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Running
on
Zero
| #!/usr/bin/env python | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import argparse | |
| import os | |
| from itertools import chain | |
| import cv2 | |
| import tqdm | |
| from detectron2.config import get_cfg | |
| from detectron2.data import ( | |
| build_detection_train_loader, | |
| DatasetCatalog, | |
| detection_utils as utils, | |
| MetadataCatalog, | |
| ) | |
| from detectron2.data.build import filter_images_with_few_keypoints | |
| from detectron2.utils.logger import setup_logger | |
| from detectron2.utils.visualizer import Visualizer | |
| def setup(args): | |
| cfg = get_cfg() | |
| if args.config_file: | |
| cfg.merge_from_file(args.config_file) | |
| cfg.merge_from_list(args.opts) | |
| cfg.DATALOADER.NUM_WORKERS = 0 | |
| cfg.freeze() | |
| return cfg | |
| def parse_args(in_args=None): | |
| parser = argparse.ArgumentParser(description="Visualize ground-truth data") | |
| parser.add_argument( | |
| "--source", | |
| choices=["annotation", "dataloader"], | |
| required=True, | |
| help="visualize the annotations or the data loader (with pre-processing)", | |
| ) | |
| parser.add_argument("--config-file", metavar="FILE", help="path to config file") | |
| parser.add_argument("--output-dir", default="./", help="path to output directory") | |
| parser.add_argument("--show", action="store_true", help="show output in a window") | |
| parser.add_argument( | |
| "opts", | |
| help="Modify config options using the command-line", | |
| default=None, | |
| nargs=argparse.REMAINDER, | |
| ) | |
| return parser.parse_args(in_args) | |
| def main() -> None: | |
| global img | |
| args = parse_args() | |
| logger = setup_logger() | |
| logger.info("Arguments: " + str(args)) | |
| cfg = setup(args) | |
| dirname = args.output_dir | |
| os.makedirs(dirname, exist_ok=True) | |
| metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]) | |
| def output(vis, fname): | |
| if args.show: | |
| print(fname) | |
| cv2.imshow("window", vis.get_image()[:, :, ::-1]) | |
| cv2.waitKey() | |
| else: | |
| filepath = os.path.join(dirname, fname) | |
| print("Saving to {} ...".format(filepath)) | |
| vis.save(filepath) | |
| scale = 1.0 | |
| if args.source == "dataloader": | |
| train_data_loader = build_detection_train_loader(cfg) | |
| for batch in train_data_loader: | |
| for per_image in batch: | |
| # Pytorch tensor is in (C, H, W) format | |
| img = per_image["image"].permute(1, 2, 0).cpu().detach().numpy() | |
| img = utils.convert_image_to_rgb(img, cfg.INPUT.FORMAT) | |
| visualizer = Visualizer(img, metadata=metadata, scale=scale) | |
| target_fields = per_image["instances"].get_fields() | |
| labels = [ | |
| metadata.thing_classes[i] for i in target_fields["gt_classes"] | |
| ] | |
| vis = visualizer.overlay_instances( | |
| labels=labels, | |
| boxes=target_fields.get("gt_boxes", None), | |
| masks=target_fields.get("gt_masks", None), | |
| keypoints=target_fields.get("gt_keypoints", None), | |
| ) | |
| output(vis, str(per_image["image_id"]) + ".jpg") | |
| else: | |
| dicts = list( | |
| chain.from_iterable([DatasetCatalog.get(k) for k in cfg.DATASETS.TRAIN]) | |
| ) | |
| if cfg.MODEL.KEYPOINT_ON: | |
| dicts = filter_images_with_few_keypoints(dicts, 1) | |
| for dic in tqdm.tqdm(dicts): | |
| img = utils.read_image(dic["file_name"], "RGB") | |
| visualizer = Visualizer(img, metadata=metadata, scale=scale) | |
| vis = visualizer.draw_dataset_dict(dic) | |
| output(vis, os.path.basename(dic["file_name"])) | |
| if __name__ == "__main__": | |
| main() # pragma: no cover | |