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delete_separator_around: simplify, eynollah: identifiers

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- use array instead of list operations
- rename identifiers:
  - `pixel` → `label`
  - `line` → `sep`
This commit is contained in:
Robert Sachunsky 2025-10-24 02:35:04 +02:00
parent 3ebbc2d693
commit a2a9fe5117
1 changed files with 47 additions and 57 deletions
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104
src/eynollah/eynollah.py
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@ -2669,45 +2669,35 @@ class Eynollah:
return layout_org, contours_new
def delete_separator_around(self, spliter_y,peaks_neg,image_by_region, pixel_line, pixel_table):
def delete_separator_around(self, splitter_y, peaks_neg, image_by_region, label_seps, label_table):
# format of subboxes: box=[x1, x2 , y1, y2]
pix_del = 100
if len(image_by_region.shape)==3:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
ys = slice(int(spliter_y[i]),
int(spliter_y[i+1]))
xs = slice(peaks_neg[i][j] - pix_del,
peaks_neg[i][j] + pix_del)
image_by_region[ys,xs,0][image_by_region[ys,xs,0]==pixel_line] = 0
image_by_region[ys,xs,0][image_by_region[ys,xs,1]==pixel_line] = 0
image_by_region[ys,xs,0][image_by_region[ys,xs,2]==pixel_line] = 0
image_by_region[ys,xs,0][image_by_region[ys,xs,0]==pixel_table] = 0
image_by_region[ys,xs,0][image_by_region[ys,xs,1]==pixel_table] = 0
image_by_region[ys,xs,0][image_by_region[ys,xs,2]==pixel_table] = 0
else:
for i in range(len(spliter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
ys = slice(int(spliter_y[i]),
int(spliter_y[i+1]))
xs = slice(peaks_neg[i][j] - pix_del,
peaks_neg[i][j] + pix_del)
image_by_region[ys,xs][image_by_region[ys,xs]==pixel_line] = 0
image_by_region[ys,xs][image_by_region[ys,xs]==pixel_table] = 0
for i in range(len(splitter_y)-1):
for j in range(1,len(peaks_neg[i])-1):
where = np.index_exp[splitter_y[i]:
splitter_y[i+1],
peaks_neg[i][j] - pix_del:
peaks_neg[i][j] + pix_del,
:]
if image_by_region.ndim < 3:
where = where[:2]
else:
print("image_by_region ndim is 3!") # rs
image_by_region[where][image_by_region[where] == label_seps] = 0
image_by_region[where][image_by_region[where] == label_table] = 0
return image_by_region
def add_tables_heuristic_to_layout(
self, image_regions_eraly_p, boxes,
slope_mean_hor, spliter_y, peaks_neg_tot, image_revised,
num_col_classifier, min_area, pixel_line):
slope_mean_hor, splitter_y, peaks_neg_tot, image_revised,
num_col_classifier, min_area, label_seps):
pixel_table =10
image_revised_1 = self.delete_separator_around(spliter_y, peaks_neg_tot, image_revised, pixel_line, pixel_table)
label_table =10
image_revised_1 = self.delete_separator_around(splitter_y, peaks_neg_tot, image_revised, label_seps, label_table)
try:
image_revised_1[:,:30][image_revised_1[:,:30]==pixel_line] = 0
image_revised_1[:,-30:][image_revised_1[:,-30:]==pixel_line] = 0
image_revised_1[:,:30][image_revised_1[:,:30]==label_seps] = 0
image_revised_1[:,-30:][image_revised_1[:,-30:]==label_seps] = 0
except:
pass
boxes = np.array(boxes, dtype=int) # to be on the safe side
@ -2718,7 +2708,7 @@ class Eynollah:
_, thresh = cv2.threshold(image_col, 0, 255, 0)
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if indiv==pixel_table:
if indiv==label_table:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy,
max_area=1, min_area=0.001)
else:
@ -2734,11 +2724,11 @@ class Eynollah:
box_xs = slice(*boxes[i][0:2])
image_box = img_comm[box_ys, box_xs]
try:
image_box_tabels_1 = (image_box == pixel_table) * 1
image_box_tabels_1 = (image_box == label_table) * 1
contours_tab,_=return_contours_of_image(image_box_tabels_1)
contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003)
image_box_tabels_1 = (image_box == pixel_line).astype(np.uint8) * 1
image_box_tabels_and_m_text = ( (image_box == pixel_table) |
image_box_tabels_1 = (image_box == label_seps).astype(np.uint8) * 1
image_box_tabels_and_m_text = ( (image_box == label_table) |
(image_box == 1) ).astype(np.uint8) * 1
image_box_tabels_1 = cv2.dilate(image_box_tabels_1, KERNEL, iterations=5)
@ -2800,7 +2790,7 @@ class Eynollah:
y_up_tabs=[]
for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii]:y_down_tabs[ii]] = pixel_table
image_box[y_up_tabs[ii]:y_down_tabs[ii]] = label_table
image_revised_last[box_ys, box_xs] = image_box
else:
@ -2811,14 +2801,14 @@ class Eynollah:
image_revised_last[box_ys, box_xs] = image_box
if num_col_classifier==1:
img_tables_col_1 = (image_revised_last == pixel_table).astype(np.uint8)
img_tables_col_1 = (image_revised_last == label_table).astype(np.uint8)
contours_table_col1, _ = return_contours_of_image(img_tables_col_1)
_,_ ,_ , _, y_min_tab_col1 ,y_max_tab_col1, _= find_new_features_of_contours(contours_table_col1)
