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| #!/usr/bin/env python | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import argparse | |
| import json | |
| import numpy as np | |
| import os | |
| from collections import defaultdict | |
| import cv2 | |
| import tqdm | |
| from detectron2.data import DatasetCatalog, MetadataCatalog | |
| from detectron2.structures import Boxes, BoxMode, Instances | |
| from detectron2.utils.file_io import PathManager | |
| from detectron2.utils.logger import setup_logger | |
| from detectron2.utils.visualizer import Visualizer | |
| def create_instances(predictions, image_size): | |
| ret = Instances(image_size) | |
| score = np.asarray([x["score"] for x in predictions]) | |
| chosen = (score > args.conf_threshold).nonzero()[0] | |
| score = score[chosen] | |
| bbox = np.asarray([predictions[i]["bbox"] for i in chosen]).reshape(-1, 4) | |
| bbox = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) | |
| labels = np.asarray([dataset_id_map(predictions[i]["category_id"]) for i in chosen]) | |
| ret.scores = score | |
| ret.pred_boxes = Boxes(bbox) | |
| ret.pred_classes = labels | |
| try: | |
| ret.pred_masks = [predictions[i]["segmentation"] for i in chosen] | |
| except KeyError: | |
| pass | |
| return ret | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser( | |
| description="A script that visualizes the json predictions from COCO or LVIS dataset." | |
| ) | |
| parser.add_argument("--input", required=True, help="JSON file produced by the model") | |
| parser.add_argument("--output", required=True, help="output directory") | |
| parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val") | |
| parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold") | |
| args = parser.parse_args() | |
| logger = setup_logger() | |
| with PathManager.open(args.input, "r") as f: | |
| predictions = json.load(f) | |
| pred_by_image = defaultdict(list) | |
| for p in predictions: | |
| pred_by_image[p["image_id"]].append(p) | |
| dicts = list(DatasetCatalog.get(args.dataset)) | |
| metadata = MetadataCatalog.get(args.dataset) | |
| if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): | |
| def dataset_id_map(ds_id): | |
| return metadata.thing_dataset_id_to_contiguous_id[ds_id] | |
| elif "lvis" in args.dataset: | |
| # LVIS results are in the same format as COCO results, but have a different | |
| # mapping from dataset category id to contiguous category id in [0, #categories - 1] | |
| def dataset_id_map(ds_id): | |
| return ds_id - 1 | |
| else: | |
| raise ValueError("Unsupported dataset: {}".format(args.dataset)) | |
| os.makedirs(args.output, exist_ok=True) | |
| for dic in tqdm.tqdm(dicts): | |
| img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1] | |
| basename = os.path.basename(dic["file_name"]) | |
| predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2]) | |
| vis = Visualizer(img, metadata) | |
| vis_pred = vis.draw_instance_predictions(predictions).get_image() | |
| vis = Visualizer(img, metadata) | |
| vis_gt = vis.draw_dataset_dict(dic).get_image() | |
| concat = np.concatenate((vis_pred, vis_gt), axis=1) | |
| cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1]) | |