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add confidence for all region types, prepare for textlines…

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- pass on probabilities from predicted class everywhere
- rename `confidence_matrix` → `confidence_regions` / `regions_confidence`
- rename `get_textregion_confidences()` → `get_region_confidences()`
- add same for tables, textlines and regionsfl (full layout model)
- aggregate per-region confidence lists for image, table, drop-capital,
  left marginal and right marginal regions
- add in writer
- simplify/re-indent some
- try to replace more number literals with class label identifiers
This commit is contained in:
Robert Sachunsky 2026-04-18 04:53:03 +02:00
parent 1164b97917
commit f28a9c9e0b
3 changed files with 255 additions and 149 deletions
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330
src/eynollah/eynollah.py
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@ -51,7 +51,7 @@ from .utils.contour import (
find_new_features_of_contours, find_new_features_of_contours,
find_features_of_contours, find_features_of_contours,
get_text_region_boxes_by_given_contours, get_text_region_boxes_by_given_contours,
get_textregion_confidences, get_region_confidences,
return_contours_of_image, return_contours_of_image,
return_contours_of_interested_region, return_contours_of_interested_region,
return_parent_contours, return_parent_contours,
@ -788,8 +788,6 @@ class Eynollah:
margin:-margin or None] margin:-margin or None]
prediction[where][inbox] = seg_in[inbox] prediction[where][inbox] = seg_in[inbox]
confidence[where][inbox] = conf_in[inbox] confidence[where][inbox] = conf_in[inbox]
# rs: why does confidence_matrix only get text-label scores?
# should be scores at final argmax
if thresholding_for_artificial_class: if thresholding_for_artificial_class:
mask_artificial_class[where][inbox] = seg_in_art[inbox] mask_artificial_class[where][inbox] = seg_in_art[inbox]
@ -1067,12 +1065,12 @@ class Eynollah:
thresholding_for_artificial_class=True, thresholding_for_artificial_class=True,
threshold_art_class=self.threshold_art_class_textline) threshold_art_class=self.threshold_art_class_textline)
if use_patches: if use_patches:
# prediction_textline, _ = self.do_prediction_new_concept_autosize( # prediction_textline, conf_textline = self.do_prediction_new_concept_autosize(
# img, self.model_zoo.get("textline_patched"), **kwargs) # img, self.model_zoo.get("textline_patched"), **kwargs)
prediction_textline, _ = self.do_prediction_new_concept( prediction_textline, conf_textline = self.do_prediction_new_concept(
True, img, self.model_zoo.get("textline"), **kwargs) True, img, self.model_zoo.get("textline"), **kwargs)
else: else:
prediction_textline, _ = self.do_prediction_new_concept( prediction_textline, conf_textline = self.do_prediction_new_concept(
False, img, self.model_zoo.get("textline"), **kwargs) False, img, self.model_zoo.get("textline"), **kwargs)
#prediction_textline_longshot = self.do_prediction(False, img, self.model_zoo.get("textline")) #prediction_textline_longshot = self.do_prediction(False, img, self.model_zoo.get("textline"))
@ -1081,9 +1079,13 @@ class Eynollah:
# suppress artificial boundary label # suppress artificial boundary label
result = (prediction_textline == 1).astype(np.uint8) result = (prediction_textline == 1).astype(np.uint8)
#, (prediction_textline_longshot==1).astype(np.uint8) #, (prediction_textline_longshot==1).astype(np.uint8)
return result return result, conf_textline
def get_regions(self, image, num_col_classifier): def get_regions(self, image, num_col_classifier,
label_text=1,
label_imgs=2,
label_seps=3,
):
self.logger.debug("enter get_regions") self.logger.debug("enter get_regions")
t_in = time.time() t_in = time.time()
erosion_hurts = False erosion_hurts = False
@ -1111,25 +1113,26 @@ class Eynollah:
self.logger.debug("detecting textlines on %s with %d colors", self.logger.debug("detecting textlines on %s with %d colors",
str(img_resized.shape), len(np.unique(img_resized))) str(img_resized.shape), len(np.unique(img_resized)))
textline_mask_tot_ea = self.run_textline(img_resized) textline_mask_tot_ea, confidence_textline = self.run_textline(img_resized)
textline_mask_tot_ea = resize_image(textline_mask_tot_ea, img_height_h, img_width_h) textline_mask_tot_ea = resize_image(textline_mask_tot_ea, img_height_h, img_width_h)
confidence_textline = resize_image(confidence_textline, img_height_h, img_width_h)
if self.plotter: if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, img_resized, image['name']) self.plotter.save_plot_of_textlines(textline_mask_tot_ea, img_resized, image['name'])
if self.skip_layout_and_reading_order: if self.skip_layout_and_reading_order:
self.logger.debug("exit get_regions") self.logger.debug("exit get_regions")
return None, erosion_hurts, None, None, textline_mask_tot_ea, None return None, erosion_hurts, None, None, textline_mask_tot_ea, None, None
#print("inside 2 ", time.time()-t_in) #print("inside 2 ", time.time()-t_in)
kwargs = dict(n_batch_inference=1, kwargs = dict(n_batch_inference=1,
thresholding_for_artificial_class=True, thresholding_for_artificial_class=True,
threshold_art_class=self.threshold_art_class_layout, threshold_art_class=self.threshold_art_class_layout,
separator_class=3) separator_class=label_seps)
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
if img_height_h / img_width_h > 2.5: if img_height_h / img_width_h > 2.5:
self.logger.debug("resized to %dx%d for %d cols", self.logger.debug("resized to %dx%d for %d cols",
img_resized.shape[1], img_resized.shape[0], num_col_classifier) img_resized.shape[1], img_resized.shape[0], num_col_classifier)
prediction_regions_org, confidence_matrix = ( prediction_regions, confidence_regions = (
# self.do_prediction_new_concept_autosize( # self.do_prediction_new_concept_autosize(
# img_resized, self.model_zoo.get("region_1_2_patched"), # img_resized, self.model_zoo.get("region_1_2_patched"),
self.do_prediction_new_concept( self.do_prediction_new_concept(
