import sys
import os
import numpy as np
import warnings
import cv2
import seaborn as sns
from tensorflow.keras.models import load_model
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras import layers
import tensorflow.keras.losses
from tensorflow.keras.layers import *
from models import *
from gt_gen_utils import *
import click
import json
from tensorflow.python.keras import backend as tensorflow_backend
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
with warnings.catch_warnings():
warnings.simplefilter("ignore")
__doc__=\
"""
Tool to load model and predict for given image.
"""
class sbb_predict:
def __init__(self,image, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area):
self.image=image
self.patches=patches
self.save=save
self.save_layout=save_layout
self.model_dir=model
self.ground_truth=ground_truth
self.task=task
self.config_params_model=config_params_model
self.xml_file = xml_file
self.out = out
if min_area:
self.min_area = float(min_area)
else:
self.min_area = 0
def resize_image(self,img_in,input_height,input_width):
return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
def color_images(self,seg):
ann_u=range(self.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", self.n_classes)
for c in ann_u:
c=int(c)
segl=(seg==c)
seg_img[:,:,0][seg==c]=c
seg_img[:,:,1][seg==c]=c
seg_img[:,:,2][seg==c]=c
return seg_img
def otsu_copy_binary(self,img):
img_r=np.zeros((img.shape[0],img.shape[1],3))
img1=img[:,:,0]
#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)
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 otsu_copy(self,img):
img_r=np.zeros((img.shape[0],img.shape[1],3))
#img1=img[:,:,0]
#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)
_, threshold1 = cv2.threshold(img[:,:,0], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img[:,:,1], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold3 = cv2.threshold(img[:,:,2], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1
img_r[:,:,1]=threshold2
img_r[:,:,2]=threshold3
###img_r=img_r/float(np.max(img_r))*255
return img_r
def soft_dice_loss(self,y_true, y_pred, epsilon=1e-6):
axes = tuple(range(1, len(y_pred.shape)-1))
numerator = 2. * K.sum(y_pred * y_true, axes)
denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
def weighted_categorical_crossentropy(self,weights=None):
def loss(y_true, y_pred):
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
return self.loss
def IoU(self,Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
IoUs = []
Nclass = np.unique(Yi)
for c in Nclass:
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
if self.n_classes>2:
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
IoUs.append(IoU)
if self.n_classes>2:
mIoU = np.mean(IoUs)
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
elif self.n_classes==2:
mIoU = IoUs[1]
print("_________________")
print("IoU: {:4.3f}".format(mIoU))
return mIoU
def start_new_session_and_model(self):
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
tensorflow_backend.set_session(session)
#tensorflow.keras.layers.custom_layer = PatchEncoder
#tensorflow.keras.layers.custom_layer = Patches
self.model = load_model(self.model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
#config = tf.ConfigProto()
#config.gpu_options.allow_growth=True
#self.session = tf.InteractiveSession()
#keras.losses.custom_loss = self.weighted_categorical_crossentropy
#self.model = load_model(self.model_dir , compile=False)
##if self.weights_dir!=None:
##self.model.load_weights(self.weights_dir)
if (self.task != 'classification' and self.task != 'reading_order'):
self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
def visualize_model_output(self, prediction, img, task):
if task == "binarization":
prediction = prediction * -1
prediction = prediction + 1
added_image = prediction * 255
layout_only = None
else:
unique_classes = np.unique(prediction[:,:,0])
rgb_colors = {'0' : [255, 255, 255],
'1' : [255, 0, 0],
'2' : [255, 125, 0],
'3' : [255, 0, 125],
'4' : [125, 125, 125],
'5' : [125, 125, 0],
'6' : [0, 125, 255],
'7' : [0, 125, 0],
'8' : [125, 125, 125],
'9' : [0, 125, 255],
'10' : [125, 0, 125],
'11' : [0, 255, 0],
'12' : [0, 0, 255],
'13' : [0, 255, 255],
'14' : [255, 125, 125],
'15' : [255, 0, 255]}
layout_only = np.zeros(prediction.shape)
for unq_class in unique_classes:
rgb_class_unique = rgb_colors[str(int(unq_class))]
layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0]
layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1]
layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2]
img = self.resize_image(img, layout_only.shape[0], layout_only.shape[1])
layout_only = layout_only.astype(np.int32)
img = img.astype(np.int32)
added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0)
return added_image, layout_only
def predict(self):
self.start_new_session_and_model()
if self.task == 'classification':
classes_names = self.config_params_model['classification_classes_name']
img_1ch = img=cv2.imread(self.image, 0)
img_1ch = img_1ch / 255.0
img_1ch = cv2.resize(img_1ch, (self.config_params_model['input_height'], self.config_params_model['input_width']), 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 = self.model.predict(img_in, verbose=0)
index_class = np.argmax(label_p_pred[0])
print("Predicted Class: {}".format(classes_names[str(int(index_class))]))
elif self.task == 'reading_order':
img_height = self.config_params_model['input_height']
img_width = self.config_params_model['input_width']
tree_xml, root_xml, bb_coord_printspace, file_name, id_paragraph, id_header, co_text_paragraph, co_text_header, tot_region_ref, x_len, y_len, index_tot_regions, img_poly = read_xml(self.xml_file)
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(co_text_header)
img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
for j in range(len(cy_main)):
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
co_text_all = co_text_paragraph + co_text_header
id_all_text = id_paragraph + id_header
##texts_corr_order_index = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ]
##texts_corr_order_index_int = [int(x) for x in texts_corr_order_index]
texts_corr_order_index_int = list(np.array(range(len(co_text_all))))
#print(texts_corr_order_index_int)
max_area = 1
#print(np.shape(co_text_all[0]), len( np.shape(co_text_all[0]) ),'co_text_all')
#co_text_all = filter_contours_area_of_image_tables(img_poly, co_text_all, _, max_area, min_area)
#print(co_text_all,'co_text_all')
co_text_all, texts_corr_order_index_int = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, self.min_area)
#print(texts_corr_order_index_int)
#co_text_all = [co_text_all[index] for index in texts_corr_order_index_int]
id_all_text = [id_all_text[index] for index in texts_corr_order_index_int]
labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
for i in range(len(co_text_all)):
img_label = np.zeros((y_len,x_len,3),dtype='uint8')
img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1))
labels_con[:,:,i] = img_label[:,:,0]
if bb_coord_printspace:
#bb_coord_printspace[x,y,w,h,_,_]
x = bb_coord_printspace[0]
y = bb_coord_printspace[1]
w = bb_coord_printspace[2]
h = bb_coord_printspace[3]
labels_con = labels_con[y:y+h, x:x+w, :]
img_poly = img_poly[y:y+h, x:x+w, :]
img_header_and_sep = img_header_and_sep[y:y+h, x:x+w]
img3= np.copy(img_poly)
labels_con = resize_image(labels_con, img_height, img_width)
img_header_and_sep = resize_image(img_header_and_sep, img_height, img_width)
img3= resize_image (img3, img_height, img_width)
img3 = img3.astype(np.uint16)
inference_bs = 1#4
input_1= np.zeros( (inference_bs, img_height, img_width,3))
starting_list_of_regions = []
starting_list_of_regions.append( list(range(labels_con.shape[2])) )
index_update = 0
index_selected = starting_list_of_regions[0]
scalibility_num = 0
while index_update>=0:
ij_list = starting_list_of_regions[index_update]
i = ij_list[0]
ij_list.pop(0)
pr_list = []
post_list = []
batch_counter = 0
tot_counter = 1
tot_iteration = len(ij_list)
full_bs_ite= tot_iteration//inference_bs
last_bs = tot_iteration % inference_bs
jbatch_indexer =[]
for j in ij_list:
img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2)
img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2)
img2[:,:,0][img3[:,:,0]==5] = 2
img2[:,:,0][img_header_and_sep[:,:]==1] = 3
img1[:,:,0][img3[:,:,0]==5] = 2
img1[:,:,0][img_header_and_sep[:,:]==1] = 3
#input_1= np.zeros( (height1, width1,3))
jbatch_indexer.append(j)
input_1[batch_counter,:,:,0] = img1[:,:,0]/3.
input_1[batch_counter,:,:,2] = img2[:,:,0]/3.
input_1[batch_counter,:,:,1] = img3[:,:,0]/5.
#input_1[batch_counter,:,:,:]= np.zeros( (batch_counter, height1, width1,3))
batch_counter = batch_counter+1
#input_1[:,:,0] = img1[:,:,0]/3.
#input_1[:,:,2] = img2[:,:,0]/3.
#input_1[:,:,1] = img3[:,:,0]/5.
