# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import glob import os import os.path as osp import cv2 import random import numpy as np import argparse import tqdm import json def mkdir_if_missing(d): if not osp.exists(d): os.makedirs(d) def bdd2mot_tracking(img_dir, label_dir, save_img_dir, save_label_dir): label_jsons = os.listdir(label_dir) for label_json in tqdm(label_jsons): with open(os.path.join(label_dir, label_json)) as f: labels_json = json.load(f) for label_json in labels_json: img_name = label_json['name'] video_name = label_json['videoName'] labels = label_json['labels'] txt_string = "" for label in labels: category = label['category'] x1 = label['box2d']['x1'] x2 = label['box2d']['x2'] y1 = label['box2d']['y1'] y2 = label['box2d']['y2'] width = x2 - x1 height = y2 - y1 x_center = (x1 + x2) / 2. / args.width y_center = (y1 + y2) / 2. / args.height width /= args.width height /= args.height identity = int(label['id']) # [class] [identity] [x_center] [y_center] [width] [height] txt_string += "{} {} {} {} {} {}\n".format( attr_id_dict[category], identity, x_center, y_center, width, height) fn_label = os.path.join(save_label_dir, img_name[:-4] + '.txt') source_img = os.path.join(img_dir, video_name, img_name) target_img = os.path.join(save_img_dir, img_name) with open(fn_label, 'w') as f: f.write(txt_string) os.system('cp {} {}'.format(source_img, target_img)) def transBbox(bbox): # bbox --> cx cy w h bbox = list(map(lambda x: float(x), bbox)) bbox[0] = (bbox[0] - bbox[2] / 2) * 1280 bbox[1] = (bbox[1] - bbox[3] / 2) * 720 bbox[2] = bbox[2] * 1280 bbox[3] = bbox[3] * 720 bbox = list(map(lambda x: str(x), bbox)) return bbox def genSingleImageMot(inputPath, classes=[]): labelPaths = glob.glob(inputPath + '/*.txt') labelPaths = sorted(labelPaths) allLines = [] result = {} for labelPath in labelPaths: frame = str(int(labelPath.split('-')[-1].replace('.txt', ''))) with open(labelPath, 'r') as labelPathFile: lines = labelPathFile.readlines() for line in lines: line = line.replace('\n', '') lineArray = line.split(' ') if len(classes) > 0: if lineArray[0] in classes: lineArray.append(frame) allLines.append(lineArray) else: lineArray.append(frame) allLines.append(lineArray) resultMap = {} for line in allLines: if line[1] not in resultMap.keys(): resultMap[line[1]] = [] resultMap[line[1]].append(line) mot_gt = [] id_idx = 0 for rid in resultMap.keys(): id_idx += 1 for id_line in resultMap[rid]: mot_line = [] mot_line.append(id_line[-1]) mot_line.append(str(id_idx)) id_line_temp = transBbox(id_line[2:6]) mot_line.extend(id_line_temp) mot_line.append('1') # origin class: id_line[0] mot_line.append('1') # permanent class => 1 mot_line.append('1') mot_gt.append(mot_line) result = list(map(lambda line: str.join(',', line), mot_gt)) resultStr = str.join('\n', result) return resultStr def writeGt(inputPath, outPath, classes=[]): singleImageResult = genSingleImageMot(inputPath, classes=classes) outPathFile = outPath + '/gt.txt' mkdir_if_missing(outPath) with open(outPathFile, 'w') as gtFile: gtFile.write(singleImageResult) def genSeqInfo(seqInfoPath): name = seqInfoPath.split('/')[-2] img1Path = osp.join(str.join('/', seqInfoPath.split('/')[0:-1]), 'img1') seqLength = len(glob.glob(img1Path + '/*.jpg')) seqInfoStr = f'''[Sequence]\nname={name}\nimDir=img1\nframeRate=30\nseqLength={seqLength}\nimWidth=1280\nimHeight=720\nimExt=.jpg''' with open(seqInfoPath, 'w') as seqFile: seqFile.write(seqInfoStr) def genMotGt(dataDir, classes=[]): seqLists = sorted(glob.glob(dataDir)) for seqList in seqLists: inputPath = osp.join(seqList, 'img1') outputPath = seqList.replace('labels_with_ids', 'images') outputPath = osp.join(outputPath, 'gt') mkdir_if_missing(outputPath) print('processing...', outputPath) writeGt(inputPath, outputPath, classes=classes) seqList = seqList.replace('labels_with_ids', 'images') seqInfoPath = osp.join(seqList, 'seqinfo.ini') genSeqInfo(seqInfoPath) def updateSeqInfo(dataDir, phase): seqPath = osp.join(dataDir, 'labels_with_ids', phase) seqList = glob.glob(seqPath + '/*') for seqName in seqList: print('seqName=>', seqName) seqName_img1_dir = osp.join(seqName, 'img1') txtLength = glob.glob(seqName_img1_dir + '/*.txt') name = seqName.split('/')[-1].replace('.jpg', '').replace('.txt', '') seqLength = len(txtLength) seqInfoStr = f'''[Sequence]\nname={name}\nimDir=img1\nframeRate=30\nseqLength={seqLength}\nimWidth=1280\nimHeight=720\nimExt=.jpg''' seqInfoPath = seqName_img1_dir.replace('labels_with_ids', 'images') seqInfoPath = seqInfoPath.replace('/img1', '') seqInfoPath = seqInfoPath + '/seqinfo.ini' with open(seqInfoPath, 'w') as seqFile: seqFile.write(seqInfoStr) def VisualDataset(datasetPath, phase='train', seqName='', frameId=1): trainPath = osp.join(datasetPath, 'labels_with_ids', phase) seq1Paths = osp.join(trainPath, seqName) seq_img1_path = osp.join(seq1Paths, 'img1') label_with_idPath = osp.join(seq_img1_path, seqName + '-' + '%07d' % frameId) + '.txt' image_path = label_with_idPath.replace('labels_with_ids', 'images').replace( '.txt', '.jpg') seqInfoPath = str.join('/', image_path.split('/')[:-2]) seqInfoPath = seqInfoPath + '/seqinfo.ini' seq_info = open(seqInfoPath).read() width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find( '\nimHeight')]) height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find( '\nimExt')]) with open(label_with_idPath, 'r') as label: allLines = label.readlines() images = cv2.imread(image_path) print('image_path => ', image_path) for line in allLines: line = line.split(' ') line = list(map(lambda x: float(x), line)) c1, c2, w, h = line[2:6] x1 = c1 - w / 2 x2 = c2 - h / 2 x3 = c1 + w / 2 x4 = c2 + h / 2 cv2.rectangle( images, (int(x1 * width), int(x2 * height)), (int(x3 * width), int(x4 * height)), (255, 0, 0), thickness=2) cv2.imwrite('test.jpg', images) def VisualGt(dataPath, phase='train'): seqList = sorted(glob.glob(osp.join(dataPath, 'images', phase) + '/*')) seqIndex = random.randint(0, len(seqList) - 1) seqPath = seqList[seqIndex] gt_path = osp.join(seqPath, 'gt', 'gt.txt') img_list_path = sorted(glob.glob(osp.join(seqPath, 'img1', '*.jpg'))) imgIndex = random.randint(0, len(img_list_path)) img_Path = img_list_path[imgIndex] frame_value = img_Path.split('/')[-1].replace('.jpg', '') frame_value = frame_value.split('-')[-1] frame_value = int(frame_value) seqNameStr = img_Path.split('/')[-1].replace('.jpg', '').replace('img', '') frame_value = int(seqNameStr.split('-')[-1]) print('frame_value => ', frame_value) gt_value = np.loadtxt(gt_path, dtype=float, delimiter=',') gt_value = gt_value[gt_value[:, 0] == frame_value] get_list = gt_value.tolist() img = cv2.imread(img_Path) colors = [[255, 0, 0], [255, 255, 0], [255, 0, 255], [0, 255, 0], [0, 255, 255], [0, 0, 255]] for seq, _id, pl, pt, w, h, _, bbox_class, _ in get_list: pl, pt, w, h = int(pl), int(pt), int(w), int(h) print('pl,pt,w,h => ', pl, pt, w, h) cv2.putText(img, str(bbox_class), (pl, pt), cv2.FONT_HERSHEY_PLAIN, 2, colors[int(bbox_class - 1)]) cv2.rectangle( img, (pl, pt), (pl + w, pt + h), colors[int(bbox_class - 1)], thickness=2) cv2.imwrite('testGt.jpg', img) print(seqPath, frame_value) return seqPath.split('/')[-1], frame_value def gen_image_list(dataPath, datType): inputPath = f'{dataPath}/labels_with_ids/{datType}' pathList = sorted(glob.glob(inputPath + '/*')) print(pathList) allImageList = [] for pathSingle in pathList: imgList = sorted(glob.glob(osp.join(pathSingle, 'img1', '*.txt'))) for imgPath in imgList: imgPath = imgPath.replace('labels_with_ids', 'images').replace( '.txt', '.jpg') allImageList.append(imgPath) with open(f'{dataPath}.