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eynollah
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pull/8/head
Konstantin Baierer 4 years ago
parent 145b2fcd98
commit 6ab7abdfcd
2 changed files with 832 additions and 842 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -1238,9 +1238,15 @@ class eynollah:
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
# all_box_coord.append(crop_coor)
mask_textline=np.zeros((textline_mask_tot_ea.shape))
mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
denoised = None
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
all_text_region_raw=(textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
all_text_region_raw = all_text_region_raw.astype(np.uint8)
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv]
@ -2750,235 +2756,260 @@ class eynollah:
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
# cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
def get_regions_from_xy_2models(self, img, is_image_enhanced):
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
def get_regions_from_xy_2models(self,img,is_image_enhanced):
img_org=np.copy(img)
img_height_h=img_org.shape[0]
img_width_h=img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
gaussian_filter = False
patches = True
binary = False
ratio_y = 1.3
ratio_x = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
gaussian_filter=False
patches=True
binary=False
ratio_y=1.3
ratio_x=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img = cv2.medianBlur(img, 5)
img=cv2.medianBlur(img,5)
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org_y = self.do_prediction(patches, img, model_region)
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org_y[:,:,0])
# plt.show()
# sys.exit()
prediction_regions_org_y = prediction_regions_org_y[:, :, 0]
mask_zeros_y = (prediction_regions_org_y[:, :] == 0) * 1
prediction_regions_org_y=self.do_prediction(patches,img,model_region)
prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
#plt.show()
#sys.exit()
prediction_regions_org_y=prediction_regions_org_y[:,:,0]
mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1
if is_image_enhanced:
ratio_x = 1.2
ratio_x=1.2
else:
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img = cv2.medianBlur(img, 5)
img=cv2.medianBlur(img,5)
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
prediction_regions_org=self.do_prediction(patches,img,model_region)
prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
##sys.exit()
prediction_regions_org = prediction_regions_org[:, :, 0]
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
gaussian_filter = False
patches = True
binary = False
ratio_x = 1
ratio_y = 1
median_blur = False
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
gaussian_filter=False
patches=True
binary=False
ratio_x=1
ratio_y=1
median_blur=False
img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if binary:
img = otsu_copy_binary(img) # otsu_copy(img)
img = self.otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint16)
if median_blur:
img = cv2.medianBlur(img, 5)
img=cv2.medianBlur(img,5)
if gaussian_filter:
img = cv2.GaussianBlur(img, (5, 5), 0)
img= cv2.GaussianBlur(img,(5,5),0)
img = img.astype(np.uint16)
prediction_regions_org2 = self.do_prediction(patches, img, model_region)
prediction_regions_org2 = resize_image(prediction_regions_org2, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org2[:,:,0])
# plt.show()
# sys.exit()
marginal_patch=0.2
prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
#sys.exit()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region.close()
del model_region
del session_region
gc.collect()
###K.clear_session()
mask_zeros2 = (prediction_regions_org2[:, :, 0] == 0) * 1
mask_lines2 = (prediction_regions_org2[:, :, 0] == 3) * 1
text_sume_early = ((prediction_regions_org[:, :] == 1) * 1).sum()
prediction_regions_org_copy = np.copy(prediction_regions_org)
prediction_regions_org_copy[(prediction_regions_org_copy[:, :] == 1) & (mask_zeros2[:, :] == 1)] = 0
text_sume_second = ((prediction_regions_org_copy[:, :] == 1) * 1).sum()
rate_two_models = text_sume_second / float(text_sume_early) * 100
print(rate_two_models, "ratio_of_two_models")
if is_image_enhanced and rate_two_models < 95.50: # 98.45:
mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
prediction_regions_org_copy=np.copy(prediction_regions_org)
prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0
text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()
rate_two_models=text_sume_second/float(text_sume_early)*100
print(rate_two_models,'ratio_of_two_models')
if is_image_enhanced and rate_two_models<95.50:#98.45:
pass
else:
prediction_regions_org = np.copy(prediction_regions_org_copy)
prediction_regions_org=np.copy(prediction_regions_org_copy)
