enable flowing from directory

sbb_binarize/cli.py

@@ -4,6 +4,7 @@ sbb_binarize CLI
 
 from click import command, option, argument, version_option, types
 from .sbb_binarize import SbbBinarizer
+import click
 
 @command()
 @version_option()
@@ -11,5 +12,24 @@ from .sbb_binarize import SbbBinarizer
 @option('--model-dir', '-m', type=click.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)
+@click.option(
+    "--dir_in",
+    "-di",
+    help="directory of images",
+    type=click.Path(exists=True, file_okay=False),
+)
+@click.option(
+    "--dir_out",
+    "-do",
+    help="directory where the binarized images will be written",
+    type=click.Path(exists=True, file_okay=False),
+)
+
+def main(patches, model_dir, input_image, output_image, dir_in, dir_out):
+    if not dir_out and (dir_in):
+        print("Error: You used -di but did not set -do")
+        sys.exit(1)
+    elif dir_out and not (dir_in):
+        print("Error: You used -do to write out binarized images but have not set -di")
+        sys.exit(1)
+    SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out)

sbb_binarize/sbb_binarize.py

@@ -7,6 +7,7 @@ from glob import glob
 from os import environ, devnull
 from os.path import join
 from warnings import catch_warnings, simplefilter
+import os
 
 import numpy as np
 from PIL import Image
@@ -242,33 +243,63 @@ class SbbBinarizer:
             prediction_true = prediction_true.astype(np.uint8)
         return prediction_true[:,:,0]
 
-    def run(self, image=None, image_path=None, save=None, use_patches=False):
-        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")
-        if image_path is not None:
-            image = cv2.imread(image_path)
-        img_last = 0
-        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)))
+    def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
+        print(dir_in,'dir_in')
+        if not dir_in:
+            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")
+            if image_path is not None:
+                image = cv2.imread(image_path)
+            img_last = 0
+            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, use_patches)
 
-            img_fin = np.zeros((res.shape[0], res.shape[1], 3))
-            res[:, :][res[:, :] == 0] = 2
-            res = res - 1
-            res = res * 255
-            img_fin[:, :, 0] = res
-            img_fin[:, :, 1] = res
-            img_fin[:, :, 2] = res
+                img_fin = np.zeros((res.shape[0], res.shape[1], 3))
+                res[:, :][res[:, :] == 0] = 2
+                res = res - 1
+                res = res * 255
+                img_fin[:, :, 0] = res
+                img_fin[:, :, 1] = res
+                img_fin[:, :, 2] = res
 
-            img_fin = img_fin.astype(np.uint8)
-            img_fin = (res[:, :] == 0) * 255
-            img_last = img_last + img_fin
+                img_fin = img_fin.astype(np.uint8)
+                img_fin = (res[:, :] == 0) * 255
+                img_last = img_last + img_fin
 
-        kernel = np.ones((5, 5), np.uint8)
-        img_last[:, :][img_last[:, :] > 0] = 255
-        img_last = (img_last[:, :] == 0) * 255
-        if save:
-            cv2.imwrite(save, img_last)
-        return img_last
+            kernel = np.ones((5, 5), np.uint8)
+            img_last[:, :][img_last[:, :] > 0] = 255
+            img_last = (img_last[:, :] == 0) * 255
+            if save:
+                cv2.imwrite(save, img_last)
+            return img_last
+        else:
+            ls_imgs  = os.listdir(dir_in)
+            for image_name in ls_imgs:
+                print(image_name,'image_name')
+                image = cv2.imread(os.path.join(dir_in,image_name) )
+                img_last = 0
+                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)
+
+                    img_fin = np.zeros((res.shape[0], res.shape[1], 3))
+                    res[:, :][res[:, :] == 0] = 2
+                    res = res - 1
+                    res = res * 255
+                    img_fin[:, :, 0] = res
+                    img_fin[:, :, 1] = res
+                    img_fin[:, :, 2] = res
+
+                    img_fin = img_fin.astype(np.uint8)
+                    img_fin = (res[:, :] == 0) * 255
+                    img_last = img_last + img_fin
+
+                kernel = np.ones((5, 5), np.uint8)
+                img_last[:, :][img_last[:, :] > 0] = 255
+                img_last = (img_last[:, :] == 0) * 255
+                
+                cv2.imwrite(os.path.join(dir_out,image_name), img_last)