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src/eynollah/eynollah.py
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@ -73,6 +73,8 @@ from .utils.contour import (
return_contours_of_interested_region_by_min_size,
return_contours_of_interested_textline,
return_parent_contours,
dilate_textregion_contours,
dilate_textline_contours,
)
from .utils.rotate import (
rotate_image,
@ -1711,9 +1713,9 @@ class Eynollah:
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
mask_images_only=(prediction_regions_org[:,:] ==2)*1
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(
mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
polygons_seplines = filter_contours_area_of_image(
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=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)
@ -1777,7 +1779,7 @@ class Eynollah:
[page_coord_img[2], page_coord_img[1]]]))
self.logger.debug("exit get_regions_extract_images_only")
return text_regions_p_true, erosion_hurts, polygons_lines_xml, polygons_of_images_fin, image_page, page_coord, cont_page
return text_regions_p_true, erosion_hurts, polygons_seplines, polygons_of_images_fin, image_page, page_coord, cont_page
def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier, skip_layout_and_reading_order=False):
self.logger.debug("enter get_regions_light_v")
@ -1893,31 +1895,31 @@ class Eynollah:
mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
mask_images_only=(prediction_regions_org[:,:] ==2)*1
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
test_khat = np.zeros(prediction_regions_org.shape)
test_khat = cv2.fillPoly(test_khat, pts=polygons_lines_xml, color=(1,1,1))
test_khat = cv2.fillPoly(test_khat, pts=polygons_seplines, color=(1,1,1))
#plt.imshow(test_khat[:,:])
#plt.show()
#for jv in range(1):
#print(jv, hir_lines_xml[0][232][3])
#print(jv, hir_seplines[0][232][3])
#test_khat = np.zeros(prediction_regions_org.shape)
#test_khat = cv2.fillPoly(test_khat, pts = [polygons_lines_xml[232]], color=(1,1,1))
#test_khat = cv2.fillPoly(test_khat, pts = [polygons_seplines[232]], color=(1,1,1))
#plt.imshow(test_khat[:,:])
#plt.show()
polygons_lines_xml = filter_contours_area_of_image(
mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
polygons_seplines = filter_contours_area_of_image(
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
test_khat = np.zeros(prediction_regions_org.shape)
test_khat = cv2.fillPoly(test_khat, pts = polygons_lines_xml, color=(1,1,1))
test_khat = cv2.fillPoly(test_khat, pts = polygons_seplines, color=(1,1,1))
#plt.imshow(test_khat[:,:])
#plt.show()
#sys.exit()
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
##polygons_of_only_texts = self.dilate_textregions_contours(polygons_of_only_texts)
##polygons_of_only_texts = dilate_textregion_contours(polygons_of_only_texts)
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)
@ -1935,7 +1937,7 @@ class Eynollah:
#plt.show()
#print("inside 4 ", time.time()-t_in)
self.logger.debug("exit get_regions_light_v")
return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin, confidence_matrix
return text_regions_p_true, erosion_hurts, polygons_seplines, textline_mask_tot_ea, img_bin, confidence_matrix
else:
img_bin = resize_image(img_bin,img_height_h, img_width_h )
self.logger.debug("exit get_regions_light_v")
@ -2018,9 +2020,9 @@ class Eynollah:
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
polygons_lines_xml = filter_contours_area_of_image(
mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
polygons_seplines = filter_contours_area_of_image(
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=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)
@ -2032,7 +2034,7 @@ class Eynollah:
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
self.logger.debug("exit get_regions_from_xy_2models")
return text_regions_p_true, erosion_hurts, polygons_lines_xml
return text_regions_p_true, erosion_hurts, polygons_seplines
except:
if self.input_binary:
prediction_bin = np.copy(img_org)
@ -2067,9 +2069,9 @@ class Eynollah:
mask_texts_only = (prediction_regions_org == 1)*1
mask_images_only= (prediction_regions_org == 2)*1
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
polygons_lines_xml = filter_contours_area_of_image(
mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
polygons_seplines = filter_contours_area_of_image(
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=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)
@ -2082,7 +2084,7 @@ class Eynollah:
erosion_hurts = True
self.logger.debug("exit get_regions_from_xy_2models")
return text_regions_p_true, erosion_hurts, polygons_lines_xml
return text_regions_p_true, erosion_hurts, polygons_seplines
def do_order_of_regions_full_layout(
self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
@ -2925,12 +2927,10 @@ class Eynollah:
#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), 2, 30, True,
map=self.executor.map, logger=self.logger, plotter=self.plotter)
slope_first = 0
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %.2f°", slope_deskew)
return slope_deskew, slope_first
return slope_deskew
def run_marginals(
self, image_page, textline_mask_tot_ea, mask_images, mask_lines,
@ -3669,312 +3669,6 @@ class Eynollah:
return x_differential_new
def dilate_textregions_contours_textline_version(self, all_found_textline_polygons):
#print(all_found_textline_polygons)
for j in range(len(all_found_textline_polygons)):
for ij in range(len(all_found_textline_polygons[j])):
con_ind = all_found_textline_polygons[j][ij]
area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(float)
x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1])
x_differential = gaussian_filter1d(x_differential, 0.1)
y_differential = gaussian_filter1d(y_differential, 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] ))
x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential]
y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential]
abs_diff=abs(abs(x_differential)- abs(y_differential) )
inc_x = np.zeros(len(x_differential)+1)
inc_y = np.zeros(len(x_differential)+1)
if (y_max-y_min) <= (x_max-x_min):
