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eynollah
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avoiding double binarization

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pull/138/head^2
vahidrezanezhad 3 months ago
parent f0b49073b7
commit 2c93904985
2 changed files with 106 additions and 53 deletions
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149
qurator/eynollah/eynollah.py
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@ -89,7 +89,7 @@ from .utils.xml import order_and_id_of_texts
from .plot import EynollahPlotter
from .writer import EynollahXmlWriter
MIN_AREA_REGION = 0.00001
MIN_AREA_REGION = 0.000001
SLOPE_THRESHOLD = 0.13
RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45:
DPI_THRESHOLD = 298
@ -237,15 +237,16 @@ class Eynollah:
self.model_region_dir_p = dir_models + "/eynollah-main-regions-aug-scaling_20210425"
self.model_region_dir_p2 = dir_models + "/eynollah-main-regions-aug-rotation_20210425"
self.model_region_dir_fully_np = dir_models + "/eynollah-full-regions-1column_20210425"
self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425"
#self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425"
self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425"
self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
self.model_region_dir_p_1_2_sp_np = dir_models + "/model_3_eraly_layout_no_patches_1_2_spaltige"
self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
if self.textline_light:
self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425"
self.model_textline_dir = dir_models + "/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_9_12_13_14_15"#"/eynollah-textline_light_20210425"#
else:
self.model_textline_dir = dir_models + "/eynollah-textline_20210425"
if self.ocr:
@ -267,7 +268,7 @@ class Eynollah:
self.model_textline = self.our_load_model(self.model_textline_dir)
self.model_region = self.our_load_model(self.model_region_dir_p_ens_light)
self.model_region_1_2 = self.our_load_model(self.model_region_dir_p_1_2_sp_np)
self.model_region_fl_new = self.our_load_model(self.model_region_dir_fully_new)
###self.model_region_fl_new = self.our_load_model(self.model_region_dir_fully_new)
self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
self.model_reading_order_machine = self.our_load_model(self.model_reading_order_machine_dir)
@ -994,8 +995,15 @@ class Eynollah:
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
seg_not_base = label_p_pred[0,:,:,4]
seg_not_base[seg_not_base>0.4] =1
seg_not_base[seg_not_base<1] =0
seg = np.argmax(label_p_pred, axis=3)[0]
seg[seg_not_base==1]=4
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)
@ -1781,7 +1789,7 @@ class Eynollah:
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):
def textline_contours(self, img, patches, scaler_h, scaler_w, num_col_classifier=None):
self.logger.debug('enter textline_contours')
if not self.dir_in:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
@ -1792,10 +1800,34 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
#print(img.shape,'bin shape textline')
if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline, n_batch_inference=3)
prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3)
if num_col_classifier==1:
prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
else:
prediction_textline = self.do_prediction(patches, img, self.model_textline, n_batch_inference=3)
prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3)
if num_col_classifier==1:
prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
prediction_textline = resize_image(prediction_textline, img_h, img_w)
textline_mask_tot_ea_art = (prediction_textline[:,:]==2)*1
old_art = np.copy(textline_mask_tot_ea_art)
textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1)
prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2
textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1
textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8')
textline_mask_tot_ea_lines = cv2.dilate(textline_mask_tot_ea_lines, KERNEL, iterations=1)
prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1
prediction_textline[:,:][old_art[:,:]==1]=2
if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
else:
@ -1855,49 +1887,58 @@ class Eynollah:
#print(num_col_classifier,'num_col_classifier')
if num_col_classifier == 1:
img_w_new = 1000
img_w_new = 900#1000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 2:
img_w_new = 1500
img_w_new = 1300#1500
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 3:
img_w_new = 2000
img_w_new = 1600#2000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 4:
img_w_new = 2500
img_w_new = 1900#2500
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 5:
img_w_new = 3000
img_w_new = 2300#3000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
else:
img_w_new = 4000
img_w_new = 3300#4000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
img_resized = resize_image(img,img_h_new, img_w_new )
t_bin = time.time()
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
#print("inside bin ", time.time()-t_bin)
prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255
#if (not self.input_binary) or self.full_layout:
#if self.input_binary:
#img_bin = np.copy(img_resized)
if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 3):
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
#print("inside bin ", time.time()-t_bin)
prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
prediction_bin = prediction_bin.astype(np.uint16)
#img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin)
prediction_bin = prediction_bin.astype(np.uint16)
#img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin)
else:
img_bin = np.copy(img_resized)
#print("inside 1 ", time.time()-t_in)
textline_mask_tot_ea = self.run_textline(img_bin)
###textline_mask_tot_ea = self.run_textline(img_bin)
textline_mask_tot_ea = self.run_textline(img_bin, num_col_classifier)
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
@ -1906,20 +1947,20 @@ class Eynollah:
#print(img_resized.shape, num_col_classifier, "num_col_classifier")
if not self.dir_in:
###if num_col_classifier == 1 or num_col_classifier == 2:
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
###prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region)
###else:
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
###prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
if num_col_classifier == 1 or num_col_classifier == 2:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region)
else:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else:
##if num_col_classifier == 1 or num_col_classifier == 2:
##prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2)
##else:
##prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
if num_col_classifier == 1 or num_col_classifier == 2:
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2)
else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
#print("inside 3 ", time.time()-t_in)
#plt.imshow(prediction_regions_org[:,:,0])
@ -1937,7 +1978,7 @@ class Eynollah:
mask_texts_only = mask_texts_only.astype('uint8')
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=3)
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=2)
mask_images_only=(prediction_regions_org[:,:] ==2)*1
@ -2899,10 +2940,11 @@ class Eynollah:
#print("enhancement in ", time.time()-t_in)
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)
def run_textline(self, image_page, num_col_classifier=None):
scaler_h_textline = 1#1.3 # 1.2#1.2
scaler_w_textline = 1#1.3 # 0.9#1
#print(image_page.shape)
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline, num_col_classifier)
if self.textline_light:
textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
@ -3147,6 +3189,17 @@ class Eynollah:
##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
drop_capital_label_in_full_layout_model = 3
drops = (regions_fully[:,:,0]==drop_capital_label_in_full_layout_model)*1
drops= drops.astype(np.uint8)
regions_fully[:,:,0][regions_fully[:,:,0]==drop_capital_label_in_full_layout_model] = 1
drops = cv2.erode(drops[:,:], KERNEL, iterations=1)
regions_fully[:,:,0][drops[:,:]==1] = drop_capital_label_in_full_layout_model
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully, drop_capital_label_in_full_layout_model)
##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
##if num_col_classifier > 2:
@ -3695,7 +3748,7 @@ class Eynollah:
"""
self.logger.debug("enter run")
skip_layout_ro = True
skip_layout_ro = False#True
t0_tot = time.time()

4
qurator/eynollah/utils/__init__.py
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@ -792,11 +792,11 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.4:
if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.8:
layout_in_patch[y : y + h, x : x + w, 0] = drop_capital_label
else:
layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = drop_capital_label
layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = 1#drop_capital_label
return layout_in_patch

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