认知服务-如何从实时流向Azure FaceList添加多个面孔?(Python) [英] Cognitive Services - How to add multiple Faces from a Live Stream to Azure FaceList ? (Python)

问题描述

问题背景:

我已经创建了一个Azure FaceList，并且正在使用网络摄像头捕获实时供稿，并且:

I have created an Azure FaceList and I am using my webcam to capture live feed and:

1. 将流发送到Azure 人脸检测
2. 获取人脸检测
返回的人脸矩形
3. 使用返回的Face矩形将从实时视频流中检测到的面部添加到我的FaceList.

1. sending the stream to Azure Face Detect
2. getting Face Rectangle returned by Face Detect
3. using the returned Face rectangle to add Face Detected from Live Video Stream to my FaceList.

((我需要创建面孔列表"以解决我在另一个问题中回答的问题，该问题由Nicolas回答了>，这就是我的关注

(I need to create Face List in order to solve the problem I explained in my other question which is answered by Nicolas, which is what I am following)

问题详细信息:

根据位于 https:/的Azure FaceList文档/docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromstream ，如果图像中有多个面孔，则需要指定目标面孔以添加到Azure FaceList.

According to Azure FaceList documentation at https://docs.microsoft.com/en-us/rest/api/cognitiveservices/face/facelist/addfacefromstream ,if there are multiple faces in the image, we need to specify the target Face to add to Azure FaceList.

问题是，如果我们需要在人脸列表"中添加所有检测到的人脸(多个人脸)，该怎么办?假设一个视频帧中有2张或更多张面孔，那么如何将这两张面孔添加到面孔列表"中?

我尝试将从 Azure Face Detect 返回的脸部矩形添加到Python列表中，然后遍历List索引，以便可以将每个脸部矩形一一传递给Azure FaceList.但是没用.

I have tried adding the face rectangles returned from Azure Face Detect into a Python List and then iterating Over List indexes, so that each face Rectangle can be passed to Azure FaceList one-by-one. But no use.

仍然出现错误:

There are more than one faces in the image

我的代码:

face_list_id = "newtest-face-list"
vid = cv2.VideoCapture(0)


count = 0


face_ids_live_Detected = []  #This list will store faceIds from detected faces
list_of_face_rectangles = []
face_rect_counter=0

while True:
        ret, frame = vid.read()
        check,buffer = cv2.imencode('.jpg', frame)
        img = cv2.imencode('.jpg', frame)[1].tobytes()
        base64_encoded = base64.b64encode(buffer).decode()
        print(type(img))
        detected_faces = utils.detect_face_stream(endpoint=ENDPOINT, key=KEY, image=img,face_attributes=attributes,recognition_model='recognition_03')
        print('Image num {} face detected {}'.format(count, detected_faces))
        count += 1
        color = (255, 0, 0)
        thickness = 2

        for face in detected_faces:
        
            detected_face_id = face['faceId']
            face_ids_live_Detected.append(detected_face_id)
            

            detected_face_rectangle = face['faceRectangle']
            list_of_face_rectangles.append(detected_face_rectangle)
            print("detected rectangle =",detected_face_rectangle)

            face_rect_for_facelist = list_of_face_rectangles[face_rect_counter]
            face_rect_counter +=1

       frame = cv2.rectangle(frame, *utils.get_rectangle(face), color, thickness)
       cv2.imshow('frame', frame)

       for face_id_live in face_ids_live_Detected:
                        similar_faces = face_client.face.find_similar(face_id=face_id_live, face_list_id=face_list_id)                
                        if not similar_faces:
                                print('No similar faces found !')
                                print('Adding Unknown Face to FaceList...')
                                facelist_result = utils.facelist_add(endpoint=ENDPOINT, key=KEY, face_list_id=face_list_id,data=img,params=face_rect_for_facelist)
                                persisted_face_id = facelist_result['persistedFaceId']
                        else:
                                print('Similar Face Found!')
                                for similar_face in similar_faces:
                                        face_id_similar = similar_face.face_id
                                        print("Confidence: "+str(similar_face.confidence))

