COCO json–person_keypoints

person_keypoins.json 结构

“info”, “licenses”,"categories"全局通用
“images”跟“annotations”放着图片跟label信息，用image id匹配。
具体结构请看下文：

one image

person_keypoint.json

{"categories": [{"skeleton": [[16,14],[14,12],[17, 5],[15,13],[12,13],[6 ,12],[7 ,13],[6 , 7],[6 , 8],[7 , 9],[8 ,10],[9 ,11],[2 , 3],[1 , 2],[1 , 3],[2 , 4],[3 , 5],[4 , 6],[5 , 7]],"name": "person", # 子类（具体类别）"supercategory": "person", # 主类"id": 1, # class id"keypoints": ["nose","left_eye","right_eye","left_ear","right_ear","left_shoulder","right_shoulder","left_elbow","right_elbow","left_wrist","right_wrist","left_hip","right_hip","left_knee","right_knee","left_ankle","right_ankle"]}],"licenses": [{"id": 1,"name": "Attribution-NonCommercial-ShareAlike License","url": "http://creativecommons.org/licenses/by-nc-sa/2.0/"},{"id": 2,"name": "Attribution-NonCommercial License","url": "http://creativecommons.org/licenses/by-nc/2.0/"},{"id": 3,"name": "Attribution-NonCommercial-NoDerivs License","url": "http://creativecommons.org/licenses/by-nc-nd/2.0/"},{"id": 4,"name": "Attribution License","url": "http://creativecommons.org/licenses/by/2.0/"},{"id": 5,"name": "Attribution-ShareAlike License","url": "http://creativecommons.org/licenses/by-sa/2.0/"},{"id": 6,"name": "Attribution-NoDerivs License","url": "http://creativecommons.org/licenses/by-nd/2.0/"},{"id": 7,"name": "No known copyright restrictions","url": "http://flickr.com/commons/usage/"},{"id": 8,"name": "United States Government Work","url": "http://www.usa.gov/copyright.shtml"}],"annotations": [{"iscrowd": 0,"bbox": [339.88, 22.16, 153.88, 300.73 # [x,y,w,h] 对象定位框],"image_id": 391895, # match the images: "id" "segmentation": [[352.55, 146.82, # 多边形(对象mask)第一个点 x，y, 如果对象被遮挡，对象被分成多个区域353.61, 137.66,356.07, 112.66,......353.61, 152.25,353.26, 149.43],[450.45, 196.54,461.71, 195.13,466.29, 209.22,......449.04, 229.57,448.33, 199.29]],"num_keypoints": 14,"id": 202758,"category_id": 1, "keypoints": [# 如果关键点在物体segment内，则认为可见.# v=0 表示这个关键点没有标注（这种情况下x=y=v=0）# v=1 表示这个关键点标注了但是不可见(被遮挡了）# v=2 表示这个关键点标注了同时也可见368,  61,   1,369,  52,   2,0,     0,   0,382,  48,   2,0,     0,   0,368,  84,   2,435,  81,   2,362, 125,   2,446, 125,   2,360, 153,   2,0,     0,   0,397, 167,   1,439, 166,   1,369, 193,   2,461, 234,   2,361, 246,   2,474, 287,   2],"area": 14107.2713},{"iscrowd": 0,"bbox": [471.64,172.82,35.92,48.1],"image_id": 391895,"segmentation": [[477.41, 217.71,475.06, 212.15,......478.27, 218.57]],"num_keypoints": 0,"id": 1260346,"category_id": 1,"keypoints": [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"area": 708.26055}],"images": [{"id": 391895, # match the annotations: "image_id" "date_captured": "2013-11-14 11:18:45","coco_url": "http://images.cocodataset.org/val2014/COCO_val2014_000000391895.jpg","height": 360,"flickr_url": "http://farm9.staticflickr.com/8186/8119368305_4e622c8349_z.jpg","file_name": "COCO_val2014_000000391895.jpg","license": 3,"width": 640}],"info": {"version": "1.0","description": "COCO 2014 Dataset","year": 2014,"contributor": "COCO Consortium","url": "http://cocodataset.org","date_created": "2017/09/01"}
}

keypoint

“keypoints”是长度为3K的数组，K是对某类定义的关键点总数，这里人体的keypoint就是17个.位置为[x,y]，关键点可见性v.
(1-‘nose’ 2-‘left_eye’ 3-‘right_eye’ 4-‘left_ear’ 5-‘right_ear’ 6-‘left_shoulder’ 7-‘right_shoulder’ 8-‘left_elbow’ -‘right_elbow’ 10-‘left_wrist’ 11-‘right_wrist’ 12-‘left_hip’ 13-‘right_hip’ 14-‘left_knee’ 15-‘right_knee’ 16-‘left_ankle’ 17-‘right_ankle’ )

OpenPose的姿态识别用了COCO数据库,coco有17个keypoint, OpenPose增加了一个, 用两侧肩膀生成一个中心点代表脖子，就是如下图openpose编号为1的人体中心点.

Display above image(ID=391895) label

# -*- coding:utf-8 -*-from __future__ import print_function
from pycocotools.coco import COCO
import os, sys, zipfile
import shutil
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
pylab.rcParams['figure.figsize'] = (8.0, 10.0)annFile='./pickOne_person_keypoints_2014_.json'
coco=COCO(annFile)# display COCO categories and supercategories
cats = coco.loadCats(coco.getCatIds())
nms=[cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(nms)))nms = set([cat['supercategory'] for cat in cats])
print('COCO supercategories: \n{}'.format(' '.join(nms)))imgIds = coco.getImgIds(imgIds = [391895])
# imgIds = coco.getImgIds()
img = coco.loadImgs(imgIds[0])[0]
dataDir = '../data/mscoco2014'
dataType = 'val2014'
I = io.imread('%s/%s/%s'%(dataDir,dataType,img['file_name']))plt.axis('off')
plt.imshow(I)
plt.show()# load and display instance annotations
# 加载实例掩膜
# catIds = coco.getCatIds(catNms=['person','dog','skateboard']);
# catIds=coco.getCatIds()
catIds=[]
for ann in coco.dataset['annotations']:if ann['image_id']==imgIds[0]:catIds.append(ann['category_id'])plt.imshow(I); plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)# initialize COCO api for person keypoints annotations
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)
coco_kps=COCO(annFile)# load and display keypoints annotations
# 加载肢体关键点
plt.imshow(I); plt.axis('off')
ax = plt.gca()
annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco_kps.loadAnns(annIds)
coco_kps.showAnns(anns)# initialize COCO api for caption annotations
annFile = '{}/annotations/captions_{}.json'.format(dataDir,dataType)
coco_caps=COCO(annFile)# load and display caption annotations
# 加载文本描述
annIds = coco_caps.getAnnIds(imgIds=img['id']);
anns = coco_caps.loadAnns(annIds)
coco_caps.showAnns(anns)
plt.imshow(I); plt.axis('off'); plt.show()

original image

segment & kepoints

caption annotations
A man with a red helmet on a small moped on a dirt road.
Man riding a motor bike on a dirt road on the countryside.
A man riding on the back of a motorcycle.
A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains.
A man in a red shirt and a red hat is on a motorcycle on a hill side.

git: https://github.com/Frizy-up/openpose-plus/tree/master

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COCO Dataset person_keypoints.json 解析

DataSet