35:
min_peaks_pos=np.mean(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
#print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
#$print(min_peaks_pos)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
#print(interest_pos)
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
#print(interest_neg,'interest_neg')
#print(grenze,'grenze')
#print(min_peaks_pos,'min_peaks_pos')
#print(dis_talaei,'dis_talaei')
#print(peaks_neg,'peaks_neg')
interest_neg_fin=interest_neg[(interest_neg<grenze)]
peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
#interest_neg_fin=interest_neg[(interest_neg<grenze)]
num_col=(len(interest_neg_fin))+1
#print(peaks_neg_fin,'peaks_neg_fin')
#print(num_col,'diz')
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_g_l=int(len(y)/4.)
p_g_u=len(y)-int(len(y)/4.)
if num_col==3:
if (peaks_neg_fin[0]>p_g_u and peaks_neg_fin[1]>p_g_u) or (peaks_neg_fin[0]<p_g_l and peaks_neg_fin[1]<p_g_l ) or ((peaks_neg_fin[0]+200)<p_m and peaks_neg_fin[1]<p_m ) or ((peaks_neg_fin[0]-200)>p_m and peaks_neg_fin[1]>p_m ):
num_col=1
peaks_neg_fin=[]
else:
pass
if num_col==2:
if (peaks_neg_fin[0]>p_g_u) or (peaks_neg_fin[0]<p_g_l):
num_col=1
peaks_neg_fin=[]
else:
pass
##print(len(peaks_neg_fin))
diff_peaks=np.abs( np.diff(peaks_neg_fin) )
cut_off=400
peaks_neg_true=[]
forest=[]
#print(len(peaks_neg_fin),'len_')
for i in range(len(peaks_neg_fin)):
if i==0:
forest.append(peaks_neg_fin[i])
if i<(len(peaks_neg_fin)-1):
if diff_peaks[i]<=cut_off:
forest.append(peaks_neg_fin[i+1])
if diff_peaks[i]>cut_off:
#print(forest[np.argmin(z[forest]) ] )
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
forest=[]
forest.append(peaks_neg_fin[i+1])
if i==(len(peaks_neg_fin)-1):
#print(print(forest[np.argmin(z[forest]) ] ))
if not self.isNaN(forest[np.argmin(z[forest]) ]):
peaks_neg_true.append(forest[np.argmin(z[forest]) ])
num_col=(len(peaks_neg_true))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_quarter=int(len(y)/5.)
p_g_l=int(len(y)/4.)
p_g_u=len(y)-int(len(y)/4.)
p_u_quarter=len(y)-p_quarter
##print(num_col,'early')
if num_col==3:
if (peaks_neg_true[0]>p_g_u and peaks_neg_true[1]>p_g_u) or (peaks_neg_true[0]<p_g_l and peaks_neg_true[1]<p_g_l ) or (peaks_neg_true[0]<p_m and (peaks_neg_true[1]+200)<p_m ) or ( (peaks_neg_true[0]-200)>p_m and peaks_neg_true[1]>p_m ):
num_col=1
peaks_neg_true=[]
elif (peaks_neg_true[0]<p_g_u and peaks_neg_true[0]>p_g_l) and (peaks_neg_true[1]>p_u_quarter):
peaks_neg_true=[ peaks_neg_true[0] ]
elif (peaks_neg_true[1]<p_g_u and peaks_neg_true[1]>p_g_l) and (peaks_neg_true[0]<p_quarter):
peaks_neg_true=[ peaks_neg_true[1] ]
else:
pass
if num_col==2:
if (peaks_neg_true[0]>p_g_u) or (peaks_neg_true[0]<p_g_l):
num_col=1
peaks_neg_true=[]
else:
pass
diff_peaks_annormal=diff_peaks[diff_peaks<360]
if len(diff_peaks_annormal)>0:
arg_help=np.array(range(len(diff_peaks)))
arg_help_ann=arg_help[diff_peaks<360]
peaks_neg_fin_new=[]
for ii in range(len(peaks_neg_fin)):
if ii in arg_help_ann:
arg_min=np.argmin([interest_neg_fin[ii],interest_neg_fin[ii+1] ] )
if arg_min==0:
peaks_neg_fin_new.append( peaks_neg_fin[ii])
else:
peaks_neg_fin_new.append( peaks_neg_fin[ii+1])
elif (ii-1) in arg_help_ann:
pass
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii] )
else:
peaks_neg_fin_new=peaks_neg_fin
#plt.plot(gaussian_filter1d(y, sigma_))
#plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
#plt.plot([0,len(y)], [grenze,grenze])
#plt.show()
##print(len(peaks_neg_true))
return len(peaks_neg_true), peaks_neg_true
def find_new_features_of_contoures(self,contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
try:
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
argmin_x_main=np.array([np.argmin(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0,0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0,1] for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
except:
x_min_main=np.array([np.min(contours_main[j][:,0]) for j in range(len(contours_main))])
argmin_x_main=np.array([np.argmin(contours_main[j][:,0]) for j in range(len(contours_main))])
x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin=np.array([contours_main[j][argmin_x_main[j],1] for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,1]) for j in range(len(contours_main))])
#dis_x=np.abs(x_max_main-x_min_main)
return cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin
def return_points_with_boundies(self,peaks_neg_fin,first_point, last_point):
peaks_neg_tot=[]
peaks_neg_tot.append(first_point)
for ii in range(len(peaks_neg_fin)):
peaks_neg_tot.append(peaks_neg_fin[ii])
peaks_neg_tot.append(last_point)
return peaks_neg_tot
def contours_in_same_horizon(self,cy_main_hor):
X1=np.zeros((len(cy_main_hor),len(cy_main_hor)))
X2=np.zeros((len(cy_main_hor),len(cy_main_hor)))
X1[0::1,:]=cy_main_hor[:]
X2=X1.T
X_dif=np.abs(X2-X1)
args_help=np.array(range(len(cy_main_hor)))
all_args=[]
for i in range(len(cy_main_hor)):
list_h=list(args_help[X_dif[i,:]<=20] )
list_h.append(i)
if len(list_h)>1:
all_args.append(list( set(list_h) ))
return np.unique(all_args)
def return_boxes_of_images_by_order_of_reading_without_seperators(self,spliter_y_new,image_p_rev,regions_without_seperators,matrix_of_lines_ch,seperators_closeup_n):
boxes=[]
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if np.abs(spliter_y_new[i+1]-spliter_y_new[i])>1./3.*regions_without_seperators.shape[0]:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
#org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
#org_img_dichte=org_img_dichte-np.min(org_img_dichte)
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
num_col, peaks_neg_fin=self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=2.4)
#num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
peaks_neg_tot=self.return_points_with_boundies(peaks_neg_fin,0, seperators_closeup_n[:,:,0].shape[1])
start_index_of_hor,newest_peaks,arg_min_hor_sort,lines_length_dels,lines_indexes_deleted=self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot,x_min_hor_some,x_max_hor_some)
arg_org_hor_some_sort=arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset=[start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]#start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset=[arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_indexes_deleted_with_subset=[lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_length_dels_with_subset=[lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
arg_org_hor_some_sort_subset=[arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
#arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
#lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
#lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
#print(len(arg_min_hor_sort),len(arg_org_hor_some_sort),'vizzzzzz')
vahid_subset=np.zeros((len(start_index_of_hor_with_subset),len(start_index_of_hor_with_subset)))-1
for kkk1 in range(len(start_index_of_hor_with_subset)):
#print(lines_indexes_deleted,'hiii')
index_del_sub=np.unique(lines_indexes_deleted_with_subset[kkk1])
for kkk2 in range(len(start_index_of_hor_with_subset)):
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1,kkk2]=kkk1
else:
pass
#print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset>0):
#print('hihoo')
# find parenets args
line_int=np.zeros(vahid_subset.shape[0])
childs_id=[]
arg_child=[]
for li in range(vahid_subset.shape[0]):
if np.all(vahid_subset[:,li]==-1):
line_int[li]=-1
else:
line_int[li]=1
#childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
#helpi=[]
#for nad in range(len(childs_args_in)):
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li] )
arg_parent=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
start_index_of_hor_parent=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
cy_hor_some_sort=cy_hor_some[arg_parent]
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
cy_help=np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent)==tj]
cy_help_sort=np.sort(cy_help)
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int=[]
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int)==-1):
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
for n in range(len(newest_y_spliter)-1):
#print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
#print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:,0][ (matrix_new[:,9]==1 ) & (matrix_new[:,6]> newest_y_spliter[n] ) & (matrix_new[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_new[:,1]) < newest_peaks[j+1] ) & (( matrix_new[:,1])> newest_peaks[j] ) ] :
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
#num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_sub, peaks_neg_fin_sub=self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.4)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
if j in start_index_of_hor_parent:
x_min_ch=x_min_hor_some[arg_child]
x_max_ch=x_max_hor_some[arg_child]
cy_hor_some_sort_child=cy_hor_some[arg_child]
cy_hor_some_sort_child=np.sort(cy_hor_some_sort_child)
for n in range(len(newest_y_spliter)-1):
cy_child_in=cy_hor_some_sort_child[( cy_hor_some_sort_child>newest_y_spliter[n] ) & ( cy_hor_some_sort_child<newest_y_spliter[n+1] ) ]
if len(cy_child_in)>0:
###num_col_ch, peaks_neg_ch=find_num_col( regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_ch, peaks_neg_ch=self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
peaks_neg_ch=peaks_neg_ch[:]+newest_peaks[j]
peaks_neg_ch_tot=self.return_points_with_boundies(peaks_neg_ch,newest_peaks[j], newest_peaks[j+1])
ss_in_ch,nst_p_ch,arg_n_ch,lines_l_del_ch,lines_in_del_ch=self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot,x_min_ch,x_max_ch)
newest_y_spliter_ch_tot=[]
for tjj in range(len(nst_p_ch)-1):
newest_y_spliter_new=[]
newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n+1])
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch)-1):
newest_y_spliter_h=newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h)-1):
matrix_new_new2=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter_h[nd] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter_h[nd+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < nst_p_ch[jn+1] ) & (( matrix_of_lines_ch[:,1]-500)>nst_p_ch[jn] ) ]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
#num_col_sub_ch, peaks_neg_fin_sub_ch=find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=2.3)
num_col_sub_ch, peaks_neg_fin_sub_ch=self.find_num_col_only_image(image_p_rev[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=2.3)
#print(peaks_neg_fin_sub_ch,'gada kutullllllll')
else:
peaks_neg_fin_sub_ch=[]
peaks_sub_ch=[]
peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj]+nst_p_ch[jn])
peaks_sub_ch.append(nst_p_ch[jn+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for khh in range(len(peaks_sub_ch)-1):
boxes.append([ peaks_sub_ch[khh], peaks_sub_ch[khh+1] ,newest_y_spliter_h[nd],newest_y_spliter_h[nd+1]])
else:
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
num_col_sub, peaks_neg_fin_sub=self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for n in range(len(newest_y_spliter)-1):
for jvt in matrix_new[:,0][ (matrix_new[:,9]==1 ) & (matrix_new[:,6]> newest_y_spliter[n] ) & (matrix_new[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_new[:,1]) < newest_peaks[j+1] ) & (( matrix_new[:,1])> newest_peaks[j] ) ] :
pass
#plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
num_col_sub, peaks_neg_fin_sub=self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
return boxes
def return_boxes_of_images_by_order_of_reading_without_seperators_2cols(self,spliter_y_new,image_p_rev,regions_without_seperators,matrix_of_lines_ch,seperators_closeup_n):
boxes=[]
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if np.abs(spliter_y_new[i+1]-spliter_y_new[i])>1./3.*regions_without_seperators.shape[0]:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
#org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
#org_img_dichte=org_img_dichte-np.min(org_img_dichte)
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
try:
num_col, peaks_neg_fin=self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=2.4)
except:
peaks_neg_fin=[]
num_col=0
peaks_neg_tot=self.return_points_with_boundies(peaks_neg_fin,0, seperators_closeup_n[:,:,0].shape[1])
for kh in range(len(peaks_neg_tot)-1):
boxes.append([ peaks_neg_tot[kh], peaks_neg_tot[kh+1] ,spliter_y_new[i],spliter_y_new[i+1]])
else:
boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
return boxes
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(self, regions_pre_p):
seperators_closeup=( (regions_pre_p[:,:]==6))*1
seperators_closeup=seperators_closeup.astype(np.uint8)
kernel = np.ones((5,5),np.uint8)
seperators_closeup = cv2.dilate(seperators_closeup,kernel,iterations = 1)
seperators_closeup = cv2.erode(seperators_closeup,kernel,iterations = 1)
seperators_closeup = cv2.erode(seperators_closeup,kernel,iterations = 1)
seperators_closeup = cv2.dilate(seperators_closeup,kernel,iterations = 1)
if len(seperators_closeup.shape)==2:
seperators_closeup_n=np.zeros((seperators_closeup.shape[0],seperators_closeup.shape[1],3))
seperators_closeup_n[:,:,0]=seperators_closeup
seperators_closeup_n[:,:,1]=seperators_closeup
seperators_closeup_n[:,:,2]=seperators_closeup
else:
seperators_closeup_n=seperators_closeup[:,:,:]
#seperators_closeup=seperators_closeup.astype(np.uint8)
seperators_closeup_n=seperators_closeup_n.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_lines)
dist_y=np.abs(y_max_main-y_min_main)
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]*0
len_y=seperators_closeup.shape[0]*.01
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
dist_y_hor=dist_y[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
y_min_main_hor=y_min_main[slope_lines==0]
y_max_main_hor=y_max_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x]
y_min_main_hor=y_min_main_hor[dist_x_hor>=len_x]
y_max_main_hor=y_max_main_hor[dist_x_hor>=len_x]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x]
dist_y_hor=dist_y_hor[dist_x_hor>=len_x]
dist_x_hor=dist_x_hor[dist_x_hor>=len_x]
args_ver=args[slope_lines==1]
dist_y_ver=dist_y[slope_lines==1]
dist_x_ver=dist_x[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
args_ver=args_ver[dist_y_ver>=len_y]
x_max_main_ver=x_max_main_ver[dist_y_ver>=len_y]
x_min_main_ver=x_min_main_ver[dist_y_ver>=len_y]
cx_main_ver=cx_main_ver[dist_y_ver>=len_y]
y_min_main_ver=y_min_main_ver[dist_y_ver>=len_y]
y_max_main_ver=y_max_main_ver[dist_y_ver>=len_y]
dist_x_ver=dist_x_ver[dist_y_ver>=len_y]
dist_y_ver=dist_y_ver[dist_y_ver>=len_y]
img_p_in_ver=np.zeros(seperators_closeup_n[:,:,2].shape)
for jv in range(len(args_ver)):
img_p_in_ver=cv2.fillPoly(img_p_in_ver, pts =[contours_lines[args_ver[jv]]], color=(1,1,1))
img_in_hor=np.zeros(seperators_closeup_n[:,:,2].shape)
for jv in range(len(args_hor)):
img_p_in_hor=cv2.fillPoly(img_in_hor, pts =[contours_lines[args_hor[jv]]], color=(1,1,1))
all_args_uniq=self.contours_in_same_horizon(cy_main_hor)
#print(all_args_uniq,'all_args_uniq')
if len(all_args_uniq)>0:
if type(all_args_uniq[0]) is list:
contours_new=[]
for dd in range(len(all_args_uniq)):
merged_all=None
some_args=args_hor[all_args_uniq[dd]]
some_cy=cy_main_hor[all_args_uniq[dd]]
some_x_min=x_min_main_hor[all_args_uniq[dd]]
some_x_max=x_max_main_hor[all_args_uniq[dd]]
img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
for jv in range(len(some_args)):
img_p_in=cv2.fillPoly(img_p_in_hor, pts =[contours_lines[some_args[jv]]], color=(1,1,1))
img_p_in[int(np.mean(some_cy))-5:int(np.mean(some_cy))+5, int(np.min(some_x_min)):int(np.max(some_x_max)) ]=1
else:
img_p_in=seperators_closeup
else:
img_p_in=seperators_closeup
sep_ver_hor=img_p_in+img_p_in_ver
sep_ver_hor_cross=(sep_ver_hor==2)*1
sep_ver_hor_cross=np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
sep_ver_hor_cross=sep_ver_hor_cross.astype(np.uint8)
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cx_cross,cy_cross ,_ , _, _ ,_,_=self.find_new_features_of_contoures(contours_cross)
for ii in range(len(cx_cross)):
sep_ver_hor[int(cy_cross[ii])-15:int(cy_cross[ii])+15,int(cx_cross[ii])+5:int(cx_cross[ii])+40]=0
sep_ver_hor[int(cy_cross[ii])-15:int(cy_cross[ii])+15,int(cx_cross[ii])-40:int(cx_cross[ii])-4]=0
img_p_in[:,:]=sep_ver_hor[:,:]
if len(img_p_in.shape)==2:
seperators_closeup_n=np.zeros((img_p_in.shape[0],img_p_in.shape[1],3))
seperators_closeup_n[:,:,0]=img_p_in
seperators_closeup_n[:,:,1]=img_p_in
seperators_closeup_n[:,:,2]=img_p_in
else:
seperators_closeup_n=img_p_in[:,:,:]
#seperators_closeup=seperators_closeup.astype(np.uint8)
seperators_closeup_n=seperators_closeup_n.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_lines)
dist_y=np.abs(y_max_main-y_min_main)
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]*.04
len_y=seperators_closeup.shape[0]*.08
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
dist_y_hor=dist_y[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
y_min_main_hor=y_min_main[slope_lines==0]
y_max_main_hor=y_max_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x]
y_min_main_hor=y_min_main_hor[dist_x_hor>=len_x]
y_max_main_hor=y_max_main_hor[dist_x_hor>=len_x]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x]
dist_y_hor=dist_y_hor[dist_x_hor>=len_x]
dist_x_hor=dist_x_hor[dist_x_hor>=len_x]
args_ver=args[slope_lines==1]
dist_y_ver=dist_y[slope_lines==1]
dist_x_ver=dist_x[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
args_ver=args_ver[dist_y_ver>=len_y]
x_max_main_ver=x_max_main_ver[dist_y_ver>=len_y]
x_min_main_ver=x_min_main_ver[dist_y_ver>=len_y]
cx_main_ver=cx_main_ver[dist_y_ver>=len_y]
y_min_main_ver=y_min_main_ver[dist_y_ver>=len_y]
y_max_main_ver=y_max_main_ver[dist_y_ver>=len_y]
dist_x_ver=dist_x_ver[dist_y_ver>=len_y]
dist_y_ver=dist_y_ver[dist_y_ver>=len_y]
matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))
matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver
matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver
matrix_of_lines_ch[:len(cy_main_hor),2]=x_min_main_hor
matrix_of_lines_ch[len(cy_main_hor):,2]=x_min_main_ver
matrix_of_lines_ch[:len(cy_main_hor),3]=x_max_main_hor
matrix_of_lines_ch[len(cy_main_hor):,3]=x_max_main_ver
matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver
matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor
matrix_of_lines_ch[:len(cy_main_hor),6]=y_min_main_hor
matrix_of_lines_ch[len(cy_main_hor):,6]=y_min_main_ver
matrix_of_lines_ch[:len(cy_main_hor),7]=y_max_main_hor
matrix_of_lines_ch[len(cy_main_hor):,7]=y_max_main_ver
matrix_of_lines_ch[:len(cy_main_hor),8]=dist_y_hor
matrix_of_lines_ch[len(cy_main_hor):,8]=dist_y_ver