dilation_m1 = round(area / (x_max-x_min) * 0.12)
else:
dilation_m1 = round(area / (y_max-y_min) * 0.12)
if dilation_m1>8:
dilation_m1 = 8
if dilation_m1<6:
dilation_m1 = 6
#print(dilation_m1, 'dilation_m1')
dilation_m1 = 6
dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)):
if abs_diff[i]==0:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0:
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and abs_diff[i]>=3:
if abs(x_differential[i])>abs(y_differential[i]):
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
else:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
else:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
inc_x[0] = inc_x[-1]
inc_y[0] = inc_y[-1]
con_scaled = con_ind*1
con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:]
con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:]
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
area_scaled = cv2.contourArea(con_scaled.astype(np.int32))
con_ind = con_ind.astype(np.int32)
results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False)
for ind in range(len(con_scaled[:,0, 1])) ]
results = np.array(results)
#print(results,'results')
results[results==0] = 1
diff_result = np.diff(results)
indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2]
indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2]
if results[0]==1:
con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1]
con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0]
#indices_2 = indices_2[1:]
indices_m2 = indices_m2[1:]
if len(indices_2)>len(indices_m2):
con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1]
con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0]
indices_2 = indices_2[:-1]
for ii in range(len(indices_2)):
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1]
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0]
all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons
def dilate_textregions_contours(self, all_found_textline_polygons):
#print(all_found_textline_polygons)
for j in range(len(all_found_textline_polygons)):
con_ind = all_found_textline_polygons[j]
#print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(float)
x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1])
x_differential = gaussian_filter1d(x_differential, 0.1)
y_differential = gaussian_filter1d(y_differential, 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] ))
x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential]
y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential]
abs_diff=abs(abs(x_differential)- abs(y_differential) )
inc_x = np.zeros(len(x_differential)+1)
inc_y = np.zeros(len(x_differential)+1)
if (y_max-y_min) <= (x_max-x_min):
dilation_m1 = round(area / (x_max-x_min) * 0.12)
else:
dilation_m1 = round(area / (y_max-y_min) * 0.12)
if dilation_m1>8:
dilation_m1 = 8
if dilation_m1<6:
dilation_m1 = 6
#print(dilation_m1, 'dilation_m1')
dilation_m1 = 6
dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)):
if abs_diff[i]==0:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0:
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and abs_diff[i]>=3:
if abs(x_differential[i])>abs(y_differential[i]):
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
else:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
else:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
inc_x[0] = inc_x[-1]
inc_y[0] = inc_y[-1]
con_scaled = con_ind*1
con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:]
con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:]
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
area_scaled = cv2.contourArea(con_scaled.astype(np.int32))
con_ind = con_ind.astype(np.int32)
results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False)
for ind in range(len(con_scaled[:,0, 1])) ]
results = np.array(results)
#print(results,'results')
results[results==0] = 1
diff_result = np.diff(results)
indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2]
indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2]
if results[0]==1:
con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1]
con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0]
#indices_2 = indices_2[1:]
indices_m2 = indices_m2[1:]
if len(indices_2)>len(indices_m2):
con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1]
con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0]
indices_2 = indices_2[:-1]
for ii in range(len(indices_2)):
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1]
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0]
all_found_textline_polygons[j][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons
def dilate_textline_contours(self, all_found_textline_polygons):
for j in range(len(all_found_textline_polygons)):
for ij in range(len(all_found_textline_polygons[j])):
con_ind = all_found_textline_polygons[j][ij]
area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(float)
x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1])
x_differential = gaussian_filter1d(x_differential, 3)
y_differential = gaussian_filter1d(y_differential, 3)
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] ))
x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential]
y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential]
abs_diff=abs(abs(x_differential)- abs(y_differential) )
inc_x = np.zeros(len(x_differential)+1)
inc_y = np.zeros(len(x_differential)+1)
if (y_max-y_min) <= (x_max-x_min):
dilation_m1 = round(area / (x_max-x_min) * 0.35)
else:
dilation_m1 = round(area / (y_max-y_min) * 0.35)
if dilation_m1>12:
dilation_m1 = 12
if dilation_m1<4:
dilation_m1 = 4
#print(dilation_m1, 'dilation_m1')
dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)):
if abs_diff[i]==0:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0:
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
elif abs_diff[i]!=0 and abs_diff[i]>=3:
if abs(x_differential[i])>abs(y_differential[i]):
inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i])
else:
inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i])
else:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
inc_x[0] = inc_x[-1]
inc_y[0] = inc_y[-1]
con_scaled = con_ind*1
