mirror of
https://github.com/qurator-spk/eynollah.git
synced 2026-04-30 19:22:03 +02:00
do_prediction*: remove autosized variants, simplify
This commit is contained in:
Robert Sachunsky
parent
de65a55a04
commit
829256df91
1 changed files with 39 additions and 49 deletions
|
|
@ -812,6 +812,10 @@ class Eynollah:
|
|||
gc.collect()
|
||||
return prediction, confidence
|
||||
|
||||
# variant of do_prediction_new_concept with no need
|
||||
# for resizing or tiling into patches - done on model
|
||||
# (Tensorflow/CUDA) side
|
||||
# (after loading wrapped resized or patched model)
|
||||
def do_prediction_new_concept_autosize(
|
||||
self, img, model,
|
||||
n_batch_inference=None,
|
||||
|
|
@ -820,7 +824,6 @@ class Eynollah:
|
|||
threshold_art_class=0.1,
|
||||
artificial_class=4,
|
||||
):
|
||||
|
||||
self.logger.debug("enter do_prediction_new_concept (%s)", model.name)
|
||||
img = img / 255.0
|
||||
img = img.astype(np.float16)
|
||||
|
|
@ -904,16 +907,11 @@ class Eynollah:
|
|||
img_height_h = img.shape[0]
|
||||
img_width_h = img.shape[1]
|
||||
|
||||
if patches:
|
||||
# prediction_regions, confidence_regions = self.do_prediction_new_concept_autosize(
|
||||
# img, self.model_zoo.get("region_fl_patched"))
|
||||
prediction_regions, confidence_regions = self.do_prediction_new_concept(
|
||||
True, img, self.model_zoo.get("region_fl"),
|
||||
n_batch_inference=1)
|
||||
else:
|
||||
prediction_regions, confidence_regions = self.do_prediction_new_concept(
|
||||
False, img, self.model_zoo.get("region_fl_np"),
|
||||
thresholding_for_heading=True)
|
||||
prediction_regions, confidence_regions = self.do_prediction_new_concept(
|
||||
patches, img, self.model_zoo.get("region_fl" if patches else "region_fl_np"),
|
||||
n_batch_inference=1,
|
||||
thresholding_for_heading=not patches)
|
||||
|
||||
self.logger.debug("exit extract_text_regions_new")
|
||||
return prediction_regions, confidence_regions
|
||||
|
||||
|
|
@ -921,9 +919,10 @@ class Eynollah:
|
|||
self.logger.debug("enter extract_text_regions")
|
||||
img_height_h = img.shape[0]
|
||||
img_width_h = img.shape[1]
|
||||
model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np")
|
||||
model_region = self.model_zoo.get("region_fl" if patches else "region_fl_np")
|
||||
|
||||
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=0.1)
|
||||
prediction_regions = self.do_prediction(patches, img, model_region,
|
||||
marginal_of_patch_percent=0.1)
|
||||
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
|
||||
self.logger.debug("exit extract_text_regions")
|
||||
return prediction_regions
|
||||
|
|
@ -1059,18 +1058,19 @@ class Eynollah:
|
|||
def textline_contours(self, img, use_patches):
|
||||
self.logger.debug('enter textline_contours')
|
||||
|
||||
kwargs = dict(artificial_class=2,
|
||||
n_batch_inference=1 if self.tables else 3, # avoid OOM
|
||||
thresholding_for_artificial_class=True,
|
||||
threshold_art_class=self.threshold_art_class_textline)
|
||||
if use_patches:
|
||||
# prediction_textline, conf_textline = self.do_prediction_new_concept_autosize(
|
||||
# img, self.model_zoo.get("textline_patched"), **kwargs)
|
||||
prediction_textline, conf_textline = self.do_prediction_new_concept(
|
||||
True, img, self.model_zoo.get("textline"), **kwargs)
|
||||
if (self.tables or
|
||||
self.reading_order_machine_based or
|
||||
self.input_binary):
|
||||
# avoid OOM
|
||||
n_batch = 1
|
||||
else:
|
||||