@ -1137,7 +1140,7 @@ class Eynollah:
**kwargs) **kwargs)
) )
else: else:
prediction_regions_org, confidence_matrix = \ prediction_regions, confidence_regions = \
self.do_prediction_new_concept( self.do_prediction_new_concept(
False, img_resized, self.model_zoo.get("region_1_2"), False, img_resized, self.model_zoo.get("region_1_2"),
**kwargs) **kwargs)
@ -1148,7 +1151,7 @@ class Eynollah:
self.logger.debug("resized to %dx%d (new_w=%d) for %d cols", self.logger.debug("resized to %dx%d (new_w=%d) for %d cols",
img_resized.shape[1], img_resized.shape[0], img_resized.shape[1], img_resized.shape[0],
new_w, num_col_classifier) new_w, num_col_classifier)
prediction_regions_org, confidence_matrix = ( prediction_regions, confidence_regions = (
# self.do_prediction_new_concept_autosize( # self.do_prediction_new_concept_autosize(
# img_resized, self.model_zoo.get("region_1_2_patched"), # img_resized, self.model_zoo.get("region_1_2_patched"),
self.do_prediction_new_concept( self.do_prediction_new_concept(
@ -1156,12 +1159,12 @@ class Eynollah:
**kwargs) **kwargs)
) )
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
confidence_matrix = resize_image(confidence_matrix, img_height_h, img_width_h ) confidence_regions = resize_image(confidence_regions, img_height_h, img_width_h)
mask_seps_only = (prediction_regions_org == 3).astype('uint8') mask_texts_only = (prediction_regions == label_text).astype('uint8')
mask_texts_only = (prediction_regions_org == 1).astype('uint8') mask_images_only = (prediction_regions == label_imgs).astype('uint8')
mask_images_only = (prediction_regions_org == 2).astype('uint8') mask_seps_only = (prediction_regions == label_seps).astype('uint8')
##if num_col_classifier == 1 or num_col_classifier == 2: ##if num_col_classifier == 1 or num_col_classifier == 2:
###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
@ -1169,18 +1172,6 @@ class Eynollah:
mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1) mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only) polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
#test_khat = np.zeros_like(prediction_regions_org)
#test_khat = cv2.fillPoly(test_khat, pts=polygons_seplines, color=1)
#plt.imshow(test_khat[:,:])
#plt.show()
#for jv in range(1):
#print(jv, hir_seplines[0][232][3])
#test_khat = np.zeros(prediction_regions_org.shape)
#test_khat = cv2.fillPoly(test_khat, pts = [polygons_seplines[232]], color=(1,1,1))
#plt.imshow(test_khat[:,:])
#plt.show()
polygons_seplines = filter_contours_area_of_image( polygons_seplines = filter_contours_area_of_image(
mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1) mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
@ -1188,13 +1179,12 @@ class Eynollah:
##polygons_of_only_texts = dilate_textregion_contours(polygons_of_only_texts) ##polygons_of_only_texts = dilate_textregion_contours(polygons_of_only_texts)
polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only,1,0.00001) polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only,1,0.00001)
text_regions_p = np.zeros_like(prediction_regions_org) text_regions_p = np.zeros_like(prediction_regions)
text_regions_p = cv2.fillPoly(text_regions_p, pts=polygons_of_only_seps, color=3) text_regions_p = cv2.fillPoly(text_regions_p, pts=polygons_of_only_seps, color=label_seps)
text_regions_p[mask_images_only == 1] = 2 text_regions_p[mask_images_only == 1] = label_imgs
text_regions_p = cv2.fillPoly(text_regions_p, pts=polygons_of_only_texts, color=1) text_regions_p = cv2.fillPoly(text_regions_p, pts=polygons_of_only_texts, color=label_text)
textline_mask_tot_ea[(text_regions_p == 0) | textline_mask_tot_ea[text_regions_p == 0] = 0
(text_regions_p == 4) ] = 0
#plt.imshow(textline_mask_tot_ea) #plt.imshow(textline_mask_tot_ea)
#plt.show() #plt.show()
#print("inside 4 ", time.time()-t_in) #print("inside 4 ", time.time()-t_in)
@ -1204,7 +1194,8 @@ class Eynollah:
polygons_seplines, polygons_seplines,
polygons_of_only_texts, polygons_of_only_texts,
textline_mask_tot_ea, textline_mask_tot_ea,
confidence_matrix) confidence_regions,
confidence_textline)
def do_order_of_regions( def do_order_of_regions(
self, self,
@ -1524,18 +1515,23 @@ class Eynollah:
return image_revised_last return image_revised_last
def get_tables_from_model(self, img): def get_tables_from_model(self, img):
prediction_table, _ = self.do_prediction_new_concept(False, img, table_prediction, table_confidence = self.do_prediction_new_concept(
False, img,
self.model_zoo.get("table"), self.model_zoo.get("table"),
thresholding_for_artificial_class=True, thresholding_for_artificial_class=True,
threshold_art_class=0.05, threshold_art_class=0.05,
artificial_class=2) artificial_class=2)
prediction_table = prediction_table.astype(np.uint8) table_prediction = table_prediction.astype(np.uint8)
return prediction_table return table_prediction, table_confidence
def run_graphics_and_columns( def run_graphics_and_columns(
self, text_regions_p_1, textline_mask_tot_ea, self, text_regions_p_1, textline_mask_tot_ea,
regions_confidence, textline_confidence,
num_col_classifier, num_column_is_classified, num_col_classifier, num_column_is_classified,
erosion_hurts, image): erosion_hurts, image,
label_imgs=2,
label_seps=3,
):
#print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics') #print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics')
#print(erosion_hurts, 'erosion_hurts') #print(erosion_hurts, 'erosion_hurts')
@ -1544,10 +1540,10 @@ class Eynollah:
image_page, page_coord, cont_page = self.extract_page(image) image_page, page_coord, cont_page = self.extract_page(image)
#print("inside graphics 1 ", time.time() - t_in_gr) #print("inside graphics 1 ", time.time() - t_in_gr)
if self.tables: if self.tables:
table_prediction = self.get_tables_from_model(image_page) table_prediction, table_confidence = self.get_tables_from_model(image_page)
else: else:
table_prediction = np.zeros((image_page.shape[0], table_prediction = np.zeros(image_page.shape[:2], dtype=np.uint8)
image_page.shape[1]), dtype=np.uint8) table_confidence = np.zeros(image_page.shape[:2], dtype=bool)