matrix_of_lines_ch[len(cy_main_hor):,9]=1
return matrix_of_lines_ch,seperators_closeup_n
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(self, img_p_in_ver,img_in_hor):
#plt.imshow(img_in_hor)
#plt.show()
#img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
img_p_in_ver=img_p_in_ver.astype(np.uint8)
img_p_in_ver=np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines_ver,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines_ver,dist_x_ver, x_min_main_ver ,x_max_main_ver ,cy_main_ver,slope_lines_org_ver,y_min_main_ver, y_max_main_ver, cx_main_ver=self.find_features_of_lines(contours_lines_ver)
for i in range(len(x_min_main_ver)):
img_p_in_ver[int(y_min_main_ver[i]):int(y_min_main_ver[i])+30,int(cx_main_ver[i])-25:int(cx_main_ver[i])+25,0]=0
img_p_in_ver[int(y_max_main_ver[i])-30:int(y_max_main_ver[i]),int(cx_main_ver[i])-25:int(cx_main_ver[i])+25,0]=0
#plt.imshow(img_p_in_ver[:,:,0])
#plt.show()
img_in_hor=img_in_hor.astype(np.uint8)
img_in_hor=np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines_hor,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines_hor,dist_x_hor, x_min_main_hor ,x_max_main_hor ,cy_main_hor,slope_lines_org_hor,y_min_main_hor, y_max_main_hor, cx_main_hor=self.find_features_of_lines(contours_lines_hor)
args_hor=np.array( range(len(slope_lines_hor) ))
all_args_uniq=self.contours_in_same_horizon(cy_main_hor)
#print(all_args_uniq,'all_args_uniq')
if len(all_args_uniq)>0:
if type(all_args_uniq[0]) is list:
special_seperators=[]
contours_new=[]
for dd in range(len(all_args_uniq)):
merged_all=None
some_args=args_hor[all_args_uniq[dd]]
some_cy=cy_main_hor[all_args_uniq[dd]]
some_x_min=x_min_main_hor[all_args_uniq[dd]]
some_x_max=x_max_main_hor[all_args_uniq[dd]]
#img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
for jv in range(len(some_args)):
img_p_in=cv2.fillPoly(img_in_hor, pts =[contours_lines_hor[some_args[jv]]], color=(1,1,1))
img_p_in[int(np.mean(some_cy))-5:int(np.mean(some_cy))+5, int(np.min(some_x_min)):int(np.max(some_x_max)) ]=1
sum_dis=dist_x_hor[some_args].sum()
diff_max_min_uniques=np.max(x_max_main_hor[some_args])-np.min(x_min_main_hor[some_args])
#print( sum_dis/float(diff_max_min_uniques) ,diff_max_min_uniques/float(img_p_in_ver.shape[1]),dist_x_hor[some_args].sum(),diff_max_min_uniques,np.mean( dist_x_hor[some_args]),np.std( dist_x_hor[some_args]) )
if diff_max_min_uniques>sum_dis and ( (sum_dis/float(diff_max_min_uniques) ) >0.85 ) and ( (diff_max_min_uniques/float(img_p_in_ver.shape[1]))>0.85 ) and np.std( dist_x_hor[some_args] )<(0.55*np.mean( dist_x_hor[some_args] )):
#print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi')
#print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha')
special_seperators.append(np.mean(cy_main_hor[some_args]))
else:
img_p_in=img_in_hor
special_seperators=[]
else:
img_p_in=img_in_hor
special_seperators=[]
img_p_in_ver[:,:,0][img_p_in_ver[:,:,0]==255]=1
#print(img_p_in_ver.shape,np.unique(img_p_in_ver[:,:,0]))
#plt.imshow(img_p_in[:,:,0])
#plt.show()
#plt.imshow(img_p_in_ver[:,:,0])
#plt.show()
sep_ver_hor=img_p_in+img_p_in_ver
#print(sep_ver_hor.shape,np.unique(sep_ver_hor[:,:,0]),'sep_ver_horsep_ver_horsep_ver_hor')
#plt.imshow(sep_ver_hor[:,:,0])
#plt.show()
sep_ver_hor_cross=(sep_ver_hor[:,:,0]==2)*1
sep_ver_hor_cross=np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
sep_ver_hor_cross=sep_ver_hor_cross.astype(np.uint8)
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cx_cross,cy_cross ,_ , _, _ ,_,_=self.find_new_features_of_contoures(contours_cross)
for ii in range(len(cx_cross)):
img_p_in[int(cy_cross[ii])-30:int(cy_cross[ii])+30,int(cx_cross[ii])+5:int(cx_cross[ii])+40,0]=0
img_p_in[int(cy_cross[ii])-30:int(cy_cross[ii])+30,int(cx_cross[ii])-40:int(cx_cross[ii])-4,0]=0
#plt.imshow(img_p_in[:,:,0])
#plt.show()
return img_p_in[:,:,0],special_seperators
def return_boxes_of_images_by_order_of_reading(self,spliter_y_new,regions_without_seperators,matrix_of_lines_ch,seperators_closeup_n):
boxes=[]
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
#org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
#org_img_dichte=org_img_dichte-np.min(org_img_dichte)
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
#num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
peaks_neg_tot=self.return_points_with_boundies(peaks_neg_fin,0, seperators_closeup_n[:,:,0].shape[1])
start_index_of_hor,newest_peaks,arg_min_hor_sort,lines_length_dels,lines_indexes_deleted=self.return_hor_spliter_by_index(peaks_neg_tot,x_min_hor_some,x_max_hor_some)
arg_org_hor_some_sort=arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset=[start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]#start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset=[arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_indexes_deleted_with_subset=[lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_length_dels_with_subset=[lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
arg_org_hor_some_sort_subset=[arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
#arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
#lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
#lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
vahid_subset=np.zeros((len(start_index_of_hor_with_subset),len(start_index_of_hor_with_subset)))-1
for kkk1 in range(len(start_index_of_hor_with_subset)):
index_del_sub=np.unique(lines_indexes_deleted_with_subset[kkk1])
for kkk2 in range(len(start_index_of_hor_with_subset)):
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1,kkk2]=kkk1
else:
pass
#print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
#print(vahid_subset,'zartt222')
# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset>0):
#print('hihoo')
# find parenets args
line_int=np.zeros(vahid_subset.shape[0])
childs_id=[]
arg_child=[]
for li in range(vahid_subset.shape[0]):
#print(vahid_subset[:,li])
if np.all(vahid_subset[:,li]==-1):
line_int[li]=-1
else:
line_int[li]=1
#childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
#helpi=[]
#for nad in range(len(childs_args_in)):
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li] )
#line_int=vahid_subset[0,:]
#print(arg_child,line_int[0],'zartt33333')
arg_parent=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
start_index_of_hor_parent=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
cy_hor_some_sort=cy_hor_some[arg_parent]
#print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
#args_indexes=np.array(range(len(start_index_of_hor) ))
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
##print(cy_hor_some_sort)
cy_help=np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent)==tj]
cy_help_sort=np.sort(cy_help)
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int=[]
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int)==-1):
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
for n in range(len(newest_y_spliter)-1):
#print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
#print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:,0][ (matrix_new[:,9]==1 ) & (matrix_new[:,6]> newest_y_spliter[n] ) & (matrix_new[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_new[:,1]) < newest_peaks[j+1] ) & (( matrix_new[:,1])> newest_peaks[j] ) ] :
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
if j in start_index_of_hor_parent:
x_min_ch=x_min_hor_some[arg_child]
x_max_ch=x_max_hor_some[arg_child]
cy_hor_some_sort_child=cy_hor_some[arg_child]
cy_hor_some_sort_child=np.sort(cy_hor_some_sort_child)
#print(cy_hor_some_sort_child,'ychilds')
for n in range(len(newest_y_spliter)-1):
cy_child_in=cy_hor_some_sort_child[( cy_hor_some_sort_child>newest_y_spliter[n] ) & ( cy_hor_some_sort_child<newest_y_spliter[n+1] ) ]
if len(cy_child_in)>0:
num_col_ch, peaks_neg_ch=self.find_num_col( regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
#print(peaks_neg_ch,'mizzzz')
#peaks_neg_ch=[]
#for djh in range(len(peaks_neg_ch)):
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
peaks_neg_ch_tot=self.return_points_with_boundies(peaks_neg_ch,newest_peaks[j], newest_peaks[j+1])
ss_in_ch,nst_p_ch,arg_n_ch,lines_l_del_ch,lines_in_del_ch=self.return_hor_spliter_by_index(peaks_neg_ch_tot,x_min_ch,x_max_ch)
newest_y_spliter_ch_tot=[]
for tjj in range(len(nst_p_ch)-1):
newest_y_spliter_new=[]
newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n+1])
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch)-1):
newest_y_spliter_h=newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h)-1):
matrix_new_new2=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter_h[nd] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter_h[nd+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < nst_p_ch[jn+1] ) & (( matrix_of_lines_ch[:,1]-500)>nst_p_ch[jn] ) ]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
num_col_sub_ch, peaks_neg_fin_sub_ch=self.find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=5.0)
else:
peaks_neg_fin_sub_ch=[]
peaks_sub_ch=[]
peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj]+nst_p_ch[jn])
peaks_sub_ch.append(nst_p_ch[jn+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for khh in range(len(peaks_sub_ch)-1):
boxes.append([ peaks_sub_ch[khh], peaks_sub_ch[khh+1] ,newest_y_spliter_h[nd],newest_y_spliter_h[nd+1]])
else:
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for n in range(len(newest_y_spliter)-1):
#plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
return boxes
def return_boxes_of_images_by_order_of_reading_new(self,spliter_y_new,regions_without_seperators,matrix_of_lines_ch):
boxes=[]
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if 1>0:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
#org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
#org_img_dichte=org_img_dichte-np.min(org_img_dichte)
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
#print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'firssst')
#plt.imshow(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:])
#plt.show()
try:
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.)
except:
peaks_neg_fin=[]
#print(peaks_neg_fin,'peaks_neg_fin')
#num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
arg_org_hor_some=matrix_new[:,0][ (matrix_new[:,9]==0) ]
peaks_neg_tot=self.return_points_with_boundies(peaks_neg_fin,0, regions_without_seperators[:,:].shape[1])
start_index_of_hor,newest_peaks,arg_min_hor_sort,lines_length_dels,lines_indexes_deleted=self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot,x_min_hor_some,x_max_hor_some)
arg_org_hor_some_sort=arg_org_hor_some[arg_min_hor_sort]
start_index_of_hor_with_subset=[start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]#start_index_of_hor[lines_length_dels>0]
arg_min_hor_sort_with_subset=[arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_indexes_deleted_with_subset=[lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
lines_length_dels_with_subset=[lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
arg_org_hor_some_sort_subset=[arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij]>0 ]
#arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
#lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
#lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
vahid_subset=np.zeros((len(start_index_of_hor_with_subset),len(start_index_of_hor_with_subset)))-1
for kkk1 in range(len(start_index_of_hor_with_subset)):
index_del_sub=np.unique(lines_indexes_deleted_with_subset[kkk1])
for kkk2 in range(len(start_index_of_hor_with_subset)):
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
vahid_subset[kkk1,kkk2]=kkk1
else:
pass
#print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
# check the len of matrix if it has no length means that there is no spliter at all
if len(vahid_subset>0):
#print('hihoo')
# find parenets args
line_int=np.zeros(vahid_subset.shape[0])
childs_id=[]
arg_child=[]
for li in range(vahid_subset.shape[0]):
#print(vahid_subset[:,li])
if np.all(vahid_subset[:,li]==-1):
line_int[li]=-1
else:
line_int[li]=1
#childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
#helpi=[]
#for nad in range(len(childs_args_in)):
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
arg_child.append(arg_min_hor_sort_with_subset[li] )
#line_int=vahid_subset[0,:]
arg_parent=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
start_index_of_hor_parent=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
#arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
start_index_of_hor_child=[start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
cy_hor_some_sort=cy_hor_some[arg_parent]
#print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
#args_indexes=np.array(range(len(start_index_of_hor) ))
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
if tj in np.unique(start_index_of_hor_parent):
#print(cy_hor_some_sort)
cy_help=np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent)==tj]
cy_help_sort=np.sort(cy_help)
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mj in range(len(cy_help_sort)):
newest_y_spliter.append(cy_help_sort[mj])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
else:
line_int=[]
newest_y_spliter_tot=[]
for tj in range(len(newest_peaks)-1):
newest_y_spliter=[]
newest_y_spliter.append(spliter_y_new[i])
newest_y_spliter.append(spliter_y_new[i+1])
newest_y_spliter_tot.append(newest_y_spliter)
# if line_int is all -1 means that big spliters have no child and we can easily go through
if np.all(np.array(line_int)==-1):
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
for n in range(len(newest_y_spliter)-1):
#print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
##plt.show()
#print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
for jvt in matrix_new[:,0][ (matrix_new[:,9]==1 ) & (matrix_new[:,6]> newest_y_spliter[n] ) & (matrix_new[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_new[:,1]) < newest_peaks[j+1] ) & (( matrix_new[:,1])> newest_peaks[j] ) ] :
pass
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
#print( int(newest_y_spliter[n]),int(newest_y_spliter[n+1]),newest_peaks[j],newest_peaks[j+1] )
try:
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=7.)
except:
peaks_neg_fin_sub=[]
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for j in range(len(newest_peaks)-1):
newest_y_spliter=newest_y_spliter_tot[j]
if j in start_index_of_hor_parent:
x_min_ch=x_min_hor_some[arg_child]
x_max_ch=x_max_hor_some[arg_child]
cy_hor_some_sort_child=cy_hor_some[arg_child]
cy_hor_some_sort_child=np.sort(cy_hor_some_sort_child)
for n in range(len(newest_y_spliter)-1):
cy_child_in=cy_hor_some_sort_child[( cy_hor_some_sort_child>newest_y_spliter[n] ) & ( cy_hor_some_sort_child<newest_y_spliter[n+1] ) ]
if len(cy_child_in)>0:
try:
num_col_ch, peaks_neg_ch=self.find_num_col( regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=7.0)
except:
peaks_neg_ch=[]
#print(peaks_neg_ch,'mizzzz')
#peaks_neg_ch=[]
#for djh in range(len(peaks_neg_ch)):
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
peaks_neg_ch_tot=self.return_points_with_boundies(peaks_neg_ch,newest_peaks[j], newest_peaks[j+1])
ss_in_ch,nst_p_ch,arg_n_ch,lines_l_del_ch,lines_in_del_ch=self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot,x_min_ch,x_max_ch)
newest_y_spliter_ch_tot=[]
for tjj in range(len(nst_p_ch)-1):
newest_y_spliter_new=[]
newest_y_spliter_new.append(newest_y_spliter[n])
if tjj in np.unique(ss_in_ch):
#print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
for mjj in range(len(cy_child_in)):
newest_y_spliter_new.append(cy_child_in[mjj])
newest_y_spliter_new.append(newest_y_spliter[n+1])
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
for jn in range(len(nst_p_ch)-1):
newest_y_spliter_h=newest_y_spliter_ch_tot[jn]
for nd in range(len(newest_y_spliter_h)-1):
matrix_new_new2=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter_h[nd] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter_h[nd+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < nst_p_ch[jn+1] ) & (( matrix_of_lines_ch[:,1]-500)>nst_p_ch[jn] ) ]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
try:
num_col_sub_ch, peaks_neg_fin_sub_ch=self.find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=7.0)
except:
peaks_neg_fin_sub_ch=[]
else:
peaks_neg_fin_sub_ch=[]
peaks_sub_ch=[]
peaks_sub_ch.append(nst_p_ch[jn])
for kjj in range(len(peaks_neg_fin_sub_ch)):
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj]+nst_p_ch[jn])
peaks_sub_ch.append(nst_p_ch[jn+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for khh in range(len(peaks_sub_ch)-1):
boxes.append([ peaks_sub_ch[khh], peaks_sub_ch[khh+1] ,newest_y_spliter_h[nd],newest_y_spliter_h[nd+1]])
else:
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
try:
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=7.0)
except:
peaks_neg_fin_sub=[]
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
for n in range(len(newest_y_spliter)-1):
#plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
#print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
matrix_new_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,9]==1 ) & (matrix_of_lines_ch[:,6]> newest_y_spliter[n] ) & (matrix_of_lines_ch[:,7]< newest_y_spliter[n+1] ) & ( ( matrix_of_lines_ch[:,1]+500) < newest_peaks[j+1] ) & (( matrix_of_lines_ch[:,1]-500)> newest_peaks[j] )]
#print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
if 1>0:#len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
try:
num_col_sub, peaks_neg_fin_sub=self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
except:
peaks_neg_fin_sub=[]
else:
peaks_neg_fin_sub=[]
peaks_sub=[]
peaks_sub.append(newest_peaks[j])
for kj in range(len(peaks_neg_fin_sub)):
peaks_sub.append(peaks_neg_fin_sub[kj]+newest_peaks[j])
peaks_sub.append(newest_peaks[j+1])
#peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
for kh in range(len(peaks_sub)-1):
boxes.append([ peaks_sub[kh], peaks_sub[kh+1] ,newest_y_spliter[n],newest_y_spliter[n+1]])
else:
boxes.append([ 0, regions_without_seperators[:,:].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
return boxes
def return_boxes_of_images_by_order_of_reading_2cols(self,spliter_y_new,regions_without_seperators,matrix_of_lines_ch,seperators_closeup_n):
boxes=[]
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
# holes in the text and also finding spliter which covers more than one columns.
for i in range(len(spliter_y_new)-1):
#print(spliter_y_new[i],spliter_y_new[i+1])
matrix_new=matrix_of_lines_ch[:,:][ (matrix_of_lines_ch[:,6]> spliter_y_new[i] ) & (matrix_of_lines_ch[:,7]< spliter_y_new[i+1] ) ]