if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs):
y_pr = self.model.predict(input_1 , verbose=0)
scalibility_num = scalibility_num+1
if batch_counter==inference_bs:
iteration_batches = inference_bs
else:
iteration_batches = last_bs
for jb in range(iteration_batches):
if y_pr[jb][0]>=0.5:
post_list.append(jbatch_indexer[jb])
else:
pr_list.append(jbatch_indexer[jb])
batch_counter = 0
jbatch_indexer = []
tot_counter = tot_counter+1
starting_list_of_regions, index_update = update_list_and_return_first_with_length_bigger_than_one(index_update, i, pr_list, post_list,starting_list_of_regions)
index_sort = [i[0] for i in starting_list_of_regions ]
id_all_text = np.array(id_all_text)[index_sort]
alltags=[elem.tag for elem in root_xml.iter()]
link=alltags[0].split('}')[0]+'}'
name_space = alltags[0].split('}')[0]
name_space = name_space.split('{')[1]
page_element = root_xml.find(link+'Page')
"""
ro_subelement = ET.SubElement(page_element, 'ReadingOrder')
#print(page_element, 'page_element')
#new_element = ET.Element('ReadingOrder')
new_element_element = ET.Element('OrderedGroup')
new_element_element.set('id', "ro357564684568544579089")
for index, id_text in enumerate(id_all_text):
new_element_2 = ET.Element('RegionRefIndexed')
new_element_2.set('regionRef', id_all_text[index])
new_element_2.set('index', str(index_sort[index]))
new_element_element.append(new_element_2)
ro_subelement.append(new_element_element)
"""
##ro_subelement = ET.SubElement(page_element, 'ReadingOrder')
ro_subelement = ET.Element('ReadingOrder')
ro_subelement2 = ET.SubElement(ro_subelement, 'OrderedGroup')
ro_subelement2.set('id', "ro357564684568544579089")
for index, id_text in enumerate(id_all_text):
new_element_2 = ET.SubElement(ro_subelement2, 'RegionRefIndexed')
new_element_2.set('regionRef', id_all_text[index])
new_element_2.set('index', str(index))
if (link+'PrintSpace' in alltags) or (link+'Border' in alltags):
page_element.insert(1, ro_subelement)
else:
page_element.insert(0, ro_subelement)
alltags=[elem.tag for elem in root_xml.iter()]
ET.register_namespace("",name_space)
tree_xml.write(os.path.join(self.out, file_name+'.xml'),xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
#tree_xml.write('library2.xml')
else:
if self.patches:
#def textline_contours(img,input_width,input_height,n_classes,model):
img=cv2.imread(self.image)
self.img_org = np.copy(img)
if img.shape[0] < self.img_height:
img = cv2.resize(img, (img.shape[1], self.img_width), interpolation=cv2.INTER_NEAREST)
if img.shape[1] < self.img_width:
img = cv2.resize(img, (self.img_height, img.shape[0]), interpolation=cv2.INTER_NEAREST)
margin = int(0.1 * self.img_width)
width_mid = self.img_width - 2 * margin
height_mid = self.img_height - 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 + self.img_width
else:
index_x_d = i * width_mid
index_x_u = index_x_d + self.img_width
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + self.img_height
else:
index_y_d = j * height_mid
index_y_u = index_y_d + self.img_height
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - self.img_width
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - self.img_height
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = self.model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
if self.task == 'enhancement':
seg = label_p_pred[0, :, :, :]
seg = seg * 255
elif self.task == 'segmentation' or self.task == 'binarization':
seg = np.argmax(label_p_pred, axis=3)[0]
seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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)
prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST)
return prediction_true
else:
img=cv2.imread(self.image)
self.img_org = np.copy(img)
width=self.img_width
height=self.img_height
img=img/255.0
img=self.resize_image(img,self.img_height,self.img_width)
label_p_pred=self.model.predict(
img.reshape(1,img.shape[0],img.shape[1],img.shape[2]))
if self.task == 'enhancement':
seg = label_p_pred[0, :, :, :]
seg = seg * 255
elif self.task == 'segmentation' or self.task == 'binarization':
seg = np.argmax(label_p_pred, axis=3)[0]
seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = seg.astype(int)
prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST)
return prediction_true
def run(self):
res=self.predict()
if (self.task == 'classification' or self.task == 'reading_order'):
pass
elif self.task == 'enhancement':
if self.save:
cv2.imwrite(self.save,res)
else:
img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task)
if self.save:
cv2.imwrite(self.save,img_seg_overlayed)
if self.save_layout:
cv2.imwrite(self.save_layout, only_layout)
if self.ground_truth:
gt_img=cv2.imread(self.ground_truth)
self.IoU(gt_img[:,:,0],res[:,:,0])
@click.command()
@click.option(
"--image",
"-i",
help="image filename",
type=click.Path(exists=True, dir_okay=False),
)
@click.option(
"--out",
"-o",
help="output directory where xml with detected reading order will be written.",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--patches/--no-patches",
"-p/-nop",
is_flag=True,
help="if this parameter set to true, this tool will try to do inference in patches.",
)
@click.option(
"--save",
"-s",
help="save prediction as a png file in current folder.",
)
@click.option(
"--save_layout",
"-sl",
help="save layout prediction only as a png file in current folder.",
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--ground_truth",
"-gt",
help="ground truth directory if you want to see the iou of prediction.",
)
@click.option(
"--xml_file",
"-xml",
help="xml file with layout coordinates that reading order detection will be implemented on. The result will be written in the same xml file.",
)
@click.option(
"--min_area",
"-min",
help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.",
)
def main(image, model, patches, save, save_layout, ground_truth, xml_file, out, min_area):
with open(os.path.join(model,'config.json')) as f:
config_params_model = json.load(f)
task = config_params_model['task']
if (task != 'classification' and task != 'reading_order'):
if not save:
print("Error: You used one of segmentation or binarization task but not set -s, you need a filename to save visualized output with -s")
sys.exit(1)
x=sbb_predict(image, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area)
x.run()
if __name__=="__main__":
main()