if self.plotter: if self.plotter:
self.plotter.save_page_image(image_page, image['name']) self.plotter.save_page_image(image_page, image['name'])
@ -1555,20 +1551,25 @@ class Eynollah:
if not self.ignore_page_extraction: if not self.ignore_page_extraction:
mask_page = np.zeros_like(text_regions_p_1) mask_page = np.zeros_like(text_regions_p_1)
mask_page = cv2.fillPoly(mask_page, pts=[cont_page[0]], color=1) mask_page = cv2.fillPoly(mask_page, pts=[cont_page[0]], color=1)
mask_page = mask_page == 0
text_regions_p_1[mask_page==0] = 0 text_regions_p_1[mask_page] = 0
textline_mask_tot_ea[mask_page==0] = 0 textline_mask_tot_ea[mask_page] = 0
regions_confidence[mask_page] = 0
textline_confidence[mask_page] = 0
ys = slice(*page_coord[0:2]) box = slice(*page_coord[0:2]), slice(*page_coord[2:4])
xs = slice(*page_coord[2:4]) text_regions_p_1 = text_regions_p_1[box]
text_regions_p_1 = text_regions_p_1[ys, xs] textline_mask_tot_ea = textline_mask_tot_ea[box]
textline_mask_tot_ea = textline_mask_tot_ea[ys, xs] regions_confidence = regions_confidence[box]
textline_confidence = textline_confidence[box]
mask_images = (text_regions_p_1 == 2).astype(np.uint8) mask_images = (text_regions_p_1 == label_imgs).astype(np.uint8)
mask_images = cv2.erode(mask_images, KERNEL, iterations=10) mask_images = cv2.erode(mask_images, KERNEL, iterations=10)
textline_mask_tot_ea[mask_images == 1] = 0 textline_mask_tot_ea[mask_images == 1] = 0
textline_confidence[mask_images == 1] = 0
img_only_regions_with_sep = ((text_regions_p_1 != 3) & img_only_regions_with_sep = ((text_regions_p_1 != label_seps) &
(text_regions_p_1 != 0)).astype(np.uint8) (text_regions_p_1 != 0)).astype(np.uint8)
#print("inside graphics 2 ", time.time() - t_in_gr) #print("inside graphics 2 ", time.time() - t_in_gr)
@ -1592,8 +1593,15 @@ class Eynollah:
except Exception as why: except Exception as why:
self.logger.exception(why) self.logger.exception(why)
num_col = None num_col = None
return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, return (num_col, num_col_classifier,
text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea) page_coord, image_page, cont_page,
text_regions_p_1,
table_prediction,
textline_mask_tot_ea,
regions_confidence,
table_confidence,
textline_confidence,
)
def run_graphics_and_columns_without_layout(self, textline_mask_tot_ea, image): def run_graphics_and_columns_without_layout(self, textline_mask_tot_ea, image):
image_page, page_coord, cont_page = self.extract_page(image) image_page, page_coord, cont_page = self.extract_page(image)
@ -1625,9 +1633,9 @@ class Eynollah:
return num_col_classifier, num_column_is_classified return num_col_classifier, num_column_is_classified
def run_textline(self, image_page): def run_textline(self, image_page):
textline_mask_tot_ea = self.textline_contours(image_page, True) textline_mask_tot_ea, textline_conf = self.textline_contours(image_page, True)
#textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) #textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
return textline_mask_tot_ea return textline_mask_tot_ea, textline_conf
def run_deskew(self, textline_mask_tot_ea): def run_deskew(self, textline_mask_tot_ea):
#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew') #print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
@ -1644,7 +1652,7 @@ class Eynollah:
try: try:
regions_without_separators = (text_regions_p == 1) * 1 regions_without_separators = (text_regions_p == 1) * 1
if self.tables: if self.tables:
regions_without_separators[table_prediction==1] = 1 regions_without_separators[table_prediction == 1] = 1
regions_without_separators = regions_without_separators.astype(np.uint8) regions_without_separators = regions_without_separators.astype(np.uint8)
text_regions_p = get_marginals( text_regions_p = get_marginals(
rotate_image(regions_without_separators, slope_deskew), text_regions_p, rotate_image(regions_without_separators, slope_deskew), text_regions_p,
@ -1731,13 +1739,16 @@ class Eynollah:
#print(time.time()-t_0_box,'time box in 5') #print(time.time()-t_0_box,'time box in 5')
self.logger.debug('exit run_boxes_no_full_layout') self.logger.debug('exit run_boxes_no_full_layout')
return (polygons_of_images, text_regions_p_d, textline_mask_tot_d, return (polygons_of_images,
regions_without_separators_d, boxes, boxes_d, text_regions_p_d, textline_mask_tot_d,
regions_without_separators_d,
boxes, boxes_d,
polygons_of_marginals, polygons_of_tables) polygons_of_marginals, polygons_of_tables)
def run_boxes_full_layout( def run_boxes_full_layout(
self, image_page, textline_mask_tot, text_regions_p, self, image_page,
slope_deskew, num_col_classifier, img_only_regions, textline_mask_tot, text_regions_p,
slope_deskew, num_col_classifier,
table_prediction, erosion_hurts, table_prediction, erosion_hurts,
label_text=1, label_text=1,
label_imgs=2, label_imgs=2,
@ -1791,7 +1802,6 @@ class Eynollah:
regions_without_separators = (text_regions_p == label_text) * 1 regions_without_separators = (text_regions_p == label_text) * 1
regions_without_separators[drops] = 1 # also cover in reading-order regions_without_separators[drops] = 1 # also cover in reading-order
# regions_without_separators = ( text_regions_p == 1 | text_regions_p == 2 ) * 1 # regions_without_separators = ( text_regions_p == 1 | text_regions_p == 2 ) * 1
#self.return_regions_without_separators_new(text_regions_p, img_only_regions)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
textline_mask_tot_d = rotate_image(textline_mask_tot, slope_deskew) textline_mask_tot_d = rotate_image(textline_mask_tot, slope_deskew)
text_regions_p_d = rotate_image(text_regions_p, slope_deskew) text_regions_p_d = rotate_image(text_regions_p, slope_deskew)
@ -1819,8 +1829,10 @@ class Eynollah:
self.logger.debug('exit run_boxes_full_layout') self.logger.debug('exit run_boxes_full_layout')
#print("full inside 3", time.time()- t_full0) #print("full inside 3", time.time()- t_full0)
return (polygons_of_images, text_regions_p_d, textline_mask_tot_d, return (polygons_of_images,