#print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
#print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
# check to see is there any vertical seperator to find holes.
if 1>0:#len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
#print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'burayaaaa galimiirrrrrrrrrrrrrrrrrrrrrrrrrrr')
#org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
#org_img_dichte=org_img_dichte-np.min(org_img_dichte)
##plt.figure(figsize=(20,20))
##plt.plot(org_img_dichte)
##plt.show()
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
try:
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
except:
peaks_neg_fin=[]
num_col=0
peaks_neg_tot=self.return_points_with_boundies(peaks_neg_fin,0, seperators_closeup_n[:,:,0].shape[1])
for kh in range(len(peaks_neg_tot)-1):
boxes.append([ peaks_neg_tot[kh], peaks_neg_tot[kh+1] ,spliter_y_new[i],spliter_y_new[i+1]])
else:
boxes.append([ 0, seperators_closeup_n[:,:,0].shape[1] ,spliter_y_new[i],spliter_y_new[i+1]])
return boxes
def return_hor_spliter_by_index(self,peaks_neg_fin_t,x_min_hor_some,x_max_hor_some):
arg_min_hor_sort=np.argsort(x_min_hor_some)
x_min_hor_some_sort=np.sort(x_min_hor_some)
x_max_hor_some_sort=x_max_hor_some[arg_min_hor_sort]
arg_minmax=np.array(range(len(peaks_neg_fin_t)))
indexer_lines=[]
indexes_to_delete=[]
indexer_lines_deletions_len=[]
indexr_uniq_ind=[]
for i in range(len(x_min_hor_some_sort)):
min_h=peaks_neg_fin_t-x_min_hor_some_sort[i]
max_h=peaks_neg_fin_t-x_max_hor_some_sort[i]
min_h[0]=min_h[0]#+20
max_h[len(max_h)-1]=max_h[len(max_h)-1]##-20
min_h_neg=arg_minmax[(min_h<0) & (np.abs(min_h)<360) ]
max_h_neg=arg_minmax[(max_h>=0) & (np.abs(max_h)<360) ]
if len(min_h_neg)>0 and len(max_h_neg)>0:
deletions=list(range(min_h_neg[0]+1,max_h_neg[0]))
unique_delets_int=[]
#print(deletions,len(deletions),'delii')
if len(deletions)>0:
#print(deletions,len(deletions),'delii2')
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
#print(deletions,indexes_to_delete,'badiii')
unique_delets=np.unique(indexes_to_delete)
#print(min_h_neg[0],unique_delets)
unique_delets_int=unique_delets[unique_delets<min_h_neg[0]]
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true=min_h_neg[0]-len(unique_delets_int)
#print(index_line_true)
if index_line_true>0 and min_h_neg[0]>=2:
index_line_true=index_line_true
else:
index_line_true=min_h_neg[0]
indexer_lines.append(index_line_true)
if len(unique_delets_int)>0:
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true=[]
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines,peaks_true,arg_min_hor_sort,indexer_lines_deletions_len,indexr_uniq_ind
def return_region_segmentation_after_implementing_not_head_maintext_parallel(self,image_regions_eraly_p,boxes):
image_revised=np.zeros((image_regions_eraly_p.shape[0] , image_regions_eraly_p.shape[1]))
for i in range(len(boxes)):
image_box=image_regions_eraly_p[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1])]
image_box=np.array(image_box)
#plt.imshow(image_box)
#plt.show()
#print(int(boxes[i][2]),int(boxes[i][3]),int(boxes[i][0]),int(boxes[i][1]),'addaa')
image_box=self.implent_law_head_main_not_parallel(image_box)
image_box=self.implent_law_head_main_not_parallel(image_box)
image_box=self.implent_law_head_main_not_parallel(image_box)
image_revised[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1])]=image_box[:,:]
return image_revised
def tear_main_texts_on_the_boundaries_of_boxes(self,img_revised_tab,boxes):
for i in range(len(boxes)):
img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 0][img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 0]==1]=0
img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 1][img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 1]==1]=0
img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 2][img_revised_tab[ int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][1]-10):int(boxes[i][1]), 2]==1]=0
return img_revised_tab
def implent_law_head_main_not_parallel(self,text_regions):
#print(text_regions.shape)
text_indexes=[1 , 2 ]# 1: main text , 2: header , 3: comments
for t_i in text_indexes:
textline_mask=(text_regions[:,:]==t_i)
textline_mask=textline_mask*255.0
textline_mask=textline_mask.astype(np.uint8)
textline_mask=np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)
kernel = np.ones((5,5),np.uint8)
#print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
if t_i==1:
contours_main,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(type(contours_main))
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
#print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
elif t_i==2:
contours_header,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(type(contours_header))
areas_header=np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
M_header=[cv2.moments(contours_header[j]) for j in range(len(contours_header))]
cx_header=[(M_header[j]['m10']/(M_header[j]['m00']+1e-32)) for j in range(len(M_header))]
cy_header=[(M_header[j]['m01']/(M_header[j]['m00']+1e-32)) for j in range(len(M_header))]
x_min_header=np.array([np.min(contours_header[j][:,0,0]) for j in range(len(contours_header))])
x_max_header=np.array([np.max(contours_header[j][:,0,0]) for j in range(len(contours_header))])
y_min_header=np.array([np.min(contours_header[j][:,0,1]) for j in range(len(contours_header))])
y_max_header=np.array([np.max(contours_header[j][:,0,1]) for j in range(len(contours_header))])
args=np.array(range(1,len(cy_header)+1))
args_main=np.array(range(1,len(cy_main)+1))
for jj in range(len(contours_main)):
headers_in_main=[(cy_header>y_min_main[jj]) & ((cy_header<y_max_main[jj]))]
mains_in_main=[(cy_main>y_min_main[jj]) & ((cy_main<y_max_main[jj]))]
args_log=args*headers_in_main
res=args_log[args_log>0]
res_true=res-1
args_log_main=args_main*mains_in_main
res_main=args_log_main[args_log_main>0]
res_true_main=res_main-1
if len(res_true)>0:
sum_header=np.sum(areas_header[res_true])
sum_main=np.sum(areas_main[res_true_main])
if sum_main>sum_header:
cnt_int=[contours_header[j] for j in res_true]
text_regions=cv2.fillPoly(text_regions, pts =cnt_int, color=(1,1,1))
else:
cnt_int=[contours_main[j] for j in res_true_main]
text_regions=cv2.fillPoly(text_regions, pts =cnt_int, color=(2,2,2))
for jj in range(len(contours_header)):
main_in_header=[(cy_main>y_min_header[jj]) & ((cy_main<y_max_header[jj]))]
header_in_header=[(cy_header>y_min_header[jj]) & ((cy_header<y_max_header[jj]))]
args_log=args_main*main_in_header
res=args_log[args_log>0]
res_true=res-1
args_log_header=args*header_in_header
res_header=args_log_header[args_log_header>0]
res_true_header=res_header-1
if len(res_true)>0:
sum_header=np.sum(areas_header[res_true_header])
sum_main=np.sum(areas_main[res_true])
if sum_main>sum_header:
cnt_int=[contours_header[j] for j in res_true_header]
text_regions=cv2.fillPoly(text_regions, pts =cnt_int, color=(1,1,1))
else:
cnt_int=[contours_main[j] for j in res_true]
text_regions=cv2.fillPoly(text_regions, pts =cnt_int, color=(2,2,2))
return text_regions
def delete_seperator_around(self,spliter_y,peaks_neg,image_by_region):
# format of subboxes box=[x1, x2 , y1, y2]
if len(image_by_region.shape)==3:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0]==6 ]=0
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),1]==6 ]=0
image_by_region[spliter_y[i]:spliter_y[i+1],peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),2]==6 ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0]==7 ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),1]==7 ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),0][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j]),2]==7 ]=0
else:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])]==6 ]=0
image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])][image_by_region[int(spliter_y[i]):int(spliter_y[i+1]),peaks_neg[i][j]-int(1./20.*peaks_neg[i][j]):peaks_neg[i][j]+int(1./20.*peaks_neg[i][j])]==7 ]=0
return image_by_region
def find_features_of_contoures(self,contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
return y_min_main ,y_max_main
def add_tables_heuristic_to_layout(self,image_regions_eraly_p,boxes,slope_mean_hor,spliter_y,peaks_neg_tot,image_revised):
image_revised_1=self.delete_seperator_around(spliter_y,peaks_neg_tot,image_revised)
img_comm_e=np.zeros(image_revised_1.shape)
img_comm=np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
for indiv in np.unique(image_revised_1):
#print(indiv,'indd')
image_col=(image_revised_1==indiv)*255
img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
img_comm_in=img_comm_in.astype(np.uint8)
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
main_contours=self.filter_contours_area_of_image_tables(thresh,contours,hirarchy,max_area=1,min_area=0.0001)
img_comm=cv2.fillPoly(img_comm, pts =main_contours, color=(indiv,indiv,indiv))
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
#img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
img_comm=img_comm.astype(np.uint8)
if not self.isNaN(slope_mean_hor):
image_revised_last=np.zeros((image_regions_eraly_p.shape[0] , image_regions_eraly_p.shape[1],3))
for i in range(len(boxes)):
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
image_box_tabels_1=(image_box[:,:,0]==7)*1
contours_tab,_=self.return_contours_of_image(image_box_tabels_1)
contours_tab=self.filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.001)
image_box_tabels_1=(image_box[:,:,0]==6)*1
image_box_tabels_and_m_text=( (image_box[:,:,0]==7) | (image_box[:,:,0]==1) )*1
image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8)
image_box_tabels_1=image_box_tabels_1.astype(np.uint8)
image_box_tabels_1 = cv2.dilate(image_box_tabels_1,self.kernel,iterations = 5)
contours_table_m_text,_=self.return_contours_of_image(image_box_tabels_and_m_text)
image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
image_box_tabels=image_box_tabels.astype(np.uint8)
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
y_min_main_line ,y_max_main_line=self.find_features_of_contoures(contours_line)
#_,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab ,y_max_main_tab=self.find_features_of_contoures(contours_tab)
cx_tab_m_text,cy_tab_m_text ,x_min_tab_m_text , x_max_tab_m_text, y_min_tab_m_text ,y_max_tab_m_text=self.find_new_features_of_contoures(contours_table_m_text)
cx_tabl,cy_tabl ,x_min_tabl , x_max_tabl, y_min_tabl ,y_max_tabl,_=self.find_new_features_of_contoures(contours_tab)
if len(y_min_main_tab )>0:
y_down_tabs=[]
y_up_tabs=[]
for i_t in range(len(y_min_main_tab )):
y_down_tab=[]
y_up_tab=[]
for i_l in range(len(y_min_main_line)):
if y_min_main_tab[i_t]>y_min_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l] and y_min_main_tab[i_t]>y_max_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l]:
pass
elif y_min_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_max_main_line[i_l] and y_max_main_tab[i_t]<y_min_main_line[i_l] and y_min_main_tab[i_t]<y_min_main_line[i_l]:
pass
elif np.abs(y_max_main_line[i_l]-y_min_main_line[i_l])<100:
pass
else:
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t] ]) )
y_down_tab.append( np.max([ y_max_main_line[i_l],y_max_main_tab[i_t] ]) )
if len(y_up_tab)==0:
for v_n in range(len(cx_tab_m_text)):
if cx_tabl[i_t]<= x_max_tab_m_text[v_n] and cx_tabl[i_t]>= x_min_tab_m_text[v_n] and cy_tabl[i_t]<= y_max_tab_m_text[v_n] and cy_tabl[i_t]>= y_min_tab_m_text[v_n] and cx_tabl[i_t]!=cx_tab_m_text[v_n] and cy_tabl[i_t]!=cy_tab_m_text[v_n]:
y_up_tabs.append(y_min_tab_m_text[v_n])
y_down_tabs.append(y_max_tab_m_text[v_n])
#y_up_tabs.append(y_min_main_tab[i_t])
#y_down_tabs.append(y_max_main_tab[i_t])
else:
y_up_tabs.append(np.min(y_up_tab))
y_down_tabs.append(np.max(y_down_tab))
else:
y_down_tabs=[]
y_up_tabs=[]
pass
for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii]:y_down_tabs[ii],:,0]=7
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
else:
for i in range(len(boxes)):
image_box=img_comm[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]
image_revised_last[int(boxes[i][2]):int(boxes[i][3]),int(boxes[i][0]):int(boxes[i][1]),:]=image_box[:,:,:]
##plt.figure(figsize=(20,20))
##plt.imshow(image_box[:,:,0])
##plt.show()
return image_revised_last
def find_features_of_contours(self,contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
return y_min_main ,y_max_main,areas_main
def remove_headers_and_mains_intersection(self,seperators_closeup_n,img_revised_tab,boxes):
for ind in range(len(boxes)):
asp=np.zeros((img_revised_tab[:,:,0].shape[0],seperators_closeup_n[:,:,0].shape[1]))
asp[ int(boxes[ind][2]):int(boxes[ind][3]),int(boxes[ind][0]):int(boxes[ind][1])] = img_revised_tab[ int(boxes[ind][2]):int(boxes[ind][3]),int(boxes[ind][0]):int(boxes[ind][1]),0]
head_patch_con=( asp[:,:]==2)*1
main_patch_con=( asp[:,:]==1)*1
#print(head_patch_con)
head_patch_con=head_patch_con.astype(np.uint8)
main_patch_con=main_patch_con.astype(np.uint8)
head_patch_con=np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con=np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con=self.return_parent_contours(contours_head_patch_con,hiearchy)
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con=self.return_parent_contours(contours_main_patch_con,hiearchy)
y_patch_head_min, y_patch_head_max,_= self.find_features_of_contours(contours_head_patch_con)
y_patch_main_min, y_patch_main_max,_= self.find_features_of_contours(contours_main_patch_con)
for i in range(len(y_patch_head_min)):
for j in range(len(y_patch_main_min)):
if y_patch_head_max[i]>y_patch_main_min[j] and y_patch_head_min[i]<y_patch_main_min[j]:
y_down=y_patch_head_max[i]
y_up=y_patch_main_min[j]
patch_intersection=np.zeros(asp.shape)
patch_intersection[y_up:y_down,:]=asp[y_up:y_down,:]
head_patch_con=( patch_intersection[:,:]==2)*1
main_patch_con=( patch_intersection[:,:]==1)*1
head_patch_con=head_patch_con.astype(np.uint8)
main_patch_con=main_patch_con.astype(np.uint8)
head_patch_con=np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con=np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con=self.return_parent_contours(contours_head_patch_con,hiearchy)
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con=self.return_parent_contours(contours_main_patch_con,hiearchy)
_,_,areas_head= self.find_features_of_contours(contours_head_patch_con)
_,_,areas_main= self.find_features_of_contours(contours_main_patch_con)
if np.sum(areas_head)>np.sum(areas_main):
img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0][img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0]==1 ]=2
else:
img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0][img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0]==2 ]=1
elif y_patch_head_min[i]<y_patch_main_max[j] and y_patch_head_max[i]>y_patch_main_max[j]:
y_down=y_patch_main_max[j]
y_up=y_patch_head_min[i]
patch_intersection=np.zeros(asp.shape)
patch_intersection[y_up:y_down,:]=asp[y_up:y_down,:]
head_patch_con=( patch_intersection[:,:]==2)*1
main_patch_con=( patch_intersection[:,:]==1)*1
head_patch_con=head_patch_con.astype(np.uint8)
main_patch_con=main_patch_con.astype(np.uint8)
head_patch_con=np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
main_patch_con=np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_head_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_head_patch_con=self.return_parent_contours(contours_head_patch_con,hiearchy)
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_main_patch_con,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con=self.return_parent_contours(contours_main_patch_con,hiearchy)
_,_,areas_head= self.find_features_of_contours(contours_head_patch_con)
_,_,areas_main= self.find_features_of_contours(contours_main_patch_con)
if np.sum(areas_head)>np.sum(areas_main):
img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0][img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0]==1 ]=2
else:
img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0][img_revised_tab[y_up:y_down,int(boxes[ind][0]):int(boxes[ind][1]),0]==2 ]=1
#print(np.unique(patch_intersection) )
##plt.figure(figsize=(20,20))
##plt.imshow(patch_intersection)
##plt.show()
else:
pass
return img_revised_tab
def order_of_regions(self,textline_mask,contours_main,contours_header, y_ref):
##plt.imshow(textline_mask)
##plt.show()
"""
print(len(contours_main),'contours_main')
mada_n=textline_mask.sum(axis=1)
y=mada_n[:]
y_help=np.zeros(len(y)+40)
y_help[20:len(y)+20]=y
x=np.array( range(len(y)) )
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
##plt.imshow(textline_mask[:,:])
##plt.show()
sigma_gaus=8
z= gaussian_filter1d(y_help, sigma_gaus)
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks_neg=peaks_neg-20-20
peaks=peaks-20
"""
textline_sum_along_width=textline_mask.sum(axis=1)
y=textline_sum_along_width[:]
y_padded=np.zeros(len(y)+40)
y_padded[20:len(y)+20]=y
x=np.array( range(len(y)) )
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
sigma_gaus=8
z= gaussian_filter1d(y_padded, sigma_gaus)
zneg_rev=-y_padded+np.max(y_padded)
zneg=np.zeros(len(zneg_rev)+40)
zneg[20:len(zneg_rev)+20]=zneg_rev
zneg= gaussian_filter1d(zneg, sigma_gaus)
peaks, _ = find_peaks(z, height=0)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks_neg=peaks_neg-20-20
peaks=peaks-20
##plt.plot(z)
##plt.show()
if contours_main!=None:
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
if len(contours_header)!=None:
