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extract_text_regions_new: fix heading thresholding…

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- re-introduce boosting `heading` thresholding broken
  when refactoring (light version and do_prediction)
- also return confidence for full layout prediction
This commit is contained in:
Robert Sachunsky 2026-04-18 04:20:25 +02:00
parent 20dc5c3188
commit 1164b97917
1 changed files with 18 additions and 30 deletions
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48
src/eynollah/eynollah.py
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@ -424,6 +424,7 @@ class Eynollah:
marginal_of_patch_percent=0.1, marginal_of_patch_percent=0.1,
thresholding_for_some_classes=False, thresholding_for_some_classes=False,
thresholding_for_heading=False, thresholding_for_heading=False,
heading_class=2,
thresholding_for_artificial_class=False, thresholding_for_artificial_class=False,
threshold_art_class=0.1, threshold_art_class=0.1,
artificial_class=2, artificial_class=2,
@ -455,8 +456,8 @@ class Eynollah:
if thresholding_for_heading: if thresholding_for_heading:
seg_mask_label( seg_mask_label(
seg, label_p_pred[:, :, 2] >= 0.2, seg, label_p_pred[:, :, heading_class] >= 0.2,
label=2) label=heading_class)
return resize_image(seg, img_h_page, img_w_page).astype(np.uint8) return resize_image(seg, img_h_page, img_w_page).astype(np.uint8)
@ -625,6 +626,8 @@ class Eynollah:
self, patches, img, model, self, patches, img, model,
n_batch_inference=1, n_batch_inference=1,
marginal_of_patch_percent=0.1, marginal_of_patch_percent=0.1,
thresholding_for_heading=False,
heading_class=2,
thresholding_for_artificial_class=False, thresholding_for_artificial_class=False,
threshold_art_class=0.1, threshold_art_class=0.1,
artificial_class=4, artificial_class=4,
@ -656,6 +659,11 @@ class Eynollah:
skeletonize=True, skeletonize=True,
dilate=3, dilate=3,
keep=separator_class) keep=separator_class)
if thresholding_for_heading:
mask = resize_image(label_p_pred[:, :, heading_class],
img_h_page, img_w_page) >= 0.2
seg_mask_label(prediction, mask,
label=heading_class)
conf = label_p_pred[tuple(np.indices(seg.shape)) + (seg,)] conf = label_p_pred[tuple(np.indices(seg.shape)) + (seg,)]
conf = resize_image(conf, img_h_page, img_w_page) conf = resize_image(conf, img_h_page, img_w_page)
@ -899,38 +907,18 @@ class Eynollah:
img_height_h = img.shape[0] img_height_h = img.shape[0]
img_width_h = img.shape[1] img_width_h = img.shape[1]
thresholding_for_heading = True
img = otsu_copy_binary(img).astype(np.uint8)
if not patches:
thresholding_for_heading = False
elif cols:
if cols == 1:
img = resize_image(img, int(img_height_h * 1000 / float(img_width_h)), 1000).astype(np.uint8)
elif cols == 2:
img = resize_image(img, int(img_height_h * 1300 / float(img_width_h)), 1300).astype(np.uint8)
elif cols == 3:
img = resize_image(img, int(img_height_h * 1600 / float(img_width_h)), 1600).astype(np.uint8)
elif cols == 4:
img = resize_image(img, int(img_height_h * 1900 / float(img_width_h)), 1900).astype(np.uint8)
elif cols == 5:
img = resize_image(img, int(img_height_h * 2200 / float(img_width_h)), 2200).astype(np.uint8)
else:
img = resize_image(img, int(img_height_h * 2500 / float(img_width_h)), 2500).astype(np.uint8)
if patches: if patches:
# prediction_regions, _ = self.do_prediction_new_concept_autosize( # prediction_regions, confidence_regions = self.do_prediction_new_concept_autosize(
# img, self.model_zoo.get("region_fl_patched"), # img, self.model_zoo.get("region_fl_patched"))
prediction_regions, _ = self.do_prediction_new_concept( prediction_regions, confidence_regions = self.do_prediction_new_concept(
True, img, self.model_zoo.get("region_fl"), True, img, self.model_zoo.get("region_fl"),
n_batch_inference=1, n_batch_inference=1)
thresholding_for_heading=True)
else: else:
prediction_regions = self.do_prediction( prediction_regions, confidence_regions = self.do_prediction_new_concept(
False, img, self.model_zoo.get("region_fl_np"), False, img, self.model_zoo.get("region_fl_np"),
thresholding_for_heading=False) thresholding_for_heading=True)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
self.logger.debug("exit extract_text_regions_new") self.logger.debug("exit extract_text_regions_new")
return prediction_regions return prediction_regions, confidence_regions
def extract_text_regions(self, img, patches, cols): def extract_text_regions(self, img, patches, cols):
self.logger.debug("enter extract_text_regions") self.logger.debug("enter extract_text_regions")
@ -1769,7 +1757,7 @@ class Eynollah:
image_page = image_page.astype(np.uint8) image_page = image_page.astype(np.uint8)
#print("full inside 1", time.time()- t_full0) #print("full inside 1", time.time()- t_full0)
regions_fully = self.extract_text_regions_new( regions_fully, regionsfl_confidence = self.extract_text_regions_new(
image_page, image_page,
False, cols=num_col_classifier) False, cols=num_col_classifier)
#print("full inside 2", time.time()- t_full0) #print("full inside 2", time.time()- t_full0)