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https://github.com/qurator-spk/sbb_binarization.git
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commit
e1575268d7
3 changed files with 53 additions and 19 deletions
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@ -19,6 +19,8 @@
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"model": {
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"description": "Directory containing HDF5 models. Can be an absolute path or a path relative to the current working directory or $SBB_BINARIZE_DATA environment variable (if set)",
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"type": "string",
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"format": "uri",
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"content-type": "text/directory",
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"required": true
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}
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}
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@ -39,32 +39,60 @@ class SbbBinarizeProcessor(Processor):
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def __init__(self, *args, **kwargs):
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kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL]
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kwargs['version'] = OCRD_TOOL['version']
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if not(kwargs.get('show_help', None) or kwargs.get('dump_json', None) or kwargs.get('show_version')):
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LOG = getLogger('processor.SbbBinarize.__init__')
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if not 'model' in kwargs['parameter']:
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raise ValueError("'model' parameter is required")
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model_path = Path(kwargs['parameter']['model'])
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if not model_path.is_absolute():
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if 'SBB_BINARIZE_DATA' in environ and environ['SBB_BINARIZE_DATA']:
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LOG.info("Environment variable SBB_BINARIZE_DATA is set to '%s' - prepending to model value '%s'. If you don't want this mechanism, unset the SBB_BINARIZE_DATA environment variable.", environ['SBB_BINARIZE_DATA'], model_path)
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model_path = Path(environ['SBB_BINARIZE_DATA']).joinpath(model_path)
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model_path = model_path.resolve()
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if not model_path.is_dir():
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raise FileNotFoundError("Does not exist or is not a directory: %s" % model_path)
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kwargs['parameter']['model'] = str(model_path)
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super().__init__(*args, **kwargs)
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if hasattr(self, 'output_file_grp'):
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# processing context
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self.setup()
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def setup(self):
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"""
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Set up the model prior to processing.
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"""
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LOG = getLogger('processor.SbbBinarize.__init__')
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if not 'model' in self.parameter:
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raise ValueError("'model' parameter is required")
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# resolve relative path via environment variable
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model_path = Path(self.parameter['model'])
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if not model_path.is_absolute():
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if 'SBB_BINARIZE_DATA' in environ and environ['SBB_BINARIZE_DATA']:
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LOG.info("Environment variable SBB_BINARIZE_DATA is set to '%s'" \
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" - prepending to model value '%s'. If you don't want this mechanism," \
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" unset the SBB_BINARIZE_DATA environment variable.",
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environ['SBB_BINARIZE_DATA'], model_path)
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model_path = Path(environ['SBB_BINARIZE_DATA']).joinpath(model_path)
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model_path = model_path.resolve()
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if not model_path.is_dir():
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raise FileNotFoundError("Does not exist or is not a directory: %s" % model_path)
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# resolve relative path via OCR-D ResourceManager
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model_path = self.resolve_resource(str(model_path))
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self.binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
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def process(self):
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"""
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Binarize with sbb_binarization
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Binarize images with sbb_binarization (based on selectional auto-encoders).
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For each page of the input file group, open and deserialize input PAGE-XML
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and its respective images. Then iterate over the element hierarchy down to
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the requested ``operation_level``.
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For each segment element, retrieve a raw (non-binarized) segment image
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according to the layout annotation (from an existing ``AlternativeImage``,
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or by cropping into the higher-level images, and deskewing when applicable).
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Pass the image to the binarizer (which runs in fixed-size windows/patches
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across the image and stitches the results together).
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Serialize the resulting bilevel image as PNG file and add it to the output
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file group (with file ID suffix ``.IMG-BIN``) along with the output PAGE-XML
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(referencing it as new ``AlternativeImage`` for the segment element).
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Produce a new PAGE output file by serialising the resulting hierarchy.
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"""
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LOG = getLogger('processor.SbbBinarize')
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assert_file_grp_cardinality(self.input_file_grp, 1)
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assert_file_grp_cardinality(self.output_file_grp, 1)
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oplevel = self.parameter['operation_level']
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model_path = self.resolve_resource(self.parameter['model'])
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binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
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for n, input_file in enumerate(self.input_files):
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file_id = make_file_id(input_file, self.output_file_grp)
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@ -78,7 +106,7 @@ class SbbBinarizeProcessor(Processor):
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if oplevel == 'page':
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LOG.info("Binarizing on 'page' level in page '%s'", page_id)
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bin_image = cv2pil(binarizer.run(image=pil2cv(page_image), use_patches=True))
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bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
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# update METS (add the image file):
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bin_image_path = self.workspace.save_image_file(bin_image,
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file_id + '.IMG-BIN',
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@ -15,6 +15,7 @@ environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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stderr = sys.stderr
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sys.stderr = open(devnull, 'w')
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from keras.models import load_model
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from keras.backend import tensorflow_backend
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sys.stderr = stderr
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import tensorflow as tf
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@ -38,12 +39,14 @@ class SbbBinarizer:
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self.models.append(self.load_model(model_file))
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def start_new_session(self):
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config = tf.ConfigProto()
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.Session(config=config) # tf.InteractiveSession()
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self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(self.session)
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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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del self.session
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@ -55,6 +58,7 @@ class SbbBinarizer:
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return model, model_height, model_width, n_classes
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def predict(self, model_in, img, use_patches):
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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img_org_h = img.shape[0]
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