img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1]
basename = os.path.basename(dic["file_name"])
vis = Visualizer(img, metadata)
vis_pred = vis.draw_proposals_separately(
proposal_by_image[dic["image_id"]], img.shape[:2],
args.conf_threshold)
predictions = create_instances(pred_by_image[dic["image_id"]],
img.shape[:2])
vis = Visualizer(img, metadata)
pred = vis.draw_instance_predictions(predictions).get_image()
vis_pred.append(pred)
vis = Visualizer(img, metadata)
gt = vis.draw_dataset_dict(dic).get_image()
concat = vis.smart_concatenate(vis_pred, min_side=1960)
vis = np.concatenate([pred, gt], axis=1)
if args.show:
webcv2.imshow(basename + ' - Press D for details', vis[:, :, ::-1])
key = webcv2.waitKey()
if key == 100: # 'd'
webcv2.imshow(basename, concat[:, :, ::-1])
webcv2.waitKey()
else:
cv2.imwrite(os.path.join(args.output, basename), vis[:, :, ::-1])
cv2.imwrite(
os.path.join(args.output, basename) + '-proposals.jpg',
concat[:, :, ::-1])
for range_name in ratios_ranges.keys():
if not len(grouped_gt[range_name]) > 0:
continue
visualized = True
vis = Visualizer(img, metadata, scale=scale)
topk_boxes, topk_indices = vis.topk_iou_boxes(
predictions.pred_boxes,
Boxes([
BoxMode.convert(x["bbox"], BoxMode.XYWH_ABS,
BoxMode.XYXY_ABS)
for x in grouped_gt[range_name]
]))
topk_indices = topk_indices.reshape((-1, ))
# Transform indices to list since shape 1 tensors will be regarded as scalars.
vis.draw_dataset_dict({"annotations": grouped_gt[range_name]})
vis_boxes = vis.draw_instance_predictions(
predictions[topk_indices.tolist()])
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()])
