def run_on_image(self, image):
"""
Args:
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
This is the format used by OpenCV.
Returns:
predictions (dict): the output of the model.
vis_output (VisImage): the visualized image output.
"""
vis_output = None
predictions = self.predictor(image)
# Convert image from OpenCV BGR format to Matplotlib RGB format.
image = image[:, :, ::-1]
visualizer = Visualizer(image,
self.metadata,
instance_mode=self.instance_mode)
if "panoptic_seg" in predictions:
panoptic_seg, segments_info = predictions["panoptic_seg"]
vis_output = visualizer.draw_panoptic_seg_predictions(
panoptic_seg.to(self.cpu_device), segments_info)
else:
if "sem_seg" in predictions:
vis_output = visualizer.draw_sem_seg(
predictions["sem_seg"].argmax(dim=0).to(self.cpu_device))
if "instances" in predictions:
instances = predictions["instances"].to(self.cpu_device)
vis_output = visualizer.draw_instance_predictions(
predictions=instances)
# add branch2 classification task result
if "classification" in predictions:
classification = predictions["classification"].to(
self.cpu_device)
vis_output = visualizer.draw_classification_predictions(
predictions=classification)
return predictions, vis_output
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()
flag = False
for ann in dic['annotations']:
category = metadata.get('thing_classes')[ann['category_id']]
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])
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()])
if args.show:
