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下面是我的代码：

def generate_dataset(nbr):
    face_classifier = cv2.CascadeClassifier("resources/haarcascade_frontalface_default.xml")

    mycursor.execute("select * from img_dataset WHERE img_person='" + str(nbr) + "'")
    data1 = mycursor.fetchall()
    for item in data1:
        imagePath = "dataset/" + nbr + "." + str(item[0]) + ".jpg"
        # print(imagePath)
        try:
            os.remove(imagePath)
        except:
            pass
    mycursor.execute("delete from img_dataset WHERE img_person='" + str(nbr) + "'")
    cnx.commit()

    def face_cropped(img):
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = face_classifier.detectMultiScale(gray, 1.3, 5)
        # scaling factor=1.3
        # Minimum neighbor = 5

        if len(faces) == 0:
            return None
        for (x, y, w, h) in faces:
            cropped_face = img[y:y + h, x:x + w]
        return cropped_face
        
    cap = cv2.VideoCapture(1)
    time.sleep(0.5)

    
    mycursor.execute("select ifnull(max(img_id), 0) from img_dataset")
    row = mycursor.fetchone()
    lastid = row[0]

    img_id = lastid
    max_imgid = img_id + 100
    count_img = 0

    while True:
        ret, img = cap.read()
        if not ret:
            print("Error: Couldn't capture image from webcam.")
            break
            
        if face_cropped(img) is None:
            frame1 = cv2.resize(img, (200, 200))
            frame1 = cv2.imencode('.jpg', frame1)[1].tobytes()
            yield (b'--frame1\r\n'b'Content-Type: image/jpeg\r\n\r\n' + frame1 + b'\r\n')
        if face_cropped(img) is not None:
            count_img += 1
            img_id += 1
            face = cv2.resize(face_cropped(img), (200, 200))
            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)

            # Convert the image data to bytes
            _, img_encoded = cv2.imencode('.jpg', face)
            img_bytes = img_encoded.tobytes()

            # Insert the image data into the database
            mycursor.execute("""
                INSERT INTO img_data (img_person, img_data)
                VALUES (%s, %s)
            """, (nbr, img_bytes))
            cnx.commit()

            file_name_path = "dataset/" + nbr + "." + str(img_id) + ".jpg"
            cv2.imwrite(file_name_path, face)
            cv2.putText(face, str(count_img) + '%', (5, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)

            mycursor.execute("""INSERT INTO `img_dataset` (`img_id`, `img_person`) VALUES
                                ('{}', '{}')""".format(img_id, nbr))
            cnx.commit()
            if int(img_id) == int(max_imgid):
                if int(img_id) == int(max_imgid):
                    cv2.putText(face, "Training Complete", (5, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
                    cv2.putText(face, "Click Train Face.", (5, 45), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
            frame = cv2.imencode('.jpg', face)[1].tobytes()
            yield (b'--frame1\r\n'b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

            if cv2.waitKey(1) == 13 or int(img_id) == int(max_imgid):
                break
                cap.release()
                cv2.destroyAllWindows()


@app.route('/train_classifier/<nbr>')
def train_classifier(nbr):
    user_id = session.get('user_id')  # Get the user's ID from the session
    # dataset_dir = "C:/Users/jd/PycharmProjects/FlaskOpencv_FaceRecognition/dataset"
    if not has_completed_training(user_id):
        img_count = get_image_count(user_id)  # Get the image count for the user

        if img_count == 100:

            dataset_dir = "dataset"

            path = [os.path.join(dataset_dir, f) for f in os.listdir(dataset_dir)]
            faces = []
            ids = []

            for image in path:
                img = Image.open(image).convert('L');
                imageNp = np.array(img, 'uint8')
                id = int(os.path.split(image)[1].split(".")[1])

                faces.append(imageNp)
                ids.append(id)
            ids = np.array(ids)

            # Train the classifier and save
            clf = cv2.face.LBPHFaceRecognizer_create()
            clf.train(faces, ids)
            clf.write("classifier.xml")

            mycursor.execute("UPDATE users SET completed_training = 1 WHERE id = %s", (user_id,))
            cnx.commit()

            flash('TRAIN SUCCESSFUL.', 'success')
        else:
            flash('SORRY, TRAIN MUST BE 100%', 'danger')

    else:
        flash('YOU CAN ONLY TRAIN ONCE.', 'danger')

    return redirect('/vfdataset_page')

字符串
这是我的错误代码：
[ WARN：email protected(https://stackoverflow.com/cdn-cgi/l/email-protection)] global cap_v4l.cpp：982 open VIDEOIO（V4 L2：/dev/video 1）：无法通过索引打开摄像头
[错误：email protected(https://stackoverflow.com/cdn-cgi/l/email-protection)] global obsensor_uvc_stream_channel.cpp：156 getStreamStream组摄像机索引超出范围
我已经尝试改变cap = cv2.VideoCapture(1)到-1，0，1，2，3，我的相机是完全工作.我希望有人能帮助我找出这个错误的可能解决方案.