在我的 utils 文件中，功能facelist_add 的代码如下:

From my utils file, code for function facelist_add is as follows:

def facelist_add(endpoint, key, face_list_id, data=None, json=None, headers=None,params=None, targetFace=None):
    # pylint: disable=too-many-arguments
    """Universal interface for request."""
    method = 'POST'
    url = endpoint + '/face/v1.0/facelists/'+face_list_id+'/persistedfaces'

    # Make it possible to call only with short name (without BaseUrl).
    if not url.startswith('https://'):
        url = BaseUrl.get() + url

    params={}

    # Setup the headers with default Content-Type and Subscription Key.
    headers = headers or {}
    if 'Content-Type' not in headers:
        headers['Content-Type'] = 'application/octet-stream'
    headers['Ocp-Apim-Subscription-Key'] = key


    params['detectionModel']='detection_03'
    

    response = requests.request(
        method,
        url,
        params=params,
        data=data,
        json=json,
        headers=headers)

    if response.text:
         result = response.json()
    else:
         result = {}

    return result

推荐答案

感谢所有提供帮助的人，尤其是 Nicolas R .我只是发现了一个错误并纠正了它.现在，程序像Charm一样运行.

Thanks to everyone who helped and especially Nicolas R. I just found a mistake and corrected it. Now the program is working like Charm.

实际上，Azure的面部检测"以顶部，左侧，宽度，高度顺序返回面部矩形，我直接将其输入到faceList的targetFace中.

Actually, Azure 'Face Detect' returns face Rectangle in top,left,width,height sequence which I was feeding directly to the faceList's targetFace.

现在我只是交换了Face Rectangle的前两个值，它变成了 left，top，width，height ，这就是文档所说的内容，并且现在可以正常工作了

Now I just swapped the first two values of Face Rectangle and it becomes left,top,width,height which is what the documentation says, and it's working fine now.

解决方案:

我添加了一个新函数，该函数采用faceRectangle字典并交换前两个值.

I have added a new function that takes the faceRectangle dictionary and swap first two values.

list_of_faceRect_valuesonly=[]

def face_rect_values(faceRect_dict):
        temp_list=[]
        for key,value in faceRect_dict.items():
                temp_list.append(value)
        temp_list[0], temp_list[1] = temp_list[1], temp_list[0]
        list_of_faceRect_valuesonly.append(temp_list)

为了从列表中提取值，我做了以下事情:

In order to extract values from list, I did following:

face_rect_counter=0
face_rect_for_facelist = list_of_faceRect_valuesonly[face_rect_counter]
face_rect_counter +=1

请求加入facelist_add功能

facelist_result = utils.facelist_add(endpoint=ENDPOINT, key=KEY, face_list_id=face_list_id,targetFace=face_rect_for_facelist,data=img)

我还稍微更改了 facelist_add 功能:

def facelist_add(endpoint, key, face_list_id, targetFace=[],data=None ,jsondata=None, headers=None):
    # pylint: disable=too-many-arguments
    """Universal interface for request."""
    method = 'POST'
    url = endpoint + '/face/v1.0/facelists/'+face_list_id+'/persistedfaces'

    # Make it possible to call only with short name (without BaseUrl).
    if not url.startswith('https://'):
        url = BaseUrl.get() + url

    params={}
    

    # Setup the headers with default Content-Type and Subscription Key.
    headers = headers or {}
    if 'Content-Type' not in headers:
        headers['Content-Type'] = 'application/octet-stream'
    headers['Ocp-Apim-Subscription-Key'] = key

    list_of_targetfaces  =[]
    list_of_targetfaces.append(targetFace)


    params={'targetFace':json.dumps(targetFace)}
    params = {'targetFace': ','.join(map(str,targetFace))}

    print("Printing TargetFaces(facelist_add function) ...",params['targetFace'])
    

    params['detectionModel']='detection_03'


    url=url + "?"


    response = requests.post(url,params=params,data=data,headers=headers)
    


    print("Request URL: ", response.url)

    result = None

    # Prevent `response.json()` complains about empty response.
    if response.text:
        result = response.json()
    else:
        result = {}

    return result

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