{datType}', 'w') as image_list_file: allImageListStr = str.join('\n', allImageList) image_list_file.write(allImageListStr) def formatOrigin(datapath, phase): label_with_idPath = osp.join(datapath, 'labels_with_ids', phase) print(label_with_idPath) for txtList in sorted(glob.glob(label_with_idPath + '/*.txt')): print(txtList) seqName = txtList.split('/')[-1] seqName = str.join('-', seqName.split('-')[0:-1]).replace('.txt', '') seqPath = osp.join(label_with_idPath, seqName, 'img1') mkdir_if_missing(seqPath) os.system(f'mv {txtList} {seqPath}') def copyImg(fromRootPath, toRootPath, phase): fromPath = osp.join(fromRootPath, 'images', phase) toPathSeqPath = osp.join(toRootPath, 'labels_with_ids', phase) seqList = sorted(glob.glob(toPathSeqPath + '/*')) for seqPath in seqList: seqName = seqPath.split('/')[-1] imgTxtList = sorted(glob.glob(osp.join(seqPath, 'img1') + '/*.txt')) img_toPathSeqPath = osp.join(seqPath, 'img1') img_toPathSeqPath = img_toPathSeqPath.replace('labels_with_ids', 'images') mkdir_if_missing(img_toPathSeqPath) for imgTxt in imgTxtList: imgName = imgTxt.split('/')[-1].replace('.txt', '.jpg') imgfromPath = osp.join(fromPath, seqName, imgName) print(f'cp {imgfromPath} {img_toPathSeqPath}') os.system(f'cp {imgfromPath} {img_toPathSeqPath}') if __name__ == "__main__": parser = argparse.ArgumentParser(description='BDD100K to MOT format') parser.add_argument("--data_path", default='bdd100k') parser.add_argument("--phase", default='train') parser.add_argument("--classes", default='2,3,4,9,10') parser.add_argument("--img_dir", default="bdd100k/images/track/") parser.add_argument("--label_dir", default="bdd100k/labels/box_track_20/") parser.add_argument("--save_path", default="bdd100kmot_vehicle") parser.add_argument("--height", default=720) parser.add_argument("--width", default=1280) args = parser.parse_args() attr_dict = dict() attr_dict["categories"] = [{ "supercategory": "none", "id": 0, "name": "pedestrian" }, { "supercategory": "none", "id": 1, "name": "rider" }, { "supercategory": "none", "id": 2, "name": "car" }, { "supercategory": "none", "id": 3, "name": "truck" }, { "supercategory": "none", "id": 4, "name": "bus" }, { "supercategory": "none", "id": 5, "name": "train" }, { "supercategory": "none", "id": 6, "name": "motorcycle" }, { "supercategory": "none", "id": 7, "name": "bicycle" }, { "supercategory": "none", "id": 8, "name": "other person" }, { "supercategory": "none", "id": 9, "name": "trailer" }, { "supercategory": "none", "id": 10, "name": "other vehicle" }] attr_id_dict = {i['name']: i['id'] for i in attr_dict['categories']} # create bdd100kmot_vehicle training set in MOT format print('Loading and converting training set...') train_img_dir = os.path.join(args.img_dir, 'train') train_label_dir = os.path.join(args.label_dir, 'train') save_img_dir = os.path.join(args.save_path, 'images', 'train') save_label_dir = os.path.join(args.save_path, 'labels_with_ids', 'train') if not os.path.exists(save_img_dir): os.makedirs(save_img_dir) if not os.path.exists(save_label_dir): os.makedirs(save_label_dir) bdd2mot_tracking(train_img_dir, train_label_dir, save_img_dir, save_label_dir) # create bdd100kmot_vehicle validation set in MOT format print('Loading and converting validation set...') val_img_dir = os.path.join(args.img_dir, 'val') val_label_dir = os.path.join(args.label_dir, 'val') save_img_dir = os.path.join(args.save_path, 'images', 'val') save_label_dir = os.path.join(args.save_path, 'labels_with_ids', 'val') if not os.path.exists(save_img_dir): os.makedirs(save_img_dir) if not os.path.exists(save_label_dir): os.makedirs(save_label_dir) bdd2mot_tracking(val_img_dir, val_label_dir, save_img_dir, save_label_dir) # gen gt file dataPath = args.data_path phase = args.phase classes = args.classes.split(',') formatOrigin(osp.join(dataPath, 'bdd100kmot_vehicle'), phase) dataDir = osp.join( osp.join(dataPath, 'bdd100kmot_vehicle'), 'labels_with_ids', phase) + '/*' genMotGt(dataDir, classes=classes) copyImg(dataPath, osp.join(dataPath, 'bdd100kmot_vehicle'), phase) updateSeqInfo(osp.join(dataPath, 'bdd100kmot_vehicle'), phase) gen_image_list(osp.join(dataPath, 'bdd100kmot_vehicle'), phase) os.system(f'rm -r {dataPath}/bdd100kmot_vehicle/images/' + phase + '/*.jpg')