DeepFace出现无法使用的情况

[Justjustifyjudge]

DeepFace处理单张图片时没有问题，但是在streamcut之后处理会发生报错：
ValueError: Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.
代码如下：(其中我是用try把报错给跳了，直接做try里面的内容时可以看到有关报错)

ret, frame = cap.read()

    # 保存摄像头捕捉的图像到临时文件
    temp_face_path = f"{base_folder}/temp_face.jpg"
    cv2.imwrite(temp_face_path, frame)

    # 调用DeepFace进行人脸验证
    try:
        # result = DeepFace.verify(img1_path=temp_face_path, img2_path=img2_path, model_name='GhostFaceNet', enforce_detection=False)
        # Debug
        # result = DeepFace.verify(img1_path=img2_pathe, img2_path=img2_pathe, model_name='DeepFace', enforce_detection=False)
        result = DeepFace.verify(img1_path=temp_face_path, img2_path=img2_pathe)

        # 提取验证结果
        verified = result['verified']
        distance = result['distance']

        if verified:
            verified_text = f"Verified: {verified} - distance: {distance:0.4}"
        else:
            verified_text = f"Not Verified - distance: {distance:0.4}"

        cv2.putText(frame, verified_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

        # 显示图像
        cv2.imshow('Face Verification', frame)
    except:
        cv2.imshow('Face Verification', frame)
        print("No face detected")

现在暂时是使用face_recognition库的方法，作为替换的补丁，大家有什么思路吗？

[Justjustifyjudge]

对stream_cut的暂存图片路径进行了修改，再加了一些魔法，似乎有时行有时不行。
face_recognition库的主要问题是会错误地将陌生人识别为合法用户，而且无法进一步特化模型。
Deepface的主要问题是合法用户必须在比较合适的位置使用比较合适的表情才能识别为合法，而且有时会因为图片中没有人脸而抛出异常
face_recognition:

import cv2
import face_recognition

# 初始化摄像头
cap = cv2.VideoCapture(0)

# 调整摄像头捕捉的图像大小
cap.set(3, 640)  # 宽度
cap.set(4, 480)  # 高度

# 加载已知人脸图像并提取特征
known_face_img = face_recognition.load_image_file(r"C:\Users\linyiwu\Desktop\datasets\face\train\0002.jpg")
known_face_encoding = face_recognition.face_encodings(known_face_img)[0]

while True:
    ret, frame = cap.read()

    # 将图像转换为 RGB 格式（face_recognition 要求输入为 RGB 格式）
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # 在图像中检测人脸
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # 对检测到的人脸进行处理
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        # 进行人脸识别
        matches = face_recognition.compare_faces([known_face_encoding], face_encoding)
        name = "Unknown"

        if matches[0]:
            name = "Known Person"

        # 在人脸周围绘制矩形并显示姓名
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
        cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

    # 显示图像
    cv2.imshow('Face Recognition', frame)

    # 按下 'q' 键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头资源并关闭窗口
cap.release()
cv2.destroyAllWindows()

deepface:

'''
Author: lin
Time: 2024年4月22日10:40:22
Description: 实现摄像头的调用，人脸检测和验证的合并
'''
import cv2
from deepface import DeepFace

# 初始化摄像头
cap = cv2.VideoCapture(0)

# 调整摄像头捕捉的图像大小
cap.set(3, 640)  # 宽度
cap.set(4, 480)  # 高度

# 设置用于比对的人脸图像文件夹路径
base_folder = r'C:\Users\linyiwu\Desktop\datasets\face\train'
img2_pathe = r"C:\Users\linyiwu\Desktop\datasets\face\train\0001.jpg"

while True:
    ret, frame = cap.read()

    # 保存摄像头捕捉的图像到临时文件
    temp_face_path = r"C:\Users\linyiwu\Desktop\datasets\face\train\temp_face.jpg"
    cv2.imwrite(temp_face_path, frame)

    # 调用DeepFace进行人脸验证
    try:
        # result = DeepFace.verify(img1_path=temp_face_path, img2_path=img2_path, model_name='GhostFaceNet', enforce_detection=False)
        # Debug
        # result = DeepFace.verify(img1_path=img2_pathe, img2_path=img2_pathe, model_name='DeepFace', enforce_detection=False)
        result = DeepFace.verify(img1_path=r'C:\Users\linyiwu\Desktop\datasets\face\train\temp_face.jpg', img2_path=r"C:\Users\linyiwu\Desktop\datasets\face\train\0001.jpg")

        # 提取验证结果
        verified = result['verified']
        distance = result['distance']

        if verified:
            verified_text = f"Verified: {verified} - distance: {distance:0.4}"
        else:
            verified_text = f"Not Verified - distance: {distance:0.4}"

        cv2.putText(frame, verified_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

        # 显示图像
        cv2.imshow('Face Verification', frame)
    except:
        cv2.imshow('Face Verification', frame)
        print("No face detected")
    # 按下 'q' 键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头资源并关闭窗口
cap.release()
cv2.destroyAllWindows()