visualize_data.py

#!/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 LazyConfig, instantiate
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data import detection_utils as utils
from detectron2.utils.logger import setup_logger
from detectron2.utils.visualizer import Visualizer


def setup(args):
    cfg = LazyConfig.load(args.config_file)
    cfg = LazyConfig.apply_overrides(cfg, args.opts)
    cfg.dataloader.train.num_workers = 0
    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)
    if isinstance(cfg.dataloader.train.dataset.names, str):
        names = [cfg.dataloader.train.dataset.names]
    else:
        names = cfg.dataloader.train.dataset.names
    metadata = MetadataCatalog.get(names[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 = instantiate(cfg.dataloader.train)
        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.dataloader.train.mapper.img_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 names]))
        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"]))