regions_without_separators_d, regions_fully, regions_without_separators, text_regions_p_d, textline_mask_tot_d,
regions_without_separators_d, regions_fully, regionsfl_confidence,
regions_without_separators,
polygons_of_marginals, polygons_of_tables) polygons_of_marginals, polygons_of_tables)
def do_order_of_regions_with_model(self, contours_only_text_parent, contours_only_text_parent_h, text_regions_p): def do_order_of_regions_with_model(self, contours_only_text_parent, contours_only_text_parent_h, text_regions_p):
@ -2181,7 +2193,7 @@ class Eynollah:
def separate_marginals_to_left_and_right_and_order_from_top_to_down( def separate_marginals_to_left_and_right_and_order_from_top_to_down(
self, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, self, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals,
slopes_marginals, mid_point_of_page_width): slopes_marginals, conf_marginals, mid_point_of_page_width):
cx_marg, cy_marg = find_center_of_contours(polygons_of_marginals) cx_marg, cy_marg = find_center_of_contours(polygons_of_marginals)
cx_marg = np.array(cx_marg) cx_marg = np.array(cx_marg)
cy_marg = np.array(cy_marg) cy_marg = np.array(cy_marg)
@ -2203,7 +2215,8 @@ class Eynollah:
return (*splitsort(polygons_of_marginals), return (*splitsort(polygons_of_marginals),
*splitsort(all_found_textline_polygons_marginals), *splitsort(all_found_textline_polygons_marginals),
*splitsort(all_box_coord_marginals), *splitsort(all_box_coord_marginals),
*splitsort(slopes_marginals)) *splitsort(slopes_marginals),
*splitsort(conf_marginals))
def run(self, def run(self,
overwrite: bool = False, overwrite: bool = False,
@ -2345,7 +2358,7 @@ class Eynollah:
self.logger.info("Step 2/5: Basic Processing Mode") self.logger.info("Step 2/5: Basic Processing Mode")
self.logger.info("Skipping layout analysis and reading order detection") self.logger.info("Skipping layout analysis and reading order detection")
_ ,_, _, _, textline_mask_tot_ea, _ = \ _ ,_, _, _, textline_mask_tot_ea, _, _ = \
self.get_regions(image, num_col_classifier) self.get_regions(image, num_col_classifier)
page_coord, image_page, textline_mask_tot_ea, cont_page = \ page_coord, image_page, textline_mask_tot_ea, cont_page = \
@ -2381,6 +2394,7 @@ class Eynollah:
all_found_textline_polygons=all_found_textline_polygons, all_found_textline_polygons=all_found_textline_polygons,
all_box_coord=page_coord, all_box_coord=page_coord,
found_polygons_images=[], found_polygons_images=[],
found_polygons_tables=[],
found_polygons_marginals_left=[], found_polygons_marginals_left=[],
found_polygons_marginals_right=[], found_polygons_marginals_right=[],
all_found_textline_polygons_marginals_left=[], all_found_textline_polygons_marginals_left=[],
@ -2392,7 +2406,6 @@ class Eynollah:
slopes_marginals_right=[], slopes_marginals_right=[],
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=[], polygons_seplines=[],
found_polygons_tables=[],
skip_layout_reading_order=True skip_layout_reading_order=True
) )
self.logger.info("Basic processing complete") self.logger.info("Basic processing complete")
@ -2405,9 +2418,13 @@ class Eynollah:
self.logger.info("Step 2/5: Layout Analysis") self.logger.info("Step 2/5: Layout Analysis")
self.logger.info("Using light version processing") self.logger.info("Using light version processing")
text_regions_p_1 ,erosion_hurts, polygons_seplines, polygons_text_early, \ (text_regions_p_1,
textline_mask_tot_ea, confidence_matrix = \ erosion_hurts,
self.get_regions(image, num_col_classifier) polygons_seplines,
polygons_text_early,
textline_mask_tot_ea,
regions_confidence,
textline_confidence) = self.get_regions(image, num_col_classifier)
#print("text region early -2 in %.1fs", time.time() - t0) #print("text region early -2 in %.1fs", time.time() - t0)
if num_col_classifier == 1 or num_col_classifier ==2: if num_col_classifier == 1 or num_col_classifier ==2:
@ -2425,9 +2442,12 @@ class Eynollah:
self.plotter.save_deskewed_image(slope_deskew, image['img'], image['name']) self.plotter.save_deskewed_image(slope_deskew, image['img'], image['name'])
#print("text region early -2,5 in %.1fs", time.time() - t0) #print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t) #self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
num_col, num_col_classifier, img_only_regions, page_coord, image_page, \ (num_col, num_col_classifier,
text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea = \ page_coord, image_page, cont_page,
text_regions_p_1, table_prediction, textline_mask_tot_ea,
regions_confidence, table_confidence, textline_confidence) = \
self.run_graphics_and_columns(text_regions_p_1, textline_mask_tot_ea, self.run_graphics_and_columns(text_regions_p_1, textline_mask_tot_ea,
regions_confidence, textline_confidence,
num_col_classifier, num_column_is_classified, num_col_classifier, num_column_is_classified,
erosion_hurts, image) erosion_hurts, image)
#self.logger.info("run graphics %.1fs ", time.time() - t1t) #self.logger.info("run graphics %.1fs ", time.time() - t1t)
@ -2448,6 +2468,7 @@ class Eynollah:
all_found_textline_polygons=[], all_found_textline_polygons=[],
all_box_coord=[], all_box_coord=[],
found_polygons_images=[], found_polygons_images=[],
found_polygons_tables=[],
found_polygons_marginals_left=[], found_polygons_marginals_left=[],
found_polygons_marginals_right=[], found_polygons_marginals_right=[],
all_found_textline_polygons_marginals_left=[], all_found_textline_polygons_marginals_left=[],
@ -2459,7 +2480,6 @@ class Eynollah:
slopes_marginals_right=[], slopes_marginals_right=[],
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=[], polygons_seplines=[],
found_polygons_tables=[],
) )
writer.write_pagexml(pcgts) writer.write_pagexml(pcgts)
self.logger.info("Job done in %.1fs", time.time() - t0) self.logger.info("Job done in %.1fs", time.time() - t0)
@ -2476,10 +2496,10 @@ class Eynollah:
img_w_new = 2400 img_w_new = 2400
img_h_new = img_w_new * textline_mask_tot_ea.shape[0] // textline_mask_tot_ea.shape[1] img_h_new = img_w_new * textline_mask_tot_ea.shape[0] // textline_mask_tot_ea.shape[1]
