| |
| |
| 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"])) |
|
|
