Add opacity option to show_result (#425)

demo/image_demo.py

@@ -15,6 +15,11 @@ def main():
         '--palette',
         default='cityscapes',
         help='Color palette used for segmentation map')
+    parser.add_argument(
+        '--opacity',
+        type=float,
+        default=0.5,
+        help='Opacity of painted segmentation map. In (0, 1] range.')
     args = parser.parse_args()
 
     # build the model from a config file and a checkpoint file
@@ -22,7 +27,12 @@ def main():
     # test a single image
     result = inference_segmentor(model, args.img)
     # show the results
-    show_result_pyplot(model, args.img, result, get_palette(args.palette))
+    show_result_pyplot(
+        model,
+        args.img,
+        result,
+        get_palette(args.palette),
+        opacity=args.opacity)
 
 
 if __name__ == '__main__':

docs/get_started.md

@@ -166,7 +166,8 @@ result = inference_segmentor(model, img)
 # visualize the results in a new window
 model.show_result(img, result, show=True)
 # or save the visualization results to image files
-model.show_result(img, result, out_file='result.jpg')
+# you can change the opacity of the painted segmentation map in (0, 1].
+model.show_result(img, result, out_file='result.jpg', opacity=0.5)
 
 # test a video and show the results
 video = mmcv.VideoReader('video.mp4')

mmseg/apis/inference.py

@@ -98,7 +98,12 @@ def inference_segmentor(model, img):
     return result
 
 
-def show_result_pyplot(model, img, result, palette=None, fig_size=(15, 10)):
+def show_result_pyplot(model,
+                       img,
+                       result,
+                       palette=None,
+                       fig_size=(15, 10),
+                       opacity=0.5):
     """Visualize the segmentation results on the image.
 
     Args:
@@ -109,10 +114,14 @@ def show_result_pyplot(model, img, result, palette=None, fig_size=(15, 10)):
             map. If None is given, random palette will be generated.
             Default: None
         fig_size (tuple): Figure size of the pyplot figure.
+        opacity(float): Opacity of painted segmentation map.
+            Default 0.5.
+            Must be in (0, 1] range.
     """
     if hasattr(model, 'module'):
         model = model.module
-    img = model.show_result(img, result, palette=palette, show=False)
+    img = model.show_result(
+        img, result, palette=palette, show=False, opacity=opacity)
     plt.figure(figsize=fig_size)
     plt.imshow(mmcv.bgr2rgb(img))
     plt.show()

mmseg/apis/test.py

@@ -35,7 +35,8 @@ def single_gpu_test(model,
                     data_loader,
                     show=False,
                     out_dir=None,
-                    efficient_test=False):
+                    efficient_test=False,
+                    opacity=0.5):
     """Test with single GPU.
 
     Args:
@@ -46,7 +47,9 @@ def single_gpu_test(model,
             the directory to save output results.
         efficient_test (bool): Whether save the results as local numpy files to
             save CPU memory during evaluation. Default: False.
-
+        opacity(float): Opacity of painted segmentation map.
+            Default 0.5.
+            Must be in (0, 1] range.
     Returns:
         list: The prediction results.
     """
@@ -82,7 +85,8 @@ def single_gpu_test(model,
                     result,
                     palette=dataset.PALETTE,
                     show=show,
-                    out_file=out_file)
+                    out_file=out_file,
+                    opacity=opacity)
 
         if isinstance(result, list):
             if efficient_test:

mmseg/models/segmentors/base.py

@@ -212,7 +212,8 @@ class BaseSegmentor(nn.Module):
                     win_name='',
                     show=False,
                     wait_time=0,
-                    out_file=None):
+                    out_file=None,
+                    opacity=0.5):
         """Draw `result` over `img`.
 
         Args:
@@ -229,7 +230,9 @@ class BaseSegmentor(nn.Module):
                 Default: False.
             out_file (str or None): The filename to write the image.
                 Default: None.
-
+            opacity(float): Opacity of painted segmentation map.
+                Default 0.5.
+                Must be in (0, 1] range.
         Returns:
             img (Tensor): Only if not `show` or `out_file`
         """
@@ -246,13 +249,14 @@ class BaseSegmentor(nn.Module):
         assert palette.shape[0] == len(self.CLASSES)
         assert palette.shape[1] == 3
         assert len(palette.shape) == 2
+        assert 0 < opacity <= 1.0
         color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
         for label, color in enumerate(palette):
             color_seg[seg == label, :] = color
         # convert to BGR
         color_seg = color_seg[..., ::-1]
 
-        img = img * 0.5 + color_seg * 0.5
+        img = img * (1 - opacity) + color_seg * opacity
         img = img.astype(np.uint8)
         # if out_file specified, do not show image in window
         if out_file is not None:

tools/test.py

@@ -55,6 +55,11 @@ def parse_args():
         choices=['none', 'pytorch', 'slurm', 'mpi'],
         default='none',
         help='job launcher')
+    parser.add_argument(
+        '--opacity',
+        type=float,
+        default=0.5,
+        help='Opacity of painted segmentation map. In (0, 1] range.')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()
     if 'LOCAL_RANK' not in os.environ:
@@ -123,7 +128,7 @@ def main():
     if not distributed:
         model = MMDataParallel(model, device_ids=[0])
         outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
-                                  efficient_test)
+                                  efficient_test, args.opacity)
     else:
         model = MMDistributedDataParallel(
             model.cuda(),