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")