con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:]
con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:]
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
con_ind = con_ind.astype(np.int32)
results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False)
for ind in range(len(con_scaled[:,0, 1])) ]
results = np.array(results)
results[results==0] = 1
diff_result = np.diff(results)
indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2]
indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2]
if results[0]==1:
con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1]
con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0]
indices_m2 = indices_m2[1:]
if len(indices_2)>len(indices_m2):
con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1]
con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0]
indices_2 = indices_2[:-1]
for ii in range(len(indices_2)):
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1]
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0]
all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons
def filter_contours_inside_a_bigger_one(self,contours, contours_d_ordered, image, marginal_cnts=None, type_contour="textregion"):
if type_contour=="textregion":
areas = [cv2.contourArea(contours[j]) for j in range(len(contours))]
@ -4112,121 +3806,6 @@ class Eynollah:
return contours, text_con_org, conf_contours_textregions, contours_textline, contours_only_text_parent_d_ordered, np.array(range(len(contours)))
def dilate_textlines(self, all_found_textline_polygons):
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]
con_ind = con_ind.astype(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:
x_biger_than_x = np.abs(x_differential) > np.abs(y_differential)
mult = x_biger_than_x*x_differential
arg_min_mult = np.argmin(mult)
arg_max_mult = np.argmax(mult)
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 0.5 * (y_differential[ind-1] + y_differential[ind+1])
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]
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
x_differential = [x_differential[ind] if x_differential[ind] != 0
else 0.5 * (x_differential[ind-1] + x_differential[ind+1])
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[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<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 delete_regions_without_textlines(
self, slopes, all_found_textline_polygons, boxes_text, txt_con_org,
contours_only_text_parent, index_by_text_par_con):
@ -4297,7 +3876,7 @@ class Eynollah:
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
if self.extract_only_images:
text_regions_p_1, erosion_hurts, polygons_lines_xml, polygons_of_images, image_page, page_coord, cont_page = \
text_regions_p_1, erosion_hurts, polygons_seplines, polygons_of_images, image_page, page_coord, cont_page = \
self.get_regions_light_v_extract_only_images(img_res, is_image_enhanced, num_col_classifier)
ocr_all_textlines = None
pcgts = self.writer.build_pagexml_no_full_layout(
@ -4325,8 +3904,7 @@ class Eynollah:
all_found_textline_polygons=[ all_found_textline_polygons ]
all_found_textline_polygons = self.dilate_textregions_contours_textline_version(
all_found_textline_polygons)
all_found_textline_polygons = dilate_textline_contours(all_found_textline_polygons)
all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(
all_found_textline_polygons, None, textline_mask_tot_ea, type_contour="textline")
@ -4340,7 +3918,7 @@ class Eynollah:
polygons_of_marginals = []
all_found_textline_polygons_marginals = []
all_box_coord_marginals = []
polygons_lines_xml = []
polygons_seplines = []
contours_tables = []
ocr_all_textlines = None
conf_contours_textregions =None
@ -4348,13 +3926,13 @@ class Eynollah:
cont_page, page_coord, order_text_new, id_of_texts_tot,
all_found_textline_polygons, page_coord, polygons_of_images, polygons_of_marginals,
all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals,
cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines, conf_contours_textregions)
cont_page, polygons_seplines, contours_tables, ocr_all_textlines, conf_contours_textregions)
return pcgts
#print("text region early -1 in %.1fs", time.time() - t0)
t1 = time.time()
if self.light_version:
text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin_light, confidence_matrix = \
text_regions_p_1, erosion_hurts, polygons_seplines, textline_mask_tot_ea, img_bin_light, confidence_matrix = \
self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier)
#print("text region early -2 in %.1fs", time.time() - t0)
@ -4367,9 +3945,9 @@ class Eynollah:
textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
slope_deskew = self.run_deskew(textline_mask_tot_ea_deskew)
else:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
slope_deskew = self.run_deskew(textline_mask_tot_ea)
#print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \
@ -4381,7 +3959,7 @@ class Eynollah:
textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea)
#print("text region early -4 in %.1fs", time.time() - t0)
else:
text_regions_p_1 ,erosion_hurts, polygons_lines_xml = \
text_regions_p_1, erosion_hurts, polygons_seplines = \
self.get_regions_from_xy_2models(img_res, is_image_enhanced,
num_col_classifier)
self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
@ -4410,7 +3988,7 @@ class Eynollah:
textline_mask_tot_ea = self.run_textline(image_page)
self.logger.info("textline detection took %.1fs", time.time() - t1)
t1 = time.time()
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
slope_deskew = self.run_deskew(textline_mask_tot_ea)
self.logger.info("deskewing took %.1fs", time.time() - t1)
elif num_col_classifier in (1,2):
org_h_l_m = textline_mask_tot_ea.shape[0]
@ -4450,14 +4028,14 @@ class Eynollah:
boxes, boxes_d, polygons_of_marginals, contours_tables = \
self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew,
num_col_classifier, table_prediction, erosion_hurts)
###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals)
###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals)
else:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, \
regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = \
self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew,
num_col_classifier, img_only_regions, table_prediction, erosion_hurts,
img_bin_light if self.light_version else None)
###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals)
###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals)
if self.light_version:
drop_label_in_full_layout = 4
textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0
@ -4580,31 +4158,30 @@ class Eynollah:
[], [], page_coord, [], [], [], [], [], [],
polygons_of_images, contours_tables, [],
polygons_of_marginals, empty_marginals, empty_marginals, [], [], [],
cont_page, polygons_lines_xml, [], [], [])
cont_page, polygons_seplines, [], [], [])
else:
pcgts = self.writer.build_pagexml_no_full_layout(
[], page_coord, [], [], [], [],
polygons_of_images,
polygons_of_marginals, empty_marginals, empty_marginals, [], [],
cont_page, polygons_lines_xml, contours_tables, [], [])
cont_page, polygons_seplines, contours_tables, [], [])
return pcgts
#print("text region early 3 in %.1fs", time.time() - t0)
if self.light_version:
contours_only_text_parent = self.dilate_textregions_contours(
contours_only_text_parent)
contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent)
contours_only_text_parent , contours_only_text_parent_d_ordered = self.filter_contours_inside_a_bigger_one(
contours_only_text_parent, contours_only_text_parent_d_ordered, text_only, marginal_cnts=polygons_of_marginals)
#print("text region early 3.5 in %.1fs", time.time() - t0)
txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light(
contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map)
#txt_con_org = self.dilate_textregions_contours(txt_con_org)
#contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
contours_only_text_parent, self.image, confidence_matrix, map=self.executor.map)
#txt_con_org = dilate_textregion_contours(txt_con_org)
#contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent)
else:
txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light(
contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map)
contours_only_text_parent, self.image, confidence_matrix, map=self.executor.map)
#print("text region early 4 in %.1fs", time.time() - t0)
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)
@ -4628,14 +4205,10 @@ class Eynollah:
#slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, polygons_of_marginals, polygons_of_marginals, _ = \
# self.delete_regions_without_textlines(slopes_marginals, all_found_textline_polygons_marginals,
# boxes_marginals, polygons_of_marginals, polygons_of_marginals, np.array(range(len(polygons_of_marginals))))
#all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons)
#####all_found_textline_polygons = self.dilate_textline_contours(all_found_textline_polygons)
all_found_textline_polygons = self.dilate_textregions_contours_textline_version(
all_found_textline_polygons)
all_found_textline_polygons = dilate_textline_contours(all_found_textline_polygons)
all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(
all_found_textline_polygons, None, textline_mask_tot_ea_org, type_contour="textline")
all_found_textline_polygons_marginals = self.dilate_textregions_contours_textline_version(
all_found_textline_polygons_marginals)
all_found_textline_polygons_marginals = dilate_textline_contours(all_found_textline_polygons_marginals)
contours_only_text_parent, txt_con_org, conf_contours_textregions, all_found_textline_polygons, contours_only_text_parent_d_ordered, \
index_by_text_par_con = self.filter_contours_without_textline_inside(
contours_only_text_parent, txt_con_org, all_found_textline_polygons, contours_only_text_parent_d_ordered, conf_contours_textregions)
@ -4779,7 +4352,7 @@ class Eynollah:
all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h,
polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals,
all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals,
cont_page, polygons_lines_xml, ocr_all_textlines, conf_contours_textregions, conf_contours_textregions_h)
cont_page, polygons_seplines, ocr_all_textlines, conf_contours_textregions, conf_contours_textregions_h)
return pcgts
contours_only_text_parent_h = None
@ -4858,7 +4431,7 @@ class Eynollah:
txt_con_org, page_coord, order_text_new, id_of_texts_tot,
all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals,
all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals,
cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines, conf_contours_textregions)
cont_page, polygons_seplines, contours_tables, ocr_all_textlines, conf_contours_textregions)
return pcgts

97
src/eynollah/utils/__init__.py
View file

@ -955,11 +955,11 @@ def check_any_text_region_in_model_one_is_main_or_header_light(
regions_model_full = cv2.resize(regions_model_full, (regions_model_full.shape[1] // zoom,
regions_model_full.shape[0] // zoom),
interpolation=cv2.INTER_NEAREST)
contours_only_text_parent = [(i / zoom).astype(int) for i in contours_only_text_parent]
contours_only_text_parent_z = [(cnt / zoom).astype(int) for cnt in contours_only_text_parent]
###
cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = \
find_new_features_of_contours(contours_only_text_parent)
find_new_features_of_contours(contours_only_text_parent_z)
length_con=x_max_main-x_min_main
height_con=y_max_main-y_min_main
@ -982,8 +982,7 @@ def check_any_text_region_in_model_one_is_main_or_header_light(
contours_only_text_parent_main_d=[]
contours_only_text_parent_head_d=[]
for ii in range(len(contours_only_text_parent)):
con=contours_only_text_parent[ii]
for ii, con in enumerate(contours_only_text_parent_z):
img=np.zeros((regions_model_1.shape[0], regions_model_1.shape[1], 3))
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
@ -994,23 +993,22 @@ def check_any_text_region_in_model_one_is_main_or_header_light(
if (pixels_header/float(pixels_main)>=0.3) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
contours_only_text_parent_head.append(con)
contours_only_text_parent_head.append(contours_only_text_parent[ii])
conf_contours_head.append(None) # why not conf_contours[ii], too?
