Merge pull request #31 from bertsky/factor-setup

Factor setup

sbb_binarize/ocrd-tool.json

@@ -19,6 +19,8 @@
         "model": {
           "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)",
           "type": "string",
+          "format": "uri",
+          "content-type": "text/directory",
           "required": true
         }
       }

sbb_binarize/ocrd_cli.py

@@ -39,32 +39,60 @@ class SbbBinarizeProcessor(Processor):
     def __init__(self, *args, **kwargs):
         kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL]
         kwargs['version'] = OCRD_TOOL['version']
-        if not(kwargs.get('show_help', None) or kwargs.get('dump_json', None) or kwargs.get('show_version')):
+        super().__init__(*args, **kwargs)
+        if hasattr(self, 'output_file_grp'):
+            # processing context
+            self.setup()
+
+    def setup(self):
+        """
+        Set up the model prior to processing.
+        """
         LOG = getLogger('processor.SbbBinarize.__init__')
-            if not 'model' in kwargs['parameter']:
+        if not 'model' in self.parameter:
             raise ValueError("'model' parameter is required")
-            model_path = Path(kwargs['parameter']['model'])
+        # resolve relative path via environment variable
+        model_path = Path(self.parameter['model'])
         if not model_path.is_absolute():
             if 'SBB_BINARIZE_DATA' in environ and environ['SBB_BINARIZE_DATA']:
-                    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)
+                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)
                 model_path = Path(environ['SBB_BINARIZE_DATA']).joinpath(model_path)
                 model_path = model_path.resolve()
                 if not model_path.is_dir():
                     raise FileNotFoundError("Does not exist or is not a directory: %s" % model_path)
-            kwargs['parameter']['model'] = str(model_path)
-        super().__init__(*args, **kwargs)
+        # resolve relative path via OCR-D ResourceManager
+        model_path = self.resolve_resource(str(model_path))
+        self.binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
 
     def process(self):
         """
-        Binarize with sbb_binarization
+        Binarize images with sbb_binarization (based on selectional auto-encoders).
+
+        For each page of the input file group, open and deserialize input PAGE-XML
+        and its respective images. Then iterate over the element hierarchy down to
+        the requested ``operation_level``.
+
+        For each segment element, retrieve a raw (non-binarized) segment image
+        according to the layout  annotation (from an existing ``AlternativeImage``,
+        or by cropping into the higher-level images, and deskewing when applicable).
+
+        Pass the image to the binarizer (which runs in fixed-size windows/patches
+        across the image and stitches the results together).
+
+        Serialize the resulting bilevel image as PNG file and add it to the output
+        file group (with file ID suffix ``.IMG-BIN``) along with the output PAGE-XML
+        (referencing it as new ``AlternativeImage`` for the segment element).
+
+        Produce a new PAGE output file by serialising the resulting hierarchy.
         """
         LOG = getLogger('processor.SbbBinarize')
         assert_file_grp_cardinality(self.input_file_grp, 1)
         assert_file_grp_cardinality(self.output_file_grp, 1)
 
         oplevel = self.parameter['operation_level']
-        model_path = self.resolve_resource(self.parameter['model'])
-        binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
 
         for n, input_file in enumerate(self.input_files):
             file_id = make_file_id(input_file, self.output_file_grp)
@@ -78,7 +106,7 @@ class SbbBinarizeProcessor(Processor):
 
             if oplevel == 'page':
                 LOG.info("Binarizing on 'page' level in page '%s'", page_id)
-                bin_image = cv2pil(binarizer.run(image=pil2cv(page_image), use_patches=True))
+                bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
                 # update METS (add the image file):
                 bin_image_path = self.workspace.save_image_file(bin_image,
                         file_id + '.IMG-BIN',

sbb_binarize/sbb_binarize.py

@@ -15,6 +15,7 @@ environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 stderr = sys.stderr
 sys.stderr = open(devnull, 'w')
 from keras.models import load_model
+from keras.backend import tensorflow_backend
 sys.stderr = stderr
 import tensorflow as tf
 
@@ -38,12 +39,14 @@ class SbbBinarizer:
             self.models.append(self.load_model(model_file))
 
     def start_new_session(self):
-        config = tf.ConfigProto()
+        config = tf.compat.v1.ConfigProto()
         config.gpu_options.allow_growth = True
 
-        self.session = tf.Session(config=config)  # tf.InteractiveSession()
+        self.session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
+        tensorflow_backend.set_session(self.session)
 
     def end_session(self):
+        tensorflow_backend.clear_session()
         self.session.close()
         del self.session
 
@@ -55,6 +58,7 @@ class SbbBinarizer:
         return model, model_height, model_width, n_classes
 
     def predict(self, model_in, img, use_patches):
+        tensorflow_backend.set_session(self.session)
         model, model_height, model_width, n_classes = model_in
         
         img_org_h = img.shape[0]