prediction_textline, conf_textline = self.do_prediction_new_concept(
|
||||
False, img, self.model_zoo.get("textline"), **kwargs)
|
||||
n_batch = 3
|
||||
prediction_textline, conf_textline = self.do_prediction_new_concept(
|
||||
use_patches, img, self.model_zoo.get("textline"),
|
||||
artificial_class=2,
|
||||
n_batch_inference=n_batch,
|
||||
thresholding_for_artificial_class=True,
|
||||
threshold_art_class=self.threshold_art_class_textline)
|
||||
|
||||
#prediction_textline_longshot = self.do_prediction(False, img, self.model_zoo.get("textline"))
|
||||
|
||||
|
|
@ -1109,9 +1109,9 @@ class Eynollah:
|
|||
img_w_new = 4000
|
||||
img_h_new = img_w_new * img.shape[0] // img.shape[1]
|
||||
img_resized = resize_image(img, img_h_new, img_w_new)
|
||||
|
||||
self.logger.debug("detecting textlines on %s with %d colors",
|
||||
str(img_resized.shape), len(np.unique(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)
|
||||
confidence_textline = resize_image(confidence_textline, img_height_h, img_width_h)
|
||||
|
|
@ -1123,26 +1123,14 @@ class Eynollah:
|
|||
return None, erosion_hurts, None, None, textline_mask_tot_ea, None, None
|
||||
|
||||
#print("inside 2 ", time.time()-t_in)
|
||||
kwargs = dict(n_batch_inference=1,
|
||||
thresholding_for_artificial_class=True,
|
||||
threshold_art_class=self.threshold_art_class_layout,
|
||||
separator_class=label_seps)
|
||||
if num_col_classifier == 1 or num_col_classifier == 2:
|
||||
if img_height_h / img_width_h > 2.5:
|
||||
self.logger.debug("resized to %dx%d for %d cols",
|
||||
img_resized.shape[1], img_resized.shape[0], num_col_classifier)
|
||||
prediction_regions, confidence_regions = (
|
||||
# self.do_prediction_new_concept_autosize(
|
||||
# img_resized, self.model_zoo.get("region_1_2_patched"),
|
||||
self.do_prediction_new_concept(
|
||||
True, img_resized, self.model_zoo.get("region_1_2"),
|
||||
**kwargs)
|
||||
)
|
||||
patches = True
|
||||
else:
|
||||
prediction_regions, confidence_regions = \
|
||||
self.do_prediction_new_concept(
|
||||
False, img_resized, self.model_zoo.get("region_1_2"),
|
||||
**kwargs)
|
||||
patches = False
|
||||
self.logger.debug("resized to %dx%d for %d cols",
|
||||
img_resized.shape[1], img_resized.shape[0],
|
||||
num_col_classifier)
|
||||
else:
|
||||
new_w = (900+ (num_col_classifier-3)*100)
|
||||
new_h = new_w * img.shape[0] // img.shape[1]
|
||||
|
|
@ -1150,13 +1138,15 @@ class Eynollah:
|
|||
self.logger.debug("resized to %dx%d (new_w=%d) for %d cols",
|
||||
img_resized.shape[1], img_resized.shape[0],
|
||||
new_w, num_col_classifier)
|
||||
prediction_regions, confidence_regions = (
|
||||
# self.do_prediction_new_concept_autosize(
|
||||
# img_resized, self.model_zoo.get("region_1_2_patched"),
|
||||
self.do_prediction_new_concept(
|
||||
True, img_resized, self.model_zoo.get("region_1_2"),
|
||||
**kwargs)
|
||||
)
|
||||
patches = True
|
||||
|
||||
prediction_regions, confidence_regions = \
|
||||
self.do_prediction_new_concept(
|
||||
patches, img_resized, self.model_zoo.get("region_1_2"),
|
||||
n_batch_inference=1,
|
||||
thresholding_for_artificial_class=True,
|
||||
threshold_art_class=self.threshold_art_class_layout,
|
||||
separator_class=label_seps)
|
||||
|
||||
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
|
||||
confidence_regions = resize_image(confidence_regions, img_height_h, img_width_h)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue