csv转coco人体特征点数据格式

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import csv
import json

image_w = 640
image_h = 640
axis_start_num = 10000
 
info = {"description": "COCO 2017 Dataset","url": "http://cocodataset.org","version": "1.0","year": 2017,"contributor": "COCO Consortium","date_created": "2017/09/01"}
licenses = [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/","id": 1,"name": "Attribution-NonCommercial-ShareAlike License"},
{"url": "http://creativecommons.org/licenses/by-nc/2.0/","id": 2,"name": "Attribution-NonCommercial License"},
{"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/","id": 3,"name": "Attribution-NonCommercial-NoDerivs License"},
{"url": "http://creativecommons.org/licenses/by/2.0/","id": 4,"name": "Attribution License"},
{"url": "http://creativecommons.org/licenses/by-sa/2.0/","id": 5,"name": "Attribution-ShareAlike License"},
{"url": "http://creativecommons.org/licenses/by-nd/2.0/","id": 6,"name": "Attribution-NoDerivs License"},
{"url": "http://flickr.com/commons/usage/","id": 7,"name": "No known copyright restrictions"},
{"url": "http://www.usa.gov/copyright.shtml","id": 8,"name": "United States Government Work"}]
images = []
annotations = []
categories = [{"supercategory": "person","id": 1,"name": "person","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"],
"skeleton": []}]
#[16,14],[14,12],[17,15],[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]]

csv_reader=csv.reader(open('/home/jiajie/pytorch/detectron2/datasets/coco/train_vertexs.csv',encoding='utf-8'))
 
max_image_id = 0

for i,row in enumerate(csv_reader):
    if(i!=0):
        #images
        if(int(row[1])>max_image_id):
            image_info = {"license": 1} 
            image_info['file_name'] = row[0]
            image_info['coco_url'] = ""
            image_info['height'] = image_h
            image_info['width'] = image_w
            image_info['date_captured'] = ""
            image_info['flickr_url'] = ""
            image_info['id'] = int(row[1])
            images.append(image_info)
            max_image_id = int(row[1])      
            
        # annotations
        annotations_info = {"segmentation":[[]]}
        annotations_info['num_keypoints'] = int(row[3])
        annotations_info['area'] = 0.0
        annotations_info['iscrowd'] = 0
        keypoints_dict = {}
        rank_kp_list = []
        for n in range(5,9):
            keypoints_dict[str(row[n])+"_"+str(n)] = pow(int(str(row[n]).split("_")[0]),2)+pow(int(str(row[n]).split("_")[1]),2)
            rank_kp_list.append(keypoints_dict[str(row[n])+"_"+str(n)])
        #rank_kp_list.sort()
        sort_list = sorted(keypoints_dict.items(),  key=lambda d: d[1], reverse=False)
        #print(keypoints_dict)
        #print(sort_list)
        
        keypoints_list = []
        keypoints_list.append(int(str(sort_list[0][0]).split("_")[0]))
        keypoints_list.append(int(str(sort_list[0][0]).split("_")[1]))
        keypoints_list.append(int(str(sort_list[0][0]).split("_")[2]))
        keypoints_list.append(int(str(sort_list[1][0]).split("_")[0]))
        keypoints_list.append(int(str(sort_list[1][0]).split("_")[1]))
        keypoints_list.append(int(str(sort_list[1][0]).split("_")[2]))
        keypoints_list.append(int(str(sort_list[2][0]).split("_")[0]))
        keypoints_list.append(int(str(sort_list[2][0]).split("_")[1]))
        keypoints_list.append(int(str(sort_list[2][0]).split("_")[2]))
        keypoints_list.append(int(str(sort_list[3][0]).split("_")[0]))
        keypoints_list.append(int(str(sort_list[3][0]).split("_")[1]))
        keypoints_list.append(int(str(sort_list[3][0]).split("_")[2])) 
        annotations_info['keypoints'] = keypoints_list
        annotations_info['image_id'] = int(row[1])
              
        # bbox
        x_list = []
        y_list = []
        for n in range(5,9):
            if(int(str(row[n]).split("_")[2])!=0 ):
                x_list.append(int(str(row[n]).split("_")[0]))
                y_list.append(int(str(row[n]).split("_")[1]))
        min_x = min(x_list)
        min_y = min(y_list)
        max_x = max(x_list)
        max_y = max(y_list)

        annotations_info['bbox'] = [min(max(0,min_x-(max_x-min_x)/4),int(image_w)),min(max(0,min_y-(max_y-min_y)/4),int(image_h)),3*(max_x-min_x)/2,3*(max_y-min_y)/2]
        
        if(int(row[1])==588):
            print(min_x,min_y,max_x,max_y)
            print(annotations_info['bbox'])
        annotations_info['category_id'] = 1
        annotations_info['id'] = axis_start_num+int(i)
        annotations.append(annotations_info)
        



image = {
    'info': info,
    'licenses': licenses,
    'images': images,
    'annotations': annotations,
    'categories': categories
}


with open('/home/jiajie/pytorch/detectron2/datasets/coco/annotations/person_keypoints_train2017.json', 'w') as f:
    json.dump(image,f)

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import os
import cv2
import json
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils
 
from skimage import io
from matplotlib import pyplot as plt


# 验证数据集 特征点
dataset_dir = "/home/jiajie/pytorch/detectron2/datasets/coco/"
 
 
coco = COCO(os.path.join(dataset_dir,'annotations','person_keypoints_train2017.json'))
catIds = coco.getCatIds(catNms=['person']) # catIds=1 表示人这一类
imgIds = 1771
while(True):
    img = coco.loadImgs(imgIds)[0]
    image_path = os.path.join(dataset_dir, 'train2017', '4axis_'+str(imgIds) + '.png')
    I = io.imread(image_path)
    plt.axis('off')
    plt.imshow(I) #绘制图像，显示交给plt.show()处理
    bg = np.zeros((img['height'], img['width'], 3))
    annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
    anns = coco.loadAnns(annIds)
    coco.showAnns(anns)
    plt.show()
    imgIds = imgIds+3
    print(imgIds)


# 验证数据集 锚框
json_file = '/home/jiajie/pytorch/detectron2/datasets/coco/annotations/person_keypoints_train2017.json'
with open(json_file) as annos:
    anno = json.load(annos)
axis_id = 1102
annotations = anno['annotations'] 
#print(annotations[imgIds-1]['bbox'])
x, y, w, h = annotations[axis_id-1]['bbox']
print(annotations[axis_id-1]['bbox'])
image_path = os.path.join(dataset_dir, 'train2017', '4axis_'+str(imgIds) + '.png') 
image = cv2.imread(image_path)
anno_image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 255), 2) 
cv2.imshow('demo', anno_image)
cv2.waitKey(100)
plt.show()