| | |
| | |
| | import argparse |
| | import os |
| | from itertools import chain |
| | import cv2 |
| | import tqdm |
| |
|
| | from detectron2.config import get_cfg |
| | from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader |
| | from detectron2.data import detection_utils as utils |
| | 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) |
| |
|
| |
|
| | if __name__ == "__main__": |
| | 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: |
| | |
| | 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"])) |
| |
|
