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https://github.com/qurator-spk/eynollah.git
synced 2025-12-01 08:44:13 +01:00
drop obsolete multi-model binarization
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kba
kba
parent
82266f8234
commit
e503c1a0b7
3 changed files with 29 additions and 74 deletions
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@ -45,42 +45,6 @@ DEFAULT_MODEL_SPECS = EynollahModelSpecSet([
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type='Keras',
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),
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EynollahModelSpec(
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category="binarization_multi_1",
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variant='',
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filename="models_eynollah/eynollah-binarization-multi_2020_01_16/model_bin1",
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dist_url=dist_url("binarization"),
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dists=['binarization'],
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type='Keras',
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),
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EynollahModelSpec(
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category="binarization_multi_2",
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variant='',
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filename="models_eynollah/eynollah-binarization-multi_2020_01_16/model_bin2",
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dist_url=dist_url("binarization"),
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dists=['binarization'],
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type='Keras',
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),
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EynollahModelSpec(
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category="binarization_multi_3",
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variant='',
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filename="models_eynollah/eynollah-binarization-multi_2020_01_16/model_bin3",
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dist_url=dist_url("binarization"),
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dists=['binarization'],
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type='Keras',
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),
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EynollahModelSpec(
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category="binarization_multi_4",
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variant='',
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filename="models_eynollah/eynollah-binarization-multi_2020_01_16/model_bin4",
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dist_url=dist_url("binarization"),
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dists=['binarization'],
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type='Keras',
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),
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EynollahModelSpec(
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category="col_classifier",
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variant='',
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@ -40,7 +40,7 @@ class SbbBinarizeProcessor(Processor):
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# resolve relative path via OCR-D ResourceManager
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assert isinstance(self.parameter, frozendict)
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model_zoo = EynollahModelZoo(basedir=self.parameter['model'])
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self.binarizer = SbbBinarizer(model_zoo=model_zoo, mode='single', logger=self.logger)
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self.binarizer = SbbBinarizer(model_zoo=model_zoo, logger=self.logger)
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def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult:
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"""
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@ -103,7 +103,7 @@ class SbbBinarizeProcessor(Processor):
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line_image_bin = cv2pil(self.binarizer.run(image=pil2cv(line_image), use_patches=True))
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# update PAGE (reference the image file):
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line_image_ref = AlternativeImageType(comments=line_xywh['features'] + ',binarized')
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line.add_AlternativeImage(region_image_ref)
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line.add_AlternativeImage(line_image_ref)
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result.images.append(OcrdPageResultImage(line_image_bin, line.id + '.IMG-BIN', line_image_ref))
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return result
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@ -33,12 +33,10 @@ class SbbBinarizer:
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self,
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*,
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model_zoo: EynollahModelZoo,
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mode: str,
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logger: Optional[logging.Logger] = None,
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):
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self.logger = logger if logger else logging.getLogger('eynollah.binarization')
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self.model_zoo = model_zoo
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self.models = self.setup_models(mode)
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self.models = (model_zoo.model_path('binarization'), model_zoo.load_model('binarization'))
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self.session = self.start_new_session()
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def start_new_session(self):
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@ -49,12 +47,6 @@ class SbbBinarizer:
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tensorflow_backend.set_session(session)
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return session
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def setup_models(self, mode: str) -> Dict[Path, AnyModel]:
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return {
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self.model_zoo.model_path(v): self.model_zoo.load_model(v)
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for v in (['binarization'] if mode == 'single' else [f'binarization_multi_{i}' for i in range(1, 5)])
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}
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def end_session(self):
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tensorflow_backend.clear_session()
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self.session.close()
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@ -330,21 +322,21 @@ class SbbBinarizer:
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if image_path is not None:
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image = cv2.imread(image_path)
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img_last = 0
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for n, (model_file, model) in enumerate(self.models.items()):
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self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file, n + 1, len(self.models.keys()))
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res = self.predict(model, image, use_patches)
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model_file, model = self.models
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self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file)
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res = self.predict(model, image, use_patches)
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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kernel = np.ones((5, 5), np.uint8)
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img_last[:, :][img_last[:, :] > 0] = 255
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@ -361,22 +353,21 @@ class SbbBinarizer:
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self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_name)
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image = cv2.imread(os.path.join(dir_in,image_name) )
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img_last = 0
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for n, (model_file, model) in enumerate(self.models.items()):
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self.logger.info('Predicting %s with model %s [%s/%s]', image_name, model_file, n + 1, len(self.models.keys()))
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model_file, model = self.models
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self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file)
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res = self.predict(model, image, use_patches)
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res = self.predict(model, image, use_patches)
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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kernel = np.ones((5, 5), np.uint8)
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img_last[:, :][img_last[:, :] > 0] = 255
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