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main
code_formatting_only
refactor
refactor_with_disable-dir-in
machine_based_reading_order_integration
extracting_images_only
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eynollah/qurator/eynollah/eynollah.py

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# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring,missing-class-docstring,too-many-branches
# pylint: disable=too-many-locals,wrong-import-position,too-many-lines,too-many-statements,chained-comparison,fixme,broad-except,c-extension-no-member
# pylint: disable=too-many-public-methods,too-many-arguments,too-many-instance-attributes,too-many-public-methods,
# pylint: disable=consider-using-enumerate
"""
tool to extract table form data from alto xml data
"""
import math
import os
import sys
import time
import warnings
from pathlib import Path
from multiprocessing import Process, Queue, cpu_count
import gc
from ocrd_utils import getLogger
import cv2
import numpy as np
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
stderr = sys.stderr
sys.stderr = open(os.devnull, "w")
from keras import backend as K
from keras.models import load_model
sys.stderr = stderr
import tensorflow as tf
tf.get_logger().setLevel("ERROR")
warnings.filterwarnings("ignore")
from .utils.contour import (
filter_contours_area_of_image,
find_contours_mean_y_diff,
find_new_features_of_contours,
get_text_region_boxes_by_given_contours,
get_textregion_contours_in_org_image,
return_contours_of_image,
return_contours_of_interested_region,
return_contours_of_interested_region_by_min_size,
return_contours_of_interested_textline,
return_parent_contours,
)
from .utils.rotate import (
rotate_image,
rotation_not_90_func,
rotation_not_90_func_full_layout)
from .utils.separate_lines import (
textline_contours_postprocessing,
separate_lines_new2,
return_deskew_slop)
from .utils.drop_capitals import (
adhere_drop_capital_region_into_corresponding_textline,
filter_small_drop_capitals_from_no_patch_layout)
from .utils.marginals import get_marginals
from .utils.resize import resize_image
from .utils import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
find_num_col,
otsu_copy_binary,
put_drop_out_from_only_drop_model,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
check_any_text_region_in_model_one_is_main_or_header,
small_textlines_to_parent_adherence2,
order_of_regions,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new)
from .utils.pil_cv2 import check_dpi, pil2cv
from .utils.xml import order_and_id_of_texts
from .plot import EynollahPlotter
from .writer import EynollahXmlWriter
SLOPE_THRESHOLD = 0.13
RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45:
DPI_THRESHOLD = 298
MAX_SLOPE = 999
KERNEL = np.ones((5, 5), np.uint8)
class Eynollah:
def __init__(
self,
dir_models,
image_filename,
image_pil=None,
image_filename_stem=None,
dir_out=None,
dir_of_cropped_images=None,
dir_of_layout=None,
dir_of_deskewed=None,
dir_of_all=None,
enable_plotting=False,
allow_enhancement=False,
curved_line=False,
full_layout=False,
allow_scaling=False,
headers_off=False,
override_dpi=None,
logger=None,
pcgts=None,
):
if image_pil:
self._imgs = self._cache_images(image_pil=image_pil)
else:
self._imgs = self._cache_images(image_filename=image_filename)
self.image_filename = image_filename
self.dir_out = dir_out
self.allow_enhancement = allow_enhancement
self.curved_line = curved_line
self.full_layout = full_layout
self.allow_scaling = allow_scaling
self.headers_off = headers_off
self.override_dpi = override_dpi
self.plotter = None if not enable_plotting else EynollahPlotter(
dir_of_all=dir_of_all,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
dir_of_layout=dir_of_layout,
image_filename_stem=Path(Path(image_filename).name).stem)
self.writer = EynollahXmlWriter(
dir_out=self.dir_out,
image_filename=self.image_filename,
curved_line=self.curved_line,
pcgts=pcgts)
self.logger = logger if logger else getLogger('eynollah')
self.dir_models = dir_models
self.model_dir_of_enhancement = 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"
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"
self.model_page_dir = dir_models + "/model_page_mixed_best.h5"
self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5"
self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
def _cache_images(self, image_filename=None, image_pil=None):
ret = {}
if image_filename:
ret['img'] = cv2.imread(image_filename)
else:
ret['img'] = pil2cv(image_pil)
ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY)
for prefix in ('', '_grayscale'):
ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
return ret
def imread(self, grayscale=False, uint8=True):
key = 'img'
if grayscale:
key += '_grayscale'
if uint8:
key += '_uint8'
return self._imgs[key].copy()
def predict_enhancement(self, img):
self.logger.debug("enter predict_enhancement")
model_enhancement, session_enhancement = self.start_new_session_and_model(self.model_dir_of_enhancement)
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]
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 = 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))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else 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
else:
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
else:
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)
session_enhancement.close()
del model_enhancement
del session_enhancement
gc.collect()
return prediction_true
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
self.logger.debug("enter calculate_width_height_by_columns")
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 = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True
return img_new, num_column_is_classified
def resize_image_with_column_classifier(self, is_image_enhanced):
self.logger.debug("enter resize_image_with_column_classifier")
img = self.imread()
_, 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 = self.imread(grayscale=True, uint8=False)
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 / 255.0
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
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
session_col_classifier.close()
del model_num_classifier
del session_col_classifier
K.clear_session()
gc.collect()
img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
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):
self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
if self.override_dpi:
return self.override_dpi
dpi = check_dpi(self.imread())
self.logger.info("Detected %s DPI", dpi)
img = self.imread()
_, 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 = self.imread(grayscale=True)
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 / 255.0
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
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
session_col_classifier.close()
K.clear_session()
if dpi < DPI_THRESHOLD:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
image_res = self.predict_enhancement(img_new)
is_image_enhanced = True
else:
is_image_enhanced = False
num_column_is_classified = True
image_res = np.copy(img)
session_col_classifier.close()
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
return is_image_enhanced, img, image_res, num_col, num_column_is_classified
# pylint: disable=attribute-defined-outside-init
def get_image_and_scales(self, img_org, img_res, scale):
self.logger.debug("enter get_image_and_scales")
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 = resize_image(self.image, self.img_hight_int, self.img_width_int)
# Also set for the plotter
if self.plotter:
self.plotter.image_org = self.image_org
self.plotter.scale_y = self.scale_y
self.plotter.scale_x = self.scale_x
# Also set for the writer
self.writer.image_org = self.image_org
self.writer.scale_y = self.scale_y
self.writer.scale_x = self.scale_x
self.writer.height_org = self.height_org
self.writer.width_org = self.width_org
def get_image_and_scales_after_enhancing(self, img_org, img_res):
self.logger.debug("enter get_image_and_scales_after_enhancing")
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])
# Also set for the plotter
if self.plotter:
self.plotter.image_org = self.image_org
self.plotter.scale_y = self.scale_y
self.plotter.scale_x = self.scale_x
# Also set for the writer
self.writer.image_org = self.image_org
self.writer.scale_y = self.scale_y
self.writer.scale_x = self.scale_x
self.writer.height_org = self.height_org
self.writer.width_org = self.width_org
def start_new_session_and_model_old(self, model_dir):
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", 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 start_new_session_and_model(self, model_dir):
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
model = load_model(model_dir, compile=False)
return model, session
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction")
img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
if not patches:
img_h_page = img.shape[0]
img_w_page = img.shape[1]
img = img / float(255.0)
img = 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 = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
else:
if img.shape[0] < img_height_model:
img = resize_image(img, img_height_model, img.shape[1])
if img.shape[1] < img_width_model:
img = resize_image(img, img.shape[0], img_width_model)
self.logger.info("Image dimensions: %sx%s", img_height_model, 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)
img = img.astype(np.float16)
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)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else 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
else:
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
else:
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 model
gc.collect()
return prediction_true
def early_page_for_num_of_column_classification(self):
self.logger.debug("enter early_page_for_num_of_column_classification")
img = self.imread()
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img = cv2.GaussianBlur(img, (5, 5), 0)
img_page_prediction = self.do_prediction(False, img, model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, 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 = crop_image_inside_box(box, img)
session_page.close()
del model_page
del session_page
gc.collect()
K.clear_session()
self.logger.debug("exit early_page_for_num_of_column_classification")
return croped_page, page_coord
def extract_page(self):
self.logger.debug("enter extract_page")
cont_page = []
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img = cv2.GaussianBlur(self.image, (5, 5), 0)
img_page_prediction = self.do_prediction(False, img, model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, 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 += 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 = crop_image_inside_box(box, self.image)
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
gc.collect()
K.clear_session()
self.logger.debug("exit extract_page")
return croped_page, page_coord, cont_page
def extract_text_regions(self, img, patches, cols):
self.logger.debug("enter extract_text_regions")
img_height_h = img.shape[0]
img_width_h = img.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
if not patches:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
prediction_regions2 = None
else:
if cols == 1:
img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8)
img2 = 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 = resize_image(prediction_regions2, img_height_h, img_width_h)
if cols == 2:
img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8)
img2 = 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 = resize_image(prediction_regions2, img_height_h, img_width_h)
elif cols > 2:
img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8)
img2 = 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 = resize_image(prediction_regions2, img_height_h, img_width_h)
if cols == 2:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
if img_width_h >= 2000:
img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
img = img.astype(np.uint8)
if cols == 1:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
img = img.astype(np.uint8)
if cols == 3:
if (self.scale_x == 1 and img_width_h > 3000) or (self.scale_x != 1 and img_width_h > 2800):
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 2800 / float(img_width_h)), 2800)
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
if cols == 4:
if (self.scale_x == 1 and img_width_h > 4000) or (self.scale_x != 1 and img_width_h > 3700):
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
if cols == 5:
if self.scale_x == 1 and img_width_h > 5000:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.7), int(img_width_h * 0.7))
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9) )
if cols >= 6:
if img_width_h > 5600:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 5600 / float(img_width_h)), 5600)
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