areas_header=np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
M_header=[cv2.moments(contours_header[j]) for j in range(len(contours_header))]
cx_header=[(M_header[j]['m10']/(M_header[j]['m00']+1e-32)) for j in range(len(M_header))]
cy_header=[(M_header[j]['m01']/(M_header[j]['m00']+1e-32)) for j in range(len(M_header))]
x_min_header=np.array([np.min(contours_header[j][:,0,0]) for j in range(len(contours_header))])
x_max_header=np.array([np.max(contours_header[j][:,0,0]) for j in range(len(contours_header))])
y_min_header=np.array([np.min(contours_header[j][:,0,1]) for j in range(len(contours_header))])
y_max_header=np.array([np.max(contours_header[j][:,0,1]) for j in range(len(contours_header))])
#print(cy_main,'mainy')
peaks_neg_new=[]
peaks_neg_new.append(0+y_ref)
for iii in range(len(peaks_neg)):
peaks_neg_new.append(peaks_neg[iii]+y_ref)
peaks_neg_new.append(textline_mask.shape[0]+y_ref)
if len(cy_main)>0 and np.max(cy_main)>np.max(peaks_neg_new):
cy_main=np.array(cy_main)*(np.max(peaks_neg_new)/np.max(cy_main) )-10
if contours_main!=None:
indexer_main=np.array(range(len(contours_main)))
if contours_main!=None:
len_main=len(contours_main)
else:
len_main=0
matrix_of_orders=np.zeros((len(contours_main)+len(contours_header),5))
matrix_of_orders[:,0]=np.array( range( len(contours_main)+len(contours_header) ) )
matrix_of_orders[:len(contours_main),1]=1
matrix_of_orders[len(contours_main):,1]=2
matrix_of_orders[:len(contours_main),2]=cx_main
matrix_of_orders[len(contours_main):,2]=cx_header
matrix_of_orders[:len(contours_main),3]=cy_main
matrix_of_orders[len(contours_main):,3]=cy_header
matrix_of_orders[:len(contours_main),4]=np.array( range( len(contours_main) ) )
matrix_of_orders[len(contours_main):,4]=np.array( range( len(contours_header) ) )
#print(peaks_neg_new,'peaks_neg_new')
#print(matrix_of_orders,'matrix_of_orders')
#print(peaks_neg_new,np.max(peaks_neg_new))
final_indexers_sorted=[]
final_types=[]
final_index_type=[]
for i in range(len(peaks_neg_new)-1):
top=peaks_neg_new[i]
down=peaks_neg_new[i+1]
#print(top,down,'topdown')
indexes_in=matrix_of_orders[:,0][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
cxs_in=matrix_of_orders[:,2][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
cys_in=matrix_of_orders[:,3][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
types_of_text=matrix_of_orders[:,1][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
index_types_of_text=matrix_of_orders[:,4][(matrix_of_orders[:,3]>=top) & ((matrix_of_orders[:,3]<down))]
#print(top,down)
#print(cys_in,'cyyyins')
#print(indexes_in,'indexes')
sorted_inside=np.argsort(cxs_in)
ind_in_int=indexes_in[sorted_inside]
ind_in_type=types_of_text[sorted_inside]
ind_ind_type=index_types_of_text[sorted_inside]
for j in range(len(ind_in_int)):
final_indexers_sorted.append(int(ind_in_int[j]) )
final_types.append(int(ind_in_type[j]))
final_index_type.append(int(ind_ind_type[j]))
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
#print(peaks_neg_new,'peaks')
#print(final_indexers_sorted,'indexsorted')
#print(final_types,'types')
#print(final_index_type,'final_index_type')
return final_indexers_sorted, matrix_of_orders,final_types,final_index_type
def order_and_id_of_texts(self,found_polygons_text_region ,found_polygons_text_region_h,matrix_of_orders ,indexes_sorted,index_of_types, kind_of_texts, ref_point ):
indexes_sorted=np.array(indexes_sorted)
index_of_types=np.array(index_of_types)
kind_of_texts=np.array(kind_of_texts)
id_of_texts=[]
order_of_texts=[]
index_of_types_1=index_of_types[kind_of_texts==1]
indexes_sorted_1=indexes_sorted[kind_of_texts==1]
index_of_types_2=index_of_types[kind_of_texts==2]
indexes_sorted_2=indexes_sorted[kind_of_texts==2]
##print(index_of_types,'index_of_types')
##print(kind_of_texts,'kind_of_texts')
##print(len(found_polygons_text_region),'found_polygons_text_region')
##print(index_of_types_1,'index_of_types_1')
##print(indexes_sorted_1,'indexes_sorted_1')
index_b=0+ref_point
for mm in range(len(found_polygons_text_region)):
id_of_texts.append('r'+str(index_b) )
interest=indexes_sorted_1[indexes_sorted_1==index_of_types_1[mm] ]
if len(interest)>0:
order_of_texts.append(interest[0])
index_b+=1
else:
pass
for mm in range(len(found_polygons_text_region_h)):
id_of_texts.append('r'+str(index_b) )
interest=indexes_sorted_2[index_of_types_2[mm]]
order_of_texts.append(interest)
index_b+=1
return order_of_texts, id_of_texts
def get_text_region_boxes_by_given_contours(self,contours):
kernel = np.ones((5,5),np.uint8)
boxes=[]
contours_new=[]
for jj in range(len(contours)):
x,y,w,h = cv2.boundingRect(contours[jj])
boxes.append([x,y,w,h])
contours_new.append(contours[jj])
del contours
return boxes,contours_new
def return_teilwiese_deskewed_lines(self,text_regions_p,textline_rotated):
kernel = np.ones((5, 5), np.uint8)
textline_rotated = cv2.erode(textline_rotated, kernel, iterations=1)
textline_rotated_new=np.zeros(textline_rotated.shape)
rgb_m=1
rgb_h=2
cnt_m,boxes_m=self.return_contours_of_interested_region_and_bounding_box(text_regions_p,rgb_m)
cnt_h,boxes_h=self.return_contours_of_interested_region_and_bounding_box(text_regions_p,rgb_h)
areas_cnt_m=np.array([cv2.contourArea(cnt_m[j]) for j in range(len(cnt_m))])
argmax=np.argmax(areas_cnt_m)
#plt.imshow(textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]])
#plt.show()
for argmax in range(len(boxes_m)):
textline_text_region=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2] ]
textline_text_region_revised=self.seperate_lines_new(textline_text_region,0)
#except:
# textline_text_region_revised=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2] ]
textline_rotated_new[boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]]=textline_text_region_revised[:,:]
#textline_rotated_new[textline_rotated_new>0]=1
#textline_rotated_new[textline_rotated_new<0]=0
#plt.imshow(textline_rotated_new)
#plt.show()
def return_contours_of_interested_region_and_bounding_box(self,region_pre_p,pixel):
# pixels of images are identified by 5
cnts_images=(region_pre_p[:,:,0]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=1,min_area=0.0003)
boxes = []
for jj in range(len(contours_imgs)):
x, y, w, h = cv2.boundingRect(contours_imgs[jj])
boxes.append([int(x), int(y), int(w), int(h)])
return contours_imgs,boxes
def find_number_of_columns_in_document(self,region_pre_p,num_col_classifier,pixel_lines,contours_h=None):
seperators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
seperators_closeup[0:110,:,:]=0
seperators_closeup[seperators_closeup.shape[0]-150:,:,:]=0
kernel = np.ones((5,5),np.uint8)
seperators_closeup=seperators_closeup.astype(np.uint8)
seperators_closeup = cv2.dilate(seperators_closeup,kernel,iterations = 1)
seperators_closeup = cv2.erode(seperators_closeup,kernel,iterations = 1)
##plt.imshow(seperators_closeup[:,:,0])
##plt.show()
seperators_closeup_new=np.zeros((seperators_closeup.shape[0] ,seperators_closeup.shape[1] ))
##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
seperators_closeup_n=np.copy(seperators_closeup)
seperators_closeup_n=seperators_closeup_n.astype(np.uint8)
##plt.imshow(seperators_closeup_n[:,:,0])
##plt.show()
seperators_closeup_n_binary=np.zeros(( seperators_closeup_n.shape[0],seperators_closeup_n.shape[1]) )
seperators_closeup_n_binary[:,:]=seperators_closeup_n[:,:,0]
seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]!=0]=1
#seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255
#seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0
#seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1
#gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
#print(np.unique(seperators_closeup_n_binary))
##plt.imshow(seperators_closeup_n_binary)
##plt.show()
#print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) )
gray = cv2.bitwise_not(seperators_closeup_n_binary)
gray=gray.astype(np.uint8)
##plt.imshow(gray)
##plt.show()
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
cv2.THRESH_BINARY, 15, -2)
##plt.imshow(bw[:,:])
##plt.show()
horizontal = np.copy(bw)
vertical = np.copy(bw)
cols = horizontal.shape[1]
horizontal_size = cols // 30
# Create structure element for extracting horizontal lines through morphology operations
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
# Apply morphology operations
horizontal = cv2.erode(horizontal, horizontalStructure)
horizontal = cv2.dilate(horizontal, horizontalStructure)
kernel = np.ones((5,5),np.uint8)
horizontal = cv2.dilate(horizontal,kernel,iterations = 2)
horizontal = cv2.erode(horizontal,kernel,iterations = 2)
#plt.imshow(horizontal)
#plt.show()
rows = vertical.shape[0]
verticalsize = rows // 30
# Create structure element for extracting vertical lines through morphology operations
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
# Apply morphology operations
vertical = cv2.erode(vertical, verticalStructure)
vertical = cv2.dilate(vertical, verticalStructure)
vertical = cv2.dilate(vertical,kernel,iterations = 1)
# Show extracted vertical lines
horizontal,special_seperators=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical,horizontal)
##plt.imshow(vertical)
##plt.show()
#print(vertical.shape,np.unique(vertical),'verticalvertical')
seperators_closeup_new[:,:][vertical[:,:]!=0]=1
seperators_closeup_new[:,:][horizontal[:,:]!=0]=1
##plt.imshow(seperators_closeup_new)
##plt.show()
##seperators_closeup_n
vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
vertical=vertical.astype(np.uint8)
##plt.plot(vertical[:,:,0].sum(axis=0))
##plt.show()
#plt.plot(vertical[:,:,0].sum(axis=1))
#plt.show()
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line_vers,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_line_vers)
#print(slope_lines,'vertical')
args=np.array( range(len(slope_lines) ))
args_ver=args[slope_lines==1]
dist_x_ver=dist_x[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
dist_y_ver=y_max_main_ver-y_min_main_ver
len_y=seperators_closeup.shape[0]/3.0
#plt.imshow(horizontal)
#plt.show()
horizontal=np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
horizontal=horizontal.astype(np.uint8)
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line_hors,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_line_hors)
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]/5.0
dist_y=np.abs(y_max_main-y_min_main)
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
y_min_main_hor=y_min_main[slope_lines==0]
y_max_main_hor=y_max_main[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
dist_y_hor=dist_y[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x/2.0]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
y_min_main_hor=y_min_main_hor[dist_x_hor>=len_x/2.0]
y_max_main_hor=y_max_main_hor[dist_x_hor>=len_x/2.0]
dist_y_hor=dist_y_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
dist_x_hor=dist_x_hor[dist_x_hor>=len_x/2.0]
matrix_of_lines_ch=np.zeros((len(cy_main_hor)+len(cx_main_ver),10))
matrix_of_lines_ch[:len(cy_main_hor),0]=args_hor
matrix_of_lines_ch[len(cy_main_hor):,0]=args_ver
matrix_of_lines_ch[len(cy_main_hor):,1]=cx_main_ver
matrix_of_lines_ch[:len(cy_main_hor),2]=x_min_main_hor+50#x_min_main_hor+150
matrix_of_lines_ch[len(cy_main_hor):,2]=x_min_main_ver
matrix_of_lines_ch[:len(cy_main_hor),3]=x_max_main_hor-50#x_max_main_hor-150
matrix_of_lines_ch[len(cy_main_hor):,3]=x_max_main_ver
matrix_of_lines_ch[:len(cy_main_hor),4]=dist_x_hor
matrix_of_lines_ch[len(cy_main_hor):,4]=dist_x_ver
matrix_of_lines_ch[:len(cy_main_hor),5]=cy_main_hor
matrix_of_lines_ch[:len(cy_main_hor),6]=y_min_main_hor
matrix_of_lines_ch[len(cy_main_hor):,6]=y_min_main_ver
matrix_of_lines_ch[:len(cy_main_hor),7]=y_max_main_hor
matrix_of_lines_ch[len(cy_main_hor):,7]=y_max_main_ver
matrix_of_lines_ch[:len(cy_main_hor),8]=dist_y_hor
matrix_of_lines_ch[len(cy_main_hor):,8]=dist_y_ver
matrix_of_lines_ch[len(cy_main_hor):,9]=1
if contours_h is not None:
slope_lines_head,dist_x_head, x_min_main_head ,x_max_main_head ,cy_main_head,slope_lines_org_head,y_min_main_head, y_max_main_head, cx_main_head=self.find_features_of_lines(contours_h)
matrix_l_n=np.zeros((matrix_of_lines_ch.shape[0]+len(cy_main_head),matrix_of_lines_ch.shape[1]))
matrix_l_n[:matrix_of_lines_ch.shape[0],:]=np.copy(matrix_of_lines_ch[:,:])
args_head=np.array(range(len(cy_main_head)))+len(cy_main_hor)
matrix_l_n[matrix_of_lines_ch.shape[0]:,0]=args_head
matrix_l_n[matrix_of_lines_ch.shape[0]:,2]=x_min_main_head+30
matrix_l_n[matrix_of_lines_ch.shape[0]:,3]=x_max_main_head-30
matrix_l_n[matrix_of_lines_ch.shape[0]:,4]=dist_x_head
matrix_l_n[matrix_of_lines_ch.shape[0]:,5]=y_min_main_head-3-8
matrix_l_n[matrix_of_lines_ch.shape[0]:,6]=y_min_main_head-5-8
matrix_l_n[matrix_of_lines_ch.shape[0]:,7]=y_min_main_head+1-8
matrix_l_n[matrix_of_lines_ch.shape[0]:,8]=4
matrix_of_lines_ch=np.copy(matrix_l_n)
#print(matrix_of_lines_ch)
"""
seperators_closeup=seperators_closeup.astype(np.uint8)
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_lines)
slope_lines_org_hor=slope_lines_org[slope_lines==0]
args=np.array( range(len(slope_lines) ))
len_x=seperators_closeup.shape[1]/4.0
args_hor=args[slope_lines==0]
dist_x_hor=dist_x[slope_lines==0]
x_min_main_hor=x_min_main[slope_lines==0]
x_max_main_hor=x_max_main[slope_lines==0]
cy_main_hor=cy_main[slope_lines==0]
args_hor=args_hor[dist_x_hor>=len_x/2.0]
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2]
slope_mean_hor=np.mean(slope_lines_org_hor)
args_ver=args[slope_lines==1]
y_min_main_ver=y_min_main[slope_lines==1]
y_max_main_ver=y_max_main[slope_lines==1]
x_min_main_ver=x_min_main[slope_lines==1]
x_max_main_ver=x_max_main[slope_lines==1]
cx_main_ver=cx_main[slope_lines==1]
dist_y_ver=y_max_main_ver-y_min_main_ver
len_y=seperators_closeup.shape[0]/3.0
print(matrix_of_lines_ch[:,8][matrix_of_lines_ch[:,9]==0],'khatlarrrr')
args_main_spliters=matrix_of_lines_ch[:,0][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
cy_main_spliters=matrix_of_lines_ch[:,5][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
"""
cy_main_spliters=cy_main_hor[ (x_min_main_hor<=.16*region_pre_p.shape[1]) & (x_max_main_hor>=.84*region_pre_p.shape[1] )]
cy_main_spliters=np.array( list(cy_main_spliters)+list(special_seperators))
if contours_h is not None:
try:
cy_main_spliters_head=cy_main_head[ (x_min_main_head<=.16*region_pre_p.shape[1]) & (x_max_main_head>=.84*region_pre_p.shape[1] )]
cy_main_spliters=np.array( list(cy_main_spliters)+list(cy_main_spliters_head))
except:
pass
args_cy_spliter=np.argsort(cy_main_spliters)
cy_main_spliters_sort=cy_main_spliters[args_cy_spliter]
spliter_y_new=[]
spliter_y_new.append(0)
for i in range(len(cy_main_spliters_sort)):
spliter_y_new.append( cy_main_spliters_sort[i] )
spliter_y_new.append(region_pre_p.shape[0])
spliter_y_new_diff=np.diff(spliter_y_new)/float(region_pre_p.shape[0])*100
args_big_parts=np.array(range(len(spliter_y_new_diff))) [ spliter_y_new_diff>22 ]
regions_without_seperators=self.return_regions_without_seperators(region_pre_p)
##print(args_big_parts,'args_big_parts')
#image_page_otsu=self.otsu_copy(image_page_deskewd)
#print(np.unique(image_page_otsu[:,:,0]))
#image_page_background_zero=self.image_change_background_pixels_to_zero(image_page_otsu)
length_y_threshold=regions_without_seperators.shape[0]/4.0
num_col_fin=0
peaks_neg_fin_fin=[]
for iteils in args_big_parts:
regions_without_seperators_teil=regions_without_seperators[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:,0]
#image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
#print(regions_without_seperators_teil.shape)
##plt.imshow(regions_without_seperators_teil)
##plt.show()
#num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
#regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
#
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=7.0)
if num_col>num_col_fin:
num_col_fin=num_col
peaks_neg_fin_fin=peaks_neg_fin
"""
#print(length_y_vertical_lines,length_y_threshold,'x_center_of_ver_linesx_center_of_ver_linesx_center_of_ver_lines')
if len(cx_main_ver)>0 and len( dist_y_ver[dist_y_ver>=length_y_threshold] ) >=1:
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
else:
#plt.imshow(image_page_background_zero_teil)
#plt.show()
#num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3)
num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3)
print(num_col,'birda')
if num_col>0:
pass
elif num_col==0:
print(num_col,'birda2222')
num_col_regions, peaks_neg_fin_regions=self.find_num_col(regions_without_seperators_teil,multiplier=10.0)
if num_col_regions==0:
pass
else:
num_col=num_col_regions
peaks_neg_fin=peaks_neg_fin_regions[:]
"""
#print(num_col+1,'num colmsssssssss')
if len(args_big_parts)==1 and (len(peaks_neg_fin_fin)+1)<num_col_classifier:
peaks_neg_fin=self.find_num_col_by_vertical_lines(vertical)
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin>=500]
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)]
peaks_neg_fin_fin=peaks_neg_fin[:]
#print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
return num_col_fin, peaks_neg_fin_fin,matrix_of_lines_ch,spliter_y_new,seperators_closeup_n
def return_contours_of_interested_region_by_size(self,region_pre_p,pixel,min_area,max_area):
# pixels of images are identified by 5
if len(region_pre_p.shape)==3:
cnts_images=(region_pre_p[:,:,0]==pixel)*1
else:
cnts_images=(region_pre_p[:,:]==pixel)*1
cnts_images=cnts_images.astype(np.uint8)
