工作中用到了opencv，使用其对视频的处理，把视频每一帧的处理为图片，然后使用项目中的AI接口对其进行处理，实现视频实时的人体关键点的处理和人脸识别的功能。初期延迟大，卡顿的较大，效果不是很好。

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环境： Python3 opencv 3

入门代码记录

# coding=utf-8
import os
import numpy as np
from PIL import Image, ImageDraw
import cv2
import requests
import json

videoCapture = cv2.VideoCapture(0)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
size = (int(videoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
        int(videoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')

while 1:
    ret, img = videoCapture.read()
    # frame_resize = cv2.resize(img, (640, 360), interpolation=cv2.INTER_AREA)
    frame_resize = cv2.resize(img, (size[0], size[1]), interpolation=cv2.INTER_AREA)
    cv2.imwrite("", frame_resize)

    # 鉴别
    try:
        #AI 人脸识别接口
        url = ''
        data = {}

        new_img = open("", 'rb')
        f = {"image": new_img}
        result = requests.post(url, files=f, data=data)
        requestdata = result.json()
        fontface = cv2.FONT_HERSHEY_SIMPLEX
        fontscale = 1
        fontcolor = (255, 255, 255)
        #识别后的视频中文字说明
        #接口中数据处理
        for per_info in requestdata['resultInfo']:
            if per_info['score'] > 0.6:
                cv2.putText(img, 'hello world!', (
                int(x), int(y)),fontface, fontscale, fontcolor)
    except Exception as e:
        pass
    #人体关键点 同理 画线

    cv2.imshow('video', img)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break
videoCapture.release()
cv2.destroyAllWindows()