session_region.close()
del model_region
del session_region
gc.collect()
self.logger.debug("exit extract_text_regions")
return prediction_regions, prediction_regions2
def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
self.logger.debug("enter get_slopes_and_deskew_new")
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()
self.logger.debug('slopes %s', slopes)
self.logger.debug("exit get_slopes_and_deskew_new")
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):
self.logger.debug("enter get_slopes_and_deskew_new_curved")
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):
self.logger.debug("enter do_work_of_slopes_new_curved")
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 = []
textline_cnt_separated = 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[:, :]
# img_int_p=cv2.erode(img_int_p,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_for_all = [slope_deskew][0]
else:
try:
textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
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 Exception as why:
self.logger.error(why)
slope_for_all = MAX_SLOPE
if slope_for_all == MAX_SLOPE:
slope_for_all = [slope_deskew][0]
slopes_per_each_subprocess.append(slope_for_all)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
_, crop_coor = 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_separated = separate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all, plotter=self.plotter)
# new line added
##print(np.shape(textline_rotated_separated),np.shape(mask_biggest))
textline_rotated_separated[mask_region_in_patch_region[:, :] != 1] = 0
# till here
textline_cnt_separated[y : y + h, x : x + w] = textline_rotated_separated
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_separated
# plt.imshow(textline_region_in_image)
# plt.show()
# plt.imshow(textline_cnt_separated)
# plt.show()
pixel_img = 1
cnt_textlines_in_image = 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, KERNEL, iterations=5)
else:
mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4)
pixel_img = 1
mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
try:
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except Exception as why:
self.logger.error(why)
else:
add_boxes_coor_into_textlines = True
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], add_boxes_coor_into_textlines)
add_boxes_coor_into_textlines = False
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
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):
self.logger.debug('enter do_work_of_slopes_new')
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 = []
for mv in range(len(boxes_text)):
_, crop_coor = crop_image_inside_box(boxes_text[mv],image_page_rotated)
mask_textline = np.zeros((textline_mask_tot_ea.shape))
mask_textline = cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
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] ]
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,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 = 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])
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
else:
try:
textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0))
if sigma_des < 1:
sigma_des = 1
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
if abs(slope_for_all) <= 0.5:
slope_for_all = [slope_deskew][0]
except Exception as why:
self.logger.error(why)
slope_for_all = MAX_SLOPE
if slope_for_all == MAX_SLOPE:
slope_for_all = [slope_deskew][0]
slopes_per_each_subprocess.append(slope_for_all)
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 = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv])
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
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 textline_contours(self, img, patches, scaler_h, scaler_w):
self.logger.debug('enter textline_contours')
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
img_w = img_org.shape[1]
img = 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 = resize_image(prediction_textline, img_h, img_w)
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
session_textline.close()
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
self.logger.debug('enter do_work_of_slopes')
slope_biggest = 0
slopes_sub = []
boxes_sub_new = []
poly_sub = []
for mv in range(len(boxes_per_process)):
crop_img, _ = 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, KERNEL, iterations=2)
try:
textline_con, hierarchy = return_contours_of_image(crop_img)
textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierarchy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
crop_img[crop_img > 0] = 1
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, plotter=self.plotter)
except Exception as why:
self.logger.error(why)
slope_corresponding_textregion = MAX_SLOPE
if slope_corresponding_textregion == MAX_SLOPE:
slope_corresponding_textregion = slope_biggest
slopes_sub.append(slope_corresponding_textregion)
cnt_clean_rot = 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_regions_from_xy_2models(self,img,is_image_enhanced):
self.logger.debug("enter get_regions_from_xy_2models")
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)
ratio_y=1.3
ratio_x=1
img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
prediction_regions_org_y = self.do_prediction(True, img, model_region)
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
#plt.show()
prediction_regions_org_y = prediction_regions_org_y[:,:,0]
mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
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()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
text_sume_early = (prediction_regions_org[:,:] == 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
self.logger.info("ratio_of_two_models: %s", rate_two_models)
if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
prediction_regions_org = np.copy(prediction_regions_org_copy)
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3))
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))
K.clear_session()
return text_regions_p_true
def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
self.logger.debug("enter do_order_of_regions_full_layout")
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours(contours_only_text_parent)
cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = find_new_features_of_contours(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
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
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 box in args_contours_box:
con_inter_box.append(contours_only_text_parent[box])
for box in args_contours_box_h:
con_inter_box_h.append(contours_only_text_parent_h[box])
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = 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 = 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]
for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for zahler, _ in enumerate(args_contours_box_h):
arg_order_v = indexes_sorted_head[zahler]
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
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 += 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)):
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
except Exception as why:
self.logger.error(why)
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
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
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 enumerate(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 box in args_contours_box:
con_inter_box.append(contours_only_text_parent[box])
for box in args_contours_box_h:
con_inter_box_h.append(contours_only_text_parent_h[box])
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = 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 = 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]
for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for zahler, _ in enumerate(args_contours_box_h):
arg_order_v = indexes_sorted_head[zahler]
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in enumerate(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 += 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)):
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
return order_text_new, id_of_texts_tot
def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
self.logger.debug("enter do_order_of_regions_no_full_layout")
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours(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
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 = 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 = 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]
for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in enumerate(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 += 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)):
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
except Exception as why:
self.logger.error(why)
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
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 = 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 = 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]
for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in enumerate(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 += 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)):
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
return order_text_new, id_of_texts_tot
def do_order_of_regions(self, *args, **kwargs):
if self.full_layout:
return self.do_order_of_regions_full_layout(*args, **kwargs)
return self.do_order_of_regions_no_full_layout(*args, **kwargs)
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified):
img_g = self.imread(grayscale=True, uint8=True)
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[:, :]
image_page, page_coord, cont_page = self.extract_page()
if self.plotter:
self.plotter.save_page_image(image_page)
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_images = mask_images.astype(np.uint8)
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
mask_lines = (text_regions_p_1[:, :] == 3) * 1
mask_lines = mask_lines.astype(np.uint8)
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[:, :], KERNEL, iterations=6)
try:
num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
num_col = num_col + 1
if not num_column_is_classified:
num_col_classifier = num_col + 1
except Exception as why:
self.logger.error(why)
num_col = None
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page
def run_enhancement(self):
self.logger.info("resize and enhance image")
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified = self.resize_and_enhance_image_with_column_classifier()
self.logger.info("Image is %senhanced", '' if is_image_enhanced else 'not ')
K.clear_session()
scale = 1
if is_image_enhanced:
if self.allow_enhancement:
img_res = img_res.astype(np.uint8)
self.get_image_and_scales(img_org, img_res, scale)
else:
self.get_image_and_scales_after_enhancing(img_org, img_res)
else:
if self.allow_enhancement:
self.get_image_and_scales(img_org, img_res, scale)
else:
self.get_image_and_scales(img_org, img_res, scale)
if self.allow_scaling:
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)
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
def run_textline(self, image_page):
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
K.clear_session()
if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
return textline_mask_tot_ea
def run_deskew(self, textline_mask_tot_ea):
sigma = 2
main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
slope_first = 0
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %s", slope_deskew)
return slope_deskew, slope_first
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1):
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
textline_mask_tot[mask_images[:, :] == 1] = 0
text_regions_p_1[mask_lines[:, :] == 1] = 3
text_regions_p = text_regions_p_1[:, :]
text_regions_p = np.array(text_regions_p)
if num_col_classifier in (1, 2):
try:
regions_without_separators = (text_regions_p[:, :] == 1) * 1
regions_without_separators = regions_without_separators.astype(np.uint8)
text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
except Exception as e:
self.logger.error("exception %s", e)
if self.plotter:
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
return textline_mask_tot, text_regions_p, image_page_rotated
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier):
self.logger.debug('enter run_boxes_no_full_layout')
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch, splitter_y_new, _ = 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) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = 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()
self.logger.info("num_col_classifier: %s", num_col_classifier)
if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
self.logger.info("detecting boxes took %ss", str(time.time() - t1))
img_revised_tab = text_regions_p[:, :]
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2)
# plt.imshow(img_revised_tab)
# plt.show()
K.clear_session()