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_head.append(all_box_coord[ii])
slopes_head.append(slopes[ii])
all_found_textline_polygons_head.append(all_found_textline_polygons[ii])
conf_contours_head.append(None)
else:
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
contours_only_text_parent_main.append(con)
contours_only_text_parent_main.append(contours_only_text_parent[ii])
conf_contours_main.append(conf_contours[ii])
if contours_only_text_parent_d_ordered is not None:
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
all_box_coord_main.append(all_box_coord[ii])
slopes_main.append(slopes[ii])
all_found_textline_polygons_main.append(all_found_textline_polygons[ii])
#print(all_pixels,pixels_main,pixels_header)
### to make it faster
@ -1018,8 +1016,6 @@ def check_any_text_region_in_model_one_is_main_or_header_light(
# regions_model_full = cv2.resize(img, (regions_model_full.shape[1] // zoom,
# regions_model_full.shape[0] // zoom),
# interpolation=cv2.INTER_NEAREST)
contours_only_text_parent_head = [(i * zoom).astype(int) for i in contours_only_text_parent_head]
contours_only_text_parent_main = [(i * zoom).astype(int) for i in contours_only_text_parent_main]
###
return (regions_model_1,
@ -1742,6 +1738,7 @@ def return_boxes_of_images_by_order_of_reading_new(
x_ending = np.array(x_ending)
y_type_2 = np.array(y_type_2)
y_diff_type_2 = np.array(y_diff_type_2)
all_columns = set(range(len(peaks_neg_tot) - 1))
if ((reading_order_type==1) or
(reading_order_type==0 and
@ -1863,19 +1860,16 @@ def return_boxes_of_images_by_order_of_reading_new(
x_end_by_order.append(len(peaks_neg_tot)-2)
else:
#print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo')
columns_covered_by_mothers = []
columns_covered_by_mothers = set()
for dj in range(len(x_start_without_mother)):
columns_covered_by_mothers = columns_covered_by_mothers + \
list(range(x_start_without_mother[dj],
columns_covered_by_mothers.update(
range(x_start_without_mother[dj],
x_end_without_mother[dj]))
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
all_columns=np.arange(len(peaks_neg_tot)-1)
columns_not_covered=list(set(all_columns) - set(columns_covered_by_mothers))
columns_not_covered = list(all_columns - columns_covered_by_mothers)
y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + len(x_start_without_mother)))
##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered))
##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered))
x_starting = np.append(x_starting, columns_not_covered)
x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype))
x_starting = np.append(x_starting, x_start_without_mother)
x_ending = np.append(x_ending, np.array(columns_not_covered) + 1)
x_ending = np.append(x_ending, x_end_without_mother)
@ -1906,32 +1900,26 @@ def return_boxes_of_images_by_order_of_reading_new(
x_end_by_order.append(x_end_column_sort[ii]-1)
else:
#print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo')
columns_covered_by_mothers = []
columns_covered_by_mothers = set()
for dj in range(len(x_start_without_mother)):
columns_covered_by_mothers = columns_covered_by_mothers + \
list(range(x_start_without_mother[dj],
columns_covered_by_mothers.update(
range(x_start_without_mother[dj],
x_end_without_mother[dj]))
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
all_columns=np.arange(len(peaks_neg_tot)-1)
columns_not_covered=list(set(all_columns) - set(columns_covered_by_mothers))
columns_not_covered = list(all_columns - columns_covered_by_mothers)
y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + len(x_start_without_mother)))
##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered))
##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered))
x_starting = np.append(x_starting, columns_not_covered)
x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype))
x_starting = np.append(x_starting, x_start_without_mother)
x_ending = np.append(x_ending, np.array(columns_not_covered) + 1)
x_ending = np.append(x_ending, np.array(columns_not_covered, x_ending.dtype) + 1)
x_ending = np.append(x_ending, x_end_without_mother)
columns_covered_by_with_child_no_mothers = []
columns_covered_by_with_child_no_mothers = set()
for dj in range(len(x_end_with_child_without_mother)):
columns_covered_by_with_child_no_mothers = columns_covered_by_with_child_no_mothers + \
list(range(x_start_with_child_without_mother[dj],