##prediction_regions_org[mask_lines2[:,:]==1]=3
prediction_regions_org[(mask_lines2[:, :] == 1) & (prediction_regions_org[:, :] == 0)] = 3
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
del mask_lines2
del mask_zeros2
del prediction_regions_org2
# if is_image_enhanced:
# pass
# else:
# model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
# gaussian_filter=False
# patches=True
# binary=False
# ratio_x=1
# ratio_y=1
# median_blur=False
# img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
# if binary:
# img = otsu_copy_binary(img)#otsu_copy(img)
# img = img.astype(np.uint16)
# if median_blur:
# img=cv2.medianBlur(img,5)
# if gaussian_filter:
# img= cv2.GaussianBlur(img,(5,5),0)
# img = img.astype(np.uint16)
# prediction_regions_org2=self.do_prediction(patches,img,model_region)
# prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
# session_region.close()
# del model_region
# del session_region
# gc.collect()
####K.clear_session()
# mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
# mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
# text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
# prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
# prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
# text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
# print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
# del mask_lines2
# del mask_zeros2
# del prediction_regions_org2
mask_lines_only = (prediction_regions_org[:, :] == 3) * 1
prediction_regions_org = cv2.erode(prediction_regions_org[:, :], self.kernel, iterations=2)
# plt.imshow(text_region2_1st_channel)
# plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:, :], self.kernel, iterations=2)
mask_texts_only = (prediction_regions_org[:, :] == 1) * 1
mask_images_only = (prediction_regions_org[:, :] == 2) * 1
pixel_img = 1
min_area_text = 0.00001
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, pixel_img, min_area_text)
polygons_of_only_images = return_contours_of_interested_region(mask_images_only, pixel_img)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, pixel_img, min_area_text)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3, 3, 3))
#if is_image_enhanced:
#pass
#else:
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
#gaussian_filter=False
#patches=True
#binary=False
#ratio_x=1
#ratio_y=1
#median_blur=False
#img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
#if binary:
#img = self.otsu_copy_binary(img)#self.otsu_copy(img)
#img = img.astype(np.uint16)
#if median_blur:
#img=cv2.medianBlur(img,5)
#if gaussian_filter:
#img= cv2.GaussianBlur(img,(5,5),0)
#img = img.astype(np.uint16)
#prediction_regions_org2=self.do_prediction(patches,img,model_region)
#prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show()
##sys.exit()
###prediction_regions_org=prediction_regions_org[:,:,0]
#session_region.close()
#del model_region
#del session_region
#gc.collect()
####K.clear_session()
#mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
#mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
#text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
#prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
###prediction_regions_org[mask_lines2[:,:]==1]=3
#prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
#text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
#print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
#del mask_lines2
#del mask_zeros2
#del prediction_regions_org2
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
pixel_img=1
min_area_text=0.00001
polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)
polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
text_regions_p_true=np.zeros(prediction_regions_org.shape)
#text_regions_p_true[:,:]=text_regions_p_1[:,:]
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
text_regions_p_true[:, :][mask_images_only[:, :] == 1] = 2
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1, 1, 1))
text_regions_p_true[:,:][mask_images_only[:,:]==1]=2
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
##print(np.unique(text_regions_p_true))
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
#text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
#text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
del polygons_of_only_texts
del polygons_of_only_images
del polygons_of_only_lines
@ -2986,13 +3017,14 @@ class eynollah:
del prediction_regions_org
del img
del mask_zeros_y
del prediction_regions_org_y
del img_org
gc.collect()
return text_regions_p_true
def write_images_into_directory(self, img_contoures, dir_of_cropped_imgs, image_page):
index = 0
for cont_ind in img_contoures:
@ -3009,215 +3041,245 @@ class eynollah:
cv2.imwrite(path, croped_page)
index += 1
def get_marginals(self, text_with_lines, text_regions, num_col, slope_deskew):
mask_marginals = np.zeros((text_with_lines.shape[0], text_with_lines.shape[1]))
mask_marginals = mask_marginals.astype(np.uint8)
text_with_lines = text_with_lines.astype(np.uint8)
def get_marginals(self,text_with_lines,text_regions,num_col,slope_deskew):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
text_with_lines=text_with_lines.astype(np.uint8)
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded = cv2.erode(text_with_lines, self.kernel, iterations=5)
if text_with_lines.shape[0] <= 1500:
text_with_lines_eroded=cv2.erode(text_with_lines,self.kernel,iterations=5)
if text_with_lines.shape[0]<=1500:
pass
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=5)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
text_with_lines=self.resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=5)
text_with_lines=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
else:
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=7)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
text_with_lines_y = text_with_lines.sum(axis=0)
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent = text_with_lines_y_eroded.max() / (float(text_with_lines.shape[0])) * 100
# print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent < 30:
min_textline_thickness = 8
elif thickness_along_y_percent >= 30 and thickness_along_y_percent < 50:
min_textline_thickness = 20
text_with_lines=self.resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=7)
text_with_lines=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
#print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent<30:
min_textline_thickness=8
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
else:
min_textline_thickness = 40
if thickness_along_y_percent >= 14:
text_with_lines_y_rev = -1 * text_with_lines_y[:]
# print(text_with_lines_y)
# print(text_with_lines_y_rev)
# plt.plot(text_with_lines_y)
# plt.show()
text_with_lines_y_rev = text_with_lines_y_rev - np.min(text_with_lines_y_rev)
# plt.plot(text_with_lines_y_rev)
# plt.show()
sigma_gaus = 1
region_sum_0 = gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev = gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
# plt.plot(region_sum_0_rev)
# plt.show()
region_sum_0_updown = region_sum_0[len(region_sum_0) :: -1]
min_textline_thickness=40
if thickness_along_y_percent>=14:
text_with_lines_y_rev=-1*text_with_lines_y[:]
#print(text_with_lines_y)
#print(text_with_lines_y_rev)
#plt.plot(text_with_lines_y)
#plt.show()
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
#plt.plot(text_with_lines_y_rev)
#plt.show()
sigma_gaus=1
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
#plt.plot(region_sum_0_rev)
#plt.show()
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
first_nonzero = next((i for i, x in enumerate(region_sum_0) if x), None)
last_nonzero = next((i for i, x in enumerate(region_sum_0_updown) if x), None)
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero = len(region_sum_0) - last_nonzero
last_nonzero=len(region_sum_0)-last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point = (last_nonzero + first_nonzero) / 2.0
one_third_right = (last_nonzero - mid_point) / 3.0
one_third_left = (mid_point - first_nonzero) / 3.0
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
mid_point=(last_nonzero+first_nonzero)/2.
one_third_right=(last_nonzero-mid_point)/3.0
one_third_left=(mid_point-first_nonzero)/3.0
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks = np.array(peaks)
# print(region_sum_0[peaks])
peaks=np.array(peaks)
#print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
# print(first_nonzero,last_nonzero,peaks)
peaks = peaks[(peaks > first_nonzero) & ((peaks < last_nonzero))]
# print(first_nonzero,last_nonzero,peaks)
# print(region_sum_0[peaks]<10)
#print(first_nonzero,last_nonzero,peaks)
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
#print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
# print(region_sum_0[peaks])
peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
# print(peaks)
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col == 1:
peaks_right = peaks[peaks > mid_point]
peaks_left = peaks[peaks < mid_point]
if num_col == 2:
peaks_right = peaks[peaks > (mid_point + one_third_right)]
peaks_left = peaks[peaks < (mid_point - one_third_left)]
#print(region_sum_0[peaks])
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
#print(peaks)
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col==1:
peaks_right=peaks[peaks>mid_point]
peaks_left=peaks[peaks<mid_point]
if num_col==2:
peaks_right=peaks[peaks>(mid_point+one_third_right)]
peaks_left=peaks[peaks<(mid_point-one_third_left)]
try:
point_right = np.min(peaks_right)
point_right=np.min(peaks_right)
except:
point_right = last_nonzero
point_right=last_nonzero
try:
point_left = np.max(peaks_left)
point_left=np.max(peaks_left)
except:
point_left = first_nonzero
# print(point_left,point_right)
# print(text_regions.shape)
if point_right >= mask_marginals.shape[1]:
point_right = mask_marginals.shape[1] - 1
point_left=first_nonzero
#print(point_left,point_right)
#print(text_regions.shape)
if point_right>=mask_marginals.shape[1]:
point_right=mask_marginals.shape[1]-1
try:
mask_marginals[:, point_left:point_right] = 1
mask_marginals[:,point_left:point_right]=1
except:
mask_marginals[:, :] = 1
# print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
# print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum != 0] = 1
index_x = np.array(range(len(mask_marginals_rotated_sum))) + 1
index_x_interest = index_x[mask_marginals_rotated_sum == 1]
min_point_of_left_marginal = np.min(index_x_interest) - 16
max_point_of_right_marginal = np.max(index_x_interest) + 16
if min_point_of_left_marginal < 0:
min_point_of_left_marginal = 0
if max_point_of_right_marginal >= text_regions.shape[1]:
max_point_of_right_marginal = text_regions.shape[1] - 1
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
# plt.imshow(mask_marginals)
# plt.show()
# plt.imshow(mask_marginals_rotated)
# plt.show()
text_regions[(mask_marginals_rotated[:, :] != 1) & (text_regions[:, :] == 1)] = 4
pixel_img = 4
min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:, :] == 4)] = 1
marginlas_should_be_main_text = []
x_min_marginals_left = []
x_min_marginals_right = []
mask_marginals[:,:]=1
#print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated=self.rotate_image(mask_marginals,-slope_deskew)
#print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
index_x_interest=index_x[mask_marginals_rotated_sum==1]
min_point_of_left_marginal=np.min(index_x_interest)-16
max_point_of_right_marginal=np.max(index_x_interest)+16
if min_point_of_left_marginal<0:
min_point_of_left_marginal=0
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
#plt.imshow(mask_marginals)
#plt.show()
#plt.imshow(mask_marginals_rotated)
#plt.show()
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
#plt.imshow(text_regions)
#plt.show()
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=self.return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=self.find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
for i in range(len(cx_text_only)):
x_width_mar = abs(x_min_text_only[i] - x_max_text_only[i])
y_height_mar = abs(y_min_text_only[i] - y_max_text_only[i])
# print(x_width_mar,y_height_mar,'y_height_mar')
if x_width_mar > 16 and y_height_mar / x_width_mar < 10:
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<18:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i] < (mid_point - one_third_left):
x_min_marginals_left_new = x_min_text_only[i]
if len(x_min_marginals_left) == 0:
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0] = min(x_min_marginals_left[0], x_min_marginals_left_new)
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
else:
x_min_marginals_right_new = x_min_text_only[i]
if len(x_min_marginals_right) == 0:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0] = min(x_min_marginals_right[0], x_min_marginals_right_new)
if len(x_min_marginals_left) == 0:
x_min_marginals_left = [0]
if len(x_min_marginals_right) == 0:
x_min_marginals_right = [text_regions.shape[1] - 1]
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions = cv2.fillPoly(text_regions, pts=marginlas_should_be_main_text, color=(4, 4))
# print(np.unique(text_regions))
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:, : int(min_point_of_left_marginal)][text_regions[:, : int(min_point_of_left_marginal)] == 1] = 0
text_regions[:, int(max_point_of_right_marginal) :][text_regions[:, int(max_point_of_right_marginal) :] == 1] = 0
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
#print(np.unique(text_regions))
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
# plt.plot(region_sum_0)
# plt.plot(peaks,region_sum_0[peaks],'*')
# plt.show()
# plt.imshow(text_regions)
# plt.show()
# sys.exit()
#plt.plot(region_sum_0)
#plt.plot(peaks,region_sum_0[peaks],'*')
#plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
else:
pass
return text_regions
@ -4119,7 +4181,7 @@ class eynollah:
num_col = None
peaks_neg_fin = []
print(num_col, "num_colnum_col")
#print(num_col, "num_colnum_col")
if num_col is None:
txt_con_org = []
order_text_new = []
@ -4144,7 +4206,7 @@ class eynollah:
K.clear_session()
gc.collect()
print(np.unique(textline_mask_tot_ea[:, :]), "textline")
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
if self.dir_of_all is not None:

866
sbb_newspapers_org_image/utils.py
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