modify script of check dataset

README.md

@@ -113,6 +113,7 @@ I recommand you to check the information of your dataset with the script:
 ```
 $ python tools/check_dataset_info.py --im_root /path/to/your/data_root --im_anns /path/to/your/anno_file
 ```
+This will print some of the information of your dataset.  
 Then you need to change the field of `im_root` and `train/val_im_anns` in the config file. I prepared a demo config file for you named [`bisenet_customer.py`](./configs/bisenet_customer.py). You can start from this conig file.
 
 

tools/check_dataset_info.py

@@ -11,8 +11,11 @@
 parse = argparse.ArgumentParser()
 parse.add_argument('--im_root', dest='im_root', type=str, default='./datasets/cityscapes',)
 parse.add_argument('--im_anns', dest='im_anns', type=str, default='./datasets/cityscapes/train.txt',)
+parse.add_argument('--lb_ignore', dest='lb_ignore', type=int, default=255)
 args = parse.parse_args()
 
+lb_ignore = args.lb_ignore
+
 
 with open(args.im_anns, 'r') as fr:
     lines = fr.read().splitlines()
@@ -54,15 +57,20 @@
     if shape[1] < min_shape_width[1]:
         min_shape_width = shape
 
-    max_lb_val = max(max_lb_val, np.max(lb.ravel()))
-    min_lb_val = min(min_lb_val, np.min(lb.ravel()))
-
+    lb = lb[lb != lb_ignore]
+    if lb.size > 0:
+        max_lb_val = max(max_lb_val, np.max(lb))
+        min_lb_val = min(min_lb_val, np.min(lb))
 
+min_lb_val = 0
+max_lb_val = 181
+lb_minlength = 182
 ## label info
 lb_minlength = max_lb_val+1-min_lb_val
 lb_hist = np.zeros(lb_minlength)
-for impth in tqdm(impaths):
-    lb = cv2.imread(lbpth, 0).ravel() + min_lb_val
+for lbpth in tqdm(lbpaths):
+    lb = cv2.imread(lbpth, 0)
+    lb = lb[lb != lb_ignore] + min_lb_val
     lb_hist += np.bincount(lb, minlength=lb_minlength)
 
 lb_missing_vals = [ind + min_lb_val
@@ -75,38 +83,39 @@
 n_pixels = 0
 for impth in tqdm(impaths):
     im = cv2.imread(impth)[:, :, ::-1].astype(np.float32)
-    im = im.reshape(-1, 3)
+    im = im.reshape(-1, 3) / 255.
     n_pixels += im.shape[0]
     rgb_mean += im.sum(axis=0)
-rgb_mean = rgb_mean / n_pixels
+rgb_mean = (rgb_mean / n_pixels)
 
 rgb_std = np.zeros(3).astype(np.float32)
 for impth in tqdm(impaths):
     im = cv2.imread(impth)[:, :, ::-1].astype(np.float32)
-    im = im.reshape(-1, 3)
+    im = im.reshape(-1, 3) / 255.
 
     a = (im - rgb_mean.reshape(1, 3)) ** 2
     rgb_std += a.sum(axis=0)
-rgb_std = (rgb_std / n_pixels) ** (0.5)
+rgb_std = (rgb_std / n_pixels) ** 0.5
+
+rgb_mean  = rgb_mean.tolist()
+rgb_std  = rgb_std.tolist()
 
 
+print('\n')
 print(f'there are {n_pairs} lines in {args.im_anns}, which means {n_pairs} image/label image pairs')
 print('\n')
 
-print('max and min image shapes by area are: ')
-print(f'\t{max_shape_area}, {min_shape_area}')
-print('max and min image shapes by height are: ')
-print(f'\t{max_shape_height}, {min_shape_height}')
-print('max and min image shapes by width are: ')
-print(f'\t{max_shape_width}, {min_shape_width}')
+print(f'max and min image shapes by area are: {max_shape_area}, {min_shape_area}')
+print(f'max and min image shapes by height are: {max_shape_height}, {min_shape_height}')
+print(f'max and min image shapes by width are: {max_shape_width}, {min_shape_width}')
 print('\n')
 
-print(f'label values are within range of ({min_lb_val}, {max_lb_val})')
-print('label values that are missing: ')
-print('\t', lb_missing_vals)
+print(f'we ignore label value of {args.lb_ignore} in label images')
+print(f'label values are within range of [{min_lb_val}, {max_lb_val}]')
+print(f'label values that are missing: {lb_missing_vals}')
 print('ratios of each label value: ')
 print('\t', lb_ratios)
 print('\n')
 
-print('pixel mean rgb: ', mean)
-print('pixel std rgb: ', std)
+print('pixel mean rgb: ', rgb_mean)
+print('pixel std rgb: ', rgb_std)