def visualize(self, img, instances):
# Visualize the image with object masks
v = Visualizer(img[:, :, ::-1], MetadataCatalog.get(self.cfg.DATASETS.TRAIN[0]), scale=1.2)
v = v.draw_instance_predictions(instances)
return v.get_image()
if args.show:
webcv2.imshow(basename + "-boxes@" + range_name,
vis_boxes.get_image()[..., ::-1])
else:
save(vis_boxes.get_image()[..., ::-1], args.output, "boxes",
basename + "@%s.jpg" % range_name)
vis_anchor = Visualizer(img, metadata)
anchors = predictions.anchors.tensor[topk_indices]
vis_anchor = vis_anchor.overlay_instances(
boxes=anchors.reshape(-1, 4),
labels=predictions.scores[topk_indices.reshape(-1).tolist()])
if args.show:
webcv2.imshow(basename + "-anchors@" + range_name,
vis_anchor.get_image()[..., ::-1])
else:
save(vis_anchor.get_image()[..., ::-1], args.output, "anchors",
basename + "@%s.jpg" % range_name)
ratio_counts[range_name] += 1
if not visualized:
continue
vis = Visualizer(img, metadata, scale=0.5)
vis_gt = vis.draw_dataset_dict(dic)
if args.show:
webcv2.imshow(basename + '@gt', vis_gt.get_image()[..., ::-1])
else:
save(vis_gt.get_image()[..., ::-1], args.output, "gt",
basename + ".jpg")