cnts_images=np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs,hiearchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_imgs=self.return_parent_contours(contours_imgs,hiearchy)
contours_imgs=self.filter_contours_area_of_image_tables(thresh,contours_imgs,hiearchy,max_area=max_area,min_area=min_area)
img_ret=np.zeros((region_pre_p.shape[0],region_pre_p.shape[1],3))
img_ret=cv2.fillPoly(img_ret,pts=contours_imgs, color=(1,1,1))
return img_ret[:,:,0]
def get_regions_from_xy_neu(self,img):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
gaussian_filter=False
patches=True
binary=True
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org=self.do_prediction(patches,img,model_region)
prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org=prediction_regions_org[:,:,0]
gaussian_filter=False
patches=False
binary=False
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_orgt=self.do_prediction(patches,img,model_region)
prediction_regions_orgt=self.resize_image(prediction_regions_orgt, img_height_h, img_width_h )
#plt.imshow(prediction_regions_orgt[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_orgt=prediction_regions_orgt[:,:,0]
mask_texts_longshot=(prediction_regions_orgt[:,:]==1)*1
mask_texts_longshot=np.uint8(mask_texts_longshot)
#mask_texts_longshot = cv2.dilate(mask_texts_longshot[:,:], self.kernel, iterations=2)
pixel_img=1
polygons_of_only_texts_longshot=self.return_contours_of_interested_region(mask_texts_longshot,pixel_img)
longshot_true=np.zeros(mask_texts_longshot.shape)
#text_regions_p_true[:,:]=text_regions_p_1[:,:]
longshot_true=cv2.fillPoly(longshot_true,pts=polygons_of_only_texts_longshot, color=(1,1,1))
#plt.imshow(longshot_true)
#plt.show()
gaussian_filter=False
patches=False
binary=False
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
one_third_upper_ny=int(img.shape[0]/3.0)
img=img[0:one_third_upper_ny,:,:]
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_longshot_one_third=self.do_prediction(patches,img,model_region)
prediction_regions_longshot_one_third=self.resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h )
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
img=img[one_third_upper_ny:int(2*one_third_upper_ny),:,:]
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_longshot_one_third_middle=self.do_prediction(patches,img,model_region)
prediction_regions_longshot_one_third_middle=self.resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h )
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
img=img[int(2*one_third_upper_ny):,:,:]
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_longshot_one_third_down=self.do_prediction(patches,img,model_region)
prediction_regions_longshot_one_third_down=self.resize_image(prediction_regions_longshot_one_third_down, img_height_h-int(2*one_third_upper_ny), img_width_h )
#plt.imshow(prediction_regions_org[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_longshot=np.zeros((img_height_h,img_width_h))
#prediction_regions_longshot=prediction_regions_longshot[:,:,0]
#prediction_regions_longshot[0:one_third_upper_ny,:]=prediction_regions_longshot_one_third[:,:,0]
#prediction_regions_longshot[one_third_upper_ny:int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_middle[:,:,0]
#prediction_regions_longshot[int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_down[:,:,0]
prediction_regions_longshot=longshot_true[:,:]
#plt.imshow(prediction_regions_longshot)
#plt.show()
gaussian_filter=False
patches=True
binary=False
ratio_x=1#1.1
ratio_y=1
median_blur=False
#img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions=self.do_prediction(patches,img,model_region)
text_region1=self.resize_image(prediction_regions, img_height_h, img_width_h )
#plt.imshow(text_region1[:,:,0])
#plt.show()
ratio_x=1
ratio_y=1.2#1.3
binary=False
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions=self.do_prediction(patches,img,model_region)
text_region2=self.resize_image(prediction_regions, img_height_h, img_width_h )
#plt.imshow(text_region2[:,:,0])
#plt.show()
session_region.close()
del model_region
del session_region
gc.collect()
#text_region1=text_region1[:,:,0]
#text_region2=text_region2[:,:,0]
#text_region1[(text_region1[:,:]==2) & (text_region2[:,:]==1)]=1
mask_zeros_from_1=(text_region2[:,:,0]==0)*1
#mask_text_from_1=(text_region1[:,:,0]==1)*1
mask_img_text_region1=(text_region1[:,:,0]==2)*1
text_region2_1st_channel=text_region1[:,:,0]
text_region2_1st_channel[mask_zeros_from_1==1]=0
##text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
#text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1=(text_region1[:,:,0]==3)*1
mask_lines2=(text_region2[:,:,0]==3)*1
mask_lines2[mask_lines1[:,:]==1]=1
#plt.imshow(text_region2_1st_channel)
#plt.show()
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:,:], self.kernel, iterations=4)
#plt.imshow(text_region2_1st_channel)
#plt.show()
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:,:], self.kernel, iterations=4)
text_region2_1st_channel[mask_lines2[:,:]==1]=3
#text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
# only in the case of model 3
text_region2_1st_channel[(prediction_regions_longshot[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
text_region2_1st_channel[ (prediction_regions_org[:,:]==2) & (text_region2_1st_channel[:,:]==0)]=2
#text_region2_1st_channel[prediction_regions_org[:,:]==0]=0
#plt.imshow(text_region2_1st_channel)
#plt.show()
#text_region2_1st_channel[:,:400]=0
mask_texts_only=(text_region2_1st_channel[:,:]==1)*1
mask_images_only=(text_region2_1st_channel[:,:]==2)*1
mask_lines_only=(text_region2_1st_channel[:,:]==3)*1
pixel_img=1
polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img)
polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)
polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img)
text_regions_p_true=np.zeros(text_region2_1st_channel.shape)
#text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
##print(np.unique(text_regions_p_true))
#text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
#text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
return text_regions_p_true#text_region2_1st_channel
def get_regions_from_xy(self,img):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
gaussian_filter=False
patches=True
binary=True
ratio_x=1
ratio_y=1
median_blur=False
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org=self.do_prediction(patches,img,model_region)
###plt.imshow(prediction_regions_org[:,:,0])
###plt.show()
##sys.exit()
prediction_regions_org=prediction_regions_org[:,:,0]
gaussian_filter=False
patches=True
binary=False
ratio_x=1.1
ratio_y=1
median_blur=False
#img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions=self.do_prediction(patches,img,model_region)
text_region1=self.resize_image(prediction_regions, img_height_h, img_width_h )
ratio_x=1
ratio_y=1.1
binary=False
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions=self.do_prediction(patches,img,model_region)
text_region2=self.resize_image(prediction_regions, img_height_h, img_width_h )
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros_from_1=(text_region1[:,:,0]==0)*1
#mask_text_from_1=(text_region1[:,:,0]==1)*1
mask_img_text_region1=(text_region1[:,:,0]==2)*1
text_region2_1st_channel=text_region2[:,:,0]
text_region2_1st_channel[mask_zeros_from_1==1]=0
text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
#text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
mask_lines1=(text_region1[:,:,0]==3)*1
mask_lines2=(text_region2[:,:,0]==3)*1
mask_lines2[mask_lines1[:,:]==1]=1
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:,:], self.kernel, iterations=5)
##plt.imshow(text_region2_1st_channel)
##plt.show()
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:,:], self.kernel, iterations=5)
text_region2_1st_channel[mask_lines2[:,:]==1]=3
text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
text_region2_1st_channel[prediction_regions_org[:,:]==3]=3
##plt.imshow(text_region2_1st_channel)
##plt.show()
return text_region2_1st_channel
def rotation_image_new(self,img,thetha):
rotated=imutils.rotate(img,thetha)
return self.rotate_max_area_new(img, rotated,thetha)
def rotate_max_area_new(self,image,rotated,angle):
wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y2 = y1 + int(hr)
x1 = w//2 - int(wr/2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2]
def rotation_not_90_func(self,img,textline,text_regions_p_1,thetha):
rotated=imutils.rotate(img,thetha)
rotated_textline=imutils.rotate(textline,thetha)
rotated_layout=imutils.rotate(text_regions_p_1,thetha)
return self.rotate_max_area(img, rotated,rotated_textline,rotated_layout,thetha)
def rotate_max_area(self,image,rotated,rotated_textline,rotated_layout,angle):
wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y2 = y1 + int(hr)
x1 = w//2 - int(wr/2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2],rotated_textline[y1:y2, x1:x2],rotated_layout[y1:y2, x1:x2]
def rotation_not_90_func_full_layout(self,img,textline,text_regions_p_1,text_regions_p_fully,thetha):
rotated=imutils.rotate(img,thetha)
rotated_textline=imutils.rotate(textline,thetha)
rotated_layout=imutils.rotate(text_regions_p_1,thetha)
rotated_layout_full=imutils.rotate(text_regions_p_fully,thetha)
return self.rotate_max_area_full_layout(img, rotated,rotated_textline,rotated_layout,rotated_layout_full,thetha)
def rotate_max_area_full_layout(self,image,rotated,rotated_textline,rotated_layout,rotated_layout_full,angle):
wr, hr =self.rotatedRectWithMaxArea(image.shape[1], image.shape[0],math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y2 = y1 + int(hr)
x1 = w//2 - int(wr/2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2],rotated_textline[y1:y2, x1:x2],rotated_layout[y1:y2, x1:x2],rotated_layout_full[y1:y2, x1:x2]
def get_regions_from_xy_2models_ens(self,img):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
gaussian_filter=False
patches=False
binary=False
ratio_x=1
ratio_y=1
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
prediction_regions_long=self.do_prediction(patches,img,model_region)
prediction_regions_long=self.resize_image(prediction_regions_long, img_height_h, img_width_h )
gaussian_filter=False
patches=True
binary=False
ratio_x=1
ratio_y=1.2
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org_y=self.do_prediction(patches,img,model_region)
prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org_y=prediction_regions_org_y[:,:,0]
mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1
ratio_x=1.2
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org=self.do_prediction(patches,img,model_region)
prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
prediction_regions_org[( prediction_regions_long[:,:,0]==1 ) & (prediction_regions_org[:,:]==2) ]=1
session_region.close()
del model_region
del session_region
gc.collect()
return prediction_regions_org
def get_regions_from_xy_2models(self,img,is_image_enhanced):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
gaussian_filter=False
patches=True
binary=False
ratio_y=1.3
ratio_x=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org_y=self.do_prediction(patches,img,model_region)
prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org_y=prediction_regions_org_y[:,:,0]
mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1
if is_image_enhanced:
ratio_x=1.2
else:
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org=self.do_prediction(patches,img,model_region)
prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
##sys.exit()
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
gaussian_filter=False
patches=True
binary=False
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img=cv2.medianBlur(img,5)
if gaussian_filter:
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
marginal_patch=0.2
prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
4 years ago
prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
#sys.exit()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
prediction_regions_org_copy=np.copy(prediction_regions_org)
prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0
text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()
rate_two_models=text_sume_second/float(text_sume_early)*100
print(rate_two_models,'ratio_of_two_models')
if is_image_enhanced and rate_two_models<95.50:#98.45:
pass
else:
prediction_regions_org=np.copy(prediction_regions_org_copy)
##prediction_regions_org[mask_lines2[:,:]==1]=3
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
del mask_lines2
del mask_zeros2
del prediction_regions_org2
#if is_image_enhanced:
#pass
#else:
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
#gaussian_filter=False
#patches=True
#binary=False
#ratio_x=1
#ratio_y=1
#median_blur=False
#img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
#if binary:
#img = self.otsu_copy_binary(img)#self.otsu_copy(img)
#img = img.astype(np.uint16)
#if median_blur:
#img=cv2.medianBlur(img,5)
#if gaussian_filter:
#img= cv2.GaussianBlur(img,(5,5),0)
#img = img.astype(np.uint16)
#prediction_regions_org2=self.do_prediction(patches,img,model_region)
#prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
#session_region.close()
#del model_region
#del session_region
#gc.collect()
####K.clear_session()
#mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
#mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
#text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
#prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
#prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
#text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
#print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
#del mask_lines2
#del mask_zeros2
#del prediction_regions_org2
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
pixel_img=1
min_area_text=0.00001
polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)
polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
text_regions_p_true=np.zeros(prediction_regions_org.shape)
#text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true[:,:][mask_images_only[:,:]==1]=2
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
##print(np.unique(text_regions_p_true))
#text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
#text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
del polygons_of_only_texts
del polygons_of_only_images
del polygons_of_only_lines
del mask_images_only
del prediction_regions_org
del img
del mask_zeros_y
del prediction_regions_org_y
del img_org
gc.collect()
return text_regions_p_true
def get_textregion_contours_in_org_image(self,cnts,img,slope_first):
cnts_org=[]
#print(cnts,'cnts')
for i in range(len(cnts)):
img_copy=np.zeros(img.shape)
img_copy=cv2.fillPoly(img_copy,pts=[cnts[i]], color=(1,1,1))
#plt.imshow(img_copy)
#plt.show()
#print(img.shape,'img')
img_copy=self.rotation_image_new(img_copy,-slope_first)
##print(img_copy.shape,'img_copy')
#plt.imshow(img_copy)
#plt.show()
img_copy=img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont_int,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cont_int[0][:,0,0]=cont_int[0][:,0,0]+np.abs(img_copy.shape[1]-img.shape[1])
cont_int[0][:,0,1]=cont_int[0][:,0,1]+np.abs(img_copy.shape[0]-img.shape[0])
#print(np.shape(cont_int[0]))
cnts_org.append(cont_int[0])
#print(cnts_org,'cnts_org')
#sys.exit()
self.y_shift=np.abs(img_copy.shape[0]-img.shape[0])
self.x_shift=np.abs(img_copy.shape[1]-img.shape[1])
return cnts_org
def write_images_into_directory(self,img_contoures,dir_of_cropped_imgs,image_page):
index=0
for cont_ind in img_contoures:
#cont_ind[:,0,0]=cont_ind[:,0,0]/self.scale_x
#cont_ind[:,0,1]=cont_ind[:,0,1]/self.scale_y
x, y, w, h = cv2.boundingRect(cont_ind)
box = [x, y, w, h]
croped_page, page_coord = self.crop_image_inside_box(box, image_page)
croped_page = self.resize_image(croped_page, int(croped_page.shape[0]/self.scale_y), int(croped_page.shape[1]/self.scale_x))
path=os.path.join(dir_of_cropped_imgs, self.f_name+'_'+str(index)+'.jpg')
cv2.imwrite(path,croped_page)
index+=1
def get_marginals(self,text_with_lines,text_regions,num_col,slope_deskew):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
text_with_lines=text_with_lines.astype(np.uint8)
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded=cv2.erode(text_with_lines,self.kernel,iterations=5)
if text_with_lines.shape[0]<=1500:
pass
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
text_with_lines=self.resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=5)
text_with_lines=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
else:
text_with_lines=self.resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=7)
text_with_lines=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
#print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent<30:
min_textline_thickness=8
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
else:
min_textline_thickness=40
if thickness_along_y_percent>=14:
text_with_lines_y_rev=-1*text_with_lines_y[:]
#print(text_with_lines_y)
#print(text_with_lines_y_rev)
#plt.plot(text_with_lines_y)
#plt.show()
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
#plt.plot(text_with_lines_y_rev)
#plt.show()
sigma_gaus=1
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
#plt.plot(region_sum_0_rev)
#plt.show()
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero=len(region_sum_0)-last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point=(last_nonzero+first_nonzero)/2.
