跟踪数据集汇总
文章目录
DanceTrack 运动跟踪数据集
简介
DanceTrack 是一个大规模的多对象跟踪数据集。用于在遮挡、频繁交叉、同样服装和多样化身体姿态条件下对人进行跟踪。强调运动分析在多对象跟踪中的重要性。
GitHub地址：https://github.com/DanceTrack/DanceTrack
数据集下载地址：https://pan.baidu.com/s/19O3IvYNzzrcLqlODHKYUwA
提取码：awew
转为Labelme标注的物体检测数据集格式
import sysimport base64import osimport cv2import shutilimport globmodule_path = os.path.abspath(os.path.join('..'))if module_path not in sys.path: sys.path.append(module_path)import jsonfrom PIL import ImageImage.MAX_IMAGE_PIXELS = NonexmlpathNames_path='../train1/*/gt/gt.txt'xmlpathNames=glob.glob(xmlpathNames_path)print(xmlpathNames)version = '3.16.7'flags = {}lineColor = [0, 255, 0, 128]fillColor = [255, 0, 0, 128]image_t='../images/'os.makedirs(image_t,exist_ok=True)for xmlpathName in xmlpathNames: xmlpathName=xmlpathName.replace("\\","/") dancetrack_name=xmlpathName.split("/")[-3] dic_info = {} with open(xmlpathName) as fs: lines = fs.readlines() lines = sorted(lines) for line in lines: line = line.replace("\n", '') line_info = line.split(',') frame = line_info[0] frame_image_name = '{:0>8d}'.format(int(frame)) + ".jpg" box = [int(line_info[2]), int(line_info[3]), int(line_info[2]) + int(line_info[4]), int(line_info[3]) + int(line_info[5])] if frame_image_name in dic_info: box_list = dic_info[frame_image_name] box_list.append(box) dic_info[frame_image_name] = box_list else: box_list = [box] dic_info[frame_image_name] = box_list for image_name in dic_info.keys(): dic = {} dic['version'] = version dic['flags'] = flags dic['shapes'] = [] img_path = "../train1/"+dancetrack_name+"/img1/" + image_name img_new_name = dancetrack_name + "_" + image_name img_new_path = image_t + img_new_name try: shutil.copy(img_path, image_t + img_new_name) except : continue img = cv2.imread(img_new_path) imageHeight, imageWidth, _ = img.shape for data in dic_info[image_name]: shape = {} shape['label'] = 'person' shape['line_color'] = None shape['fill_color'] = None x1 = int(data[0]) y1 = int(data[1]) x2 = int(data[2]) y2 = int(data[3]) shape['points'] = [[x1, y1], [x2, y2]] shape['shape_type'] = 'rectangle' shape['flags'] = {} dic['shapes'].append(shape) dic['lineColor'] = lineColor dic['fillColor'] = fillColor dic['imagePath'] = img_new_name dic['imageData'] = base64.b64encode( open('{}'.format(img_new_path), "rb").read()).decode('utf-8') dic['imageHeight'] = imageHeight dic['imageWidth'] = imageWidth fw = open('{}json'.format(img_new_path.replace(img_new_path.split('.')[-1], "")), 'w') json.dump(dic, fw) fw.close()WiderPerson行人检测数据集
简介
WiderPerson 是关于户外行人检测的基准数据集。该数据集图像场景多样，不再局限于交通场景。该数据集包含 13,382 张图像，40 万个遮挡物的标注，其中 8,000 张图像用于训练，1,000 张图像用于验证，4,382 张图像用于测试。与 CityPersons 和 WIDER FACE 数据集类似，该数据集不公布测试图像的 bounding box ground truth。该数据集包含密集的行人和各种遮挡，适合进行户外环境的行人检测评估。
官网地址：http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson/
百度网盘：https://pan.baidu.com/s/1ulMlbw_zhNUYwdyXONLrwg
提取码：uq3u
转为Labelme标注的物体检测数据集格式
import osimport numpy as npimport scipy.io as sioimport shutilfrom lxml.etree import Element, SubElement, tostringfrom xml.dom.minidom import parseStringimport cv2import base64import jsonif __name__ == '__main__': # 行人 # 骑车的 # 遮挡的部分行人 # 一些假人，比如图画上的人 # 拥挤人群，直接大框覆盖了 version = '3.16.7' flags = {} lineColor = [0, 255, 0, 128] fillColor = [255, 0, 0, 128] classes = {'1': 'person', '2': 'person', '3': 'person', '4': 'person', '5': 'person', # 不需要哪个类的话直接删去} # 这里如果自己只要人，可以把1-5全标记为people，也可以根据自己场景需要筛选 VOCRoot = './images/' # 生成的voc2007的位置 os.makedirs(VOCRoot,exist_ok=True) widerDir = './WiderPerson' # widerperson文件夹所在的路径 wider_path = './WiderPerson/train.txt' # widerperson文件夹所中训练集+验证集txt标签所在位置 with open(wider_path, 'r') as f: imgIds = [x for x in f.read().splitlines()] for imgId in imgIds: objCount = 0 # 一个标志位，用来判断该img是否包含我们需要的标注 filename = imgId + '.jpg' img_path = './WiderPerson/images/' + filename file_new_name='wider_'+filename print('Img :%s' % img_path) img = cv2.imread(img_path) width = img.shape[1] # 获取图片尺寸 height = img.shape[0] # 获取图片尺寸 360 dic = {} dic['version'] = version dic['flags'] = flags dic['shapes'] = [] label_path = img_path.replace('images', 'Annotations') + '.txt' with open(label_path) as file: line = file.readline() count = int(line.split('\n')[0]) # 里面行人个数 lines = file.readlines() for line in lines: cls_id = line.split(' ')[0] if cls_id not in classes: print(cls_id) continue shape = {} shape['label'] = cls_name = classes[cls_id] shape['line_color'] = None shape['fill_color'] = None x1 = int(line.split(' ')[1]) + 1 y1 = int(line.split(' ')[2]) + 1 x2 = int(line.split(' ')[3]) + 1 y2 = int(line.split(' ')[4].split('\n')[0]) + 1 shape['points'] = [[x1, y1], [x2, y2]] shape['shape_type'] = 'rectangle' shape['flags'] = {} dic['shapes'].append(shape) dic['lineColor'] = lineColor dic['fillColor'] = fillColor dic['imagePath'] = filename dic['imageData'] = base64.b64encode( open('{}'.format(img_path), "rb").read()).decode('utf-8') dic['imageHeight'] = height dic['imageWidth'] = width suffix=(VOCRoot + file_new_name).split('.')[-1] fw = open('{}'.format((VOCRoot + file_new_name).replace(suffix,"json")), 'w') json.dump(dic, fw) fw.close() shutil.copy(img_path, VOCRoot + file_new_name) 