image_page = resize_image(image_page,img_h_new, img_w_new ) image_page = resize_image(image_page, img_h_new, img_w_new)
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_h_new, img_w_new ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea, img_h_new, img_w_new)
text_regions_p_1 = resize_image(text_regions_p_1,img_h_new, img_w_new ) text_regions_p_1 = resize_image(text_regions_p_1, img_h_new, img_w_new)
table_prediction = resize_image(table_prediction,img_h_new, img_w_new ) table_prediction = resize_image(table_prediction, img_h_new, img_w_new)
text_regions_p = \ text_regions_p = \
self.run_marginals(num_col_classifier, slope_deskew, text_regions_p_1, table_prediction) self.run_marginals(num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
@ -2488,17 +2508,20 @@ class Eynollah:
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page, image['name']) self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page, image['name'])
self.plotter.save_plot_of_layout_main(text_regions_p, image_page, image['name']) self.plotter.save_plot_of_layout_main(text_regions_p, image_page, image['name'])
label_text = 1
label_marg = 4
if image_page.size: if image_page.size:
# if ratio of text regions to page area is smaller that 30%, # if ratio of text regions to page area is smaller that 30%,
# then deskew angle will not be allowed to exceed 45 # then deskew angle will not be allowed to exceed 45
if (abs(slope_deskew) > 45 and if (abs(slope_deskew) > 45 and
((text_regions_p == 1).sum() + ((text_regions_p == label_text).sum() +
(text_regions_p == 4).sum()) / float(image_page.size) <= 0.3): (text_regions_p == label_marg).sum()) <=
0.3 * image_page.size):
slope_deskew = 0 slope_deskew = 0
# if there is no main text, then relabel marginalia as main # if there is no main text, then relabel marginalia as main
if (text_regions_p == 1).sum() == 0: if not np.any(text_regions_p == label_text):
text_regions_p[text_regions_p == 4] = 1 text_regions_p[text_regions_p == label_marg] = label_text
self.logger.info("Step 3/5: Text Line Detection") self.logger.info("Step 3/5: Text Line Detection")
@ -2506,36 +2529,53 @@ class Eynollah:
self.logger.info("Mode: Curved line detection") self.logger.info("Mode: Curved line detection")
if num_col_classifier in (1,2): if num_col_classifier in (1,2):
image_page = resize_image(image_page,org_h_l_m, org_w_l_m ) image_page = resize_image(image_page, org_h_l_m, org_w_l_m)
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,org_h_l_m, org_w_l_m ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea, org_h_l_m, org_w_l_m)
text_regions_p = resize_image(text_regions_p,org_h_l_m, org_w_l_m ) text_regions_p = resize_image(text_regions_p, org_h_l_m, org_w_l_m)
text_regions_p_1 = resize_image(text_regions_p_1,org_h_l_m, org_w_l_m ) text_regions_p_1 = resize_image(text_regions_p_1, org_h_l_m, org_w_l_m)
table_prediction = resize_image(table_prediction,org_h_l_m, org_w_l_m ) table_prediction = resize_image(table_prediction, org_h_l_m, org_w_l_m)
self.logger.info(f"Detection of marginals took {time.time() - t1:.1f}s") self.logger.info(f"Detection of marginals took {time.time() - t1:.1f}s")
## birdan sora chock chakir ## birdan sora chock chakir
t1 = time.time() t1 = time.time()
args = (image_page,
textline_mask_tot_ea,
text_regions_p,
slope_deskew,
num_col_classifier,
table_prediction,
erosion_hurts)
if not self.full_layout: if not self.full_layout:
polygons_of_images, text_regions_p_d, \ (polygons_of_images, text_regions_p_d,
textline_mask_tot_ea_d, regions_without_separators_d, \ textline_mask_tot_ea_d,
boxes, boxes_d, polygons_of_marginals, contours_tables = \ regions_without_separators_d,
self.run_boxes_no_full_layout(image_page, textline_mask_tot_ea, text_regions_p, slope_deskew, boxes, boxes_d,
num_col_classifier, table_prediction, erosion_hurts) polygons_of_marginals, contours_tables) = self.run_boxes_no_full_layout(*args)
###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals) ###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals)
else: else:
polygons_of_images, text_regions_p_d, \ (polygons_of_images, text_regions_p_d,
textline_mask_tot_ea_d, regions_without_separators_d, \ textline_mask_tot_ea_d,
regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = \ regions_without_separators_d,
self.run_boxes_full_layout(image_page, textline_mask_tot_ea, text_regions_p, slope_deskew, regions_fully, regionsfl_confidence,
num_col_classifier, img_only_regions, table_prediction, erosion_hurts) regions_without_separators,
# boxes, boxes_d: done separately further below
polygons_of_marginals, contours_tables) = self.run_boxes_full_layout(*args)
###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals) ###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals)
# suppress drop capitals for deskewing (but keep for reading order) # suppress drop capitals for deskewing (but keep for reading order)
label_drop = 4 label_drop_fl = 4
textline_mask_tot_ea_org[text_regions_p == label_drop] = 0 textline_mask_tot_ea_org[text_regions_p == label_drop_fl] = 0
polygons_of_drop_capitals = return_contours_of_interested_region(text_regions_p,
label_drop_fl,
min_area=0.00003)
conf_drops = get_region_confidences(polygons_of_drop_capitals, regionsfl_confidence)
text_only = (text_regions_p == 1) * 1 conf_marginals = get_region_confidences(polygons_of_marginals, regions_confidence)
conf_images = get_region_confidences(polygons_of_images, regions_confidence)
conf_tables = get_region_confidences(contours_tables, table_confidence)
text_only = (text_regions_p == label_text) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = (text_regions_p_d == 1) * 1 text_only_d = (text_regions_p_d == label_text) * 1
#print("text region early 2 in %.1fs", time.time() - t0) #print("text region early 2 in %.1fs", time.time() - t0)
###min_con_area = 0.000005 ###min_con_area = 0.000005
@ -2694,6 +2734,9 @@ class Eynollah:
if not len(contours_only_text_parent): if not len(contours_only_text_parent):
