AttributeError：'NoneType'对象在pycharm中没有属性'split'

Question

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.logging.set_verbosity(tf.logging.ERROR)
classifier = tf.estimator.Estimator(model_dir="tmp/cnn_model2", model_fn=cnn_model_fn)
prediction = None
model = load_model('cnn_model_keras2isl.h5')


def get_image_size():
    img = cv2.imread('gesturesisl/0/100.jpg', 0)
    return img.shape


image_x, image_y = get_image_size()


def tf_process_image(img):
    img = cv2.resize(img, (image_x, image_y))
    img = np.array(img, dtype=np.float32)
    np_array = np.array(img)
    return np_array


def tf_predict(classifier, image):
    '''
    need help with prediction using tensorflow
    '''
    global prediction
    processed_array = tf_process_image(image)
    pred_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": processed_array}, shuffle=False)
    pred = classifier.predict(input_fn=pred_input_fn)
    prediction = next(pred)
    print(prediction)


def keras_process_image(img):
    img = cv2.resize(img, (image_x, image_y))
    img = np.array(img, dtype=np.float32)
    img = np.reshape(img, (1, image_x, image_y, 1))
    return img


def keras_predict(model, image):
    processed = keras_process_image(image)
    pred_probab = model.predict(processed)[0]
    pred_class = list(pred_probab).index(max(pred_probab))
    return max(pred_probab), pred_class


def get_pred_text_from_db(pred_class):
    conn = sqlite3.connect("gestureisl_db.db")
    cmd = "SELECT g_name FROM gestureisl WHERE g_id=" + str(pred_class)
    cursor = conn.execute(cmd)
    for row in cursor:
        return row[0]


def split_sentence(text, num_of_words):
    '''
    Splits a text into group of num_of_words
    '''
    **list_words = text.split(" ")**
    length = len(list_words)
    splitted_sentence = []
    b_index = 0
    e_index = num_of_words
    while length > 0:
        part = ""
        for word in list_words[b_index:e_index]:
            part = part + " " + word
        splitted_sentence.append(part)
        b_index += num_of_words
        e_index += num_of_words
        length -= num_of_words
    return splitted_sentence


def put_splitted_text_in_blackboard(blackboard, splitted_text):
    y = 200
    for text in splitted_text:
        cv2.putText(blackboard, text, (4, y), cv2.FONT_HERSHEY_TRIPLEX, 2, (255, 255, 255))
        y += 50


def get_hand_hist():
    with open("hist", "rb") as f:
        hist = pickle.load(f)
    return hist


def recognize():
    global prediction
    cam = cv2.VideoCapture(1)
    if cam.read()[0] == False:
        cam = cv2.VideoCapture(0)
    hist = get_hand_hist()
    x, y, w, h = 300, 100, 300, 300
    while True:
        text = ""
        img = cam.read()[1]
        img = cv2.flip(img, 1)
        img = cv2.resize(img, (640, 480))
        imgCrop = img[y:y + h, x:x + w]
        imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([imgHSV], [0, 1], hist, [0, 180, 0, 256], 1)
        disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
        cv2.filter2D(dst, -1, disc, dst)
        blur = cv2.GaussianBlur(dst, (11, 11), 0)
        blur = cv2.medianBlur(blur, 15)
        thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
        thresh = cv2.merge((thresh, thresh, thresh))
        thresh = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
        thresh = thresh[y:y + h, x:x + w]
        (openCV_ver, _, __) = cv2.__version__.split(".")
        if openCV_ver == '3':
            contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
        elif openCV_ver == '4':
            contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
        if len(contours) > 0:
            contour = max(contours, key=cv2.contourArea)
            # print(cv2.contourArea(contour))
            if cv2.contourArea(contour) > 10000:
                x1, y1, w1, h1 = cv2.boundingRect(contour)
                save_img = thresh[y1:y1 + h1, x1:x1 + w1]

                if w1 > h1:
                    save_img = cv2.copyMakeBorder(save_img, int((w1 - h1) / 2), int((w1 - h1) / 2), 0, 0,
                                                  cv2.BORDER_CONSTANT, (0, 0, 0))
                elif h1 > w1:
                    save_img = cv2.copyMakeBorder(save_img, 0, 0, int((h1 - w1) / 2), int((h1 - w1) / 2),
                                                  cv2.BORDER_CONSTANT, (0, 0, 0))

                pred_probab, pred_class = keras_predict(model, save_img)

                if pred_probab * 100 > 80:
                    text = get_pred_text_from_db(pred_class)
                    print(text)
        blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
        **splitted_text = split_sentence(text, 2)**
        put_splitted_text_in_blackboard(blackboard, splitted_text)
        # cv2.putText(blackboard, text, (30, 200), cv2.FONT_HERSHEY_TRIPLEX, 1.3, (255, 255, 255))
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        res = np.hstack((img, blackboard))
        cv2.imshow("Recognizing gesture", res)
        cv2.imshow("thresh", thresh)
        if cv2.waitKey(1) == ord('q'):
            break


keras_predict(model, np.zeros((50, 50), dtype=np.uint8))
recognize()

**我收到此错误 AttributeError: 'NoneType' object has no attribute 'split' 对于上述代码。有人可以帮我解决这个问题吗？

Traceback（最近一次调用最后一次）： 文件“C:/”，第 158 行，在 认出（） 文件“C:/”，第 146 行，在识别中 split_text = split_sentence（文本，2） 文件“C:/”，第 69 行，在 split_sentence 中 list_words = text.split(" ") AttributeError: 'NoneType' 对象没有属性 'split'**

Answer 1

get_pred_text_from_db 如果游标中没有行，则返回None。如果存在多行，它也只会返回第一行。考虑yield 而不是return，并决定如何处理没有可用行的情况。