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。
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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 sys
import base64
import os
import cv2
import shutil
import glob
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
    sys.path.append(module_path)
import json
from PIL import Image

Image.MAX_IMAGE_PIXELS = None

xmlpathNames_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。

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