columns_covered_by_with_child_no_mothers.update(
range(x_start_with_child_without_mother[dj],
x_end_with_child_without_mother[dj]))
columns_covered_by_with_child_no_mothers = list(set(columns_covered_by_with_child_no_mothers))
all_columns = np.arange(len(peaks_neg_tot)-1)
columns_not_covered_child_no_mother = list(set(all_columns) - set(columns_covered_by_with_child_no_mothers))
columns_not_covered_child_no_mother = list(all_columns - columns_covered_by_with_child_no_mothers)
#indexes_to_be_spanned=[]
for i_s in range(len(x_end_with_child_without_mother)):
columns_not_covered_child_no_mother.append(x_start_with_child_without_mother[i_s])
@ -1967,21 +1955,19 @@ def return_boxes_of_images_by_order_of_reading_new(
if len(x_diff_all_between_nm_wc)>0:
biggest=np.argmax(x_diff_all_between_nm_wc)
columns_covered_by_mothers = []
columns_covered_by_mothers = set()
for dj in range(len(x_starting_all_between_nm_wc)):
columns_covered_by_mothers = columns_covered_by_mothers + \
list(range(x_starting_all_between_nm_wc[dj],
columns_covered_by_mothers.update(
range(x_starting_all_between_nm_wc[dj],
x_ending_all_between_nm_wc[dj]))
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
all_columns=np.arange(i_s_nc, x_end_biggest_column)
columns_not_covered = list(set(all_columns) - set(columns_covered_by_mothers))
child_columns = set(range(i_s_nc, x_end_biggest_column))
columns_not_covered = list(child_columns - columns_covered_by_mothers)
should_longest_line_be_extended=0
if (len(x_diff_all_between_nm_wc) > 0 and
set(list(range(x_starting_all_between_nm_wc[biggest],
x_ending_all_between_nm_wc[biggest])) +
list(columns_not_covered)) != set(all_columns)):
list(columns_not_covered)) != child_columns):
should_longest_line_be_extended=1
index_lines_so_close_to_top_separator = \
np.arange(len(y_all_between_nm_wc))[(y_all_between_nm_wc>y_column_nc[i_c]) &
@ -2092,36 +2078,31 @@ def return_boxes_of_images_by_order_of_reading_new(
x_start_by_order=[]
x_end_by_order=[]
if len(x_starting)>0:
all_columns = np.arange(len(peaks_neg_tot)-1)
columns_covered_by_lines_covered_more_than_2col = []
columns_covered_by_lines_covered_more_than_2col = set()
for dj in range(len(x_starting)):
if set(list(range(x_starting[dj],x_ending[dj]))) == set(all_columns):
pass
else:
columns_covered_by_lines_covered_more_than_2col = columns_covered_by_lines_covered_more_than_2col + \
list(range(x_starting[dj],x_ending[dj]))
columns_covered_by_lines_covered_more_than_2col = list(set(columns_covered_by_lines_covered_more_than_2col))
columns_not_covered = list(set(all_columns) - set(columns_covered_by_lines_covered_more_than_2col))
if set(range(x_starting[dj], x_ending[dj])) != all_columns:
columns_covered_by_lines_covered_more_than_2col.update(
range(x_starting[dj], x_ending[dj]))
columns_not_covered = list(all_columns - columns_covered_by_lines_covered_more_than_2col)
y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + 1))
##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered))
##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered))
x_starting = np.append(x_starting, columns_not_covered)
x_ending = np.append(x_ending, np.array(columns_not_covered) + 1)
x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype))
x_ending = np.append(x_ending, np.array(columns_not_covered, x_ending.dtype) + 1)
if len(new_main_sep_y) > 0:
x_starting = np.append(x_starting, 0)
x_ending = np.append(x_ending, len(peaks_neg_tot)-1)
x_ending = np.append(x_ending, len(peaks_neg_tot) - 1)
else:
x_starting = np.append(x_starting, x_starting[0])
x_ending = np.append(x_ending, x_ending[0])
else:
all_columns = np.arange(len(peaks_neg_tot)-1)
columns_not_covered = list(set(all_columns))
columns_not_covered = list(all_columns)
y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * len(columns_not_covered))
##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered))
##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered))
x_starting = np.append(x_starting, columns_not_covered)
x_ending = np.append(x_ending, np.array(columns_not_covered) + 1)
x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype))
x_ending = np.append(x_ending, np.array(columns_not_covered, x_ending.dtype) + 1)
ind_args=np.array(range(len(y_type_2)))
#ind_args=np.array(ind_args)

150
src/eynollah/utils/contour.py
View file

@ -1,7 +1,15 @@
from typing import Sequence, Union
