allow passing image directly, return image on binarize

sbb_binarize/cli.py

@@ -16,7 +16,12 @@ def main():
 
     options = parser.parse_args()
 
-    binarizer = SbbBinarizer(options.image, options.model, options.patches, options.save)
+    binarizer = SbbBinarizer(
+            image_path=options.image,
+            model=options.model,
+            patches=options.patches,
+            save=options.save
+    )
     binarizer.run()
 
 if __name__ == "__main__":

sbb_binarize/sbb_binarize.py

@@ -15,18 +15,23 @@ import tensorflow as tf
 with catch_warnings():
     simplefilter("ignore")
 
+def resize_image(img_in, input_height, input_width):
+    return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
+
 class SbbBinarizer:
 
     # TODO use True/False for patches
-    def __init__(self, image, model, patches='false', save=None):
+    def __init__(self, model, image=None, image_path=None, patches='false', save=None):
+        if not(image or image_path) or (image and image_path):
+            raise ValueError("Must pass either a PIL image or an image_path")
+        if image:
             self.image = image
+        else:
+            self.image = cv2.imread(self.image)
         self.patches = patches
         self.save = save
         self.model_dir = model
 
-    def resize_image(self,img_in,input_height,input_width):
-        return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
-
     def start_new_session_and_model(self):
         config = tf.ConfigProto()
         config.gpu_options.allow_growth = True
@@ -48,11 +53,11 @@ class SbbBinarizer:
 
     def predict(self,model_name):
         self.load_model(model_name)
-        img = cv2.imread(self.image)
+        img = self.image
         img_width_model = self.img_width
         img_height_model = self.img_height
 
-        if self.patches=='true' or self.patches=='True':
+        if self.patches in ('true', 'True'):
 
             margin = int(0.1 * img_width_model)
 
@@ -181,14 +186,14 @@ class SbbBinarizer:
             img_h_page = img.shape[0]
             img_w_page = img.shape[1]
             img = img / float(255.0)
-            img = self.resize_image(img, img_height_model, img_width_model)
+            img = resize_image(img, img_height_model, img_width_model)
 
             label_p_pred = self.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 = self.resize_image(seg_color, img_h_page, img_w_page)
+            prediction_true = resize_image(seg_color, img_h_page, img_w_page)
             prediction_true = prediction_true.astype(np.uint8)
         return prediction_true[:,:,0]
 
@@ -217,3 +222,4 @@ class SbbBinarizer:
         img_last = (img_last[:, :] == 0)*255
         if self.save:
             cv2.imwrite(self.save, img_last)
+        return img_last