face++进行姿态估计

从视频中提取图片（cv2）–>调用face++的api返回json数据（posts）–>骨骼线描绘–>图片和mp3重组为mp4（moviepy）

face++ API调用

Face⁺⁺ (faceplusplus.com.cn)里面有很多旷世提供的AI算法调用的API，只需要通过post请求就能得到结果。本次实验调用旷世的人体关键点检测API，文档见Face⁺⁺ - 文档中心 (faceplusplus.com.cn)。在调用face++的API文档前，需要先申请一个api_key和api_secret。

视频预处理

本次对一个视频进行逐帧的关键点检测，需要先将视频处理成图片，同时后续需要将处理好的推按重新组合成视频，这里选择moviepy库和cv2库来处理。

首先是从视频中提取图片

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from moviepy.editor import *
import cv2
# 调整图片的分辨率大小
NEW_SIZE = [720, 1280]
# 设置音频的采样率
audio_fps = 48000

video = VideoFileClip('wind.mp4', target_resolution=NEW_SIZE, audio_fps=audio_fps)

# 提取音频部分并保存
audio = video.audio
audio.write_audiofile('wind.mp3', fps=None, bitrate='315k')

# 将修改分辨率的视频重新保存，并设置比特率调整压缩程度
video.write_videofile('new_wind.mp4', bitrate='2590k', audio_fps=None, audio_bitrate='315k')

# 从视频中逐帧读取图片并保存
videoCapture = cv2.VideoCapture('new_wind.mp4')
sucess, frame = videoCapture.read()
i = 0
while sucess:
	i = i+1
	cv2.imwrite('img/%05d.jpg'%i, frame)
	sucess, frame = videoCapture.read()

使用moviepy库能够实现音视频的剪辑任务，如将一段无声视频和音频组合起来

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from moviepy.editor import *
import moviepy
import os

video = VideoFileClip('test.mp4')

audio = AudioFileClip('test.mp3', fps=48000)

# 必须要使用新的变量来承接改变后的视频，原始的video不会改变
new_video = video.set_audio(audio) 

new_video.write_videofile('new_vis_wind.mp4', bitrate='5000k', audio_fps=None, audio_bitrate='315k')

发post请求，得到包含关键点的json数据

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import requests
import numpy as np 
import json
import os

url = 'https://api-cn.faceplusplus.com/humanbodypp/v1/skeleton'
api_key = '自己的api_key'
api_secret = '自己的api_secret'

# 逐张图片调用旷世的api，保存每个人的坐标信息和关键点信息的json
imgpath = 'img'
for filename in os.listdir(imgpath):
	img = os.path.join(imgpath, filename)
	files = {'image_file': open(img, 'rb')}
	form = {'api_key': api_key, 'api_secret': api_secret}
	res = requests.post(url, form, files=files).json()
	json_path = os.path.join('json', filename.replace('.jpg', '.json'))
	with open(json_path, 'w') as f:
		json.dump(res, f)

描绘骨骼线

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import cv2
import os
import json

imgdir = 'img'
jsondir = 'json'
drawdir = 'img_2'

poskeys = ['head', 'neck', 'left_hand', 'left_elbow', 'left_shoulder', 'right_hand', 'right_elbow',
			'right_shoulder', 'left_buttocks', 'left_knee', 'left_foot', 'right_buttocks', 'right_knee',
			'right_foot']
# 描绘14根线条
# 需要来确定骨架线的颜色

colors = {'head-neck': (153, 0, 0), 'neck-left_shoulder': (0, 0, 153), 'neck-right_shoulder': (0, 51, 153), 
		  'left_shoulder-left_elbow': (0, 102, 153), 'left_elbow-left_hand': (0, 153, 153), 'right_shoulder-right_elbow': (0, 153, 102),
		  'right_elbow-right_hand': (0, 153, 51), 'left_shoulder-left_buttocks': (0, 153, 0), 'left_buttocks-left_knee': (51, 153, 0),
		  'left_knee-left_foot': (102, 153, 0), 'right_shoulder-right_buttocks': (153, 153, 0), 'right_buttocks-right_knee': (153, 102, 0),
		  'right_knee-right_foot': (153, 51, 0), 'left_buttocks-right_buttocks': (61, 0, 153)}
linewidth = 4

def draw_skeleton(img, poses):
	for pos in poses:
		body = pos['body_rectangle']
		loc = [body['left'], body['top'], body['width'], body['height']]
		# 是否描绘每个人的框
		# cv2.rectangle(img, pt1=(loc[0], loc[1]), pt2=(loc[0]+loc[2], loc[1]+loc[3]), color=colors['head-neck'], thickness=linewidth)

		# 得到关键点的相对坐标
		landmarks = pos['landmark']
		head = (landmarks['head']['x'], landmarks['head']['y'])
		neck = (landmarks['neck']['x'], landmarks['neck']['y'])
		left_hand = (landmarks['left_hand']['x'], landmarks['left_hand']['y'])
		left_elbow = (landmarks['left_elbow']['x'], landmarks['left_elbow']['y'])
		left_shoulder = (landmarks['left_shoulder']['x'], landmarks['left_shoulder']['y'])
		right_hand = (landmarks['right_hand']['x'], landmarks['right_hand']['y'])
		right_elbow = (landmarks['right_elbow']['x'], landmarks['right_elbow']['y'])
		right_shoulder = (landmarks['right_shoulder']['x'], landmarks['right_shoulder']['y'])
		left_buttocks = (landmarks['left_buttocks']['x'], landmarks['left_buttocks']['y'])
		left_knee = (landmarks['left_knee']['x'], landmarks['left_knee']['y'])
		left_foot = (landmarks['left_foot']['x'], landmarks['left_foot']['y'])
		right_buttocks = (landmarks['right_buttocks']['x'], landmarks['right_buttocks']['y'])
		right_knee = (landmarks['right_knee']['x'], landmarks['right_knee']['y'])
		right_foot = (landmarks['right_foot']['x'], landmarks['right_foot']['y'])

		# 由关键点的相对坐标得到绝对坐标
		head = (head[0]+loc[0], head[1]+loc[1])
		neck = (neck[0]+loc[0], neck[1]+loc[1])
		left_hand = (left_hand[0]+loc[0], left_hand[1]+loc[1])
		left_elbow = (left_elbow[0]+loc[0], left_elbow[1]+loc[1])
		left_shoulder = (left_shoulder[0]+loc[0], left_shoulder[1]+loc[1])
		right_hand = (right_hand[0]+loc[0], right_hand[1]+loc[1])
		right_elbow = (right_elbow[0]+loc[0], right_elbow[1]+loc[1])
		right_shoulder = (right_shoulder[0]+loc[0], right_shoulder[1]+loc[1])
		left_buttocks = (left_buttocks[0]+loc[0], left_buttocks[1]+loc[1])
		left_knee = (left_knee[0]+loc[0], left_knee[1]+loc[1])
		left_foot = (left_foot[0]+loc[0], left_foot[1]+loc[1])
		right_buttocks = (right_buttocks[0]+loc[0], right_buttocks[1]+loc[1])
		right_knee = (right_knee[0]+loc[0], right_knee[1]+loc[1])
		right_foot = (right_foot[0]+loc[0], right_foot[1]+loc[1])

		# 描绘骨骼线
		cv2.line(img, pt1=head, pt2=neck, color=colors['head-neck'], thickness=linewidth)
		cv2.line(img, pt1=neck, pt2=left_shoulder, color=colors['neck-left_shoulder'], thickness=linewidth)
		cv2.line(img, pt1=neck, pt2=right_shoulder, color=colors['neck-right_shoulder'], thickness=linewidth)
		cv2.line(img, pt1=left_shoulder, pt2=left_elbow, color=colors['left_shoulder-left_elbow'], thickness=linewidth)
		cv2.line(img, pt1=left_elbow, pt2=left_hand, color=colors['left_elbow-left_hand'], thickness=linewidth)
		cv2.line(img, pt1=right_shoulder, pt2=right_elbow, color=colors['right_shoulder-right_elbow'], thickness=linewidth)
		cv2.line(img, pt1=right_elbow, pt2=right_hand, color=colors['right_elbow-right_hand'], thickness=linewidth)
		cv2.line(img, pt1=left_shoulder, pt2=left_buttocks, color=colors['left_shoulder-left_buttocks'], thickness=linewidth)
		cv2.line(img, pt1=left_buttocks, pt2=left_knee, color=colors['left_buttocks-left_knee'], thickness=linewidth)
		cv2.line(img, pt1=left_knee, pt2=left_foot, color=colors['left_knee-left_foot'], thickness=linewidth)
		cv2.line(img, pt1=right_shoulder, pt2=right_buttocks, color=colors['right_shoulder-right_buttocks'], thickness=linewidth)
		cv2.line(img, pt1=right_buttocks, pt2=right_knee, color=colors['right_buttocks-right_knee'], thickness=linewidth)
		cv2.line(img, pt1=right_knee, pt2=right_foot, color=colors['right_knee-right_foot'], thickness=linewidth)
		cv2.line(img, pt1=left_buttocks, pt2=right_buttocks, color=colors['left_buttocks-right_buttocks'], thickness=linewidth)

	return img

for file in os.listdir(imgdir):
	imgpath = os.path.join(imgdir, file)
	# 这种写法是每张图片使用自己的骨骼线，为了避免视频中骨骼线显示不稳定，每三张图片共享一个骨骼线
	# jsonpath = os.path.join(jsondir, file.replace('.jpg', '.json'))

	# 每3张图片共享骨骼线
	jsonidx = (int(file.replace('.jpg', ''))-1)//3+1
	jsonpath = os.path.join(jsondir, '%05d.json'%jsonidx)
	img =  cv2.imread(imgpath)

	with open(jsonpath) as f:
		poses = json.load(f)['skeletons']
	img = draw_skeleton(img, poses)

	# img_2保存每张图一个骨骼线，img_3共享骨骼线
	newimgpath = os.path.join('img_3', file)

	cv2.imwrite(newimgpath, img)

将图片和mp3重组为视频

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from moviepy.editor import *
import moviepy
import os

imgdir = 'img_2'

# 标记了骨骼线的图像
imgpaths = [os.path.join(imgdir, file) for file in os.listdir(imgdir)] # 需要确保按照时间先后顺序排列

video = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(imgpaths, fps=30)

audio = AudioFileClip('video_wind.mp3', fps=48000)

new_video = video.set_audio(audio) # 必须要使用新的变量来承接改变后的视频

new_video.write_videofile('new_video_wind_1.mp4', bitrate='2590k', audio_fps=None, audio_bitrate='315k')

效果图如下

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    缺失模块。
    1、请确保node版本大于6.2
    2、在博客根目录（注意不是yilia根目录）执行以下命令：
    npm i hexo-generator-json-content --save

    3、在根目录_config.yml里添加配置： 

      jsonContent:
    meta: false
    pages: false
    posts:
      title: true
      date: true
      path: true
      text: false
      raw: false
      content: false
      slug: false
      updated: false
      comments: false
      link: false
      permalink: false
      excerpt: false
      categories: false
      tags: true

很惭愧<br><br>模板不是我写的<br><br>想学cpp&算法<br><br>谢谢大家