94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
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#!/usr/bin/env python
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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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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# Pytorch tensor is in (C, H, W) format
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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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