def visualize_predict_data(self,
per_image,
per_instalce,
save_to_file=False):
metadata = self.data_meta
def output(vis, fname):
if not save_to_file:
print(fname)
cv2.imshow("window", vis.get_image()[:, :, ::-1])
cv2.waitKey()
else:
filepath = os.path.join("./", fname)
print("Saving to {} ...".format(filepath))
vis.save(filepath)
scale = 1.0
# Pytorch tensor is in (C, H, W) format
img = per_image["image"].permute(1, 2, 0)
if cfg.INPUT.FORMAT == "BGR":
img = img[:, :, [2, 1, 0]]
else:
img = np.asarray(
Image.fromarray(img, mode=cfg.INPUT.FORMAT).convert("RGB"))
visualizer = Visualizer(img, metadata=metadata, scale=scale)
vis = visualizer.draw_instance_predictions(per_instalce)
output(vis, str(per_image["image_id"]) + ".jpg")
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)
return predictions, vis_output
metadata = dataset.meta
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
def dataset_id_map(ds_id):
return metadata.thing_dataset_id_to_contiguous_id[ds_id]
elif "lvis" in args.dataset:
# LVIS results are in the same format as COCO results, but have a different
# mapping from dataset category id to contiguous category id in [0, #categories - 1]
def dataset_id_map(ds_id):
return ds_id - 1
else:
raise ValueError("Unsupported dataset: {}".format(args.dataset))
os.makedirs(args.output, exist_ok=True)
for dic in tqdm.tqdm(dicts):
img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1]
basename = os.path.basename(dic["file_name"])
predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2])
vis = Visualizer(img, metadata)
vis_pred = vis.draw_instance_predictions(predictions).get_image()
vis = Visualizer(img, metadata)
vis_gt = vis.draw_dataset_dict(dic).get_image()
concat = np.concatenate((vis_pred, vis_gt), axis=1)
cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1])