百度AI人脸识别案例详解（API调用与开发实战）

人脸分析

import requests, base64  def get_access_token():    url = 'https://aip.baidubce.com/oauth/2.0/token'    data = {        'grant_type': 'client_credentials',  # 固定值        'client_id': '',  # 在开放平台注册后所建应用的API Key        'client_secret': ''  # 所建应用的Secret Key    }    res = requests.post(url, data=data)    res = res.json()    access_token = res['access_token']    return access_token  def get_json(img):    access_token=get_access_token()    request_url = "https://aip.baidubce.com/rest/2.0/face/v3/detect"    file = open(img, 'rb')  # 二进制读取图片    base64_data = base64.b64encode(file.read())  # 将图片进行base64编码    base64_code = base64_data.decode()    params = {        'image': base64_code,        'image_type': 'BASE64',        'face_field': 'age,beauty,expression,face_shape,gender,glasses,landmark,landmark150,quality,eye_status,emotion,face_type,mask,spoofing'    }    request_url = request_url + "?access_token=" + access_token    headers = {'Content-Type': 'application/json'}    response = requests.post(request_url, data=params, headers=headers)    if response:        return response.json() if __name__ == '__main__':    img = './img/mjl.jpg'    json_data=get_json(img)

返回结果说明

人脸对比

import requests, base64  def get_access_token():    url = 'https://aip.baidubce.com/oauth/2.0/token'    data = {        'grant_type': 'client_credentials',  # 固定值        'client_id': '',  # 在开放平台注册后所建应用的API Key        'client_secret': ''  # 所建应用的Secret Key    }    res = requests.post(url, data=data)    res = res.json()    access_token = res['access_token']    return access_token # 根据图片名读取图片，并转换成base64def read_img(img):    with open(img, 'rb') as f:        base64_data = base64.b64encode(f.read())        base64_code = base64_data.decode()    return base64_code def get_json(img1,img2):    request_url = "https://aip.baidubce.com/rest/2.0/face/v3/match"    params = [        {            "image": img1,            "image_type": "BASE64",            "face_type": "LIVE",            "quality_control": "LOW",            "liveness_control": "HIGH"        },        {            "image": img2,            "image_type": "BASE64",            "face_type": "LIVE",            "quality_control": "LOW",            "liveness_control": "HIGH"        }    ]    access_token = get_access_token()    request_url = request_url + "?access_token=" + access_token    headers = {'content-type': 'application/json'}    response = requests.post(request_url, json=params, headers=headers)    if response:        return response.json()  if __name__ == '__main__':    img1 = read_img('img/mm2.jpeg')    img2 = read_img('img/mjl.jpg')    json_data=get_json(img1,img2)    print(json_data)    if json_data['error_msg'] == 'SUCCESS':        score = json_data['result']['score']         if score > 80:            print("照片相似度为：" + str(score) + "基本确定是本人")        else:            print("照片相似度为：" + str(score) + "基本确定不是本人")        print(score)    else:        print('错误信息：', json_data['error_msg'])

返回结果说明

人脸融合

import requestsimport base64import json   # 获取tokendef get_access_token():    url = 'https://aip.baidubce.com/oauth/2.0/token'    data = {        'grant_type': 'client_credentials',  # 固定值        'client_id': '',  # 在开放平台注册后所建应用的API Key        'client_secret': ''  # 所建应用的Secret Key    }    res = requests.post(url, data=data)    res = res.json()    access_token = res['access_token']    return access_token  # 根据图片名读取图片，并转换成base64def read_photo(name):    with open(name, 'rb') as f:        base64_data = base64.b64encode(f.read())        base64_code = base64_data.decode()    return base64_code  # 调用百度的接口，实现融合图片def face_fusion(template, target):    access_token = get_access_token()    url = 'https://aip.baidubce.com/rest/2.0/face/v1/merge'    request_url = url + '?access_token=' + access_token    params = {        "image_template": {            "image": template,            "image_type": "BASE64",            "quality_control": "NONE"        },        "image_target": {            "image": target,            "image_type": "BASE64",            "quality_control": "NONE"        },        "merge_degree": "NORMAL"    }    params = json.dumps(params)    headers = {'content-type': 'application/json'}    result = requests.post(request_url, data=params, headers=headers).json()    if result['error_code'] == 0:        res = result["result"]["merge_image"]        down_photo(res)    else:        print(str(result['error_code'])+result['error_msg']) # 下载融合后图片def down_photo(data):    imagedata = base64.b64decode(data)    file = open('./result.jpg', "wb")    file.write(imagedata) # 主程序if __name__ == '__main__':    template = read_photo('img/jjy.jpg')    target = read_photo('img/mzc.jpeg')    face_fusion(template, target)

返回结果说明

人像动漫画

import requests, base64  # 百度AI开放平台鉴权函数def get_access_token():    url = 'https://aip.baidubce.com/oauth/2.0/token'    data = {        'grant_type': 'client_credentials',  # 固定值        'client_id': '',  # 在开放平台注册后所建应用的API Key        'client_secret': ''  # 所建应用的Secret Key    }    res = requests.post(url, data=data)    res = res.json()    access_token = res['access_token']    return access_token  def image_process(img_before, img_after, how_to_deal):    # 函数的三个参数，一个是转化前的文件名，一个是转化后的文件名，均在同一目录下，第三个是图像处理能力选择    request_url = 'https://aip.baidubce.com/rest/2.0/image-process/v1/' + how_to_deal    if how_to_deal == 'style_trans':  # 判断如果是 图像风格化，需要额外添加一个风格配置        others = 'cartoon'  # 风格化参数，具体可设置范围参见下面注释        ''' cartoon：卡通画风格 pencil：铅笔风格 color_pencil：彩色铅笔画风格 warm：彩色糖块油画风格 wave：神奈川冲浪里油画风格 lavender：薰衣草油画风格 mononoke：奇异油画风格 scream：呐喊油画风格 gothic：哥特油画风格 '''    else:        others = ''     file = open(img_before, 'rb')  # 二进制读取图片    origin_img = base64.b64encode(file.read())  # 将图片进行base64编码    headers = {'Content-Type': 'application/x-www-form-urlencoded'}    data = {        'access_token': get_access_token(),        'image': origin_img,        "type":'anime_mask',        "mask_id":"2"    }     res = requests.post(request_url, data=data, headers=headers)     res = res.json()    print(res)    if res:        f = open(img_after, 'wb')        after_img = res['image']        after_img = base64.b64decode(after_img)        f.write(after_img)        f.close()  if __name__ == '__main__':    img_before = 'img/mm1.jpg'  # 当前目录下的图片    img_after = img_before.split('.')  # 将原文件名分成列表    img_after = img_after[0] + '_2.' + img_after[1]  # 新生成的文件名为原文件名上加 _1     image_process(img_before, img_after, 'selfie_anime')    # 第三个参数： selfie_anime 为人像动漫化，colourize 图像上色，style_trans 为图像风格化    print('done!')

返回结果说明