# stop early # stop early
# FIXME: Why not just (convert polygons_of_marginals to contours_only_text_parent and)
# continue processing normally?
# Why not (at least) split marginals left vs right and get textlines?
empty_marginals = [[]] * len(polygons_of_marginals) empty_marginals = [[]] * len(polygons_of_marginals)
if self.full_layout: if self.full_layout:
pcgts = writer.build_pagexml_full_layout( pcgts = writer.build_pagexml_full_layout(
@ -2719,7 +2762,7 @@ class Eynollah:
slopes_marginals_left=[], slopes_marginals_left=[],
slopes_marginals_right=[], slopes_marginals_right=[],
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=polygons_seplines polygons_seplines=polygons_seplines,
) )
else: else:
pcgts = writer.build_pagexml_no_full_layout( pcgts = writer.build_pagexml_no_full_layout(
@ -2729,6 +2772,7 @@ class Eynollah:
all_found_textline_polygons=[], all_found_textline_polygons=[],
all_box_coord=[], all_box_coord=[],
found_polygons_images=polygons_of_images, found_polygons_images=polygons_of_images,
found_polygons_tables=contours_tables,
found_polygons_marginals_left=polygons_of_marginals, found_polygons_marginals_left=polygons_of_marginals,
found_polygons_marginals_right=polygons_of_marginals, found_polygons_marginals_right=polygons_of_marginals,
all_found_textline_polygons_marginals_left=empty_marginals, all_found_textline_polygons_marginals_left=empty_marginals,
@ -2740,7 +2784,6 @@ class Eynollah:
slopes_marginals_right=[], slopes_marginals_right=[],
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=polygons_seplines, polygons_seplines=polygons_seplines,
found_polygons_tables=contours_tables
) )
writer.write_pagexml(pcgts) writer.write_pagexml(pcgts)
self.logger.info("Job done in %.1fs", time.time() - t0) self.logger.info("Job done in %.1fs", time.time() - t0)
@ -2752,8 +2795,7 @@ class Eynollah:
contours_only_text_parent, contours_only_text_parent_d_ordered, text_only, contours_only_text_parent, contours_only_text_parent_d_ordered, text_only,
marginal_cnts=polygons_of_marginals) marginal_cnts=polygons_of_marginals)
#print("text region early 3.5 in %.1fs", time.time() - t0) #print("text region early 3.5 in %.1fs", time.time() - t0)
conf_contours_textregions = get_textregion_confidences( conf_textregions = get_region_confidences(contours_only_text_parent, regions_confidence)
contours_only_text_parent, confidence_matrix)
#contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent) #contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent)
#print("text region early 4 in %.1fs", time.time() - t0) #print("text region early 4 in %.1fs", time.time() - t0)
boxes_text = get_text_region_boxes_by_given_contours(contours_only_text_parent) boxes_text = get_text_region_boxes_by_given_contours(contours_only_text_parent)
@ -2777,10 +2819,10 @@ class Eynollah:
all_found_textline_polygons_marginals = dilate_textline_contours( all_found_textline_polygons_marginals = dilate_textline_contours(
all_found_textline_polygons_marginals) all_found_textline_polygons_marginals)
contours_only_text_parent, all_found_textline_polygons, \ contours_only_text_parent, all_found_textline_polygons, \
contours_only_text_parent_d_ordered, conf_contours_textregions = \ contours_only_text_parent_d_ordered, conf_textregions = \
self.filter_contours_without_textline_inside( self.filter_contours_without_textline_inside(
contours_only_text_parent, all_found_textline_polygons, contours_only_text_parent, all_found_textline_polygons,
contours_only_text_parent_d_ordered, conf_contours_textregions) contours_only_text_parent_d_ordered, conf_textregions)
else: else:
scale_param = 1 scale_param = 1
textline_mask_tot_ea_erode = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=2) textline_mask_tot_ea_erode = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=2)
@ -2799,33 +2841,53 @@ class Eynollah:
all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2( all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2(
all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier) all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
mid_point_of_page_width = text_regions_p.shape[1] / 2. (polygons_of_marginals_left,
(polygons_of_marginals_left, polygons_of_marginals_right, polygons_of_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right, all_found_textline_polygons_marginals_left,
all_box_coord_marginals_left, all_box_coord_marginals_right, all_found_textline_polygons_marginals_right,
slopes_marginals_left, slopes_marginals_right) = \ all_box_coord_marginals_left,
all_box_coord_marginals_right,
slopes_marginals_left,
slopes_marginals_right,
conf_marginals_left,
conf_marginals_right) = \
self.separate_marginals_to_left_and_right_and_order_from_top_to_down( self.separate_marginals_to_left_and_right_and_order_from_top_to_down(
polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, polygons_of_marginals,
slopes_marginals, mid_point_of_page_width) all_found_textline_polygons_marginals,
all_box_coord_marginals,
slopes_marginals,
conf_marginals,
0.5 * text_regions_p.shape[1])