self.logger.debug('exit run_boxes_no_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions):
self.logger.debug('enter run_boxes_full_layout')
# 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()
image_page = image_page.astype(np.uint8)
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, True, cols=num_col_classifier)
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
regions_fully_only_drop = 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()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
K.clear_session()
regions_fully_np, _ = self.extract_text_regions(image_page, False, 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 = 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()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = 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_np[:, :, 0] == 4] = 4
#plt.imshow(text_regions_p)
#plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
else:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions)
K.clear_session()
img_revised_tab = np.copy(text_regions_p[:, :])
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
self.logger.debug('exit run_boxes_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators
def run(self):
"""
Get image and scales, then extract the page of scanned image
"""
self.logger.debug("enter run")
t0 = time.time()
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement()
self.logger.info("Enhancing took %ss ", str(time.time() - t0))
t1 = time.time()
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
self.logger.info("Textregion detection took %ss ", str(time.time() - t1))
t1 = time.time()
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page = \
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified)
self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
self.logger.info('cont_page %s', cont_page)
if not num_col:
self.logger.info("No columns detected, outputting an empty PAGE-XML")
pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [], cont_page)
self.logger.info("Job done in %ss", str(time.time() - t1))
return pcgts
t1 = time.time()
textline_mask_tot_ea = self.run_textline(image_page)
self.logger.info("textline detection took %ss", str(time.time() - t1))
t1 = time.time()
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
self.logger.info("deskewing took %ss", str(time.time() - t1))
t1 = time.time()
textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1)
self.logger.info("detection of marginals took %ss", str(time.time() - t1))
t1 = time.time()
if not self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier)
pixel_img = 4
min_area_mar = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
if self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions)
text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
min_con_area = 0.000005
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = 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])
self.logger.info('areas_cnt_text %s', areas_cnt_text)
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, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = 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)]
index_con_parents_d=np.argsort(areas_cnt_text_d)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d)
try:
if len(cx_bigest_d) >= 5:
cx_bigest_d_last5 = cx_bigest_d[-5:]
cy_biggest_d_last5 = cy_biggest_d[-5:]
dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))]
ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d)
else:
cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):]
cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):]
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d)
cx_bigest_d_big[0] = cx_bigest_d[ind_largest]
cy_biggest_d_big[0] = cy_biggest_d[ind_largest]
except Exception as why:
self.logger.error(why)
(h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0)
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
contours_only_text_parent_d_ordered = []
for i in range(len(contours_only_text_parent)):
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0]
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))]
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 = return_contours_of_image(text_only)
contours_only_text_parent = 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, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
# self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
if not self.curved_line:
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, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
else:
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=KERNEL, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
K.clear_session()
if self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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, _, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = 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, _, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = 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)
if self.plotter:
self.plotter.save_plot_of_layout(text_regions_p, image_page)
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
K.clear_session()
polygons_of_tabels = []
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_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, kernel=KERNEL, curved_line=self.curved_line)
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = 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:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = 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:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = 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(splitter_y_new,splitter_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) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_d.shape[1])
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
random_pixels_for_image[random_pixels_for_image != 0] = 1
regions_without_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
if self.full_layout:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, 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, cont_page)
self.logger.info("Job done in %ss", str(time.time() - t0))
return pcgts
else:
contours_only_text_parent_h = None
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, 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, cont_page)
self.logger.info("Job done in %ss", str(time.time() - t0))
return pcgts
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