# This script converts MOT labels into COCO style. # Official website of the MOT dataset: https://motchallenge.net/ # # Label format of MOT dataset: # GTs: # # starts from 1 but COCO style starts from 0, # , , , , , # # conf is annotated as 0 if the object is ignored, # , # # DETs and Results: # , , , , , , , # , , # for 3D objects # # Classes in MOT: # 1: 'pedestrian' # 2: 'person on vehicle' # 3: 'car' # 4: 'bicycle' # 5: 'motorbike' # 6: 'non motorized vehicle' # 7: 'static person' # 8: 'distractor' # 9: 'occluder' # 10: 'occluder on the ground', # 11: 'occluder full' # 12: 'reflection' # # USELESS classes are not included into the json file. # IGNORES classes are included with `ignore=True`. import argparse from collections import defaultdict import json import os import os.path as osp import pickle import numpy as np from tqdm import tqdm USELESS = [3, 4, 5, 6, 9, 10, 11] IGNORES = [2, 7, 8, 12, 13] def parse_args(): parser = argparse.ArgumentParser( description="Convert MOT label and detections to COCO-VID format." ) parser.add_argument("-i", "--input", help="path of MOT data") parser.add_argument("-o", "--output", help="path to save coco formatted label file") parser.add_argument( "--convert-det", action="store_true", help="convert official detection results." ) parser.add_argument( "--split-train", action="store_true", help="split the train set into half-train and half-validate.", ) return parser.parse_args() def parse_gts(gts, is_mot15): outputs = defaultdict(list) for gt in gts: gt = gt.strip().split(",") frame_id, ins_id = map(int, gt[:2]) bbox = list(map(float, gt[2:6])) if is_mot15: conf = 1.0 class_id = 1 visibility = 1.0 else: conf = float(gt[6]) class_id = int(gt[7]) visibility = float(gt[8]) if class_id in USELESS: continue elif class_id in IGNORES: continue anns = dict( category_id=1, bbox=bbox, area=bbox[2] * bbox[3], iscrowd=False, visibility=visibility, mot_instance_id=ins_id, mot_conf=conf, mot_class_id=class_id, ) outputs[frame_id].append(anns) return outputs def parse_dets(dets): outputs = defaultdict(list) for det in dets: det = det.strip().split(",") frame_id, ins_id = map(int, det[:2]) assert ins_id == -1 bbox = list(map(float, det[2:7])) # [x1, y1, x2, y2] to be consistent with mmdet bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3], bbox[4]] outputs[frame_id].append(bbox) return outputs def list_from_file(file_name): with open(file_name) as f: lines = [line.rstrip() for line in f] return lines def dump_to_json(json_object, json_file_name): with open(json_file_name, "w") as outfile: json_str = json.dumps(json_object) outfile.write(json_str) def dump_to_pickle(pickle_object, pickle_file_name): with open(pickle_file_name, "wb") as outfile: pickle.dump(pickle_object, outfile) def main(args): if not osp.isdir(args.output): os.makedirs(args.output) sets = ["train", "test"] if args.split_train: sets += ["half-train", "half-val"] vid_id, img_id, ann_id = 1, 1, 1 for subset in sets: ins_id = 0 print(f"Converting {subset} set to COCO format") if "half" in subset: in_folder = osp.join(args.input, "train") else: in_folder = osp.join(args.input, subset) out_file = osp.join(args.output, f"{subset}_cocoformat.json") outputs = defaultdict(list) outputs["categories"] = [dict(id=1, name="pedestrian")] if args.convert_det: det_file = osp.join(args.output, f"{subset}_detections.pkl") detections = dict(det_bboxes=dict()) video_names = os.listdir(in_folder) for video_name in tqdm(video_names): # basic params parse_gt = "test" not in subset ins_maps = dict() # load video infos video_folder = osp.join(in_folder, video_name) infos = list_from_file(f"{video_folder}/seqinfo.ini") # video-level infos assert video_name == infos[1].strip().split("=")[1] img_folder = infos[2].strip().split("=")[1] img_names = [ fname for fname in os.listdir(f"{video_folder}/{img_folder}") if fname.endswith(".jpg") or fname.endswith(".png") ] img_names = sorted(img_names) fps = int(infos[3].strip().split("=")[1]) num_imgs = int(infos[4].strip().split("=")[1]) assert num_imgs == len(img_names) width = int(infos[5].strip().split("=")[1]) height = int(infos[6].strip().split("=")[1]) video = dict( id=vid_id, name=video_name, fps=fps, width=width, height=height ) # parse annotations if parse_gt: gts = list_from_file(f"{video_folder}/gt/gt.txt") if "MOT15" in video_folder: img2gts = parse_gts(gts, True) else: img2gts = parse_gts(gts, False) if args.convert_det: dets = list_from_file(f"{video_folder}/det/det.txt") img2dets = parse_dets(dets) # make half sets if "half" in subset: split_frame = num_imgs // 2 + 1 if "train" in subset: img_names = img_names[:split_frame] elif "val" in subset: img_names = img_names[split_frame:] else: raise ValueError("subset must be named with `train` or `val`") mot_frame_ids = [str(int(_.split(".")[0])) for _ in img_names] with open(f"{video_folder}/gt/gt_{subset}.txt", "wt") as f: for gt in gts: if gt.split(",")[0] in mot_frame_ids: f.writelines(f"{gt}\n") # image and box level infos for frame_id, name in enumerate(img_names): img_name = osp.join(video_name, img_folder, name) mot_frame_id = int(name.split(".")[0]) image = dict( id=img_id, video_id=vid_id, file_name=img_name, height=height, width=width, frame_id=frame_id, mot_frame_id=mot_frame_id, ) if parse_gt: gts = img2gts[mot_frame_id] for gt in gts: gt.update(id=ann_id, image_id=img_id) mot_ins_id = gt["mot_instance_id"] if mot_ins_id in ins_maps: gt["instance_id"] = ins_maps[mot_ins_id] else: gt["instance_id"] = ins_id ins_maps[mot_ins_id] = ins_id ins_id += 1 outputs["annotations"].append(gt) ann_id += 1 if args.convert_det: dets = np.array(img2dets[mot_frame_id]) if dets.ndim == 1: assert len(dets) == 0 dets = np.zeros((0, 5)) detections["det_bboxes"][img_name] = [dets] outputs["images"].append(image) img_id += 1 outputs["videos"].append(video) vid_id += 1 outputs["num_instances"] = ins_id print(f"{subset} has {ins_id} instances.") dump_to_json(outputs, out_file) if args.convert_det: dump_to_pickle(detections, det_file) print(f"Done! Saved as {out_file} and {det_file}") else: print(f"Done! Saved as {out_file}") if __name__ == "__main__": args = parse_args() main(args)