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import argparse |
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import os |
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from itertools import chain |
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import cv2 |
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import tqdm |
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from detectron2.config import get_cfg |
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from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader |
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from detectron2.data import detection_utils as utils |
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from detectron2.data.build import filter_images_with_few_keypoints |
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from detectron2.utils.logger import setup_logger |
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from detectron2.utils.visualizer import Visualizer |
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def setup(args): |
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cfg = get_cfg() |
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if args.config_file: |
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cfg.merge_from_file(args.config_file) |
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cfg.merge_from_list(args.opts) |
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cfg.freeze() |
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return cfg |
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def parse_args(in_args=None): |
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parser = argparse.ArgumentParser(description="Visualize ground-truth data") |
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parser.add_argument( |
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"--source", |
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choices=["annotation", "dataloader"], |
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required=True, |
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help="visualize the annotations or the data loader (with pre-processing)", |
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) |
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parser.add_argument("--config-file", metavar="FILE", help="path to config file") |
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parser.add_argument("--output-dir", default="./", help="path to output directory") |
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parser.add_argument("--show", action="store_true", help="show output in a window") |
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parser.add_argument( |
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"opts", |
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help="Modify config options using the command-line", |
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default=None, |
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nargs=argparse.REMAINDER, |
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) |
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return parser.parse_args(in_args) |
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if __name__ == "__main__": |
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args = parse_args() |
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logger = setup_logger() |
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logger.info("Arguments: " + str(args)) |
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cfg = setup(args) |
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dirname = args.output_dir |
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os.makedirs(dirname, exist_ok=True) |
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metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]) |
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def output(vis, fname): |
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if args.show: |
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print(fname) |
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cv2.imshow("window", vis.get_image()[:, :, ::-1]) |
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cv2.waitKey() |
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else: |
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filepath = os.path.join(dirname, fname) |
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print("Saving to {} ...".format(filepath)) |
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vis.save(filepath) |
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scale = 2.0 if args.show else 1.0 |
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if args.source == "dataloader": |
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train_data_loader = build_detection_train_loader(cfg) |
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for batch in train_data_loader: |
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for per_image in batch: |
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img = per_image["image"].permute(1, 2, 0).cpu().detach().numpy() |
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img = utils.convert_image_to_rgb(img, cfg.INPUT.FORMAT) |
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visualizer = Visualizer(img, metadata=metadata, scale=scale) |
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target_fields = per_image["instances"].get_fields() |
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labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]] |
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vis = visualizer.overlay_instances( |
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labels=labels, |
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boxes=target_fields.get("gt_boxes", None), |
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masks=target_fields.get("gt_masks", None), |
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keypoints=target_fields.get("gt_keypoints", None), |
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) |
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output(vis, str(per_image["image_id"]) + ".jpg") |
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else: |
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dicts = list(chain.from_iterable([DatasetCatalog.get(k) for k in cfg.DATASETS.TRAIN])) |
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if cfg.MODEL.KEYPOINT_ON: |
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dicts = filter_images_with_few_keypoints(dicts, 1) |
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for dic in tqdm.tqdm(dicts): |
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img = utils.read_image(dic["file_name"], "RGB") |
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visualizer = Visualizer(img, metadata=metadata, scale=scale) |
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vis = visualizer.draw_dataset_dict(dic) |
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output(vis, os.path.basename(dic["file_name"])) |
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