百度智能云的使用

2024-07-16 15:17| 来源: 网络整理| 查看: 265

先进入这个界面：https://console.bce.baidu.com/ai/#/ai/face/overview/index

创建好应用后点击图片右下角的免费获取资源（把能获取的都获取来）

点击管理应用然后查看左边的技术文档

在API文档中找到想要的技术

根据提示获得需要的access_token 这里的AK和SK在上面的第二张图中从resopnse.json()中选择["access_token"]并返回

def getAccess(AK="CZ1HCPYGjXvlRVEGKR5XCIWT",SK="xT1CfbqzUa9xFAittlnS8ibc4tL4nTFn"): # client_id 为官网获取的AK， client_secret 为官网获取的SK host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={}&client_secret={}'.format(AK,SK) response = requests.get(host) if response: print(response.json()["access_token"]) return (response.json()["access_token"])

将access_token替换并根据所需要的内容替换params中的参数

代码：AIFaceDetect.py

''' 人脸检测与属性分析 ''' def AI_facedetect(Path="./face_img/face1.jpg"): # Path="./face1.jpg" request_url = "https://aip.baidubce.com/rest/2.0/face/v3/detect" f = open(Path, 'rb') #base64编码后的图片 img = base64.b64encode(f.read()) params = {"image":img,"image_type":"BASE64","face_field":"faceshape,age,beauty,emotion,gender"} access_token = getAccess("CZ1HCPYGjXvlRVEGKR5XCIWT","xT1CfbqzUa9xFAittlnS8ibc4tL4nTFn") request_url = request_url + "?access_token=" + access_token headers = {'content-type': 'application/json'} response = requests.post(request_url, data=params, headers=headers) if response: print(response.json()['result'],end="\n") print((response.json()['result']['face_list'][0]['location']),\ (response.json()['result']['face_list'][0]['face_shape']['type']),\ (response.json()['result']['face_list'][0]['beauty']), \ (response.json()['result']['face_list'][0]['emotion']['type']), \ (response.json()['result']['face_list'][0]['gender']['type']), \ (response.json()['result']['face_list'][0]['age']), \ (response.json()['result']['face_num']) ) return(response.json()['result']['face_list'][0]['location']),\ (response.json()['result']['face_list'][0]['face_shape']['type']),\ (response.json()['result']['face_list'][0]['beauty']), \ (response.json()['result']['face_list'][0]['emotion']['type']), \ (response.json()['result']['face_list'][0]['gender']['type']), \ (response.json()['result']['face_list'][0]['age']), \ (response.json()['result']['face_num']) AI_facedetect()

输出的结果：

{'face_num': 1, 'face_list': [{'face_token': 'b4051e5145fa8e0b86c608805a64d80b', 'location': {'left': 423.64, 'top': 669.23, 'width': 245, 'height': 239, 'rotation': -5}, 'face_probability': 1, 'angle': {'yaw': -15.34, 'pitch': 18.32, 'roll': -1.93}, 'face_shape': {'type': 'heart', 'probability': 0.73}, 'age': 23, 'beauty': 66.05, 'emotion': {'type': 'neutral', 'probability': 1}, 'gender': {'type': 'female', 'probability': 1}}]} {'left': 423.64, 'top': 669.23, 'width': 245, 'height': 239, 'rotation': -5} heart 66.05 neutral female 23 1

将其封装成函数后便可以在界面中进行调用响应按钮

槽函数：face_detect.py

def facedet(self): #人脸检测 result_face_path="./face_img/result_face1.jpg" self.location,self.face_shape,self.beauty,self.emotion,self.gender,self.age,self.facenum=AI_facedetect(self.imgPath) img2 = cv2.imread(self.imgPath) #绘制矩形 # 矩形需要得到左上角和右下角的坐标即第二和第三个参数 print("location:",self.location) print((int(self.location['left']),int(self.location['top'])), (int(self.location['left']+self.location['width']), int(self.location["top"]+self.location["height"]))) cv2.rectangle(img2, (int(self.location['left']),int(self.location['top'])), (int(self.location['left']+self.location['width']), int(self.location["top"]+self.location["height"])) , color=(0, 0, 255),thickness=6) # 给对角线上的点pointlist[左上，右上，右下，坐下] # cv2.imshow("res",img2) # cv2.waitKey() cv2.imwrite(result_face_path, img2) self.graylabPic.setPixmap(QPixmap(result_face_path).scaled(self.graylabPic.size())) self.lineEditL.setText("人脸："+str(self.facenum)) self.lineEditM.setText("性别："+str(self.gender)) self.lineEditR.setText("年龄："+str(self.age)) self.lineEditDL.setText("脸型："+str(self.face_shape)) #注意这里要先强制类型转化成string self.lineEditDM.setText("表情："+str(self.emotion)) self.lineEditDR.setText("魅力值："+str(self.beauty))

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