CV2：关闭网络摄像头并以不同的功能重新打开

Question

在此代码中，我想使用 open cv 和face_recognition 显示 3 种类型的分类：“已知”、“未知”和“敌对”。 第一个函数（firtsCase()）包含“已知”、“未知”并且一切正常。 我想做的是：当程序知道函数 secondaryCase() 中的函数是“敌对”并且我按“q”时，网络摄像头必须关闭并启动将我发送回“已知”案例的函数，并且“未知”（在 secondaryCase() 末尾与firstCase() 一起调用）重新打开网络摄像头并重新分类。 您可以在函数 SecondCase() 的最新部分中找到它。

我有这个错误：

small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) cv2.error：OpenCV（4.5.5）/Users/runner/work/opencv-python/opencv-python/opencv/modules/imgproc/src/resize.cpp:4052：错误：（-215：断言失败）！ssize函数“resize”中的 .empty()

我知道这个错误出现是因为它试图调整已经被操纵的东西的大小，但我不知道知道我该如何解决它。

这是代码：

video_capture = cv2.VideoCapture(0)


face2 = face_recognition.load_image_file(os.path.abspath("path2"))
face2_face_encoding = face_recognition.face_encodings(face2)[0]

known_face_encodings = [
    face2_face_encoding
]

known_face_names = [
    "Giulia"
]

face_location = []
face_encodings = []
face_names = []

def firstCase():
    while True:
            ret, frame = video_capture.read()
            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
            rgb_small_frame = small_frame[:, :, ::-1]
            process_this_frame = True
            face = False
            if process_this_frame:
                face_locations = face_recognition.face_locations(rgb_small_frame)
                face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

                face_names = []
                for face_encoding in face_encodings:
                    matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.6)
                    name = "Sconosciuto"
                    face = True
                    face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                    best_match_index = np.argmin(face_distances)
                    if matches[best_match_index]:
                        name = known_face_names[best_match_index]
                        face = False
                        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                        best_match_index = np.argmin(face_distances) 
                    face_names.append(name)
            process_this_frame = not process_this_frame
            if face:
                for (top, right, bottom, left), name in zip(face_locations, face_names):
                    top *= 4
                    right *= 4
                    bottom *= 4
                    left *= 4
                    cv2.rectangle(frame, (left, top), (right, bottom), (40,48,48), 2)
                    cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (40,48,48), cv2.FILLED)
                    font = cv2.FONT_HERSHEY_DUPLEX
                    cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.6, (255, 255, 255), 1)
                cv2.imshow('Video', frame)
            else:
                for (top, right, bottom, left), name in zip(face_locations, face_names):
                    top *= 4
                    right *= 4
                    bottom *= 4
                    left *= 4
                    cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
                    cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
                    font = cv2.FONT_HERSHEY_DUPLEX
                    cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.6, (255, 255, 255), 1)
                cv2.imshow('Video', frame)    
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
    video_capture.release()
    cv2.destroyAllWindows()

def secondCase():
    while True:
        ret, frame = video_capture.read()
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        process_this_frame = True
        face = False
        if process_this_frame:
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

            face_names = []
            for face_encoding in face_encodings:
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.6)
                name = "Ostile"
                face = True
                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = known_face_names[best_match_index]
                    face = False
                face_names.append(name)
        process_this_frame = not process_this_frame
        if face:
            for (top, right, bottom, left), name in zip(face_locations, face_names):
                top *= 4
                right *= 4
                bottom *= 4
                left *= 4
                cv2.rectangle(frame, (left, top), (right, bottom), (0,0,0), 2)
                cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0,0,0), cv2.FILLED)
                font = cv2.FONT_HERSHEY_DUPLEX
                cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.6, (255, 255, 255), 1)
            cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    video_capture.release()
    cv2.destroyAllWindows()
    
    firstCase()
    

print("Inizio scansione :" + time.ctime() + "\nCitofono attivo. Citofona premendo a... ")

sel = selectors.DefaultSelector()
sel.register(sys.stdin, selectors.EVENT_READ)
sys.stdout.flush()
pairs = sel.select(timeout=5)

if pairs:
    if input("Citofona premendo 'a' \n"):
        firstCase()
else:
    secondCase()

谢谢大家的帮助。