if len(y_min_tab_col1)>0:
for ijv in range(len(y_min_tab_col1)):
image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv])] = pixel_table
image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv])] = label_table
return image_revised_last
def get_tables_from_model(self, img, num_col_classifier):
@ -3153,14 +3143,14 @@ class Eynollah:
text_regions_p_1_n = None
textline_mask_tot_d = None
regions_without_separators_d = None
pixel_lines = 3
label_seps = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(
text_regions_p, num_col_classifier, self.tables, pixel_lines)
_, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
text_regions_p, num_col_classifier, self.tables, label_seps)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
text_regions_p_1_n, num_col_classifier, self.tables, pixel_lines)
_, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
text_regions_p_1_n, num_col_classifier, self.tables, label_seps)
#print(time.time()-t_0_box,'time box in 2')
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -3175,7 +3165,7 @@ class Eynollah:
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new, regions_without_separators, matrix_of_lines_ch,
splitter_y_new, regions_without_separators, matrix_of_seps_ch,
num_col_classifier, erosion_hurts, self.tables, self.right2left)
boxes_d = None
self.logger.debug("len(boxes): %s", len(boxes))
@ -3187,17 +3177,17 @@ class Eynollah:
else:
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[(table_prediction == 1)] = 10
pixel_line = 3
label_seps = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(
text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables,
num_col_classifier , 0.000005, pixel_line)
num_col_classifier , 0.000005, label_seps)
#print(time.time()-t_0_box,'time box in 3.2')
img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(
img_revised_tab2, table_prediction, 10, num_col_classifier)
#print(time.time()-t_0_box,'time box in 3.3')
else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d,
splitter_y_new_d, regions_without_separators_d, matrix_of_seps_ch_d,
num_col_classifier, erosion_hurts, self.tables, self.right2left)
boxes = None
self.logger.debug("len(boxes): %s", len(boxes_d))
@ -3210,11 +3200,11 @@ class Eynollah:
text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
pixel_line = 3
label_seps = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(
text_regions_p_tables, boxes_d, 0, splitter_y_new_d,
peaks_neg_tot_tables_d, text_regions_p_tables,
num_col_classifier, 0.000005, pixel_line)
num_col_classifier, 0.000005, label_seps)
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(
img_revised_tab2, table_prediction_n, 10, num_col_classifier)
@ -3333,14 +3323,14 @@ class Eynollah:
regions_without_separators = (text_regions_p[:,:] == 1)*1
regions_without_separators[table_prediction == 1] = 1
pixel_lines=3
label_seps=3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(
text_regions_p, num_col_classifier, self.tables, pixel_lines)
text_regions_p, num_col_classifier, self.tables, label_seps)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
text_regions_p_1_n, num_col_classifier, self.tables, pixel_lines)
text_regions_p_1_n, num_col_classifier, self.tables, label_seps)
if num_col_classifier>=3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@ -3359,10 +3349,10 @@ class Eynollah:
num_col_classifier, erosion_hurts, self.tables, self.right2left)
text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
pixel_line = 3
label_seps = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(
text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables,
num_col_classifier , 0.000005, pixel_line)
num_col_classifier , 0.000005, label_seps)
img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables(
img_revised_tab2, table_prediction, 10, num_col_classifier)
@ -3374,11 +3364,11 @@ class Eynollah:
text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
pixel_line = 3
label_seps = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout(
text_regions_p_tables, boxes_d, 0, splitter_y_new_d,
peaks_neg_tot_tables_d, text_regions_p_tables,
num_col_classifier, 0.000005, pixel_line)
num_col_classifier, 0.000005, label_seps)
img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables(
img_revised_tab2, table_prediction_n, 10, num_col_classifier)
@ -4721,12 +4711,12 @@ class Eynollah:
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(
boxes, _ = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new, regions_without_separators, matrix_of_lines_ch,
num_col_classifier, erosion_hurts, self.tables, self.right2left,
logger=self.logger)
else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(
boxes_d, _ = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d,
num_col_classifier, erosion_hurts, self.tables, self.right2left,
logger=self.logger)