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eynollah
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update of light versions

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pull/138/head^2
vahidrezanezhad 3 months ago
parent 2d18739d9b
commit b9e8959c4a
1 changed files with 126 additions and 112 deletions
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qurator/eynollah/eynollah.py
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@ -1862,7 +1862,10 @@ class Eynollah:
def textline_contours(self, img, patches, scaler_h, scaler_w, num_col_classifier=None):
self.logger.debug('enter textline_contours')
thresholding_for_artificial_class_in_light_version = True#False
if self.textline_light:
thresholding_for_artificial_class_in_light_version = True#False
else:
thresholding_for_artificial_class_in_light_version = False
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)
#img = img.astype(np.uint8)
@ -2016,7 +2019,7 @@ class Eynollah:
#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, num_col_classifier)
textline_mask_tot_ea = self.run_textline(img_resized, num_col_classifier)
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
@ -2057,7 +2060,8 @@ class Eynollah:
mask_texts_only = mask_texts_only.astype('uint8')
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=2)
#mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
#mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
mask_images_only=(prediction_regions_org[:,:] ==2)*1
@ -2097,6 +2101,7 @@ class Eynollah:
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)
@ -3845,132 +3850,139 @@ class Eynollah:
return x_differential_new
def dilate_textlines(self,all_found_textline_polygons):
for i in range(len(all_found_textline_polygons[0])):
con_ind = all_found_textline_polygons[0][i]
con_ind = con_ind.astype(np.float)
x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1])
x_min = float(np.min( con_ind[:,0,0] ))
y_min = float(np.min( con_ind[:,0,1] ))
x_max = float(np.max( con_ind[:,0,0] ))
y_max = float(np.max( con_ind[:,0,1] ))
if (y_max - y_min) > (x_max - x_min) and (x_max - x_min)<70:
for j in range(len(all_found_textline_polygons)):
for i in range(len(all_found_textline_polygons[j])):
con_ind = all_found_textline_polygons[j][i]
x_biger_than_x = np.abs(x_differential) > np.abs(y_differential)
con_ind = con_ind.astype(np.float)
mult = x_biger_than_x*x_differential
x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1])
arg_min_mult = np.argmin(mult)
arg_max_mult = np.argmax(mult)
x_min = float(np.min( con_ind[:,0,0] ))
y_min = float(np.min( con_ind[:,0,1] ))
if y_differential[0]==0:
y_differential[0] = 0.1
x_max = float(np.max( con_ind[:,0,0] ))
y_max = float(np.max( con_ind[:,0,1] ))
if y_differential[-1]==0:
y_differential[-1]= 0.1
if (y_max - y_min) > (x_max - x_min) and (x_max - x_min)<70:
x_biger_than_x = np.abs(x_differential) > np.abs(y_differential)
mult = x_biger_than_x*x_differential
y_differential = [y_differential[ind] if y_differential[ind]!=0 else (y_differential[ind-1] + y_differential[ind+1])/2. for ind in range(len(y_differential)) ]
if y_differential[0]==0.1:
y_differential[0] = y_differential[1]
if y_differential[-1]==0.1:
y_differential[-1] = y_differential[-2]
arg_min_mult = np.argmin(mult)
arg_max_mult = np.argmax(mult)
y_differential.append(y_differential[0])
y_differential = [-1 if y_differential[ind]<0 else 1 for ind in range(len(y_differential))]
y_differential = self.return_it_in_two_groups(y_differential)
y_differential = np.array(y_differential)
con_scaled = con_ind*1
con_scaled[:,0, 0] = con_ind[:,0,0] - 8*y_differential
con_scaled[arg_min_mult,0, 1] = con_ind[arg_min_mult,0,1] + 8
con_scaled[arg_min_mult+1,0, 1] = con_ind[arg_min_mult+1,0,1] + 8
try:
con_scaled[arg_min_mult-1,0, 1] = con_ind[arg_min_mult-1,0,1] + 5
con_scaled[arg_min_mult+2,0, 1] = con_ind[arg_min_mult+2,0,1] + 5
except:
pass
con_scaled[arg_max_mult,0, 1] = con_ind[arg_max_mult,0,1] - 8
con_scaled[arg_max_mult+1,0, 1] = con_ind[arg_max_mult+1,0,1] - 8
try:
con_scaled[arg_max_mult-1,0, 1] = con_ind[arg_max_mult-1,0,1] - 5
con_scaled[arg_max_mult+2,0, 1] = con_ind[arg_max_mult+2,0,1] - 5
except:
pass
else:
y_biger_than_x = np.abs(y_differential) > np.abs(x_differential)
mult = y_biger_than_x*y_differential
arg_min_mult = np.argmin(mult)
arg_max_mult = np.argmax(mult)
if x_differential[0]==0:
x_differential[0] = 0.1
if x_differential[-1]==0:
x_differential[-1]= 0.1
if y_differential[0]==0:
y_differential[0] = 0.1
if y_differential[-1]==0:
y_differential[-1]= 0.1
y_differential = [y_differential[ind] if y_differential[ind]!=0 else (y_differential[ind-1] + y_differential[ind+1])/2. for ind in range(len(y_differential)) ]
x_differential = [x_differential[ind] if x_differential[ind]!=0 else (x_differential[ind-1] + x_differential[ind+1])/2. for ind in range(len(x_differential)) ]
if x_differential[0]==0.1:
x_differential[0] = x_differential[1]
if x_differential[-1]==0.1:
x_differential[-1] = x_differential[-2]
if y_differential[0]==0.1:
y_differential[0] = y_differential[1]
if y_differential[-1]==0.1:
y_differential[-1] = y_differential[-2]
y_differential.append(y_differential[0])
x_differential.append(x_differential[0])
x_differential = [-1 if x_differential[ind]<0 else 1 for ind in range(len(x_differential))]
x_differential = self.return_it_in_two_groups(x_differential)
x_differential = np.array(x_differential)
con_scaled = con_ind*1
con_scaled[:,0, 1] = con_ind[:,0,1] + 8*x_differential
con_scaled[arg_min_mult,0, 0] = con_ind[arg_min_mult,0,0] + 8
con_scaled[arg_min_mult+1,0, 0] = con_ind[arg_min_mult+1,0,0] + 8
y_differential = [-1 if y_differential[ind]<0 else 1 for ind in range(len(y_differential))]
y_differential = self.return_it_in_two_groups(y_differential)
y_differential = np.array(y_differential)
con_scaled = con_ind*1
con_scaled[:,0, 0] = con_ind[:,0,0] - 8*y_differential
con_scaled[arg_min_mult,0, 1] = con_ind[arg_min_mult,0,1] + 8
con_scaled[arg_min_mult+1,0, 1] = con_ind[arg_min_mult+1,0,1] + 8
try:
con_scaled[arg_min_mult-1,0, 1] = con_ind[arg_min_mult-1,0,1] + 5
con_scaled[arg_min_mult+2,0, 1] = con_ind[arg_min_mult+2,0,1] + 5
except:
pass
con_scaled[arg_max_mult,0, 1] = con_ind[arg_max_mult,0,1] - 8
con_scaled[arg_max_mult+1,0, 1] = con_ind[arg_max_mult+1,0,1] - 8
try:
con_scaled[arg_max_mult-1,0, 1] = con_ind[arg_max_mult-1,0,1] - 5
con_scaled[arg_max_mult+2,0, 1] = con_ind[arg_max_mult+2,0,1] - 5
except:
pass
con_scaled[arg_min_mult-1,0, 0] = con_ind[arg_min_mult-1,0,0] + 5
con_scaled[arg_min_mult+2,0, 0] = con_ind[arg_min_mult+2,0,0] + 5
con_scaled[arg_max_mult,0, 0] = con_ind[arg_max_mult,0,0] - 8
con_scaled[arg_max_mult+1,0, 0] = con_ind[arg_max_mult+1,0,0] - 8
else:
y_biger_than_x = np.abs(y_differential) > np.abs(x_differential)
mult = y_biger_than_x*y_differential
arg_min_mult = np.argmin(mult)
arg_max_mult = np.argmax(mult)
if x_differential[0]==0:
x_differential[0] = 0.1
if x_differential[-1]==0:
x_differential[-1]= 0.1
x_differential = [x_differential[ind] if x_differential[ind]!=0 else (x_differential[ind-1] + x_differential[ind+1])/2. for ind in range(len(x_differential)) ]
if x_differential[0]==0.1:
x_differential[0] = x_differential[1]
if x_differential[-1]==0.1:
x_differential[-1] = x_differential[-2]
x_differential.append(x_differential[0])
x_differential = [-1 if x_differential[ind]<0 else 1 for ind in range(len(x_differential))]
x_differential = self.return_it_in_two_groups(x_differential)
x_differential = np.array(x_differential)
con_scaled = con_ind*1
con_scaled[:,0, 1] = con_ind[:,0,1] + 8*x_differential
con_scaled[arg_min_mult,0, 0] = con_ind[arg_min_mult,0,0] + 8
con_scaled[arg_min_mult+1,0, 0] = con_ind[arg_min_mult+1,0,0] + 8
try:
con_scaled[arg_min_mult-1,0, 0] = con_ind[arg_min_mult-1,0,0] + 5
con_scaled[arg_min_mult+2,0, 0] = con_ind[arg_min_mult+2,0,0] + 5
except:
pass
con_scaled[arg_max_mult,0, 0] = con_ind[arg_max_mult,0,0] - 8
con_scaled[arg_max_mult+1,0, 0] = con_ind[arg_max_mult+1,0,0] - 8
try:
con_scaled[arg_max_mult-1,0, 0] = con_ind[arg_max_mult-1,0,0] - 5
con_scaled[arg_max_mult+2,0, 0] = con_ind[arg_max_mult+2,0,0] - 5
except:
pass
con_scaled[arg_max_mult-1,0, 0] = con_ind[arg_max_mult-1,0,0] - 5
con_scaled[arg_max_mult+2,0, 0] = con_ind[arg_max_mult+2,0,0] - 5
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
all_found_textline_polygons[0][i][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[0][i][:,0,0] = con_scaled[:,0, 0]
all_found_textline_polygons[j][i][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][i][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons
def run(self):
@ -4207,6 +4219,8 @@ class Eynollah:
slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea_org, image_page_rotated, boxes_marginals, slope_deskew)
all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons)
else:
slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)

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