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eynollah
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🎨 cleanup, remove unused vars, use max over if-else

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pull/19/head
Konstantin Baierer 4 years ago
parent 8f0455590a
commit df3e161325
1 changed files with 33 additions and 73 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -1,4 +1,4 @@
# pylint: disable=no-member # pylint: disable=no-member,invalid-name,line-too-long
""" """
tool to extract table form data from alto xml data tool to extract table form data from alto xml data
""" """
@ -419,18 +419,12 @@ class eynollah:
del img_in del img_in
del img_1ch del img_1ch
del page_coord del page_coord
K.clear_session() K.clear_session()
gc.collect() gc.collect()
print(dpi) print(dpi)
if dpi < 298: if dpi < 298:
# sys.exit()
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred) img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
# img_new=resize_image(img,img_h_new,img_w_new)
image_res = self.predict_enhancement(img_new) image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem) + ".tif",self.image) # cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem) + ".tif",self.image)
# self.image=self.image.astype(np.uint16) # self.image=self.image.astype(np.uint16)
@ -480,7 +474,6 @@ class eynollah:
return model, session return model, session
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1): def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
img_height_model = model.layers[len(model.layers) - 1].output_shape[1] img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
@ -495,48 +488,34 @@ class eynollah:
img = resize_image(img, img.shape[0], img_width_model) img = resize_image(img, img.shape[0], img_width_model)
# print(img_height_model,img_width_model) # print(img_height_model,img_width_model)
# margin = int(0.2 * img_width_model)
margin = int(marginal_of_patch_percent * img_height_model) margin = int(marginal_of_patch_percent * img_height_model)
width_mid = img_width_model - 2 * margin width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin height_mid = img_height_model - 2 * margin
img = img / float(255.0) img = img / float(255.0)
# print(sys.getsizeof(img))
# print(np.max(img))
img = img.astype(np.float16) img = img.astype(np.float16)
# print(sys.getsizeof(img))
img_h = img.shape[0] img_h = img.shape[0]
img_w = img.shape[1] img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3)) prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w)) mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid) nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid) nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
if i == 0: if i == 0:
index_x_d = i * width_mid index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model index_x_u = index_x_d + img_width_model
else: else:
index_x_d = i * width_mid index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model index_x_u = index_x_d + img_width_model
if j == 0: if j == 0:
index_y_d = j * height_mid index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model index_y_u = index_y_d + img_height_model
else: else:
index_y_d = j * height_mid index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model index_y_u = index_y_d + img_height_model
if index_x_u > img_w: if index_x_u > img_w:
index_x_u = img_w index_x_u = img_w
index_x_d = img_w - img_width_model index_x_d = img_w - img_width_model
@ -681,7 +660,7 @@ class eynollah:
x, y, w, h = cv2.boundingRect(cnt) x, y, w, h = cv2.boundingRect(cnt)
if x <= 30: if x <= 30:
w = w + x w += x
x = 0 x = 0
if (self.image.shape[1] - (x + w)) <= 30: if (self.image.shape[1] - (x + w)) <= 30:
w = w + (self.image.shape[1] - (x + w)) w = w + (self.image.shape[1] - (x + w))
@ -717,33 +696,31 @@ class eynollah:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
if patches and cols == 1: if patches and cols == 1:
img2 = otsu_copy_binary(img) # otsu_copy(img) img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7)) img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if patches and cols == 2: if patches and cols == 2:
img2 = otsu_copy_binary(img) # otsu_copy(img) img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4)) img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
elif patches and cols > 2: elif patches and cols > 2:
img2 = otsu_copy_binary(img) # otsu_copy(img) img2 = otsu_copy_binary(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3)) img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if patches and cols == 2: if patches and cols == 2:
img = otsu_copy_binary(img) # otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if img_width_h >= 2000: if img_width_h >= 2000:
img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)) img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
@ -752,60 +729,55 @@ class eynollah:
img = img.astype(np.uint8) img = img.astype(np.uint8)
if patches and cols == 1: if patches and cols == 1:
img = otsu_copy_binary(img) # otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5)) img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
img = img.astype(np.uint8) img = img.astype(np.uint8)
if patches and cols==3: if patches and cols == 3:
if (self.scale_x==1 and img_width_h>3000) or (self.scale_x!=1 and img_width_h>2800): if (self.scale_x == 1 and img_width_h > 3000) or (self.scale_x != 1 and img_width_h > 2800):
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.8), int(img_width_h*0.8) ) img = resize_image(img, int(img_height_h * 2800 / float(img_width_h)), 2800)
img= resize_image(img, int(img_height_h*2800/float(img_width_h)), 2800 )
else: else:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols==4: if patches and cols == 4:
#print(self.scale_x,img_width_h,'scale') #print(self.scale_x,img_width_h,'scale')
if (self.scale_x==1 and img_width_h>4000) or (self.scale_x!=1 and img_width_h>3700): if (self.scale_x == 1 and img_width_h > 4000) or (self.scale_x != 1 and img_width_h > 3700):
