OpenCV2窗口启动时冻结并关闭问题求助（人脸识别项目）

核心问题分析与修复点： #

• 循环内错误释放资源：video_capture.release()和cv2.destroyAllWindows()被放在while True循环内部，第一次循环就会执行资源释放和窗口销毁，导致窗口直接关闭。需将这两行移到循环结束后执行。
• 帧处理逻辑缺失：self.process_current_frame = not self.process_current_frame导致程序仅交替处理帧，且显示操作只在处理帧时执行，后续无画面更新引发窗口未响应。需移除该交替逻辑，保证每帧都处理并显示。
• 路径大小写不统一：加载人脸图片时，os.listdir("Faces")与f'faces/{image}'路径大小写不一致，在区分大小写的系统中会导致人脸编码失败，需统一路径写法。
• 颜色参数无效：矩形框颜色(0,0,25)数值过小，几乎不可见，改为标准BGR红色值(0,0,255)。

修正后的完整代码： #

import face_recognition
import os, sys
import cv2
import numpy as np
import math


def face_confidence(face_distance, face_match_threshold=0.6):
    range_val = (1.0 - face_match_threshold)
    linear_val = (1.0 - face_distance) / (range_val * 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)) + '%'

class FaceRecognition:
    face_locations = []
    face_encodings = []
    face_names = []
    known_face_encodings = []
    known_face_names = []

    def __init__(self):
        self.encode_faces()

    def encode_faces(self):
        # 统一路径大小写
        for image in os.listdir("faces"):
            face_image = face_recognition.load_image_file(f'faces/{image}')
            # 增加判断，避免无人脸的图片引发索引错误
            face_encodings = face_recognition.face_encodings(face_image)
            if face_encodings:
                face_encoding = face_encodings[0]
                self.known_face_encodings.append(face_encoding)
                # 去掉图片扩展名，显示更友好的名字
                self.known_face_names.append(os.path.splitext(image)[0])
            else:
                print(f"警告：图片 {image} 中未检测到人脸，已跳过")
        print("已编码的人脸名单：", self.known_face_names)

    def run_recognition(self):
        video_capture = cv2.VideoCapture(0)

        if not video_capture.isOpened():
            sys.exit('未找到视频源')

        while True:
            ret, frame = video_capture.read()
            # 判断是否成功读取帧
            if not ret:
                print("无法读取视频帧")
                break

            # 处理每一帧，移除交替处理逻辑
            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
            rgb_small_frame = small_frame[:, :, ::-1]

            # 检测人脸位置和编码
            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.known_face_encodings, face_encoding)
                name = 'Unknown'
                confidence = 'Unknown'

                face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
                if len(face_distances) > 0:
                    best_match_index = np.argmin(face_distances)
                    if matches[best_match_index]:
                        name = self.known_face_names[best_match_index]
                        confidence = face_confidence(face_distances[best_match_index])

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

            # 在原帧上绘制标注
            for (top, right, bottom, left), name in zip(self.face_locations, self.face_names):
                top *= 4
                right *= 4
                bottom *= 4
                left *= 4

                # 修改为可见的红色框
                cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
                cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), -1)
                cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 1)

            # 显示画面
            cv2.imshow('Face Recognition', frame)

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

        # 循环结束后再释放资源
        video_capture.release()
        cv2.destroyAllWindows()

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

内容的提问来源于stack exchange，提问作者Augustas Judickas