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get_textregion_contours_in_org_image_light: no back rotation, drop slope_first (always 0)

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Robert Sachunsky 2025-08-20 14:28:14 +02:00
parent b6d1c43a85
commit 277d00579e
2 changed files with 14 additions and 23 deletions
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14
src/eynollah/eynollah.py
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@ -2927,12 +2927,10 @@ class Eynollah:
#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), 2, 30, True,
map=self.executor.map, logger=self.logger, plotter=self.plotter)
slope_first = 0
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %.2f°", slope_deskew)
return slope_deskew, slope_first
return slope_deskew
def run_marginals(
self, image_page, textline_mask_tot_ea, mask_images, mask_lines,
@ -4173,9 +4171,9 @@ class Eynollah:
textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
slope_deskew = self.run_deskew(textline_mask_tot_ea_deskew)
else:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
slope_deskew = self.run_deskew(textline_mask_tot_ea)
#print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \
@ -4216,7 +4214,7 @@ class Eynollah:
textline_mask_tot_ea = self.run_textline(image_page)
self.logger.info("textline detection took %.1fs", time.time() - t1)
t1 = time.time()
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
slope_deskew = self.run_deskew(textline_mask_tot_ea)
self.logger.info("deskewing took %.1fs", time.time() - t1)
elif num_col_classifier in (1,2):
org_h_l_m = textline_mask_tot_ea.shape[0]
@ -4405,12 +4403,12 @@ class Eynollah:
contours_only_text_parent, contours_only_text_parent_d_ordered, text_only, marginal_cnts=polygons_of_marginals)
#print("text region early 3.5 in %.1fs", time.time() - t0)
txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light(
contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map)
contours_only_text_parent, self.image, confidence_matrix, map=self.executor.map)
#txt_con_org = self.dilate_textregions_contours(txt_con_org)
#contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
else:
txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light(
contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map)
contours_only_text_parent, self.image, confidence_matrix, map=self.executor.map)
#print("text region early 4 in %.1fs", time.time() - t0)
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)

23
src/eynollah/utils/contour.py
View file

@ -247,23 +247,16 @@ def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
return cont_int[0], index_r_con, confidence_contour
def get_textregion_contours_in_org_image_light(cnts, img, slope_first, confidence_matrix, map=map):
def get_textregion_contours_in_org_image_light(cnts, img, confidence_matrix, map=map):
if not len(cnts):
return [], []
confidence_matrix = cv2.resize(confidence_matrix, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
##cnts = list( (np.array(cnts)/2).astype(np.int16) )
#cnts = cnts/2
cnts = [(i/6).astype(int) for i in cnts]
results = map(partial(do_back_rotation_and_get_cnt_back,
img=img,
slope_first=slope_first,
confidence_matrix=confidence_matrix,
),
cnts, range(len(cnts)))
contours, indexes, conf_contours = tuple(zip(*results))
return [i*6 for i in contours], list(conf_contours)
confs = []
for cnt in cnts:
cnt_mask = np.zeros(confidence_matrix.shape)
cnt_mask = cv2.fillPoly(cnt_mask, pts=[cnt], color=1.0)
confs.append(np.sum(confidence_matrix * cnt_mask) / np.sum(cnt_mask))
return cnts, confs
def return_contours_of_interested_textline(region_pre_p, pixel):
# pixels of images are identified by 5