借用百度智能云完成<驾驶行为识别>视频检测【python】

作者: 小小杨树 | 来源:发表于2022-02-10 11:05 被阅读0次

借用百度智能云完成<驾驶行为识别>视频检测【python】
opencv，tensorflow，cnn实现人脸识别
智能驾驶和发展
经典图像分类程序融入到视频检测中【MATLAB】
国朗疲劳驾驶报警预警系统如何实时抓拍自动识别？
用Python玩人脸融合，想知道你未来孩子的长相？
AI加速，魔屏未来会议快车
视频监控下的人脸识别
DAY004 常识判断（三）科技
基于深度学习的视频行为分析总结

9QS`]NUYQ5QX$ZSK1N`1OWY.png

一.API Key和Secret Key准备工作

1.1在百度智能云平台上注册账号，

1.2进入百度智能云管理中心：

1.3.汇集信息

驾驶行为分析
API Key:    【官网获取的AK】     
Secret Key: 【官网获取的SK】
host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=【官网获取的AK】&client_secret=【官网获取的SK】'

二.检测图像中的驾驶行为

2.1根据官网给的样例，更改自己的ak和sk,如下所示

def detect_image(frame):
    request_url = "https://aip.baidubce.com/rest/2.0/image-classify/v1/driver_behavior"
    image = cv2.imencode('.jpg', frame)[1]
    img = str(base64.b64encode(image))[2:-1]
    params = {"image": img}
    host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=【官网获取的AK】&client_secret=【官网获取的SK】'
    response_host = requests.get(host)
    access_token = []
    useriddatas = json.loads(response_host.text)
    access_token.append(str(useriddatas["access_token"]))
    access = (''.join(access_token))
    access_token = access
    request_url = request_url + "?access_token=" + access_token
    headers = {'content-type': 'application/x-www-form-urlencoded'}
    response = requests.post(request_url, data=params, headers=headers)
    data = response.json()
    datas = str(data)
    if len(datas) > 200: #防止检测过快无法触及并发，这里我是使用的免费的次数
        find_behavior(data)
    else:
        pass

2.2从response.json()中解析行为：

def find_behavior(data):
    '''
    smoke //吸烟，
    cellphone //打手机 ，
    not_buckling_up // 未系安全带，
    not_facing_front // 视角未看前方，
    yawning // 打哈欠，
    eyes_closed // 闭眼，
    head_lowered // 低头
    '''
    # print(data)
    data_smoke = data['person_info'][0]['attributes']['smoke']['score']
    data_cellphone = data['person_info'][0]['attributes']['cellphone']['score']
    data_not_buckling_up = data['person_info'][0]['attributes']['not_buckling_up']['score']
    data_not_facing_front = data['person_info'][0]['attributes']['not_facing_front']['score']
    data_yawning = data['person_info'][0]['attributes']['yawning']['score']
    data_eyes_closed = data['person_info'][0]['attributes']['eyes_closed']['score']
    data_head_lowered = data['person_info'][0]['attributes']['head_lowered']['score']
    All_behavior = [data_smoke,
                    data_cellphone,
                    data_not_buckling_up,
                    data_not_facing_front,
                    data_yawning,
                    data_eyes_closed,
                    data_head_lowered]
    All_behavior = np.array(All_behavior)  # 转格式
    Danger_behavior = np.sum(All_behavior >= 0.4)  # 存在危险驾驶行为（该行为概率大于0.4）的个数
    if Danger_behavior > 0:
        """
        from pygame import mixer     #控制音频流模块
        mixer.init()                 #初始化混音器模块
        mixer.music.load('./存在危险驾驶行为.mp3')  #载入待播放音乐文件 假定为2秒
        mixer.music.play()           #开始播放音乐流
        sleep(2)  #休眠2秒供播放使用
        mixer.music.stop()  # 停止播放
        """
        print("存在危险驾驶行为")
    else:
        print("安全驾驶中")

2.3.将图像检测怼到视频中

if __name__ == "__main__":
    capture = cv2.VideoCapture(0)
    fps = 0.0
    while (True):
        t1 = time.time()
        # 读取某一帧
        ref, frame = capture.read()
        # 格式转变，BGRtoRGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 转变成Image
        # 进行检测
        detect_image(frame)
        # time.sleep(0.2)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # fps = int(round(capture.get(cv2.CAP_PROP_FPS)))
        fps = (fps + (1. / (time.time() - t1))) / 2
        frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("video", frame)
        c = cv2.waitKey(1) & 0xff
        if c == 27:
            capture.release()
            break
    capture.release()
    cv2.destroyAllWindows()

三.demo代码：

# encoding:utf-8

import json
import time
import numpy as np
import requests
import base64
import cv2

'''
驾驶行为分析
API Key:    USIybtKpK5CB2B1o6RSyYYz1     
Secret Key: CFSlR51sTQnocifu8mmrn8sv7XNb5vwS
host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=【官网获取的AK】&client_secret=【官网获取的SK】'
'''

def find_behavior(data):
    '''
    smoke //吸烟，
    cellphone //打手机 ，
    not_buckling_up // 未系安全带，
    not_facing_front // 视角未看前方，
    yawning // 打哈欠，
    eyes_closed // 闭眼，
    head_lowered // 低头
    '''
    # print(data)
    data_smoke = data['person_info'][0]['attributes']['smoke']['score']
    data_cellphone = data['person_info'][0]['attributes']['cellphone']['score']
    data_not_buckling_up = data['person_info'][0]['attributes']['not_buckling_up']['score']
    data_not_facing_front = data['person_info'][0]['attributes']['not_facing_front']['score']
    data_yawning = data['person_info'][0]['attributes']['yawning']['score']
    data_eyes_closed = data['person_info'][0]['attributes']['eyes_closed']['score']
    data_head_lowered = data['person_info'][0]['attributes']['head_lowered']['score']
    All_behavior = [data_smoke,
                    data_cellphone,
                    data_not_buckling_up,
                    data_not_facing_front,
                    data_yawning,
                    data_eyes_closed,
                    data_head_lowered]
    All_behavior = np.array(All_behavior)  # 转格式
    Danger_behavior = np.sum(All_behavior >= 0.4)  # 存在危险驾驶行为（该行为概率大于0.4）的个数
    if Danger_behavior > 0:
        """
        from pygame import mixer     #控制音频流模块
        mixer.init()                 #初始化混音器模块
        mixer.music.load('./存在危险驾驶行为.mp3')  #载入待播放音乐文件 假定为2秒
        mixer.music.play()           #开始播放音乐流
        sleep(2)  #休眠2秒供播放使用
        mixer.music.stop()  # 停止播放
        """
        print("存在危险驾驶行为")
    else:
        print("安全驾驶中")




def detect_image(frame):
    request_url = "https://aip.baidubce.com/rest/2.0/image-classify/v1/driver_behavior"
    image = cv2.imencode('.jpg', frame)[1]
    img = str(base64.b64encode(image))[2:-1]
    params = {"image": img}
    host ='https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=【官网获取的AK】&client_secret=【官网获取的SK】'
    response_host = requests.get(host)
    access_token = []
    useriddatas = json.loads(response_host.text)
    access_token.append(str(useriddatas["access_token"]))
    access = (''.join(access_token))
    access_token = access
    request_url = request_url + "?access_token=" + access_token
    headers = {'content-type': 'application/x-www-form-urlencoded'}
    response = requests.post(request_url, data=params, headers=headers)
    data = response.json()
    datas = str(data)
    if len(datas) > 200:
        find_behavior(data)
    else:
        pass




if __name__ == "__main__":
    capture = cv2.VideoCapture(0)
    fps = 0.0
    while (True):
        t1 = time.time()
        # 读取某一帧
        ref, frame = capture.read()
        # 格式转变，BGRtoRGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 转变成Image
        # 进行检测
        detect_image(frame)
        # time.sleep(0.2)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # fps = int(round(capture.get(cv2.CAP_PROP_FPS)))
        fps = (fps + (1. / (time.time() - t1))) / 2
        frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("video", frame)
        c = cv2.waitKey(1) & 0xff
        if c == 27:
            capture.release()
            break
    capture.release()
    cv2.destroyAllWindows()

借用百度智能云完成<驾驶行为识别>视频检测【python】
一.API Key和Secret Key准备工作 1.1在百度智能云平台上注册账号， 1.2进入百度智能云管理中心...
opencv，tensorflow，cnn实现人脸识别
opencv实现人脸检测，tensorflow利用cnn实现人脸识别，python完成github地址: http...
智能驾驶和发展
关键技术当前涉及的几大关键技术人脸识别，应用于鉴权、疲劳检测人体姿态识别，应用于危险行为驾驶眼球跟踪，应用...
经典图像分类程序融入到视频检测中【MATLAB】
一.程序流程： 1.借用函数：imaqhwinfo调用摄像头实现视频检测 2.载入卷积识别网络 3.初始化FPS并...
国朗疲劳驾驶报警预警系统如何实时抓拍自动识别？
国朗科技疲劳驾报警预警测系统基于先进的图像智能识别分析技术，检测驾驶员的头部运动、眼皮运动、眼睛闭合频率、凝...
用Python玩人脸融合，想知道你未来孩子的长相？
这次花了点时间，将百度智能云里面的人脸识别功能与python结合，进行了一场实验。结果还是蛮有成就感的，过程也挺简...
AI加速，魔屏未来会议快车
语音识别 Voice Recognition > 智能语音唤醒检测到唤醒词指令时，“魔屏智能语音识别”功能自动开...
视频监控下的人脸识别
2017智能视频监控环境下的人脸识别算法研究总体框图 1.视频—图集(运动目标检测）(1)基于关键帧的方法基于关...
DAY004 常识判断（三）科技
科技常识高新技术人工智能：AI。机器人、语言识别、自然语言。百度ALL IN AI，自动驾驶。虚拟现实：VR...
基于深度学习的视频行为分析总结
一、什么是视频行为分析？基于视频的行为识别包括两个主要问题，即行为定位和行为识别。行为定位即找到有行为的视频片段...