基于OpenCV的人脸识别程序运行后意外闪退问题求助

程序启动摄像头后立刻停止运行，报错信息：

Process finished with exit code -1073741819 (0xC0000005)
（翻译：内存访问冲突，通常是非法内存读写导致的程序崩溃）

以下是我的代码：

import cv2 as cv
import numpy as np
import face_recognition
import os, sys
from datetime import datetime
import datetime as dt
import math

def face_conf (face_distance, face_match_threshold=0.6):
    range = (1.0 - face_match_threshold)
    linear_val = (1.0 - face_distance) / (range * 2.0)

    if face_distance > face_match_threshold:
        return str(round(linear_val * 100, 2)) + '%'
    else:
        value = (linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2 ) )) * 100
        return str (round(value, 2)) + '%'


def markAttendance(name):

    with open ('FaceAttendance.csv','r+') as f:
        myList = f.readlines()
        nameList = []
        for line in myList:
            entry = line.split(',')
            nameList.append(entry[0])

        if name not in nameList:
            now = datetime.now()
            current_date = now.strftime('%B-%d-%Y')
            time_arr = dt.time(10, 30, 00)
            dateString = now.strftime('%H:%M:%S')
            time_arr_string = time_arr.strftime('%H:%M:%S')
            status_late = ('LATE')
            status_present = ('PRESENT')

            if dateString > time_arr_string:
                f.writelines(f'\n{name}, {dateString}, {current_date}, {status_late}')

            elif dateString < time_arr_string:
                f.writelines(f'\n{name}, {dateString}, {current_date}, {status_present}')



markAttendance('Attendance Start: ')

class FaceRecognition:

    face_locations = []
    face_encodings = []
    face_names = []
    knownFaceEncodings = []
    knownFaceNames = []
    process_current_frame = True

    def __init__(self):
        self.encode_faces()

    def encode_faces(self):
        for img in os.listdir('Attendance'):
            face_image = face_recognition.load_image_file(f'Attendance/{img}')
            face_encoding = face_recognition.face_encodings(face_image)[0]

            self.knownFaceEncodings.append(face_encoding)
            self.knownFaceNames.append(os.path.splitext(img)[0])

        print(self.knownFaceNames)

    def run_recognition(self):
        video_cap = cv.VideoCapture(0)

        if not video_cap.isOpened():
            sys.exit('Error: Video Not Found!')

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

            if self.process_current_frame:
                small_frame = cv.resize(frame, (0,0), fx=0.25, fy=0.25)
                rgb_small_frame = small_frame [:, :, ::-1]

                #Find the face in current frame
                self.face_locations = face_recognition.face_locations(rgb_small_frame)
                self.face_encodings = face_recognition.face_encodings(rgb_small_frame, self.face_locations)

                self.face_names = []
                for face_encoding in self.face_encodings:
                    matches = face_recognition.compare_faces(self.knownFaceEncodings, face_encoding)
                    name = 'Unknown'
                    confidence = 'Unknown'

                    face_distances = face_recognition.face_distance(self.knownFaceEncodings, face_encoding)
                    matchIndex = np.argmin(face_distances)

                    if matches[matchIndex]:
                        name = self.knownFaceNames[matchIndex]
                        confidence = face_conf(face_distances[matchIndex])
                        markAttendance(name)


                    self.face_names.append(f'{name} ({confidence})')

            self.process_current_frame = not self.process_current_frame

            #display annotations

            for (top, right, bottom, left), name in zip (self.face_locations, self.face_names):
                top *=4
                right *= 4
                bottom *= 4
                left *= 4


                cv.rectangle (frame, (left, top), (right, bottom), (0, 255, 0), 2)
                cv.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), -1)
                cv.putText(frame, name, (left + 6, bottom - 6), cv.FONT_HERSHEY_COMPLEX, .75, (255, 255, 255), 2)


            cv.imshow ('Face Recognition', frame)
            if cv.waitKey(1) & 0xFF == ord('s'):
                break

        video_cap.release()
        cv.destroyAllWindows()


if __name__ == '__main__':
    fr = FaceRecognition()
    fr.run_recognition()

错误码0xC0000005是Windows系统的内存访问冲突，核心原因是程序尝试读写非法内存区域。针对你的代码，以下是具体修复点：

1. 修复人脸编码的索引越界问题 #

face_recognition.face_encodings(face_image)[0]直接取索引0，如果图片中没有检测到人脸，会抛出索引错误并导致崩溃。修改encode_faces方法：

def encode_faces(self):
    for img in os.listdir('Attendance'):
        face_image = face_recognition.load_image_file(f'Attendance/{img}')
        face_encodings = face_recognition.face_encodings(face_image)
        if face_encodings:  # 确保检测到人脸
            face_encoding = face_encodings[0]
            self.knownFaceEncodings.append(face_encoding)
            self.knownFaceNames.append(os.path.splitext(img)[0])
        else:
            print(f"Warning: No face detected in image {img}")
    print(self.knownFaceNames)

2. 检查摄像头帧读取有效性 #

如果摄像头读取失败（ret为False），frame是空值，后续resize操作会触发内存错误。在处理前加判断：

while True:
    ret, frame = video_cap.read()
    if not ret:
        print("Failed to read frame from camera")
        break  # 或添加重试逻辑

    if self.process_current_frame:
        # 后续处理代码...

3. 将类属性改为实例属性 #

你的FaceRecognition类中，face_locations、face_encodings等是类级属性，所有实例共享，可能在帧处理时引发并发冲突。改为实例属性：

class FaceRecognition:
    def __init__(self):
        # 初始化实例属性
        self.face_locations = []
        self.face_encodings = []
        self.face_names = []
        self.knownFaceEncodings = []
        self.knownFaceNames = []
        self.process_current_frame = True
        self.encode_faces()

4. 修复考勤文件的异常处理 #

如果FaceAttendance.csv不存在，open('FaceAttendance.csv','r+')会报错。修改markAttendance函数，确保文件存在：

def markAttendance(name):
    # 先检查文件是否存在，不存在则创建并写入表头
    if not os.path.exists('FaceAttendance.csv'):
        with open('FaceAttendance.csv', 'w') as f:
            f.write('Name,Time,Date,Status\n')
    
    with open('FaceAttendance.csv','r+') as f:
        myList = f.readlines()
        nameList = []
        for line in myList:
            entry = line.strip().split(',')
            if entry:  # 避免空行导致索引错误
                nameList.append(entry[0])

        if name not in nameList:
            now = datetime.now()
            current_date = now.strftime('%B-%d-%Y')
            time_arr = dt.time(10, 30, 00)
            dateString = now.strftime('%H:%M:%S')
            time_arr_string = time_arr.strftime('%H:%M:%S')
            status_late = 'LATE'
            status_present = 'PRESENT'

            if dateString > time_arr_string:
                f.writelines(f'\n{name}, {dateString}, {current_date}, {status_late}')
            else:  # 补充等于时间点的情况
                f.writelines(f'\n{name}, {dateString}, {current_date}, {status_present}')

5. 排查依赖库版本兼容性 #

Windows下face_recognition依赖的dlib库容易出现版本不兼容问题，建议：

• 卸载现有库：pip uninstall opencv-python face-recognition numpy dlib
• 重新安装兼容版本：pip install opencv-python face-recognition numpy（让pip自动匹配兼容版本）
• 若仍有问题，手动安装对应Python版本和系统架构的预编译dlib包

内容的提问来源于stack exchange，提问作者Ecn Nllr