one_third_right=(last_nonzero-mid_point)/3.0
one_third_left=(mid_point-first_nonzero)/3.0
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks=np.array(peaks)
#print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
#print(first_nonzero,last_nonzero,peaks)
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
#print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
#print(region_sum_0[peaks])
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
#print(peaks)
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col==1:
peaks_right=peaks[peaks>mid_point]
peaks_left=peaks[peaks<mid_point]
if num_col==2:
peaks_right=peaks[peaks>(mid_point+one_third_right)]
peaks_left=peaks[peaks<(mid_point-one_third_left)]
try:
point_right=np.min(peaks_right)
except:
point_right=last_nonzero
try:
point_left=np.max(peaks_left)
except:
point_left=first_nonzero
#print(point_left,point_right)
#print(text_regions.shape)
if point_right>=mask_marginals.shape[1]:
point_right=mask_marginals.shape[1]-1
try:
mask_marginals[:,point_left:point_right]=1
except:
mask_marginals[:,:]=1
#print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated=self.rotate_image(mask_marginals,-slope_deskew)
#print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
index_x_interest=index_x[mask_marginals_rotated_sum==1]
min_point_of_left_marginal=np.min(index_x_interest)-16
max_point_of_right_marginal=np.max(index_x_interest)+16
if min_point_of_left_marginal<0:
min_point_of_left_marginal=0
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
#plt.imshow(mask_marginals)
#plt.show()
#plt.imshow(mask_marginals_rotated)
#plt.show()
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
#plt.imshow(text_regions)
#plt.show()
4 years ago
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=self.return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=self.find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<18:
4 years ago
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
else:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
#print(np.unique(text_regions))
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
#plt.plot(region_sum_0)
#plt.plot(peaks,region_sum_0[peaks],'*')
#plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
else:
pass
return text_regions
def do_work_of_textline_seperation(self,queue_of_all_params,polygons_per_process, index_polygons_per_process,con_par_org,textline_mask_tot,mask_texts_only,num_col,scale_par,boxes_text):
textregions_cnt_tot_per_process=[]
textlines_cnt_tot_per_process=[]
index_polygons_per_process_per_process=[]
polygons_per_par_process_per_process=[]
textline_cnt_seperated=np.zeros(textline_mask_tot.shape)
for iiii in range(len(polygons_per_process)):
#crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
#arg_max=np.argmax(areas_cnt_only_text)
textregions_cnt_tot_per_process.append(polygons_per_process[iiii]/scale_par)
textline_region_in_image=np.zeros(textline_mask_tot.shape)
cnt_o_t_max=polygons_per_process[iiii]
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
mask_biggest=np.zeros(mask_texts_only.shape)
mask_biggest=cv2.fillPoly(mask_biggest, pts =[cnt_o_t_max], color=(1,1,1))
mask_region_in_patch_region=mask_biggest[y:y+h,x:x+w]
textline_biggest_region=mask_biggest*textline_mask_tot
textline_rotated_seperated=self.seperate_lines_new2(textline_biggest_region[y:y+h,x:x+w],0,num_col)
#new line added
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
textline_rotated_seperated[mask_region_in_patch_region[:,:]!=1]=0
#till here
textline_cnt_seperated[y:y+h,x:x+w]=textline_rotated_seperated
textline_region_in_image[y:y+h,x:x+w]=textline_rotated_seperated
#plt.imshow(textline_region_in_image)
#plt.show()
#plt.imshow(textline_cnt_seperated)
#plt.show()
pixel_img=1
cnt_textlines_in_image=self.return_contours_of_interested_textline(textline_region_in_image,pixel_img)
textlines_cnt_per_region=[]
for jjjj in range(len(cnt_textlines_in_image)):
mask_biggest2=np.zeros(mask_texts_only.shape)
mask_biggest2=cv2.fillPoly(mask_biggest2, pts =[cnt_textlines_in_image[jjjj]], color=(1,1,1))
if num_col+1==1:
mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=5)
else:
mask_biggest2=cv2.dilate(mask_biggest2, self.kernel, iterations=4)
pixel_img=1
cnt_textlines_in_image_ind=self.return_contours_of_interested_textline(mask_biggest2,pixel_img)
try:
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
except:
pass
#print(len(cnt_textlines_in_image_ind))
#plt.imshow(mask_biggest2)
#plt.show()
textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
polygons_per_par_process_per_process.append(con_par_org[iiii])
queue_of_all_params.put([index_polygons_per_process_per_process,polygons_per_par_process_per_process,textregions_cnt_tot_per_process, textlines_cnt_tot_per_process ])
def small_textlines_to_parent_adherence2(self,textlines_con,textline_iamge,num_col):
#print(textlines_con)
#textlines_con=textlines_con.astype(np.uint32)
textlines_con_changed=[]
for m1 in range(len(textlines_con)):
#textlines_tot=textlines_con[m1]
#textlines_tot=textlines_tot.astype()
textlines_tot=[]
textlines_tot_org_form=[]
#print(textlines_tot)
for nn in range(len(textlines_con[m1])):
textlines_tot.append(np.array( textlines_con[m1][nn],dtype=np.int32) )
textlines_tot_org_form.append(textlines_con[m1][nn])
##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1))
##plt.imshow(img_text_all)
##plt.show()
areas_cnt_text=np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))])
areas_cnt_text=areas_cnt_text/float(textline_iamge.shape[0]*textline_iamge.shape[1])
indexes_textlines=np.array(range(len(textlines_tot)))
#print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text))
if num_col==0:
min_area=0.0004
elif num_col==1:
min_area=0.0003
else:
min_area=0.0001
indexes_textlines_small=indexes_textlines[areas_cnt_text<min_area]
#print(indexes_textlines)
textlines_small=[]
textlines_small_org_form=[]
for i in indexes_textlines_small:
textlines_small.append(textlines_tot[i])
textlines_small_org_form.append(textlines_tot_org_form[i])
textlines_big=[]
textlines_big_org_form=[]
for i in list(set(indexes_textlines)-set(indexes_textlines_small) ):
textlines_big.append(textlines_tot[i])
textlines_big_org_form.append(textlines_tot_org_form[i])
img_textline_s=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
img_textline_s=cv2.fillPoly(img_textline_s, pts =textlines_small , color=(1,1,1))
img_textline_b=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
img_textline_b=cv2.fillPoly(img_textline_b, pts =textlines_big , color=(1,1,1))
sum_small_big_all=img_textline_s+img_textline_b
sum_small_big_all2=(sum_small_big_all[:,:]==2)*1
sum_intersection_sb=sum_small_big_all2.sum(axis=1).sum()
if sum_intersection_sb>0:
dis_small_from_bigs_tot=[]
for z1 in range(len(textlines_small)):
#print(len(textlines_small),'small')
intersections=[]
for z2 in range(len(textlines_big)):
img_text=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
img_text=cv2.fillPoly(img_text, pts =[textlines_small[z1] ] , color=(1,1,1))
img_text2=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
img_text2=cv2.fillPoly(img_text2, pts =[textlines_big[z2] ] , color=(1,1,1))
sum_small_big=img_text2+img_text
sum_small_big_2=(sum_small_big[:,:]==2)*1
sum_intersection=sum_small_big_2.sum(axis=1).sum()
#print(sum_intersection)
intersections.append(sum_intersection)
if len(np.array(intersections)[np.array(intersections)>0] )==0:
intersections=[]
try:
dis_small_from_bigs_tot.append(np.argmax(intersections))
except:
dis_small_from_bigs_tot.append(-1)
smalls_list=np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot)>=0]
#index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) )
textlines_big_with_change=[]
textlines_big_with_change_con=[]
textlines_small_with_change=[]
for z in list(set(smalls_list)):
index_small_textlines=list(np.where(np.array(dis_small_from_bigs_tot)==z)[0])
#print(z,index_small_textlines)
img_text2=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1],3))
img_text2=cv2.fillPoly(img_text2, pts =[textlines_big[z]] , color=(255,255,255))
textlines_big_with_change.append(z)
for k in index_small_textlines:
img_text2=cv2.fillPoly(img_text2, pts =[textlines_small[k]] , color=(255,255,255))
textlines_small_with_change.append(k)
img_text2=img_text2.astype(np.uint8)
imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(cont[0],type(cont))
textlines_big_with_change_con.append(cont)
textlines_big_org_form[z]=cont[0]
#plt.imshow(img_text2)
#plt.show()
#print(textlines_big_with_change,'textlines_big_with_change')
#print(textlines_small_with_change,'textlines_small_with_change')
#print(textlines_big)
textlines_con_changed.append(textlines_big_org_form)
else:
textlines_con_changed.append(textlines_big_org_form)
return textlines_con_changed
def check_any_text_region_in_model_one_is_main_or_header(self,regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
#text_only=(regions_model_1[:,:]==1)*1
#contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
4 years ago
"""
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
"""
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=self.find_new_features_of_contoures(contours_only_text_parent)
length_con=x_max_main-x_min_main
height_con=y_max_main-y_min_main
all_found_texline_polygons_main=[]
all_found_texline_polygons_head=[]
all_box_coord_main=[]
all_box_coord_head=[]
slopes_main=[]
slopes_head=[]
contours_only_text_parent_main=[]
contours_only_text_parent_head=[]
contours_only_text_parent_main_d=[]
contours_only_text_parent_head_d=[]
for ii in range(len(contours_only_text_parent)):
con=contours_only_text_parent[ii]
img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3))
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
all_pixels=((img[:,:,0]==255)*1).sum()
pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum()
pixels_main=all_pixels-pixels_header
if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
contours_only_text_parent_head.append(con)
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_head.append(all_box_coord[ii])
slopes_head.append(slopes[ii])
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
else:
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
contours_only_text_parent_main.append(con)
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_main.append(all_box_coord[ii])
slopes_main.append(slopes[ii])
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
#print(all_pixels,pixels_main,pixels_header)
#plt.imshow(img[:,:,0])
#plt.show()
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(self,layout_in_patch):
drop_only=(layout_in_patch[:,:,0]==4)*1
contours_drop,hir_on_drop=self.return_contours_of_image(drop_only)
contours_drop_parent=self.return_parent_contours( contours_drop,hir_on_drop)
areas_cnt_text=np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text=areas_cnt_text/float(drop_only.shape[0]*drop_only.shape[1])
contours_drop_parent=[contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz]>0.00001]
areas_cnt_text=[areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz]>0.001]
contours_drop_parent_final=[]
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
layout_in_patch[y:y+h,x:x+w,0]=4
return layout_in_patch
def filter_small_drop_capitals_from_no_patch_layout(self,layout_no_patch,layout1):
drop_only=(layout_no_patch[:,:,0]==4)*1
contours_drop,hir_on_drop=self.return_contours_of_image(drop_only)
contours_drop_parent=self.return_parent_contours( contours_drop,hir_on_drop)
areas_cnt_text=np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text=areas_cnt_text/float(drop_only.shape[0]*drop_only.shape[1])
contours_drop_parent=[contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz]>0.001]
areas_cnt_text=[areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz]>0.001]
contours_drop_parent_final=[]
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
#boxes.append([int(x), int(y), int(w), int(h)])
iou_of_box_and_contoure=float(drop_only.shape[0]*drop_only.shape[1])*areas_cnt_text[jj]/float(w*h)*100
height_to_weight_ratio=h/float(w)
weigh_to_height_ratio=w/float(h)
if iou_of_box_and_contoure>60 and weigh_to_height_ratio<1.2 and height_to_weight_ratio<2:
map_of_drop_contour_bb=np.zeros((layout1.shape[0],layout1.shape[1]))
map_of_drop_contour_bb[y:y+h,x:x+w]=layout1[y:y+h,x:x+w]
if ( ((map_of_drop_contour_bb==1)*1).sum()/float(((map_of_drop_contour_bb==5)*1).sum()) *100)>=15:
contours_drop_parent_final.append(contours_drop_parent[jj])
layout_no_patch[:,:,0][layout_no_patch[:,:,0]==4]=0
layout_no_patch=cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4,4, 4))
return layout_no_patch
def put_drop_out_from_only_drop_model(self,layout_no_patch,layout1):
drop_only=(layout_no_patch[:,:,0]==4)*1
contours_drop,hir_on_drop=self.return_contours_of_image(drop_only)
contours_drop_parent=self.return_parent_contours( contours_drop,hir_on_drop)
areas_cnt_text=np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text=areas_cnt_text/float(drop_only.shape[0]*drop_only.shape[1])
contours_drop_parent=[contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz]>0.00001]
areas_cnt_text=[areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz]>0.00001]
contours_drop_parent_final=[]
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
#boxes.append([int(x), int(y), int(w), int(h)])
map_of_drop_contour_bb=np.zeros((layout1.shape[0],layout1.shape[1]))
map_of_drop_contour_bb[y:y+h,x:x+w]=layout1[y:y+h,x:x+w]
if ( ((map_of_drop_contour_bb==1)*1).sum()/float(((map_of_drop_contour_bb==5)*1).sum()) *100)>=15:
contours_drop_parent_final.append(contours_drop_parent[jj])
layout_no_patch[:,:,0][layout_no_patch[:,:,0]==4]=0
layout_no_patch=cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4,4, 4))
return layout_no_patch
def do_order_of_regions(self,contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot):
if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
cx_text_only,cy_text_only ,x_min_text_only, _, _ ,_,y_cor_x_min_main=self.find_new_features_of_contoures(contours_only_text_parent)
cx_text_only_h,cy_text_only_h ,x_min_text_only_h, _, _ ,_,y_cor_x_min_main_h=self.find_new_features_of_contoures(contours_only_text_parent_h)
try:
arg_text_con=[]
for ii in range(len(cx_text_only)):
for jj in range(len(boxes)):
if (x_min_text_only[ii]+80) >=boxes[jj][0] and (x_min_text_only[ii]+80) < boxes[jj][1] and y_cor_x_min_main[ii] >=boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
arg_text_con.append(jj)
break
arg_arg_text_con=np.argsort(arg_text_con)
args_contours=np.array(range( len(arg_text_con)) )
arg_text_con_h=[]
for ii in range(len(cx_text_only_h)):
for jj in range(len(boxes)):
if (x_min_text_only_h[ii]+80) >=boxes[jj][0] and (x_min_text_only_h[ii]+80) < boxes[jj][1] and y_cor_x_min_main_h[ii] >=boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
arg_text_con_h.append(jj)
break
arg_arg_text_con=np.argsort(arg_text_con_h)
args_contours_h=np.array(range( len(arg_text_con_h)) )
order_by_con_head=np.zeros(len(arg_text_con_h))
order_by_con_main=np.zeros(len(arg_text_con))
ref_point=0
order_of_texts_tot=[]
id_of_texts_tot=[]
for iij in range(len(boxes)):
args_contours_box=args_contours[np.array(arg_text_con)==iij]
args_contours_box_h=args_contours_h[np.array(arg_text_con_h)==iij]
con_inter_box=[]
con_inter_box_h=[]
for i in range(len(args_contours_box)):
con_inter_box.append( contours_only_text_parent[args_contours_box[i] ] )
for i in range(len(args_contours_box_h)):
con_inter_box_h.append( contours_only_text_parent_h[args_contours_box_h[i] ] )
indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=self.order_of_regions(textline_mask_tot[int(boxes[iij][2]):int(boxes[iij][3]), int(boxes[iij][0]):int(boxes[iij][1])],con_inter_box,con_inter_box_h,boxes[iij][2])
order_of_texts, id_of_texts=self.order_and_id_of_texts(con_inter_box ,con_inter_box_h,matrix_of_orders ,indexes_sorted ,index_by_kind_sorted, kind_of_texts_sorted, ref_point)
indexes_sorted_main=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_by_type_main=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_sorted_head=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==2]
indexes_by_type_head=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==2]
zahler=0
for mtv in args_contours_box:
arg_order_v=indexes_sorted_main[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
zahler=zahler+1
zahler=0
for mtv in args_contours_box_h:
arg_order_v=indexes_sorted_head[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
#print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
order_by_con_head[ args_contours_box_h[indexes_by_type_head[zahler] ]]=tartib+ref_point
zahler=zahler+1
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji]+ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point=ref_point+len(id_of_texts)
order_of_texts_tot=[]
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1] ))
for tj1 in range(len(contours_only_text_parent_h)):
order_of_texts_tot.append(int(order_by_con_head[tj1]) )
order_text_new=[]
for iii in range(len(order_of_texts_tot)):
tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
order_text_new.append(tartib_new)
except:
arg_text_con=[]
for ii in range(len(cx_text_only)):
for jj in range(len(boxes)):
if cx_text_only[ii] >=boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >=boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
arg_text_con.append(jj)
break
arg_arg_text_con=np.argsort(arg_text_con)
args_contours=np.array(range( len(arg_text_con)) )
order_by_con_main=np.zeros(len(arg_text_con))
############################# head
arg_text_con_h=[]
for ii in range(len(cx_text_only_h)):
for jj in range(len(boxes)):
if cx_text_only_h[ii] >=boxes[jj][0] and cx_text_only_h[ii] < boxes[jj][1] and cy_text_only_h[ii] >=boxes[jj][2] and cy_text_only_h[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
arg_text_con_h.append(jj)
break
arg_arg_text_con_h=np.argsort(arg_text_con_h)
args_contours_h=np.array(range( len(arg_text_con_h)) )
order_by_con_head=np.zeros(len(arg_text_con_h))
#####
ref_point=0
order_of_texts_tot=[]
id_of_texts_tot=[]
for iij in range(len(boxes)):
args_contours_box=args_contours[np.array(arg_text_con)==iij]
args_contours_box_h=args_contours_h[np.array(arg_text_con_h)==iij]
con_inter_box=[]
con_inter_box_h=[]
for i in range(len(args_contours_box)):
con_inter_box.append( contours_only_text_parent[args_contours_box[i] ] )
for i in range(len(args_contours_box_h)):
con_inter_box_h.append( contours_only_text_parent_h[args_contours_box_h[i] ] )
indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=self.order_of_regions(textline_mask_tot[int(boxes[iij][2]):int(boxes[iij][3]), int(boxes[iij][0]):int(boxes[iij][1])],con_inter_box,con_inter_box_h,boxes[iij][2])
order_of_texts, id_of_texts=self.order_and_id_of_texts(con_inter_box ,con_inter_box_h,matrix_of_orders ,indexes_sorted ,index_by_kind_sorted, kind_of_texts_sorted, ref_point)
indexes_sorted_main=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_by_type_main=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_sorted_head=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==2]
indexes_by_type_head=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==2]
zahler=0
for mtv in args_contours_box:
arg_order_v=indexes_sorted_main[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
zahler=zahler+1
zahler=0
for mtv in args_contours_box_h:
arg_order_v=indexes_sorted_head[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
#print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
order_by_con_head[ args_contours_box_h[indexes_by_type_head[zahler] ]]=tartib+ref_point
zahler=zahler+1
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji]+ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point=ref_point+len(id_of_texts)
order_of_texts_tot=[]
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1] ))
for tj1 in range(len(contours_only_text_parent_h)):
order_of_texts_tot.append(int(order_by_con_head[tj1]) )
order_text_new=[]
for iii in range(len(order_of_texts_tot)):
tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
order_text_new.append(tartib_new)
return order_text_new,id_of_texts_tot
else:
cx_text_only,cy_text_only ,x_min_text_only, _, _ ,_,y_cor_x_min_main=self.find_new_features_of_contoures(contours_only_text_parent)
try:
arg_text_con=[]
for ii in range(len(cx_text_only)):
for jj in range(len(boxes)):
if (x_min_text_only[ii]+80) >=boxes[jj][0] and (x_min_text_only[ii]+80) < boxes[jj][1] and y_cor_x_min_main[ii] >=boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
arg_text_con.append(jj)
break
arg_arg_text_con=np.argsort(arg_text_con)
args_contours=np.array(range( len(arg_text_con)) )
order_by_con_main=np.zeros(len(arg_text_con))
ref_point=0
order_of_texts_tot=[]
id_of_texts_tot=[]
for iij in range(len(boxes)):
args_contours_box=args_contours[np.array(arg_text_con)==iij]
con_inter_box=[]
con_inter_box_h=[]
for i in range(len(args_contours_box)):
con_inter_box.append( contours_only_text_parent[args_contours_box[i] ] )
indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=self.order_of_regions(textline_mask_tot[int(boxes[iij][2]):int(boxes[iij][3]), int(boxes[iij][0]):int(boxes[iij][1])],con_inter_box,con_inter_box_h,boxes[iij][2])
order_of_texts, id_of_texts=self.order_and_id_of_texts(con_inter_box ,con_inter_box_h,matrix_of_orders ,indexes_sorted ,index_by_kind_sorted, kind_of_texts_sorted, ref_point)
indexes_sorted_main=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_by_type_main=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_sorted_head=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==2]
