Visualization of the new panoptic output format

Summary:
Currently only supports visualization of the new output format.
The masks for `stuff` classes are still not shown, but I think this is an old issue.

TEST=python demo/demo.py --config-file projects/Panoptic-DeepLab/configs/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024.yaml --input ~/github/detectron2/datasets/cityscapes/leftImg8bit/test/berlin/berlin_000030_000019_leftImg8bit.png --output projects/output/vis --opts MODEL.WEIGHTS /checkpoint/bowencheng/experiments/30841561/output/model_final.pth

![vis](https://user-images.githubusercontent.com/25308473/94322846-8c9c4080-ff48-11ea-925c-0b9fe6bfca40.png)

Pull Request resolved: fairinternal/detectron2#463

Reviewed By: ppwwyyxx

Differential Revision: D23956202

Pulled By: bowenc0221

fbshipit-source-id: efdd21aae470c067da03099e47fef34b47e6f38b

Author: bowenc0221

demo/demo.py

@@ -20,6 +20,9 @@
 def setup_cfg(args):
     # load config from file and command-line arguments
     cfg = get_cfg()
+    # To use demo for Panoptic-DeepLab, please uncomment the following two lines.
+    # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config  # noqa
+    # add_panoptic_deeplab_config(cfg)
     cfg.merge_from_file(args.config_file)
     cfg.merge_from_list(args.opts)
     # Set score_threshold for builtin models

detectron2/utils/video_visualizer.py

@@ -125,7 +125,7 @@ def draw_panoptic_seg_predictions(
         self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5
     ):
         frame_visualizer = Visualizer(frame, self.metadata)
-        pred = _PanopticPrediction(panoptic_seg, segments_info)
+        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
 
         if self._instance_mode == ColorMode.IMAGE_BW:
             frame_visualizer.output.img = frame_visualizer._create_grayscale_image(

detectron2/utils/visualizer.py

@@ -147,7 +147,30 @@ def bbox(self):
 
 
 class _PanopticPrediction:
-    def __init__(self, panoptic_seg, segments_info):
+    def __init__(self, panoptic_seg, segments_info, metadata=None):
+        if segments_info is None:
+            assert metadata is not None
+            # If "segments_info" is None, we assume "panoptic_img" is a
+            # H*W int32 image storing the panoptic_id in the format of
+            # category_id * label_divisor + instance_id. We reserve -1 for
+            # VOID label.
+            label_divisor = metadata.label_divisor
+            segments_info = []
+            for panoptic_label in np.unique(panoptic_seg.numpy()):
+                if panoptic_label == -1:
+                    # VOID region.
+                    continue
+                pred_class = panoptic_label // label_divisor
+                isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
+                segments_info.append(
+                    {
+                        "id": int(panoptic_label),
+                        "category_id": int(pred_class),
+                        "isthing": bool(isthing),
+                    }
+                )
+        del metadata
+
         self._seg = panoptic_seg
 
         self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
@@ -433,7 +456,7 @@ def draw_panoptic_seg_predictions(
         Returns:
             output (VisImage): image object with visualizations.
         """
-        pred = _PanopticPrediction(panoptic_seg, segments_info)
+        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
 
         if self._instance_mode == ColorMode.IMAGE_BW:
             self.output.img = self._create_grayscale_image(pred.non_empty_mask())
@@ -470,7 +493,9 @@ def draw_panoptic_seg_predictions(
         labels = _create_text_labels(category_ids, scores, self.metadata.thing_classes)
 
         try:
-            colors = [random_color(rgb=True, maximum=1) for k in category_ids]
+            colors = [
+                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids
+            ]
         except AttributeError:
             colors = None
         self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)