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.7), int(img_width_h*0.7) ) img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
img= resize_image(img, int(img_height_h*3700/float(img_width_h)), 3700 )
else: else:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)#self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
if patches and cols==5: if patches and cols==5:
if (self.scale_x==1 and img_width_h>5000): if self.scale_x == 1 and img_width_h > 5000:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h*0.7), int(img_width_h*0.7) ) img= resize_image(img, int(img_height_h * 0.7), int(img_width_h * 0.7))
#img= self.resize_image(img, int(img_height_h*4700/float(img_width_h)), 4700 )
else: else:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9) )
if patches and cols>=6: if patches and cols>=6:
if img_width_h>5600: if img_width_h > 5600:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
#img= self.resize_image(img, int(img_height_h*0.7), int(img_width_h*0.7) ) img= resize_image(img, int(img_height_h * 5600 / float(img_width_h)), 5600)
img= resize_image(img, int(img_height_h*5600/float(img_width_h)), 5600 )
else: else:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
if not patches: if not patches:
img = otsu_copy_binary(img)#self.otsu_copy(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
prediction_regions2=None prediction_regions2 = None
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent) prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
@ -847,7 +819,6 @@ class eynollah:
for i in range(num_cores): for i in range(num_cores):
list_all_par = queue_of_all_params.get(True) list_all_par = queue_of_all_params.get(True)
slopes_for_sub_process = list_all_par[0] slopes_for_sub_process = list_all_par[0]
polys_for_sub_process = list_all_par[1] polys_for_sub_process = list_all_par[1]
boxes_for_sub_process = list_all_par[2] boxes_for_sub_process = list_all_par[2]
@ -855,7 +826,6 @@ class eynollah:
contours_par_for_subprocess = list_all_par[4] contours_par_for_subprocess = list_all_par[4]
boxes_coord_for_subprocess = list_all_par[5] boxes_coord_for_subprocess = list_all_par[5]
indexes_for_subprocess = list_all_par[6] indexes_for_subprocess = list_all_par[6]
for j in range(len(slopes_for_sub_process)): for j in range(len(slopes_for_sub_process)):
slopes.append(slopes_for_sub_process[j]) slopes.append(slopes_for_sub_process[j])
all_found_texline_polygons.append(polys_for_sub_process[j]) all_found_texline_polygons.append(polys_for_sub_process[j])
@ -899,7 +869,6 @@ class eynollah:
for i in range(num_cores): for i in range(num_cores):
list_all_par = queue_of_all_params.get(True) list_all_par = queue_of_all_params.get(True)
polys_for_sub_process = list_all_par[0] polys_for_sub_process = list_all_par[0]
boxes_for_sub_process = list_all_par[1] boxes_for_sub_process = list_all_par[1]
contours_for_subprocess = list_all_par[2] contours_for_subprocess = list_all_par[2]
@ -907,7 +876,6 @@ class eynollah:
boxes_coord_for_subprocess = list_all_par[4] boxes_coord_for_subprocess = list_all_par[4]
indexes_for_subprocess = list_all_par[5] indexes_for_subprocess = list_all_par[5]
slopes_for_sub_process = list_all_par[6] slopes_for_sub_process = list_all_par[6]
for j in range(len(polys_for_sub_process)): for j in range(len(polys_for_sub_process)):
slopes.append(slopes_for_sub_process[j]) slopes.append(slopes_for_sub_process[j])
all_found_texline_polygons.append(polys_for_sub_process[j]) all_found_texline_polygons.append(polys_for_sub_process[j])
@ -930,7 +898,6 @@ class eynollah:
contours_textregion_par_per_each_subprocess = [] contours_textregion_par_per_each_subprocess = []
all_box_coord_per_process = [] all_box_coord_per_process = []
index_by_text_region_contours = [] index_by_text_region_contours = []
slope_biggest = 0
textline_cnt_seperated = np.zeros(textline_mask_tot_ea.shape) textline_cnt_seperated = np.zeros(textline_mask_tot_ea.shape)
@ -938,9 +905,9 @@ class eynollah:
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[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) all_text_region_raw = all_text_region_raw.astype(np.uint8)
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv] img_int_p = all_text_region_raw[:, :]
##img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2) # img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
# plt.imshow(img_int_p) # plt.imshow(img_int_p)
# plt.show() # plt.show()
@ -953,13 +920,9 @@ class eynollah:
textline_con, hierachy = return_contours_of_image(img_int_p) textline_con, hierachy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0)) sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
if sigma_des < 1:
sigma_des = 1
img_int_p[img_int_p > 0] = 1 img_int_p[img_int_p > 0] = 1
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, image_filename_stem=self.image_filename_stem) slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, image_filename_stem=self.image_filename_stem)
if abs(slope_for_all) < 0.5: if abs(slope_for_all) < 0.5:
@ -978,9 +941,7 @@ class eynollah:
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated) crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
if abs(slope_for_all) < 45: if abs(slope_for_all) < 45:
# all_box_coord.append(crop_coor) # all_box_coord.append(crop_coor)
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape) textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
cnt_o_t_max = contours_par_per_process[mv] cnt_o_t_max = contours_par_per_process[mv]
x, y, w, h = cv2.boundingRect(cnt_o_t_max) x, y, w, h = cv2.boundingRect(cnt_o_t_max)
@ -1049,7 +1010,6 @@ class eynollah:
contours_textregion_par_per_each_subprocess = [] contours_textregion_par_per_each_subprocess = []
all_box_coord_per_process = [] all_box_coord_per_process = []
index_by_text_region_contours = [] index_by_text_region_contours = []
slope_biggest = 0
for mv in range(len(boxes_text)): for mv in range(len(boxes_text)):
crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated) crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)

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