# FIXME: get_region_confidences w/ textline_confidence on all types of textlines...
#print(len(polygons_of_marginals), len(ordered_left_marginals), len(ordered_right_marginals), 'marginals ordred') #print(len(polygons_of_marginals), len(ordered_left_marginals), len(ordered_right_marginals), 'marginals ordred')
if self.full_layout: if self.full_layout:
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, \ (text_regions_p,
all_found_textline_polygons, all_found_textline_polygons_h, slopes, slopes_h, \ contours_only_text_parent,
contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, \ contours_only_text_parent_h,
conf_contours_textregions, conf_contours_textregions_h = split_textregion_main_vs_head( all_box_coord,
text_regions_p, regions_fully, contours_only_text_parent, all_box_coord_h,
all_box_coord, all_found_textline_polygons, all_found_textline_polygons,
slopes, contours_only_text_parent_d_ordered, conf_contours_textregions) all_found_textline_polygons_h,
slopes,
slopes_h,
contours_only_text_parent_d_ordered,
contours_only_text_parent_h_d_ordered,
conf_textregions,
conf_textregions_h) = split_textregion_main_vs_head(
text_regions_p,
regions_fully,
contours_only_text_parent,
all_box_coord,
all_found_textline_polygons,
slopes,
contours_only_text_parent_d_ordered,
conf_textregions)
if self.plotter: if self.plotter:
self.plotter.save_plot_of_layout(text_regions_p, image_page, image['name']) self.plotter.save_plot_of_layout(text_regions_p, image_page, image['name'])
self.plotter.save_plot_of_layout_all(text_regions_p, image_page, image['name']) self.plotter.save_plot_of_layout_all(text_regions_p, image_page, image['name'])
label_drop = 4
polygons_of_drop_capitals = return_contours_of_interested_region(text_regions_p, label_drop,
min_area=0.00003)
##all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline( ##all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline(
##text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, ##text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h,
##all_box_coord, all_box_coord_h, all_found_textline_polygons, all_found_textline_polygons_h, ##all_box_coord, all_box_coord_h, all_found_textline_polygons, all_found_textline_polygons_h,
@ -2933,8 +2995,13 @@ class Eynollah:
slopes_marginals_right=slopes_marginals_right, slopes_marginals_right=slopes_marginals_right,
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=polygons_seplines, polygons_seplines=polygons_seplines,
conf_contours_textregions=conf_contours_textregions, conf_textregions=conf_textregions,
conf_contours_textregions_h=conf_contours_textregions_h conf_textregions_h=conf_textregions_h,
conf_marginals_left=conf_marginals_left,
conf_marginals_right=conf_marginals_right,
conf_images=conf_images,
conf_tables=conf_tables,
conf_drops=conf_drops,
) )
else: else:
pcgts = writer.build_pagexml_no_full_layout( pcgts = writer.build_pagexml_no_full_layout(
@ -2944,6 +3011,7 @@ class Eynollah:
all_found_textline_polygons=all_found_textline_polygons, all_found_textline_polygons=all_found_textline_polygons,
all_box_coord=all_box_coord, all_box_coord=all_box_coord,
found_polygons_images=polygons_of_images, found_polygons_images=polygons_of_images,
found_polygons_tables=contours_tables,
found_polygons_marginals_left=polygons_of_marginals_left, found_polygons_marginals_left=polygons_of_marginals_left,
found_polygons_marginals_right=polygons_of_marginals_right, found_polygons_marginals_right=polygons_of_marginals_right,
all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left,
@ -2955,7 +3023,11 @@ class Eynollah:
slopes_marginals_right=slopes_marginals_right, slopes_marginals_right=slopes_marginals_right,
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=polygons_seplines, polygons_seplines=polygons_seplines,
found_polygons_tables=contours_tables, conf_textregions=conf_textregions,
conf_marginals_left=conf_marginals_left,
conf_marginals_right=conf_marginals_right,
conf_images=conf_images,
conf_tables=conf_tables,
) )
writer.write_pagexml(pcgts) writer.write_pagexml(pcgts)

2
src/eynollah/utils/contour.py
View file

@ -207,7 +207,7 @@ 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]) 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 return cont_int[0], index_r_con, confidence_contour
def get_textregion_confidences(cnts, confidence_matrix): def get_region_confidences(cnts, confidence_matrix):
if not len(cnts): if not len(cnts):
return [] return []

64
src/eynollah/writer.py
View file

@ -82,6 +82,7 @@ class EynollahXmlWriter:
all_found_textline_polygons, all_found_textline_polygons,
all_box_coord, all_box_coord,
found_polygons_images, found_polygons_images,
found_polygons_tables,
found_polygons_marginals_left, found_polygons_marginals_left,
found_polygons_marginals_right, found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_left,
@ -93,7 +94,15 @@ class EynollahXmlWriter:
slopes_marginals_right, slopes_marginals_right,
cont_page, cont_page,
polygons_seplines, polygons_seplines,
found_polygons_tables, ocr_all_textlines=None,
ocr_all_textlines_marginals_left=None,
ocr_all_textlines_marginals_right=None,
ocr_all_textlines_drop=None,
conf_textregions=None,
conf_marginals_left=None,
conf_marginals_right=None,
conf_images=None,
conf_tables=None,
): ):
return self.build_pagexml_full_layout( return self.build_pagexml_full_layout(
found_polygons_text_region=found_polygons_text_region, found_polygons_text_region=found_polygons_text_region,
@ -119,6 +128,14 @@ class EynollahXmlWriter:
slopes_marginals_right=slopes_marginals_right, slopes_marginals_right=slopes_marginals_right,
cont_page=cont_page, cont_page=cont_page,
