[Feature] add -with-labels arg to inferencer for visualization without labels (#3466)

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## Motivation

It is difficult to visualize without "labels" when using the inferencer.

- While using the `MMSegInferencer`, the visualized prediction contains
labels on the mask, but it is difficult to pass `withLabels=False`
without rewriting the config (which is harder to do when you initialize
the inferencer with a model name rather than the config).
- I thought it would be easier to just pass `withLabels=False` to
`inferencer.__call__()` since you can also pass `opacity` and other
parameters anyway.

## Modification

Please briefly describe what modification is made in this PR.

- Added `with_labels` to `visualize_kwargs` inside `MMSegInferencer`.
- Modified to `visualize()` function.

## BC-breaking (Optional)

Does the modification introduce changes that break the
backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the
downstream projects should modify their code to keep compatibility with
this PR.

## Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases
here, and update the documentation.

## Checklist

1. Pre-commit or other linting tools are used to fix the potential lint
issues.
2. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
4. The documentation has been modified accordingly, like docstring or
example tutorials.

---------

Co-authored-by: xiexinch <[email protected]>

Author: haruishi43

demo/image_demo.py

@@ -19,6 +19,11 @@ def main():
         type=float,
         default=0.5,
         help='Opacity of painted segmentation map. In (0, 1] range.')
+    parser.add_argument(
+        '--with-labels',
+        action='store_true',
+        default=False,
+        help='Whether to display the class labels.')
     parser.add_argument(
         '--title', default='result', help='The image identifier.')
     args = parser.parse_args()
@@ -36,6 +41,7 @@ def main():
         result,
         title=args.title,
         opacity=args.opacity,
+        with_labels=args.with_labels,
         draw_gt=False,
         show=False if args.out_file is not None else True,
         out_file=args.out_file)

demo/image_demo_with_inferencer.py

@@ -27,6 +27,11 @@ def main():
         type=float,
         default=0.5,
         help='Opacity of painted segmentation map. In (0, 1] range.')
+    parser.add_argument(
+        '--with-labels',
+        action='store_true',
+        default=False,
+        help='Whether to display the class labels.')
     args = parser.parse_args()
 
     # build the model from a config file and a checkpoint file
@@ -38,7 +43,11 @@ def main():
 
     # test a single image
     mmseg_inferencer(
-        args.img, show=args.show, out_dir=args.out_dir, opacity=args.opacity)
+        args.img,
+        show=args.show,
+        out_dir=args.out_dir,
+        opacity=args.opacity,
+        with_labels=args.with_labels)
 
 
 if __name__ == '__main__':

mmseg/apis/inference.py

@@ -127,7 +127,7 @@ def show_result_pyplot(model: BaseSegmentor,
                        draw_pred: bool = True,
                        wait_time: float = 0,
                        show: bool = True,
-                       withLabels: Optional[bool] = True,
+                       with_labels: Optional[bool] = True,
                        save_dir=None,
                        out_file=None):
     """Visualize the segmentation results on the image.
@@ -147,7 +147,7 @@ def show_result_pyplot(model: BaseSegmentor,
             that means "forever". Defaults to 0.
         show (bool): Whether to display the drawn image.
             Default to True.
-        withLabels(bool, optional): Add semantic labels in visualization
+        with_labels(bool, optional): Add semantic labels in visualization
             result, Default to True.
         save_dir (str, optional): Save file dir for all storage backends.
             If it is None, the backend storage will not save any data.
@@ -183,7 +183,7 @@ def show_result_pyplot(model: BaseSegmentor,
         wait_time=wait_time,
         out_file=out_file,
         show=show,
-        withLabels=withLabels)
+        with_labels=with_labels)
     vis_img = visualizer.get_image()
 
     return vis_img

mmseg/apis/mmseg_inferencer.py

@@ -60,7 +60,8 @@ class MMSegInferencer(BaseInferencer):
     preprocess_kwargs: set = set()
     forward_kwargs: set = {'mode', 'out_dir'}
     visualize_kwargs: set = {
-        'show', 'wait_time', 'img_out_dir', 'opacity', 'return_vis'
+        'show', 'wait_time', 'img_out_dir', 'opacity', 'return_vis',
+        'with_labels'
     }
     postprocess_kwargs: set = {'pred_out_dir', 'return_datasample'}
 
