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make switching between autosized and looped tiling easier

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Robert Sachunsky 2026-03-14 02:16:26 +01:00
parent 2f3b622cf5
commit c514bbc661
1 changed files with 28 additions and 16 deletions
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src/eynollah/eynollah.py
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@ -802,6 +802,7 @@ class Eynollah:
def do_prediction_new_concept_autosize( def do_prediction_new_concept_autosize(
self, img, model, self, img, model,
n_batch_inference=None,
thresholding_for_heading=False, thresholding_for_heading=False,
thresholding_for_artificial_class=False, thresholding_for_artificial_class=False,
threshold_art_class=0.1, threshold_art_class=0.1,
@ -890,9 +891,6 @@ class Eynollah:
self.logger.debug("enter extract_text_regions") self.logger.debug("enter extract_text_regions")
img_height_h = img.shape[0] img_height_h = img.shape[0]
img_width_h = img.shape[1] img_width_h = img.shape[1]
#model_name = "region_fl_patched" if patches else "region_fl_np_resized"
model_name = "region_fl_patched" if patches else "region_fl_np"
model_region = self.model_zoo.get(model_name)
thresholding_for_heading = True thresholding_for_heading = True
img = otsu_copy_binary(img).astype(np.uint8) img = otsu_copy_binary(img).astype(np.uint8)
@ -914,11 +912,14 @@ class Eynollah:
if patches: if patches:
prediction_regions, _ = self.do_prediction_new_concept_autosize( prediction_regions, _ = self.do_prediction_new_concept_autosize(
img, model_region, img, self.model_zoo.get("region_fl_patched"),
# prediction_regions, _ = self.do_prediction_new_concept(
# True, img, self.model_zoo.get("region_fl"),
n_batch_inference=2,
thresholding_for_heading=True) thresholding_for_heading=True)
else: else:
prediction_regions = self.do_prediction( prediction_regions = self.do_prediction(
False, img, model_region, False, img, self.model_zoo.get("region_fl_np"),
n_batch_inference=2, n_batch_inference=2,
thresholding_for_heading=False) thresholding_for_heading=False)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
@ -1067,11 +1068,18 @@ class Eynollah:
def textline_contours(self, img, use_patches): def textline_contours(self, img, use_patches):
self.logger.debug('enter textline_contours') self.logger.debug('enter textline_contours')
prediction_textline, _ = self.do_prediction_new_concept_autosize( kwargs = dict(artificial_class=2,
img, self.model_zoo.get("textline_patched" if use_patches else "textline_resized"), n_batch_inference=3,
artificial_class=2,
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:
prediction_textline, _ = self.do_prediction_new_concept_autosize(
img, self.model_zoo.get("textline_patched"), **kwargs)
# prediction_textline, _ = self.do_prediction_new_concept(
# True, img, self.model_zoo.get("textline"), **kwargs)
else:
prediction_textline, _ = self.do_prediction_new_concept(
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"))
@ -1119,6 +1127,9 @@ class Eynollah:
return None, erosion_hurts, None, None, textline_mask_tot_ea, None return None, erosion_hurts, None, None, textline_mask_tot_ea, None
#print("inside 2 ", time.time()-t_in) #print("inside 2 ", time.time()-t_in)
kwargs = dict(n_batch_inference=2,
thresholding_for_artificial_class=True,
threshold_art_class=self.threshold_art_class_layout)
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",
@ -1126,16 +1137,16 @@ class Eynollah:
prediction_regions_org, confidence_matrix = \ prediction_regions_org, confidence_matrix = \
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"),
thresholding_for_artificial_class=True, # self.do_prediction_new_concept(
threshold_art_class=self.threshold_art_class_layout) # True, img_resized, self.model_zoo.get("region_1_2"),
**kwargs)
else: else:
prediction_regions_org = np.zeros((img_height_org, img_width_org), dtype=np.uint8) prediction_regions_org = np.zeros((img_height_org, img_width_org), dtype=np.uint8)
confidence_matrix = np.zeros((img_height_org, img_width_org)) confidence_matrix = np.zeros((img_height_org, img_width_org))
prediction_regions_page, confidence_matrix_page = \ prediction_regions_page, confidence_matrix_page = \
self.do_prediction_new_concept( self.do_prediction_new_concept(
False, image['img_page'], self.model_zoo.get("region_1_2"), False, image['img_page'], self.model_zoo.get("region_1_2"),
thresholding_for_artificial_class=True, **kwargs)
threshold_art_class=self.threshold_art_class_layout)
ys = slice(*image['coord_page'][0:2]) ys = slice(*image['coord_page'][0:2])
xs = slice(*image['coord_page'][2:4]) xs = slice(*image['coord_page'][2:4])
prediction_regions_org[ys, xs] = prediction_regions_page prediction_regions_org[ys, xs] = prediction_regions_page
@ -1151,8 +1162,9 @@ class Eynollah:
prediction_regions_org, confidence_matrix = \ prediction_regions_org, confidence_matrix = \
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"),
thresholding_for_artificial_class=True, # self.do_prediction_new_concept(
threshold_art_class=self.threshold_art_class_layout) # True, img_resized, self.model_zoo.get("region_1_2"),
**kwargs)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
confidence_matrix = resize_image(confidence_matrix, img_height_h, img_width_h ) confidence_matrix = resize_image(confidence_matrix, img_height_h, img_width_h )