基于coco数据集的人体关键点分布示意图与数据集解析

本文绘制了coco中人体姿态关键点的分布示意图，并解释了每个关键点的含义。

目录

1、数据集介绍

2、示意图

3、数据集解析

1、数据集介绍

        有pose标注的部分数据样式如下：

        每张图中有若干个segment标注，每个标注包含的信息如下：

{"segmentation":[[0.43,299.58,2.25,299.58,9.05,287.78,32.66,299.13,39.01,296.4,48.09,290.96,43.55,286.87,62.16,291.86,61.25,286.87,37.65,279.15,18.13,272.8,0,262.81]],
"num_keypoints":1,
"area":1037.7819,
"iscrowd":0,
"keypoints":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,277,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
"image_id":397133,
"bbox":[0,262.81,62.16,36.77],
"category_id":1,
"id":1218137}

        我们所需要的就是其中的'keypoints'部分，每三个数字为一组，代表一个关键点，三个值分别为x坐标、y坐标、标志位，其中，标志位有三个值：

• 0：未标注
• 1：标注，但被遮挡
• 2：标注，未遮挡

2、示意图

        下图中，共17个关节点（鼻子x1、眼睛x2、耳朵x2、肩部x2、肘部x2、手腕x2、髋部x2、膝关节x2、脚腕x2）：

3、数据集解析

        我们从coco2017中解析数据集并保存为YOLO格式，这种格式可以直接用YOLOv5或者YOLOv8进行训练：

"""
get person instance segmentation annotations from coco data set.
"""import argparse
import osimport numpy as np
import tqdm
import shutil
from pycocotools.coco import COCOdef main(args):annotation_file = os.path.join(args.input_dir, 'annotations', 'person_keypoints_{}.json'.format(args.split))# init pathsubdir = args.split[:-4] + '_coco2'img_save_dir = os.path.join(args.output_dir, subdir, 'images')txt_save_dir = os.path.join(args.output_dir, subdir, 'labels')os.makedirs(img_save_dir, exist_ok=True)os.makedirs(txt_save_dir, exist_ok=True)coco = COCO(annotation_file)catIds = coco.getCatIds()imgIds = coco.getImgIds()print("catIds len:{}, imgIds len:{}".format(len(catIds), len(imgIds)))for imgId in tqdm.tqdm(imgIds, ncols=100):img = coco.loadImgs(imgId)[0]annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)anns = coco.loadAnns(annIds)if len(annIds) > 0:img_origin_path = os.path.join(args.input_dir, args.split, img['file_name'])img_height, img_width = img['height'], img['width']lines = []for ann in anns:# if ann['iscrowd'] != 0 or ann['category_id'] != 1:#     continuebbox = np.asarray(ann['bbox'], dtype=float)  # x1y1whbbox[::2] = bbox[::2] / img_widthbbox[1::2] = bbox[1::2] / img_height# x1y1wh2xywhbbox[0] += bbox[2] / 2bbox[1] += bbox[3] / 2bbox_str = [str(b) for b in bbox]keypoints = np.asarray(ann['keypoints'], dtype=float)keypoints[::3] = keypoints[::3] / img_widthkeypoints[1::3] = keypoints[1::3] / img_heightkeypoints_str = [str(k) for k in keypoints]line = '{} {} {}'.format(0, ' '.join(bbox_str), ' '.join(keypoints_str))lines.append(line)if len(lines) > 0:txt_output_path = os.path.join(txt_save_dir, os.path.splitext(img['file_name'])[0] + '.txt')with open(txt_output_path, 'a') as f:for line in lines:f.write(line + '\n')img_output_path = os.path.join(img_save_dir, img['file_name'])shutil.copy(img_origin_path, img_output_path)def get_args():parser = argparse.ArgumentParser()parser.add_argument("--input_dir", default="/data/public_datasets/coco2017", type=str,help="input dataset directory")parser.add_argument("--split", default="val2017", type=str,help="train2017 or val2017")parser.add_argument("--output_dir", default="/data/datasets/person_pose", type=str,help="output dataset directory")return parser.parse_args()if __name__ == '__main__':args = get_args()main(args)

参考：

COCO - Common Objects in Context