@@ -201,7 +202,8 @@ def visualize(self,
                   show: bool = False,
                   wait_time: int = 0,
                   img_out_dir: str = '',
-                  opacity: float = 0.8) -> List[np.ndarray]:
+                  opacity: float = 0.8,
+                  with_labels: Optional[bool] = True) -> List[np.ndarray]:
         """Visualize predictions.
 
         Args:
@@ -254,7 +256,8 @@ def visualize(self,
                 wait_time=wait_time,
                 draw_gt=False,
                 draw_pred=True,
-                out_file=out_file)
+                out_file=out_file,
+                with_labels=with_labels)
             if return_vis:
                 results.append(self.visualizer.get_image())
             self.num_visualized_imgs += 1

mmseg/visualization/local_visualizer.py

@@ -103,7 +103,7 @@ def _draw_sem_seg(self,
                       sem_seg: PixelData,
                       classes: Optional[List],
                       palette: Optional[List],
-                      withLabels: Optional[bool] = True) -> np.ndarray:
+                      with_labels: Optional[bool] = True) -> np.ndarray:
         """Draw semantic seg of GT or prediction.
 
         Args:
@@ -119,7 +119,7 @@ def _draw_sem_seg(self,
             palette (list, optional): Input palette for result rendering, which
                 is a list of color palette responding to the classes.
                 Defaults to None.
-            withLabels(bool, optional): Add semantic labels in visualization
+            with_labels(bool, optional): Add semantic labels in visualization
                 result, Default to True.
 
         Returns:
@@ -139,7 +139,7 @@ def _draw_sem_seg(self,
         for label, color in zip(labels, colors):
             mask[sem_seg[0] == label, :] = color
 
-        if withLabels:
+        if with_labels:
             font = cv2.FONT_HERSHEY_SIMPLEX
             # (0,1] to change the size of the text relative to the image
             scale = 0.05
@@ -265,7 +265,7 @@ def add_datasample(
             # TODO: Supported in mmengine's Viusalizer.
             out_file: Optional[str] = None,
             step: int = 0,
-            withLabels: Optional[bool] = True) -> None:
+            with_labels: Optional[bool] = True) -> None:
         """Draw datasample and save to all backends.
 
         - If GT and prediction are plotted at the same time, they are
@@ -291,7 +291,7 @@ def add_datasample(
             wait_time (float): The interval of show (s). Defaults to 0.
             out_file (str): Path to output file. Defaults to None.
             step (int): Global step value to record. Defaults to 0.
-            withLabels(bool, optional): Add semantic labels in visualization
+            with_labels(bool, optional): Add semantic labels in visualization
                 result, Defaults to True.
         """
         classes = self.dataset_meta.get('classes', None)
@@ -307,7 +307,7 @@ def add_datasample(
                                             'visualizing semantic ' \
                                             'segmentation results.'
                 gt_img_data = self._draw_sem_seg(image, data_sample.gt_sem_seg,
-                                                 classes, palette, withLabels)
+                                                 classes, palette, with_labels)
 
             if 'gt_depth_map' in data_sample:
                 gt_img_data = gt_img_data if gt_img_data is not None else image
@@ -325,7 +325,7 @@ def add_datasample(
                 pred_img_data = self._draw_sem_seg(image,
                                                    data_sample.pred_sem_seg,
                                                    classes, palette,
-                                                   withLabels)
+                                                   with_labels)
 
             if 'pred_depth_map' in data_sample:
                 pred_img_data = pred_img_data if pred_img_data is not None \