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sbb_pixelwise_segmentation
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early dilation for textline artificial class

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pull/18/head
vahidrezanezhad 4 months ago
parent 9904846776
commit 4f0e3efa2b
1 changed files with 13 additions and 7 deletions
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20
gt_gen_utils.py
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@ -88,12 +88,15 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=min_area) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=min_area)
return contours_imgs return contours_imgs
def update_region_contours(co_text, img_boundary, erosion_rate, dilation_rate, y_len, x_len): def update_region_contours(co_text, img_boundary, erosion_rate, dilation_rate, y_len, x_len, dilation_early=None):
co_text_eroded = [] co_text_eroded = []
for con in co_text: for con in co_text:
img_boundary_in = np.zeros( (y_len,x_len) ) img_boundary_in = np.zeros( (y_len,x_len) )
img_boundary_in = cv2.fillPoly(img_boundary_in, pts=[con], color=(1, 1, 1)) img_boundary_in = cv2.fillPoly(img_boundary_in, pts=[con], color=(1, 1, 1))
if dilation_early:
img_boundary_in = cv2.dilate(img_boundary_in[:,:], KERNEL, iterations=dilation_early)
#img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=7)#asiatica #img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=7)#asiatica
if erosion_rate > 0: if erosion_rate > 0:
img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=erosion_rate) img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=erosion_rate)
@ -258,22 +261,25 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
if "artificial_class_label" in keys: if "artificial_class_label" in keys:
img_boundary = np.zeros((y_len, x_len)) img_boundary = np.zeros((y_len, x_len))
erosion_rate = 1 erosion_rate = 0#1
dilation_rate = 3 dilation_rate = 3
co_use_case, img_boundary = update_region_contours(co_use_case, img_boundary, erosion_rate, dilation_rate, y_len, x_len ) dilation_early = 2
co_use_case, img_boundary = update_region_contours(co_use_case, img_boundary, erosion_rate, dilation_rate, y_len, x_len, dilation_early=dilation_early )
img = np.zeros((y_len, x_len, 3)) img = np.zeros((y_len, x_len, 3))
if output_type == '2d': if output_type == '2d':
img_poly = cv2.fillPoly(img, pts=co_use_case, color=(1, 1, 1)) img_poly = cv2.fillPoly(img, pts=co_use_case, color=(1, 1, 1))
if "artificial_class_label" in keys: if "artificial_class_label" in keys:
img_poly[:,:][img_boundary[:,:]==1] = artificial_class_label img_mask = np.copy(img_poly)
img_poly[:,:][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=1)] = artificial_class_label
elif output_type == '3d': elif output_type == '3d':
img_poly = cv2.fillPoly(img, pts=co_use_case, color=textline_rgb_color) img_poly = cv2.fillPoly(img, pts=co_use_case, color=textline_rgb_color)
if "artificial_class_label" in keys: if "artificial_class_label" in keys:
img_poly[:,:,0][img_boundary[:,:]==1] = artificial_class_rgb_color[0] img_mask = np.copy(img_poly)
img_poly[:,:,1][img_boundary[:,:]==1] = artificial_class_rgb_color[1] img_poly[:,:,0][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[0]
img_poly[:,:,2][img_boundary[:,:]==1] = artificial_class_rgb_color[2] img_poly[:,:,1][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[1]
img_poly[:,:,2][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[2]
if printspace and config_params['use_case']!='printspace': if printspace and config_params['use_case']!='printspace':

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