from numbers import Number
from functools import partial
import itertools
import cv2
import numpy as np
from shapely import geometry
from scipy.sparse.csgraph import minimum_spanning_tree
from shapely.geometry import Polygon, LineString
from shapely.geometry.polygon import orient
from shapely import set_precision
from shapely.ops import unary_union, nearest_points
from .rotate import rotate_image, rotation_image_new
@ -37,29 +45,28 @@ def get_text_region_boxes_by_given_contours(contours):
return boxes, contours_new
def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0):
found_polygons_early = []
for jv,c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
for jv, contour in enumerate(contours):
if len(contour) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
polygon = contour2polygon(contour, dilate=dilate)
area = polygon.area
if (area >= min_area * np.prod(image.shape[:2]) and
area <= max_area * np.prod(image.shape[:2]) and
hierarchy[0][jv][3] == -1):
found_polygons_early.append(np.array([[point]
for point in polygon.exterior.coords], dtype=np.uint))
found_polygons_early.append(polygon2contour(polygon))
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0):
found_polygons_early = []
for jv,c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
for jv, contour in enumerate(contours):
if len(contour) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
polygon = contour2polygon(contour, dilate=dilate)
# area = cv2.contourArea(contour)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
@ -68,9 +75,8 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m
area <= max_area * np.prod(image.shape[:2]) and
# hierarchy[0][jv][3]==-1
True):
# print(c[0][0][1])
found_polygons_early.append(np.array([[point]
for point in polygon.exterior.coords], dtype=np.int32))
# print(contour[0][0][1])
found_polygons_early.append(polygon2contour(polygon))
return found_polygons_early
def find_new_features_of_contours(contours_main):
@ -247,23 +253,16 @@ def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
return cont_int[0], index_r_con, confidence_contour
def get_textregion_contours_in_org_image_light(cnts, img, slope_first, confidence_matrix, map=map):
def get_textregion_contours_in_org_image_light(cnts, img, confidence_matrix, map=map):
if not len(cnts):
return [], []
confidence_matrix = cv2.resize(confidence_matrix, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
##cnts = list( (np.array(cnts)/2).astype(np.int16) )
#cnts = cnts/2
cnts = [(i/6).astype(int) for i in cnts]
results = map(partial(do_back_rotation_and_get_cnt_back,
img=img,
slope_first=slope_first,
confidence_matrix=confidence_matrix,
),
cnts, range(len(cnts)))
contours, indexes, conf_contours = tuple(zip(*results))
return [i*6 for i in contours], list(conf_contours)
confs = []
for cnt in cnts:
cnt_mask = np.zeros(confidence_matrix.shape)
cnt_mask = cv2.fillPoly(cnt_mask, pts=[cnt], color=1.0)
confs.append(np.sum(confidence_matrix * cnt_mask) / np.sum(cnt_mask))
return cnts, confs
def return_contours_of_interested_textline(region_pre_p, pixel):
# pixels of images are identified by 5
@ -332,3 +331,96 @@ def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area,
return img_ret[:, :, 0]
def dilate_textline_contours(all_found_textline_polygons):
return [[polygon2contour(contour2polygon(contour, dilate=6))
for contour in region]
for region in all_found_textline_polygons]
def dilate_textregion_contours(all_found_textline_polygons):
return [polygon2contour(contour2polygon(contour, dilate=6))
for contour in all_found_textline_polygons]
def contour2polygon(contour: Union[np.ndarray, Sequence[Sequence[Sequence[Number]]]], dilate=0):
polygon = Polygon([point[0] for point in contour])
if dilate:
polygon = polygon.buffer(dilate)
if polygon.geom_type == 'GeometryCollection':
# heterogeneous result: filter zero-area shapes (LineString, Point)
polygon = unary_union([geom for geom in polygon.geoms if geom.area > 0])
if polygon.geom_type == 'MultiPolygon':
# homogeneous result: construct convex hull to connect
polygon = join_polygons(polygon.geoms)
return make_valid(polygon)
def polygon2contour(polygon: Polygon) -> np.ndarray:
return np.array(polygon.exterior.coords[:-1], dtype=np.uint)[:, np.newaxis]
def make_valid(polygon: Polygon) -> Polygon:
"""Ensures shapely.geometry.Polygon object is valid by repeated rearrangement/simplification/enlargement."""
