Use Python to interface with Tencent Cloud to realize face key point detection and comparison functions

Use Python to interface with Tencent Cloud to realize face key point detection and comparison functions

Face technology has been widely used in today's society, such as face recognition, face comparison, etc. As the leading cloud computing service provider in China, Tencent Cloud provides a rich set of face recognition and analysis APIs, as well as a Python SDK, which can be easily connected with Python. This article will introduce how to use Python to connect with Tencent Cloud interface to realize facial key point detection and comparison functions.

First, we need to create a project in the console of Tencent Cloud Face Recognition and Analysis API and obtain the API key. In this project, we can configure the interface for face detection and key point analysis.

Next, we need to install the Python SDK, which can be installed using pip in the command line:

pip install -U tencentcloud-sdk-python

After the installation is complete, we can introduce the Tencent Cloud SDK into the Python code and create a client :

from tencentcloud.common import credential
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
from tencentcloud.fmu.v20191213 import fmu_client, models

# 配置客户端
httpProfile = HttpProfile()
httpProfile.endpoint = "fmu.tencentcloudapi.com"

# 密钥信息
cred = credential.Credential("your-secret-id", "your-secret-key")
clientProfile = ClientProfile()
clientProfile.httpProfile = httpProfile
client = fmu_client.FmuClient(cred, "ap-guangzhou", clientProfile)

In the code, we set the key, client region, client configuration information, etc.

Next, we can write code to implement the function of detecting and comparing facial key points. Take face key point detection as an example:

def detect_face(image_path):
    try:
        # 读取图片
        with open(image_path, "rb") as f:
            image_data = f.read()
            f.close()

        # 构建请求
        req = models.DetectFaceRequest()
        params = {
            "Image": {
                "ImageBase64": base64.b64encode(image_data).decode("utf-8")
            },
            "Url": ""
        }
        req.from_json_string(json.dumps(params))

        # 发送请求并解析结果
        resp = client.DetectFace(req)
        face_infos = json.loads(resp.to_json_string())["FaceInfos"]
        for face_info in face_infos:
            # 获取关键点坐标
            landmarks = face_info["Landmarks"]
            for landmark in landmarks:
                x = landmark["X"]
                y = landmark["Y"]
                print("关键点坐标：({},{})".format(x, y))

    except TencentCloudSDKException as err:
        print(err)

In the code, we first read the image and convert the image data to Base64 encoding, then build the request object, put the image data into the request, and finally initiate Request and parse the results. Through the results, we can obtain the coordinates of key points on the face.

The usage of Tencent Cloud's face comparison interface is similar to face key point detection. You only need to change the request parameters. The following is a code example for face comparison:

def face_match(image_path1, image_path2):
    try:
        # 读取图片1
        with open(image_path1, "rb") as f1:
            image_data1 = f1.read()
            f1.close()

        # 读取图片2
        with open(image_path2, "rb") as f2:
            image_data2 = f2.read()
            f2.close()

        # 构建请求
        req = models.CompareFaceRequest()
        params = {
            "ImageA": base64.b64encode(image_data1).decode("utf-8"),
            "ImageB": base64.b64encode(image_data2).decode("utf-8")
        }
        req.from_json_string(json.dumps(params))

        # 发送请求并解析结果
        resp = client.CompareFace(req)
        similarity = json.loads(resp.to_json_string())["Score"]
        print("人脸相似度为：{}%".format(similarity))

    except TencentCloudSDKException as err:
        print(err)

In the code, we first read the data of the two pictures and convert the data to Base64 encoding, then build the request object and put the picture data into During the request, the request is finally initiated and the results are parsed. Through the results, we can obtain the similarity between the two faces.

Through the above code examples, we can easily use Python to connect with the Tencent Cloud interface to realize the detection and comparison of facial key points. This provides convenience for us to develop face-related applications. Of course, the above code is just a simple example, and readers can expand and optimize it according to their own needs.

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