polygons_seplines=polygons_seplines, polygons_seplines=polygons_seplines,
ocr_all_textlines=ocr_all_textlines,
ocr_all_textlines_marginals_left=ocr_all_textlines_marginals_left,
ocr_all_textlines_marginals_right=ocr_all_textlines_marginals_right,
conf_textregions=conf_textregions,
conf_marginals_left=conf_marginals_left,
conf_marginals_right=conf_marginals_right,
conf_images=conf_images,
conf_tables=conf_tables,
) )
def build_pagexml_full_layout( def build_pagexml_full_layout(
@ -152,8 +169,13 @@ class EynollahXmlWriter:
ocr_all_textlines_marginals_left=None, ocr_all_textlines_marginals_left=None,
ocr_all_textlines_marginals_right=None, ocr_all_textlines_marginals_right=None,
ocr_all_textlines_drop=None, ocr_all_textlines_drop=None,
conf_contours_textregions=None, conf_textregions=None,
conf_contours_textregions_h=None, conf_textregions_h=None,
conf_marginals_left=None,
conf_marginals_right=None,
conf_images=None,
conf_tables=None,
conf_drops=None,
skip_layout_reading_order=False, skip_layout_reading_order=False,
): ):
self.logger.debug('enter build_pagexml') self.logger.debug('enter build_pagexml')
@ -184,8 +206,8 @@ class EynollahXmlWriter:
Coords=CoordsType(points=self.calculate_points(region_contour, offset)) Coords=CoordsType(points=self.calculate_points(region_contour, offset))
) )
assert textregion.Coords assert textregion.Coords
if conf_contours_textregions: if conf_textregions:
textregion.Coords.set_conf(conf_contours_textregions[mm]) textregion.Coords.set_conf(conf_textregions[mm])
page.add_TextRegion(textregion) page.add_TextRegion(textregion)
if ocr_all_textlines: if ocr_all_textlines:
ocr_textlines = ocr_all_textlines[mm] ocr_textlines = ocr_all_textlines[mm]
@ -201,8 +223,8 @@ class EynollahXmlWriter:
Coords=CoordsType(points=self.calculate_points(region_contour, offset)) Coords=CoordsType(points=self.calculate_points(region_contour, offset))
) )
assert textregion.Coords assert textregion.Coords
if conf_contours_textregions_h: if conf_textregions_h:
textregion.Coords.set_conf(conf_contours_textregions_h[mm]) textregion.Coords.set_conf(conf_textregions_h[mm])
page.add_TextRegion(textregion) page.add_TextRegion(textregion)
if ocr_all_textlines_h: if ocr_all_textlines_h:
ocr_textlines = ocr_all_textlines_h[mm] ocr_textlines = ocr_all_textlines_h[mm]
@ -216,6 +238,8 @@ class EynollahXmlWriter:
id=counter.next_region_id, type_='drop-capital', id=counter.next_region_id, type_='drop-capital',
Coords=CoordsType(points=self.calculate_points(region_contour, offset)) Coords=CoordsType(points=self.calculate_points(region_contour, offset))
) )
if conf_drops:
dropcapital.Coords.set_conf(conf_drops[mm])
page.add_TextRegion(dropcapital) page.add_TextRegion(dropcapital)
all_box_coord_drop = [[0, 0, 0, 0]] all_box_coord_drop = [[0, 0, 0, 0]]
slopes_drop = [0] slopes_drop = [0]
@ -231,6 +255,8 @@ class EynollahXmlWriter:
id=counter.next_region_id, type_='marginalia', id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_points(region_contour, offset)) Coords=CoordsType(points=self.calculate_points(region_contour, offset))
) )
if conf_marginals_left:
marginal.Coords.set_conf(conf_marginals_left[mm])
page.add_TextRegion(marginal) page.add_TextRegion(marginal)
if ocr_all_textlines_marginals_left: if ocr_all_textlines_marginals_left:
ocr_textlines = ocr_all_textlines_marginals_left[mm] ocr_textlines = ocr_all_textlines_marginals_left[mm]
@ -243,6 +269,8 @@ class EynollahXmlWriter:
id=counter.next_region_id, type_='marginalia', id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_points(region_contour, offset)) Coords=CoordsType(points=self.calculate_points(region_contour, offset))
) )
if conf_marginals_right:
marginal.Coords.set_conf(conf_marginals_right[mm])
page.add_TextRegion(marginal) page.add_TextRegion(marginal)
if ocr_all_textlines_marginals_right: if ocr_all_textlines_marginals_right:
ocr_textlines = ocr_all_textlines_marginals_right[mm] ocr_textlines = ocr_all_textlines_marginals_right[mm]
@ -251,20 +279,26 @@ class EynollahXmlWriter:
self.serialize_lines_in_region(marginal, all_found_textline_polygons_marginals_right, mm, page_coord, self.serialize_lines_in_region(marginal, all_found_textline_polygons_marginals_right, mm, page_coord,
all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines) all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines)
for region_contour in found_polygons_images: for mm, region_contour in enumerate(found_polygons_images):
page.add_ImageRegion( image = ImageRegionType(
ImageRegionType(id=counter.next_region_id, id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_points(region_contour, offset)))) Coords=CoordsType(points=self.calculate_points(region_contour, offset)))
if conf_images:
image.Coords.set_conf(conf_images[mm])
page.add_ImageRegion(image)
for region_contour in polygons_seplines: for region_contour in polygons_seplines:
page.add_SeparatorRegion( page.add_SeparatorRegion(
SeparatorRegionType(id=counter.next_region_id, SeparatorRegionType(id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_points(region_contour, None)))) Coords=CoordsType(points=self.calculate_points(region_contour, None))))
for region_contour in found_polygons_tables: for mm, region_contour in enumerate(found_polygons_tables):
page.add_TableRegion( table = TableRegionType(
TableRegionType(id=counter.next_region_id, id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_points(region_contour, offset)))) Coords=CoordsType(points=self.calculate_points(region_contour, offset)))
if conf_tables:
table.Coords.set_conf(conf_tables[mm])
page.add_TableRegion(table)
return pcgts return pcgts