issue #45 the patches option is omitted and it means that documents will be processed in patches while no patches is not desired by the tool

sbb_binarize/cli.py

@@ -7,9 +7,8 @@ from .sbb_binarize import SbbBinarizer
 
 @command()
 @version_option()
-@option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
 @option('--model-dir', '-m', type=types.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
 @argument('input_image')
 @argument('output_image')
-def main(patches, model_dir, input_image, output_image):
-    SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image)
+def main(model_dir, input_image, output_image):
+    SbbBinarizer(model_dir).run(image_path=input_image, save=output_image)

sbb_binarize/ocrd_cli.py

@@ -110,7 +110,7 @@ class SbbBinarizeProcessor(Processor):
 
             if oplevel == 'page':
                 LOG.info("Binarizing on 'page' level in page '%s'", page_id)
-                bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
+                bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image)))
                 # update METS (add the image file):
                 bin_image_path = self.workspace.save_image_file(bin_image,
                         file_id + '.IMG-BIN',
@@ -124,7 +124,7 @@ class SbbBinarizeProcessor(Processor):
                     LOG.warning("Page '%s' contains no text/table regions", page_id)
                 for region in regions:
                     region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized')
-                    region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image), use_patches=True))
+                    region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image)))
                     region_image_bin_path = self.workspace.save_image_file(
                             region_image_bin,
                             "%s_%s.IMG-BIN" % (file_id, region.id),
@@ -139,7 +139,7 @@ class SbbBinarizeProcessor(Processor):
                     LOG.warning("Page '%s' contains no text lines", page_id)
                 for region_id, line in region_line_tuples:
                     line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
-                    line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image), use_patches=True))
+                    line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image)))
                     line_image_bin_path = self.workspace.save_image_file(
                             line_image_bin,
                             "%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),

sbb_binarize/sbb_binarize.py

@@ -62,7 +62,7 @@ class SbbBinarizer:
         n_classes = model.layers[len(model.layers)-1].output_shape[3]
         return model, model_height, model_width, n_classes
 
-    def predict(self, model_in, img, use_patches):
+    def predict(self, model_in, img):
         tensorflow_backend.set_session(self.session)
         model, model_height, model_width, n_classes = model_in
         
@@ -102,10 +102,6 @@ class SbbBinarizer:
             
         img = np.copy(img_padded)
 
-            
-
-        if use_patches:
-
         margin = int(0.1 * model_width)
 
         width_mid = model_width - 2 * margin
@@ -232,21 +228,9 @@ class SbbBinarizer:
         prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
         prediction_true = prediction_true.astype(np.uint8)
 
-        else:
-            img_h_page = img.shape[0]
-            img_w_page = img.shape[1]
-            img = img / float(255.0)
-            img = resize_image(img, model_height, model_width)
-
-            label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
-
-            seg = np.argmax(label_p_pred, axis=3)[0]
-            seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
-            prediction_true = resize_image(seg_color, img_h_page, img_w_page)
-            prediction_true = prediction_true.astype(np.uint8)
         return prediction_true[:,:,0]
 
-    def run(self, image=None, image_path=None, save=None, use_patches=False):
+    def run(self, image=None, image_path=None, save=None):
         if (image is not None and image_path is not None) or \
                (image is None and image_path is None):
             raise ValueError("Must pass either a opencv2 image or an image_path")
@@ -256,7 +240,7 @@ class SbbBinarizer:
         for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
             self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
 
-            res = self.predict(model, image, use_patches)
+            res = self.predict(model, image)
 
             img_fin = np.zeros((res.shape[0], res.shape[1], 3))
             res[:, :][res[:, :] == 0] = 2