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eynollah
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issue raised by Clemens, strong erosion causing

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pull/34/head
vahid 4 years ago
parent 98f9272c4b
commit 1184d3d2fc
1 changed files with 108 additions and 51 deletions
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qurator/eynollah/eynollah.py
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@ -1073,8 +1073,9 @@ class Eynollah:
poly.put(poly_sub)
box_sub.put(boxes_sub_new)
def get_regions_from_xy_2models(self,img,is_image_enhanced):
def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
self.logger.debug("enter get_regions_from_xy_2models")
erosion_hurts = False
img_org = np.copy(img)
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
@ -1093,70 +1094,119 @@ class Eynollah:
#plt.show()
prediction_regions_org_y = prediction_regions_org_y[:,:,0]
mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1
img_only_regions_with_sep = ( (prediction_regions_org_y[:,:] != 3) & (prediction_regions_org_y[:,:] != 0) )*1
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
try:
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
_, _ = find_num_col(img_only_regions, multiplier=6.0)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
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()
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
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()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
session_region.close()
del model_region
del session_region
gc.collect()
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
text_sume_early = (prediction_regions_org[:,:] == 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()
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
text_sume_early = (prediction_regions_org[:,:] == 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
rate_two_models = text_sume_second / float(text_sume_early) * 100
self.logger.info("ratio_of_two_models: %s", rate_two_models)
if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
prediction_regions_org = np.copy(prediction_regions_org_copy)
self.logger.info("ratio_of_two_models: %s", rate_two_models)
if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
prediction_regions_org = np.copy(prediction_regions_org_copy)
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*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)
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3))
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
text_regions_p_true = np.zeros(prediction_regions_org.shape)
text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3))
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=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
K.clear_session()
return text_regions_p_true, erosion_hurts
except:
img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
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()
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
mask_images_only=(prediction_regions_org[:,:] ==2)*1
K.clear_session()
return text_regions_p_true
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
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))
erosion_hurts = True
K.clear_session()
return text_regions_p_true, erosion_hurts
def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
self.logger.debug("enter do_order_of_regions_full_layout")
@ -1406,7 +1456,7 @@ class Eynollah:
return self.do_order_of_regions_full_layout(*args, **kwargs)
return self.do_order_of_regions_no_full_layout(*args, **kwargs)
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified):
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
img_g = self.imread(grayscale=True, uint8=True)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
@ -1427,7 +1477,14 @@ class Eynollah:
mask_lines = mask_lines.astype(np.uint8)
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], KERNEL, iterations=6)
if erosion_hurts:
img_only_regions = np.copy(img_only_regions_with_sep[:,:])
else:
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
try:
num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
num_col = num_col + 1
@ -1628,12 +1685,12 @@ class Eynollah:
self.logger.info("Enhancing took %ss ", str(time.time() - t0))
t1 = time.time()
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
text_regions_p_1 ,erosion_hurts = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
self.logger.info("Textregion detection took %ss ", str(time.time() - t1))
t1 = time.time()
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page = \
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified)
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts)
self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
self.logger.info('cont_page %s', cont_page)

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