indexes_by_type_head=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==2]
zahler=0
for mtv in args_contours_box:
arg_order_v=indexes_sorted_main[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
zahler=zahler+1
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji]+ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point=ref_point+len(id_of_texts)
order_of_texts_tot=[]
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1] ))
order_text_new=[]
for iii in range(len(order_of_texts_tot)):
tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
order_text_new.append(tartib_new)
except:
arg_text_con=[]
for ii in range(len(cx_text_only)):
for jj in range(len(boxes)):
if cx_text_only[ii] >=boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >=boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]:#this is valid if the center of region identify in which box it is located
arg_text_con.append(jj)
break
arg_arg_text_con=np.argsort(arg_text_con)
args_contours=np.array(range( len(arg_text_con)) )
order_by_con_main=np.zeros(len(arg_text_con))
ref_point=0
order_of_texts_tot=[]
id_of_texts_tot=[]
for iij in range(len(boxes)):
args_contours_box=args_contours[np.array(arg_text_con)==iij]
con_inter_box=[]
con_inter_box_h=[]
for i in range(len(args_contours_box)):
con_inter_box.append( contours_only_text_parent[args_contours_box[i] ] )
indexes_sorted, matrix_of_orders,kind_of_texts_sorted,index_by_kind_sorted=self.order_of_regions(textline_mask_tot[int(boxes[iij][2]):int(boxes[iij][3]), int(boxes[iij][0]):int(boxes[iij][1])],con_inter_box,con_inter_box_h,boxes[iij][2])
order_of_texts, id_of_texts=self.order_and_id_of_texts(con_inter_box ,con_inter_box_h,matrix_of_orders ,indexes_sorted ,index_by_kind_sorted, kind_of_texts_sorted, ref_point)
indexes_sorted_main=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_by_type_main=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==1]
indexes_sorted_head=np.array(indexes_sorted)[np.array(kind_of_texts_sorted)==2]
indexes_by_type_head=np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted)==2]
zahler=0
for mtv in args_contours_box:
arg_order_v=indexes_sorted_main[zahler]
tartib=np.where(indexes_sorted==arg_order_v )[0][0]
order_by_con_main[ args_contours_box[indexes_by_type_main[zahler] ]]=tartib+ref_point
zahler=zahler+1
for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji]+ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point=ref_point+len(id_of_texts)
order_of_texts_tot=[]
for tj1 in range(len(contours_only_text_parent)):
order_of_texts_tot.append(int(order_by_con_main[tj1] ))
order_text_new=[]
for iii in range(len(order_of_texts_tot)):
tartib_new=np.where(np.array(order_of_texts_tot)==iii)[0][0]
order_text_new.append(tartib_new)
return order_text_new,id_of_texts_tot
def adhere_drop_capital_region_into_cprresponding_textline(self,text_regions_p,polygons_of_drop_capitals,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h):
#print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
#print(all_found_texline_polygons[3])
cx_m,cy_m ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)
cx_h,cy_h ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent_h)
cx_d,cy_d ,_ , _, y_min_d ,y_max_d,_=self.find_new_features_of_contoures(polygons_of_drop_capitals)
img_con_all=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
for j_cont in range(len(contours_only_text_parent)):
img_con_all[all_box_coord[j_cont][0]:all_box_coord[j_cont][1],all_box_coord[j_cont][2]:all_box_coord[j_cont][3],0]=(j_cont+1)*3
#img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
#plt.imshow(img_con_all[:,:,0])
#plt.show()
#img_con_all=cv2.dilate(img_con_all, self.kernel, iterations=3)
#plt.imshow(img_con_all[:,:,0])
#plt.show()
#print(np.unique(img_con_all[:,:,0]))
for i_drop in range(len(polygons_of_drop_capitals)):
#print(i_drop,'i_drop')
img_con_all_copy=np.copy(img_con_all)
img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(1,1,1))
#plt.imshow(img_con[:,:,0])
#plt.show()
##img_con=cv2.dilate(img_con, self.kernel, iterations=30)
#plt.imshow(img_con[:,:,0])
#plt.show()
#print(np.unique(img_con[:,:,0]))
img_con_all_copy[:,:,0]=img_con_all_copy[:,:,0]+img_con[:,:,0]
img_con_all_copy[:,:,0][img_con_all_copy[:,:,0]==1]=0
kherej_ghesmat=np.unique(img_con_all_copy[:,:,0])/3
res_summed_pixels=np.unique(img_con_all_copy[:,:,0])%3
region_with_intersected_drop=kherej_ghesmat[res_summed_pixels==1]
#region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop=region_with_intersected_drop.astype(np.uint8)
#print(len(region_with_intersected_drop),'region_with_intersected_drop1')
if len(region_with_intersected_drop)==0:
img_con_all_copy=np.copy(img_con_all)
img_con=cv2.dilate(img_con, self.kernel, iterations=4)
img_con_all_copy[:,:,0]=img_con_all_copy[:,:,0]+img_con[:,:,0]
img_con_all_copy[:,:,0][img_con_all_copy[:,:,0]==1]=0
kherej_ghesmat=np.unique(img_con_all_copy[:,:,0])/3
res_summed_pixels=np.unique(img_con_all_copy[:,:,0])%3
region_with_intersected_drop=kherej_ghesmat[res_summed_pixels==1]
#region_with_intersected_drop=region_with_intersected_drop/3
region_with_intersected_drop=region_with_intersected_drop.astype(np.uint8)
#print(np.unique(img_con_all_copy[:,:,0]))
if self.curved_line=='True' or self.curved_line=='true':
if len(region_with_intersected_drop)>1:
sum_pixels_of_intersection=[]
for i in range(len(region_with_intersected_drop)):
#print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection.append( ((img_con_all_copy[:,:,0]==(region_with_intersected_drop[i]*3+1))*1).sum() )
#print(sum_pixels_of_intersection)
region_final=region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)]-1
#print(region_final,'region_final')
#cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try:
cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(all_box_coord[j_cont])
#print(cx_t)
#print(cy_t)
#print(cx_d[i_drop])
#print(cy_d[i_drop])
y_lines=np.array(cy_t)#all_box_coord[int(region_final)][0]+np.array(cy_t)
#print(y_lines)
y_lines[y_lines<y_min_d[i_drop]]=0
#print(y_lines)
arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
#print(arg_min)
cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
img_textlines=img_textlines.astype(np.uint8)
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours_combined),'len textlines mixed')
areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
#print(np.shape(contours_biggest))
#print(contours_biggest[:])
#contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
#contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
#contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except:
#print('gordun1')
pass
elif len(region_with_intersected_drop)==1:
region_final=region_with_intersected_drop[0]-1
#areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
#cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(all_box_coord[j_cont])
#print(cx_t)
#print(cy_t)
#print(cx_d[i_drop])
#print(cy_d[i_drop])
y_lines=np.array(cy_t)#all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines[y_lines<y_min_d[i_drop]]=0
#print(y_lines)
arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
#print(arg_min)
cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
img_textlines=img_textlines.astype(np.uint8)
#plt.imshow(img_textlines)
#plt.show()
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours_combined),'len textlines mixed')
areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
#print(np.shape(contours_biggest))
#print(contours_biggest[:])
#contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
#contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
#print(np.shape(contours_biggest),'contours_biggest')
#print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
#print(cx_t,'print')
try:
#print(all_found_texline_polygons[j_cont][0])
cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(all_box_coord[j_cont])
#print(cx_t)
#print(cy_t)
#print(cx_d[i_drop])
#print(cy_d[i_drop])
y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines[y_lines<y_min_d[i_drop]]=0
#print(y_lines)
arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
#print(arg_min)
cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
img_textlines=img_textlines.astype(np.uint8)
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours_combined),'len textlines mixed')
areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
#print(np.shape(contours_biggest))
#print(contours_biggest[:])
contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
#all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except:
pass
else:
pass
##cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
###print(cx_d[i_drop])
###print(cy_d[i_drop])
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
##y_lines[y_lines<y_min_d[i_drop]]=0
###print(y_lines)
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
###print(arg_min)
##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
##img_textlines=img_textlines.astype(np.uint8)
##plt.imshow(img_textlines)
##plt.show()
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
##print(len(contours_combined),'len textlines mixed')
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
###print(np.shape(contours_biggest))
###print(contours_biggest[:])
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
else:
if len(region_with_intersected_drop)>1:
sum_pixels_of_intersection=[]
for i in range(len(region_with_intersected_drop)):
#print((region_with_intersected_drop[i]*3+1))
sum_pixels_of_intersection.append( ((img_con_all_copy[:,:,0]==(region_with_intersected_drop[i]*3+1))*1).sum() )
#print(sum_pixels_of_intersection)
region_final=region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)]-1
#print(region_final,'region_final')
#cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try:
cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(all_box_coord[j_cont])
#print(cx_t)
#print(cy_t)
#print(cx_d[i_drop])
#print(cy_d[i_drop])
y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
#print(y_lines)
y_lines[y_lines<y_min_d[i_drop]]=0
#print(y_lines)
arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
#print(arg_min)
cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0]+all_box_coord[int(region_final)][2]
cnt_nearest[:,1]=all_found_texline_polygons[int(region_final)][arg_min][:,1]+all_box_coord[int(region_final)][0]
img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
img_textlines=img_textlines.astype(np.uint8)
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours_combined),'len textlines mixed')
areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
#print(np.shape(contours_biggest))
#print(contours_biggest[:])
contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[int(region_final)][2]
contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[int(region_final)][0]
contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except:
#print('gordun1')
pass
elif len(region_with_intersected_drop)==1:
region_final=region_with_intersected_drop[0]-1
#areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
#cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(cx_t,'print')
try:
#print(all_found_texline_polygons[j_cont][0])
cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
#print(all_box_coord[j_cont])
#print(cx_t)
#print(cy_t)
#print(cx_d[i_drop])
#print(cy_d[i_drop])
y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
y_lines[y_lines<y_min_d[i_drop]]=0
#print(y_lines)
arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
#print(arg_min)
cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
cnt_nearest[:,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0]+all_box_coord[int(region_final)][2]
cnt_nearest[:,1]=all_found_texline_polygons[int(region_final)][arg_min][:,1]+all_box_coord[int(region_final)][0]
img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
img_textlines=img_textlines.astype(np.uint8)
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours_combined),'len textlines mixed')
areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
#print(np.shape(contours_biggest))
#print(contours_biggest[:])
contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[int(region_final)][2]
contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[int(region_final)][0]
contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
#all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
except:
pass
else:
pass
#####for i_drop in range(len(polygons_of_drop_capitals)):
#####for j_cont in range(len(contours_only_text_parent)):
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
#####img_con=img_con.astype(np.uint8)
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####contours_new,hir_new=self.return_contours_of_image(img_con)
#####contours_new_parent=self.return_parent_contours( contours_new,hir_new)
######plt.imshow(img_con)
######plt.show()
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_texline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)
######print(cx_d[i_drop])
######print(cy_d[i_drop])
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
######print(y_lines)
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
######print(arg_min)
#####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
#####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
#####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_textlines=img_textlines.astype(np.uint8)
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
#####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
######print(np.shape(contours_biggest))
######print(contours_biggest[:])
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
#####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
######print(contours_biggest)
######plt.imshow(img_textlines[:,:,0])
######plt.show()
#####else:
#####pass
#####except:
#####pass
return all_found_texline_polygons
def save_plot_of_layout_main(self,text_regions_p,image_page):
values=np.unique(text_regions_p[:,:])
#pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Images' , 'Seperators','Marginalia']
values_indexes=[0 , 1 , 2 , 3,4]
plt.figure(figsize=(40,40))
plt.rcParams['font.size']='40'
im=plt.imshow(text_regions_p[:,:])
colors=[im.cmap(im.norm(value)) for value in values]
patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int( np.where(values_indexes==i)[0][0] )])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout,self.f_name+'_layout_main.png') )
def save_plot_of_layout_main_all(self,text_regions_p,image_page):
values=np.unique(text_regions_p[:,:])
#pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Images' , 'Seperators','Marginalia']
values_indexes=[0 , 1 , 2 , 3,4]
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='40'
plt.subplot(1,2,1)
plt.imshow(image_page)
plt.subplot(1,2,2)
im=plt.imshow(text_regions_p[:,:])
colors=[im.cmap(im.norm(value)) for value in values]
patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int( np.where(values_indexes==i)[0][0] )])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=60)
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_layout_main_and_page.png') )
def save_plot_of_layout(self,text_regions_p,image_page):
values=np.unique(text_regions_p[:,:])
#pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Header' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators']
values_indexes=[0 , 1 , 2 , 8, 4, 5, 6]
plt.figure(figsize=(40,40))
plt.rcParams['font.size']='40'
im=plt.imshow(text_regions_p[:,:])
colors=[im.cmap(im.norm(value)) for value in values]
patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int( np.where(values_indexes==i)[0][0] )])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout,self.f_name+'_layout.png') )
def save_plot_of_layout_all(self,text_regions_p,image_page):
values=np.unique(text_regions_p[:,:])
#pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Header' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators']
values_indexes=[0 , 1 , 2 , 8, 4, 5, 6]
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='40'
plt.subplot(1,2,1)
plt.imshow(image_page)
plt.subplot(1,2,2)
im=plt.imshow(text_regions_p[:,:])
colors=[im.cmap(im.norm(value)) for value in values]
patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int( np.where(values_indexes==i)[0][0] )])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=60)
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_layout_and_page.png') )
def save_deskewed_image(self,slope_deskew):
img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all,self.f_name+'_org.png'),self.image_org)
cv2.imwrite(os.path.join(self.dir_of_deskewed,self.f_name+'_deskewed.png'),img_rotated)
del img_rotated
def run(self):
is_image_enhanced=False
#get image and sclaes, then extract the page of scanned image
t1=time.time()
##########
###is_image_enhanced,img_org,img_res=self.resize_and_enhance_image(is_image_enhanced)
is_image_enhanced,img_org,img_res,num_col_classifier,num_column_is_classified=self.resize_and_enhance_image_with_column_classifier(is_image_enhanced)
print(is_image_enhanced,'is_image_enhanced')
K.clear_session()
scale=1
if (self.allow_enhancement=='true' or self.allow_enhancement=='True' or self.allow_enhancement=='TRUE') and is_image_enhanced:
cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",img_res)
img_res=img_res.astype(np.uint8)
self.get_image_and_scales(img_org,img_res,scale)
if (self.allow_enhancement=='False' or self.allow_enhancement=='false' or self.allow_enhancement=='FALSE' or self.allow_enhancement==None) and is_image_enhanced:
self.get_image_and_scales_after_enhancing(img_org,img_res)
if (self.allow_enhancement=='true' or self.allow_enhancement=='True' or self.allow_enhancement=='TRUE') and not is_image_enhanced:
self.get_image_and_scales(img_org,img_res,scale)
if (self.allow_enhancement=='False' or self.allow_enhancement=='false' or self.allow_enhancement=='FALSE' or self.allow_enhancement==None) and not is_image_enhanced:
self.get_image_and_scales(img_org,img_res,scale)
if (self.allow_scaling=='true' or self.allow_scaling=='True' or self.allow_scaling=='TRUE') and not is_image_enhanced :
img_org,img_res,is_image_enhanced=self.resize_image_with_column_classifier(is_image_enhanced)
self.get_image_and_scales_after_enhancing(img_org,img_res)
#print(self.scale_x)
print('enhancing: '+str(time.time()-t1))
text_regions_p_1=self.get_regions_from_xy_2models(img_res,is_image_enhanced)
K.clear_session()
gc.collect()
print('textregion: '+str(time.time()-t1))
img_g=cv2.imread(self.image_dir,0)
img_g=img_g.astype(np.uint8)
img_g3=np.zeros((img_g.shape[0],img_g.shape[1],3))
img_g3 = img_g3.astype(np.uint8)
img_g3[:,:,0]=img_g[:,:]
img_g3[:,:,1]=img_g[:,:]
img_g3[:,:,2]=img_g[:,:]
###self.produce_groundtruth_for_textline()
image_page,page_coord=self.extract_page()
#print(image_page.shape,'page')
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all,self.f_name+'_page.png'),image_page)
##########
K.clear_session()
gc.collect()
img_g3_page=img_g3[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3],:]
del img_g3
del img_g
text_regions_p_1=text_regions_p_1[page_coord[0]:page_coord[1],page_coord[2]:page_coord[3]]
mask_images=(text_regions_p_1[:,:]==2)*1
mask_lines=(text_regions_p_1[:,:]==3)*1
mask_images=mask_images.astype(np.uint8)
mask_lines=mask_lines.astype(np.uint8)
mask_images=cv2.erode(mask_images[:,:], self.kernel, iterations=10)
img_only_regions_with_sep=( (text_regions_p_1[:,:]!=3) & (text_regions_p_1[:,:]!=0) )*1
img_only_regions_with_sep=img_only_regions_with_sep.astype(np.uint8)
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], self.kernel, iterations=6)
try:
num_col, peaks_neg_fin=self.find_num_col(img_only_regions,multiplier=6.0)
if not num_column_is_classified:
num_col_classifier=num_col+1
except:
num_col=None
peaks_neg_fin=[]
#print(num_col,'num_colnum_col')
4 years ago
if num_col is None:
txt_con_org=[]
order_text_new=[]
id_of_texts_tot=[]
all_found_texline_polygons=[]
all_box_coord=[]
polygons_of_images=[]
polygons_of_marginals=[]
all_found_texline_polygons_marginals=[]
all_box_coord_marginals=[]
slopes=[]
slopes_marginals=[]
self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)
else:
#pass
try:
patches=True
scaler_h_textline=1#1.2#1.2
scaler_w_textline=1#0.9#1
textline_mask_tot_ea,textline_mask_tot_long_shot=self.textline_contours(image_page,patches,scaler_h_textline,scaler_w_textline)
K.clear_session()
gc.collect()
#print(np.unique(textline_mask_tot_ea[:,:]),'textline')
4 years ago
if self.dir_of_all is not None:
values=np.unique(textline_mask_tot_ea[:,:])
pixels=['Background' , 'Textlines']
values_indexes=[0 , 1]
plt.figure(figsize=(70,40))
plt.rcParams['font.size']='40'
plt.subplot(1,2,1)
plt.imshow(image_page)
plt.subplot(1,2,2)
im=plt.imshow(textline_mask_tot_ea[:,:])
colors=[im.cmap(im.norm(value)) for value in values]
patches=[ mpatches.Patch( color=colors[np.where(values==i)[0][0]], label="{l}".format(l=pixels[int( np.where(values_indexes==i)[0][0] )])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fontsize=60)
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_textline_and_page.png') )
print('textline: '+str(time.time()-t1))
#plt.imshow(textline_mask_tot_ea)
#plt.show()
#sys.exit()
sigma=2
main_page_deskew=True
slope_deskew=self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma,main_page_deskew)
slope_first=0#self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma)
if self.dir_of_deskewed is not None:
self.save_deskewed_image(slope_deskew)
#img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
print(slope_deskew,'slope_deskew')
##plt.imshow(img_rotated)
##plt.show()
##sys.exit()
print('deskewing: '+str(time.time()-t1))
image_page_rotated,textline_mask_tot=image_page[:,:],textline_mask_tot_ea[:,:]#self.rotation_not_90_func(image_page,textline_mask_tot_ea,slope_first)
textline_mask_tot[mask_images[:,:]==1]=0
pixel_img=1
min_area=0.00001
max_area=0.0006
textline_mask_tot_small_size=self.return_contours_of_interested_region_by_size(textline_mask_tot,pixel_img,min_area,max_area)
#text_regions_p_1[(textline_mask_tot[:,:]==1) & (text_regions_p_1[:,:]==2)]=1
text_regions_p_1[mask_lines[:,:]==1]=3
##text_regions_p_1[textline_mask_tot_small_size[:,:]==1]=1
text_regions_p=text_regions_p_1[:,:]#long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p=np.array(text_regions_p)
if num_col_classifier==1 or num_col_classifier==2:
try:
regions_without_seperators=(text_regions_p[:,:]==1)*1
regions_without_seperators=regions_without_seperators.astype(np.uint8)
text_regions_p=self.get_marginals(self.rotate_image(regions_without_seperators,slope_deskew),text_regions_p,num_col_classifier,slope_deskew)
except:
pass
else:
pass
#plt.imshow(text_regions_p)
#plt.show()
if self.dir_of_all is not None:
self.save_plot_of_layout_main_all(text_regions_p,image_page)
if self.dir_of_layout is not None:
self.save_plot_of_layout_main(text_regions_p,image_page)
print('marginals: '+str(time.time()-t1))
if self.full_layout is None or self.full_layout=='False' or self.full_layout=='false' or self.full_layout=='FALSE':
if np.abs(slope_deskew)>=.13:
image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n=self.rotation_not_90_func(image_page,textline_mask_tot,text_regions_p,slope_deskew)
text_regions_p_1_n=self.resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
textline_mask_tot_d=self.resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
regions_without_seperators_d=(text_regions_p_1_n[:,:]==1)*1
regions_without_seperators=(text_regions_p[:,:]==1)*1#( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
pixel_lines=3
if np.abs(slope_deskew)<0.13:
num_col,peaks_neg_fin,matrix_of_lines_ch,spliter_y_new,seperators_closeup_n=self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines)
if np.abs(slope_deskew)>=.13:
num_col_d,peaks_neg_fin_d,matrix_of_lines_ch_d,spliter_y_new_d,seperators_closeup_n_d=self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines)
K.clear_session()
gc.collect()
#print(peaks_neg_fin,num_col,'num_col2')
print(num_col_classifier,'num_col_classifier')
if num_col_classifier>=3:
if np.abs(slope_deskew)<0.13:
regions_without_seperators=regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:,:], self.kernel, iterations=6)
random_pixels_for_image=np.random.randn(regions_without_seperators.shape[0],regions_without_seperators.shape[1])
random_pixels_for_image[random_pixels_for_image<-.5]=0
random_pixels_for_image[random_pixels_for_image!=0]=1
regions_without_seperators[(random_pixels_for_image[:,:]==1) & (text_regions_p[:,:]==2)]=1
if np.abs(slope_deskew)>=.13:
regions_without_seperators_d=regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:,:], self.kernel, iterations=6)
random_pixels_for_image=np.random.randn(regions_without_seperators_d.shape[0],regions_without_seperators_d.shape[1])
random_pixels_for_image[random_pixels_for_image<-.5]=0
random_pixels_for_image[random_pixels_for_image!=0]=1
regions_without_seperators_d[(random_pixels_for_image[:,:]==1) & (text_regions_p_1_n[:,:]==2)]=1
else:
pass
if np.abs(slope_deskew)<0.13:
boxes=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new,regions_without_seperators,matrix_of_lines_ch)
else:
boxes_d=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d,regions_without_seperators_d,matrix_of_lines_ch_d)
#print(len(boxes),'boxes')
#sys.exit()
print('boxes in: '+str(time.time()-t1))
img_revised_tab=text_regions_p[:,:]
#plt.imshow(img_revised_tab)
#plt.show()
K.clear_session()
pixel_img=4
min_area_mar=0.00001
polygons_of_marginals=self.return_contours_of_interested_region(text_regions_p,pixel_img,min_area_mar)
if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
# set first model with second model
text_regions_p[:,:][text_regions_p[:,:]==2]=5
text_regions_p[:,:][text_regions_p[:,:]==3]=6
text_regions_p[:,:][text_regions_p[:,:]==4]=8
K.clear_session()
#gc.collect()
patches=True
image_page=image_page.astype(np.uint8)
#print(type(image_page))
regions_fully,regions_fully_only_drop=self.extract_text_regions(image_page,patches,cols=num_col_classifier)
regions_fully_only_drop=self.put_drop_out_from_only_drop_model(regions_fully_only_drop,text_regions_p)
regions_fully[:,:,0][regions_fully_only_drop[:,:,0]==4]=4
K.clear_session()
gc.collect()
#plt.imshow(regions_fully[:,:,0])
#plt.show()
regions_fully=self.putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
#plt.imshow(regions_fully[:,:,0])
#plt.show()
K.clear_session()
gc.collect()
patches=False
regions_fully_np,_=self.extract_text_regions(image_page,patches,cols=num_col_classifier)
#plt.imshow(regions_fully_np[:,:,0])
#plt.show()
if num_col_classifier>2:
regions_fully_np[:,:,0][regions_fully_np[:,:,0]==4]=0
else:
regions_fully_np=self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)
#regions_fully_np=self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)
#plt.imshow(regions_fully_np[:,:,0])
#plt.show()
K.clear_session()
gc.collect()
#plt.imshow(regions_fully[:,:,0])
#plt.show()
regions_fully=self.boosting_headers_by_longshot_region_segmentation(regions_fully,regions_fully_np,img_only_regions)
#plt.imshow(regions_fully[:,:,0])
#plt.show()
text_regions_p[:,:][regions_fully[:,:,0]==4]=4
##text_regions_p[:,:][(regions_fully[:,:,0]==7) & (text_regions_p[:,:]!=0)]=7
text_regions_p[:,:][regions_fully_np[:,:,0]==4]=4
#plt.imshow(text_regions_p)
#plt.show()
if np.abs(slope_deskew)>=0.13:
image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n,regions_fully_n=self.rotation_not_90_func_full_layout(image_page,textline_mask_tot,text_regions_p,regions_fully,slope_deskew)
text_regions_p_1_n=self.resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1])
textline_mask_tot_d=self.resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1])
regions_fully_n=self.resize_image(regions_fully_n,text_regions_p.shape[0],text_regions_p.shape[1])
regions_without_seperators_d=(text_regions_p_1_n[:,:]==1)*1
regions_without_seperators=(text_regions_p[:,:]==1)*1#( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
K.clear_session()
gc.collect()
img_revised_tab=np.copy(text_regions_p[:,:])
print('full layout in: '+str(time.time()-t1))
#sys.exit()
pixel_img=5
polygons_of_images=self.return_contours_of_interested_region(img_revised_tab,pixel_img)
#plt.imshow(img_revised_tab)
#plt.show()
#print(img_revised_tab.shape,text_regions_p_1_n.shape)
#text_regions_p_1_n=self.resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
#print(np.unique(text_regions_p_1_n),'uni')
text_only=( (img_revised_tab[:,:]==1) )*1
if np.abs(slope_deskew)>=0.13:
text_only_d=( (text_regions_p_1_n[:,:]==1) )*1
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
#print(text_only.shape,text_only_d.shape)
#plt.imshow(text_only)
#plt.show()
#plt.imshow(text_only_d)
#plt.show()
min_con_area=0.000005
if np.abs(slope_deskew)>=0.13:
contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
contours_biggest=contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>min_con_area]
areas_cnt_text_parent=[areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz]>min_con_area]
index_con_parents=np.argsort(areas_cnt_text_parent)
contours_only_text_parent=list(np.array(contours_only_text_parent)[index_con_parents] )
areas_cnt_text_parent=list(np.array(areas_cnt_text_parent)[index_con_parents] )
cx_bigest_big,cy_biggest_big ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest])
cx_bigest,cy_biggest ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)
contours_only_text_d,hir_on_text_d=self.return_contours_of_image(text_only_d)
contours_only_text_parent_d=self.return_parent_contours( contours_only_text_d,hir_on_text_d)
areas_cnt_text_d=np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d=areas_cnt_text_d/float(text_only_d.shape[0]*text_only_d.shape[1])
contours_biggest_d=contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
cx_bigest_d_big,cy_biggest_d_big ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest_d])
cx_bigest_d,cy_biggest_d ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent_d)
(h, w) = text_only.shape[:2]
center = (w // 2., h // 2.)
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
M_22=np.array(M)[:2,:2]
p_big=np.dot(M_22,[cx_bigest_big,cy_biggest_big])
x_diff=p_big[0]-cx_bigest_d_big
y_diff=p_big[1]-cy_biggest_d_big
#print(p_big)
#print(cx_bigest_d_big,cy_biggest_d_big)
#print(x_diff,y_diff)
contours_only_text_parent_d_ordered=[]
for i in range(len(contours_only_text_parent)):
#img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
#img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
#plt.imshow(img1[:,:,0])
#plt.show()
p=np.dot(M_22,[cx_bigest[i],cy_biggest[i]])
#print(p)
p[0]=p[0]-x_diff[0]
p[1]=p[1]-y_diff[0]
#print(p)
#print(cx_bigest_d)
#print(cy_biggest_d)
dists = [math.sqrt((p[0]-cx_bigest_d[j])**2 + (p[1]-cy_biggest_d[j])**2) for j in range(len(cx_bigest_d))]
#print(np.argmin(dists))
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
#img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
#img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
#plt.imshow(img2[:,:,0])
#plt.show()
else:
contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
contours_biggest=contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>min_con_area]
areas_cnt_text_parent=[areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz]>min_con_area]
index_con_parents=np.argsort(areas_cnt_text_parent)
contours_only_text_parent=list(np.array(contours_only_text_parent)[index_con_parents] )
areas_cnt_text_parent=list(np.array(areas_cnt_text_parent)[index_con_parents] )
cx_bigest_big,cy_biggest_big ,_, _, _ ,_,_=self.find_new_features_of_contoures([contours_biggest])
cx_bigest,cy_biggest ,_, _, _ ,_,_=self.find_new_features_of_contoures(contours_only_text_parent)
#print(areas_cnt_text_parent,'areas_cnt_text_parent')
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
##contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d])
###areas_cnt_text_parent_d=list(np.array(areas_cnt_text_parent_d)[index_con_parents_d])
##print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d')
#print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz')
txt_con_org=self.get_textregion_contours_in_org_image(contours_only_text_parent,self.image,slope_first)
###boxes_text,_=self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_text,_=self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals,_=self.get_text_region_boxes_by_given_contours(polygons_of_marginals)
####boxes_text_h,_=self.get_text_region_boxes_by_given_contours(text_only_h,contours_only_text_parent_h,image_page)
if self.curved_line is None or self.curved_line=='false' or self.curved_line=='False' or self.curved_line=='FALSE' :
slopes,all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord,index_by_text_par_con=self.get_slopes_and_deskew_new(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,slope_deskew)
slopes_marginals,all_found_texline_polygons_marginals,boxes_marginals,_,polygons_of_marginals,all_box_coord_marginals,index_by_text_par_con_marginal=self.get_slopes_and_deskew_new(polygons_of_marginals,polygons_of_marginals,textline_mask_tot_ea,image_page_rotated,boxes_marginals,slope_deskew)
4 years ago
if self.curved_line=='true' or self.curved_line=='True' or self.curved_line=='TRUE' :
scale_param=1
all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord,index_by_text_par_con,slopes=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,cv2.erode(textline_mask_tot_ea,kernel=self.kernel,iterations=1),image_page_rotated,boxes_text,text_only,num_col_classifier,scale_param,slope_deskew)
#all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
all_found_texline_polygons=self.small_textlines_to_parent_adherence2(all_found_texline_polygons,textline_mask_tot_ea,num_col_classifier)
#slopes=list(np.zeros(len(contours_only_text_parent)))
all_found_texline_polygons_marginals,boxes_marginals,_,polygons_of_marginals,all_box_coord_marginals,index_by_text_par_con_marginal,slopes_marginals=self.get_slopes_and_deskew_new_curved(polygons_of_marginals,polygons_of_marginals,cv2.erode(textline_mask_tot_ea,kernel=self.kernel,iterations=1),image_page_rotated,boxes_marginals,text_only,num_col_classifier,scale_param,slope_deskew)
#all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
all_found_texline_polygons_marginals=self.small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals,textline_mask_tot_ea,num_col_classifier)
index_of_vertical_text_contours=np.array(range(len(slopes)) )[(abs(np.array(slopes))>60)]
contours_text_vertical=[contours_only_text_parent[i] for i in index_of_vertical_text_contours]
K.clear_session()
gc.collect()
#contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
###print(index_by_text_par_con,'index_by_text_par_con')
if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
##for iii in range(len(contours_only_text_parent)):
##img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
##img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[iii]] ,color=(1,1,1))
##plt.imshow(img1[:,:,0])
##plt.show()
##img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
##img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d_ordered[iii]] ,color=(1,1,1))
##plt.imshow(img2[:,:,0])
##plt.show()
if np.abs(slope_deskew)>=0.13:
contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h,slopes,slopes_h,contours_only_text_parent_d_ordered,contours_only_text_parent_h_d_ordered=self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p,regions_fully,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered)
else:
contours_only_text_parent_d_ordered=None
text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h,slopes,slopes_h,contours_only_text_parent_d_ordered,contours_only_text_parent_h_d_ordered=self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p,regions_fully,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered)
###text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h=self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p,regions_fully,contours_only_text_parent,all_box_coord,all_found_texline_polygons)
#text_regions_p=self.return_region_segmentation_after_implementing_not_head_maintext_parallel(text_regions_p,boxes)
# if you want to save the layout result just uncommet following plot
if self.dir_of_layout is not None:
self.save_plot_of_layout(text_regions_p,image_page)
if self.dir_of_all is not None:
self.save_plot_of_layout_all(text_regions_p,image_page)
K.clear_session()
gc.collect()
##print('Job done in: '+str(time.time()-t1))
polygons_of_tabels=[]
pixel_img=4
polygons_of_drop_capitals=self.return_contours_of_interested_region_by_min_size(text_regions_p,pixel_img)
#polygons_of_drop_capitals=[]
all_found_texline_polygons=self.adhere_drop_capital_region_into_cprresponding_textline(text_regions_p,polygons_of_drop_capitals,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h)
#print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines=6
if self.headers_off is None or self.headers_off=='false' or self.headers_off=='False' or self.headers_off=='FALSE':
if np.abs(slope_deskew)<0.13:
num_col,peaks_neg_fin,matrix_of_lines_ch,spliter_y_new,seperators_closeup_n=self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines,contours_only_text_parent_h)
else:
num_col_d,peaks_neg_fin_d,matrix_of_lines_ch_d,spliter_y_new_d,seperators_closeup_n_d=self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines,contours_only_text_parent_h_d_ordered)
elif self.headers_off=='true' or self.headers_off=='True' or self.headers_off=='TRUE':
if np.abs(slope_deskew)<0.13:
num_col,peaks_neg_fin,matrix_of_lines_ch,spliter_y_new,seperators_closeup_n=self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines)
else:
num_col_d,peaks_neg_fin_d,matrix_of_lines_ch_d,spliter_y_new_d,seperators_closeup_n_d=self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier,pixel_lines)
#print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
#print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
#print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
if num_col_classifier>=3:
if np.abs(slope_deskew)<0.13:
regions_without_seperators=regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:,:], self.kernel, iterations=6)
random_pixels_for_image=np.random.randn(regions_without_seperators.shape[0],regions_without_seperators.shape[1])
random_pixels_for_image[random_pixels_for_image<-.5]=0
random_pixels_for_image[random_pixels_for_image!=0]=1
regions_without_seperators[(random_pixels_for_image[:,:]==1) & (text_regions_p[:,:]==5)]=1
else:
regions_without_seperators_d=regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:,:], self.kernel, iterations=6)
random_pixels_for_image=np.random.randn(regions_without_seperators_d.shape[0],regions_without_seperators_d.shape[1])
random_pixels_for_image[random_pixels_for_image<-.5]=0
random_pixels_for_image[random_pixels_for_image!=0]=1
regions_without_seperators_d[(random_pixels_for_image[:,:]==1) & (text_regions_p_1_n[:,:]==5)]=1
else:
pass
if np.abs(slope_deskew)<0.13:
boxes=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new,regions_without_seperators,matrix_of_lines_ch)
else:
boxes_d=self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d,regions_without_seperators_d,matrix_of_lines_ch_d)
#print(boxes_d,len(boxes_d),'boxes_d')
#for mv in range(len(boxes_d)):
#img_box=image_page[boxes_d[mv][1]:boxes_d[mv][1]+boxes_d[mv][3] , boxes_d[mv][0]:boxes_d[mv][0]+boxes_d[mv][2],: ]
#plt.imshow(img_box)
#plt.show()
4 years ago
#print(slopes)
if self.dir_of_cropped_images is not None:
self.write_images_into_directory(polygons_of_images,self.dir_of_cropped_images,image_page)
if self.full_layout=='true' or self.full_layout=='True' or self.full_layout=='TRUE':
if np.abs(slope_deskew)<0.13:
order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
else:
order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent_d_ordered,contours_only_text_parent_h_d_ordered,boxes_d,textline_mask_tot_d)
self.write_into_page_xml_full(contours_only_text_parent,contours_only_text_parent_h,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,all_found_texline_polygons_h,
all_box_coord,all_box_coord_h,polygons_of_images ,polygons_of_tabels,polygons_of_drop_capitals,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,slopes,slopes_marginals)
else:
contours_only_text_parent_h=None
#print('bura galmir?')
if np.abs(slope_deskew)<0.13:
contours_only_text_parent=list(np.array(contours_only_text_parent)[index_by_text_par_con])
order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
else:
contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent_d_ordered,contours_only_text_parent_h,boxes_d,textline_mask_tot_d)
#order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)
except:
txt_con_org=[]
order_text_new=[]
id_of_texts_tot=[]
all_found_texline_polygons=[]
all_box_coord=[]
polygons_of_images=[]
polygons_of_marginals=[]
all_found_texline_polygons_marginals=[]
all_box_coord_marginals=[]
slopes=[]
slopes_marginals=[]
self.write_into_page_xml(txt_con_org,page_coord,self.dir_out , order_text_new , id_of_texts_tot,all_found_texline_polygons,
all_box_coord,polygons_of_images,polygons_of_marginals,all_found_texline_polygons_marginals,all_box_coord_marginals,self.curved_line,slopes,slopes_marginals)
print('Job done in: '+str(time.time()-t1))
@click.command()
@click.option('--image', '-i', help='image filename', type=click.Path(exists=True, dir_okay=False))
@click.option('--out', '-o', help='directory to write output xml data', type=click.Path(exists=True, file_okay=False))
@click.option('--model', '-m', help='directory of models', type=click.Path(exists=True, file_okay=False))
@click.option('--save_images', '-si', help='if a directory is given, images in documents will be cropped and saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_layout', '-sl', help='if a directory is given, plot of layout will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_deskewed', '-sd', help='if a directory is given, deskewed image will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--save_all', '-sa', help='if a directory is given, all plots needed for documentation will be saved there', type=click.Path(exists=True, file_okay=False))
@click.option('--allow_enhancement', '-ae', help='if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory')
@click.option('--curved_line', '-cl', help='if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.')
@click.option('--full_layout', '-fl', help='if this parameter set to true, this tool will try to return all elements of layout.')
@click.option('--allow_scaling', '-as', help='if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection')
@click.option('--headers_off', '-ho', help='if this parameter set to true, this tool would ignore headers role in reading order')
def main(image,out, model,save_images,save_layout,save_deskewed,save_all,allow_enhancement,curved_line,full_layout,allow_scaling,headers_off):
possibles = globals() # XXX unused?
possibles.update(locals())
x = eynollah(image, None, out, model,save_images,save_layout,save_deskewed,save_all,allow_enhancement,curved_line,full_layout,allow_scaling,headers_off)
x.run()
if __name__ == "__main__":
main()