Fix error in tools.

Author: nepeplwu

tools/convert_voc2010.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 File: convert_voc2010.py
 This file is based on https://www.cs.stanford.edu/~roozbeh/pascal-context/ to generate PASCAL-Context Dataset.
@@ -24,33 +23,31 @@
 |--ImageSets
 |
 |--SegmentationClass
-|  
+|
 |--JPEGImages
 |
 |--SegmentationObject
 |
 |--trainval_merged.json
 """
 
+import argparse
 import os
 
-import argparse
 import tqdm
 import numpy as np
 from detail import Detail
 from PIL import Image
+from paddleseg.utils.download import _download_file
+
+JSON_URL = 'https://codalabuser.blob.core.windows.net/public/trainval_merged.json'
 
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description=
-        'Generate PASCAL-Context dataset'
-    )
+        description='Generate PASCAL-Context dataset')
     parser.add_argument(
-        '--voc_path',
-        dest='voc_path',
-        help='pascal voc path',
-        type=str)
+        '--voc_path', dest='voc_path', help='pascal voc path', type=str)
     parser.add_argument(
         '--annotation_path',
         dest='annotation_path',
@@ -66,17 +63,23 @@ def __init__(self, voc_path, annotation_path):
         self.annotation_path = annotation_path
         self.label_dir = os.path.join(self.voc_path, 'Context')
         self._image_dir = os.path.join(self.voc_path, 'JPEGImages')
-        self.annFile = os.path.join(self.annotation_path, 'trainval_merged.json')
-        
+        self.annFile = os.path.join(self.annotation_path,
+                                    'trainval_merged.json')
+
         if not os.path.exists(self.annFile):
-             _download_file(url=JSON_URL, savepath=self.annotation_path, print_progress=True)
-            
-        self._mapping = np.sort(np.array([
-            0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22,
-            23, 397, 25, 284, 158, 159, 416, 33, 162, 420, 454, 295, 296,
-            427, 44, 45, 46, 308, 59, 440, 445, 31, 232, 65, 354, 424,
-            68, 326, 72, 458, 34, 207, 80, 355, 85, 347, 220, 349, 360,
-            98, 187, 104, 105, 366, 189, 368, 113, 115]))
+            _download_file(
+                url=JSON_URL,
+                savepath=self.annotation_path,
+                print_progress=True)
+
+        self._mapping = np.sort(
+            np.array([
+                0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25,
+                284, 158, 159, 416, 33, 162, 420, 454, 295, 296, 427, 44, 45,
+                46, 308, 59, 440, 445, 31, 232, 65, 354, 424, 68, 326, 72, 458,
+                34, 207, 80, 355, 85, 347, 220, 349, 360, 98, 187, 104, 105,
+                366, 189, 368, 113, 115
+            ]))
         self._key = np.array(range(len(self._mapping))).astype('uint8') - 1
 
         self.train_detail = Detail(self.annFile, self._image_dir, 'train')
@@ -94,12 +97,20 @@ def _class_to_index(self, mask, _mapping, _key):
             assert (values[i] in _mapping)
         index = np.digitize(mask.ravel(), _mapping, right=True)
         return _key[index].reshape(mask.shape)
-    
+
     def save_mask(self, img_id, mode):
         if mode == 'train':
-            mask = Image.fromarray(self._class_to_index(self.train_detail.getMask(img_id), _mapping=self._mapping, _key=self._key))
+            mask = Image.fromarray(
+                self._class_to_index(
+                    self.train_detail.getMask(img_id),
+                    _mapping=self._mapping,
+                    _key=self._key))
         elif mode == 'val':
-            mask = Image.fromarray(self._class_to_index(self.val_detail.getMask(img_id), _mapping=self._mapping, _key=self._key))
+            mask = Image.fromarray(
+                self._class_to_index(
+                    self.val_detail.getMask(img_id),
+                    _mapping=self._mapping,
+                    _key=self._key))
         filename = img_id['file_name']
         basename, _ = os.path.splitext(filename)
         if filename.endswith(".jpg"):
@@ -109,27 +120,38 @@ def save_mask(self, img_id, mode):
 
     def generate_label(self):
 
-        with open(os.path.join(self.voc_path, 'ImageSets/Segmentation/train_context.txt'), 'w') as f:
+        with open(
+                os.path.join(self.voc_path,
+                             'ImageSets/Segmentation/train_context.txt'),
+                'w') as f:
             for img_id in tqdm.tqdm(self.train_ids, desc='train'):
                 basename = self.save_mask(img_id, 'train')
                 f.writelines(''.join([basename, '\n']))
 
-        with open(os.path.join(self.voc_path, 'ImageSets/Segmentation/val_context.txt'), 'w') as f:
+        with open(
+                os.path.join(self.voc_path,
+                             'ImageSets/Segmentation/val_context.txt'),
+                'w') as f:
             for img_id in tqdm.tqdm(self.val_ids, desc='val'):
                 basename = self.save_mask(img_id, 'val')
                 f.writelines(''.join([basename, '\n']))
-        
-        with open(os.path.join(self.voc_path, 'ImageSets/Segmentation/trainval_context.txt'), 'w') as f:
+
+        with open(
+                os.path.join(self.voc_path,
+                             'ImageSets/Segmentation/trainval_context.txt'),
+                'w') as f:
             for img in tqdm.tqdm(os.listdir(self.label_dir), desc='trainval'):
                 if img.endswith('.png'):
                     basename = img.split('.', 1)[0]
                     f.writelines(''.join([basename, '\n']))
-                    
-                    
+
+
 def main():
     args = parse_args()
-    generator = PascalContextGenerator(voc_path=args.voc_path, annotation_path=args.annotation_path)
+    generator = PascalContextGenerator(
+        voc_path=args.voc_path, annotation_path=args.annotation_path)
     generator.generate_label()
 
+
 if __name__ == '__main__':
     main()