allow mask visualization

mmdet/models/detectors/base.py

@@ -4,6 +4,7 @@ from abc import ABCMeta, abstractmethod
 import mmcv
 import numpy as np
 import torch.nn as nn
+import pycocotools.mask as maskUtils
 
 from mmdet.core import tensor2imgs, get_classes
 
@@ -86,6 +87,11 @@ class BaseDetector(nn.Module):
                     img_norm_cfg,
                     dataset='coco',
                     score_thr=0.3):
+        if isinstance(result, tuple):
+            bbox_result, segm_result = result
+        else:
+            bbox_result, segm_result = result, None
+
         img_tensor = data['img'][0]
         img_metas = data['img_meta'][0].data[0]
         imgs = tensor2imgs(img_tensor, **img_norm_cfg)
@@ -102,12 +108,23 @@ class BaseDetector(nn.Module):
         for img, img_meta in zip(imgs, img_metas):
             h, w, _ = img_meta['img_shape']
             img_show = img[:h, :w, :]
+
+            bboxes = np.vstack(bbox_result)
+            # draw segmentation masks
+            if segm_result is not None:
+                segms = mmcv.concat_list(segm_result)
+                inds = np.where(bboxes[:, -1] > score_thr)[0]
+                for i in inds:
+                    color_mask = np.random.randint(
+                        0, 256, (1, 3), dtype=np.uint8)
+                    mask = maskUtils.decode(segms[i]).astype(np.bool)
+                    img_show[mask] = img_show[mask] * 0.5 + color_mask * 0.5
+            # draw bounding boxes
             labels = [
                 np.full(bbox.shape[0], i, dtype=np.int32)
-                for i, bbox in enumerate(result)
+                for i, bbox in enumerate(bbox_result)
             ]
             labels = np.concatenate(labels)
-            bboxes = np.vstack(result)
             mmcv.imshow_det_bboxes(
                 img_show,
                 bboxes,

mmdet/models/detectors/cascade_rcnn.py

@@ -306,14 +306,13 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
         raise NotImplementedError
 
     def show_result(self, data, result, img_norm_cfg, **kwargs):
-        # TODO: show segmentation masks
         if self.with_mask:
             ms_bbox_result, ms_segm_result = result
+            if isinstance(ms_bbox_result, dict):
+                result = (ms_bbox_result['ensemble'],
+                          ms_segm_result['ensemble'])
         else:
-            ms_bbox_result = result
-        if isinstance(ms_bbox_result, dict):
-            bbox_result = ms_bbox_result['ensemble']
-        else:
-            bbox_result = ms_bbox_result
-        super(CascadeRCNN, self).show_result(data, bbox_result, img_norm_cfg,
+            if isinstance(result, dict):
+                result = result['ensemble']
+        super(CascadeRCNN, self).show_result(data, result, img_norm_cfg,
                                              **kwargs)

mmdet/models/detectors/mask_rcnn.py

@@ -25,10 +25,3 @@ class MaskRCNN(TwoStageDetector):
             train_cfg=train_cfg,
             test_cfg=test_cfg,
             pretrained=pretrained)
-
-    def show_result(self, data, result, img_norm_cfg, **kwargs):
-        # TODO: show segmentation masks
-        assert isinstance(result, tuple)
-        assert len(result) == 2  # (bbox_results, segm_results)
-        super(MaskRCNN, self).show_result(data, result[0], img_norm_cfg,
-                                          **kwargs)