def isint(x):
return isinstance(x, int) or int(x) == x
# make sure rounding does not invalidate
if not all(map(isint, np.array(polygon.exterior.coords).flat)) and polygon.minimum_clearance < 1.0:
polygon = Polygon(np.round(polygon.exterior.coords))
points = list(polygon.exterior.coords[:-1])
# try by re-arranging points
for split in range(1, len(points)):
if polygon.is_valid or polygon.simplify(polygon.area).is_valid:
break
# simplification may not be possible (at all) due to ordering
# in that case, try another starting point
polygon = Polygon(points[-split:]+points[:-split])
# try by simplification
for tolerance in range(int(polygon.area + 1.5)):
if polygon.is_valid:
break
# simplification may require a larger tolerance
polygon = polygon.simplify(tolerance + 1)
# try by enlarging
for tolerance in range(1, int(polygon.area + 2.5)):
if polygon.is_valid:
break
# enlargement may require a larger tolerance
polygon = polygon.buffer(tolerance)
assert polygon.is_valid, polygon.wkt
return polygon
def join_polygons(polygons: Sequence[Polygon], scale=20) -> Polygon:
"""construct concave hull (alpha shape) from input polygons by connecting their pairwise nearest points"""
# ensure input polygons are simply typed and all oriented equally
polygons = [orient(poly)
for poly in itertools.chain.from_iterable(
[poly.geoms
if poly.geom_type in ['MultiPolygon', 'GeometryCollection']
else [poly]
for poly in polygons])]
npoly = len(polygons)
if npoly == 1:
return polygons[0]
# find min-dist path through all polygons (travelling salesman)
pairs = itertools.combinations(range(npoly), 2)
dists = np.zeros((npoly, npoly), dtype=float)
for i, j in pairs:
dist = polygons[i].distance(polygons[j])
if dist < 1e-5:
dist = 1e-5 # if pair merely touches, we still need to get an edge
dists[i, j] = dist
dists[j, i] = dist
dists = minimum_spanning_tree(dists, overwrite=True)
# add bridge polygons (where necessary)
for prevp, nextp in zip(*dists.nonzero()):
prevp = polygons[prevp]
nextp = polygons[nextp]
nearest = nearest_points(prevp, nextp)
bridgep = orient(LineString(nearest).buffer(max(1, scale/5), resolution=1), -1)
polygons.append(bridgep)
jointp = unary_union(polygons)
assert jointp.geom_type == 'Polygon', jointp.wkt
# follow-up calculations will necessarily be integer;
# so anticipate rounding here and then ensure validity
jointp2 = set_precision(jointp, 1.0)
if jointp2.geom_type != 'Polygon' or not jointp2.is_valid:
jointp2 = Polygon(np.round(jointp.exterior.coords))
jointp2 = make_valid(jointp2)
assert jointp2.geom_type == 'Polygon', jointp2.wkt
return jointp2

46
src/eynollah/utils/separate_lines.py
View file

@ -1345,24 +1345,26 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
return contours_rotated_clean
def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, plotter=None):
def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, plotter=None):
if logger is None:
logger = getLogger(__package__)
if not np.prod(img_crop.shape):
return img_crop
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
num_patches = int(img_crop.shape[1] / 200.0)
else:
num_patches = int(img_path.shape[1] / 140.0)
# num_patches=int(img_path.shape[1]/200.)
num_patches = int(img_crop.shape[1] / 140.0)
# num_patches=int(img_crop.shape[1]/200.)
if num_patches == 0:
num_patches = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
# plt.imshow(img_patch_ineterst)
# plt.imshow(img_patch_interest)
# plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
length_x = int(img_crop.shape[1] / float(num_patches))
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
margin = int(0.04 * length_x)
# if margin<=4:
@ -1370,7 +1372,7 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
# margin=0
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
nxf = img_crop.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
@ -1386,12 +1388,12 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
if index_x_u > img_crop.shape[1]:
index_x_u = img_crop.shape[1]
index_x_d = img_crop.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_xline = img_patch_interest[:, index_x_d:index_x_u]
try:
assert img_xline.any()
@ -1407,9 +1409,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
img_line_rotated = rotate_image(img_xline, slope_xline)
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
img_patch_interest_revised = np.zeros(img_patch_interest.shape)
for i in range(nxf):
if i == 0:
@ -1419,11 +1421,11 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
if index_x_u > img_crop.shape[1]:
index_x_u = img_crop.shape[1]
index_x_d = img_crop.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_xline = img_patch_interest[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
@ -1446,9 +1448,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]]
img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
img_patch_interest_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
return img_patch_ineterst_revised
return img_patch_interest_revised
def do_image_rotation(angle, img, sigma_des, logger=None):
if logger is None:
@ -1546,7 +1548,7 @@ def do_work_of_slopes_new(
img_int_p = all_text_region_raw[:,:]
img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2)
if img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
if not np.prod(img_int_p.shape) or img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
slope = 0
slope_for_all = slope_deskew
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w]
@ -1603,7 +1605,7 @@ def do_work_of_slopes_new_curved(
# plt.imshow(img_int_p)
# plt.show()
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
if not np.prod(img_int_p.shape) or img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
slope = 0
slope_for_all = slope_deskew
else: