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Parcurcik
Asked:2022-08-03 17:06:38 +0000 UTC2022-08-03 17:06:38 +0000 UTC

如何在子线程的主线程的 QLineEdit 中显示值？

  • 772

该类通过haar级联检测车牌号，也直接显示在应用程序中。

我需要以某种方式将a带有车号的变量输出到预先准备好的表格QLineEdit中。

下面的方法不起作用，程序崩溃。

class PredictNumber(QThread):
    ImageUpdate = pyqtSignal(QImage)
    NuberUpdate = pyqtSignal(QTextLine)

    def run(self):
        self.ThreadActive = True
        capture = cv2.VideoCapture(0)
        a = None
        while self.ThreadActive:
            face_cascade = cv2.CascadeClassifier('cascade/haarcascade_russian_plate_number.xml')
            ret, frame = capture.read()
            if ret:
                image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                with_cascade = face_cascade.detectMultiScale(image, 1.3, 7)
                for i, (x, y, w, h) in enumerate(with_cascade):
                    roi_color = image[y:y + h, x:x + w]
                    r = 300.0 / roi_color.shape[1]
                    dim = (400, int(roi_color.shape[0] * r))
                    resized = cv2.resize(roi_color, dim, interpolation=cv2.INTER_AREA)
                    w_resized = resized.shape[0]
                    h_resized = resized.shape[1]
                    image[380:380 + w_resized, 235:235 + h_resized] = resized
                    letters = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'E', 'H', 'K', 'M', 'O',
                               'P', 'T', 'X', 'Y']

                    def decode_batch(out):
                        ret = []
                        for j in range(out.shape[0]):
                            out_best = list(np.argmax(out[j, 2:], 1))
                            out_best = [k for k, g in itertools.groupby(out_best)]
                            outstr = ''
                            for c in out_best:
                                if c < len(letters):
                                    outstr += letters[c]
                            ret.append(outstr)
                        return ret

                    paths = 'model1_nomer.tflite'
                    interpreter = tf.lite.Interpreter(model_path=paths)
                    interpreter.allocate_tensors()
                    # Get input and output tensors.
                    input_details = interpreter.get_input_details()
                    output_details = interpreter.get_output_details()
                    img = resized
                    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                    img = cv2.resize(img, (128, 64))
                    img = img.astype(np.float32)
                    img /= 255
                    img1 = img.T
                    img1.shape
                    X_data1 = np.float32(img1.reshape(1, 128, 64, 1))
                    input_index = (interpreter.get_input_details()[0]['index'])
                    interpreter.set_tensor(input_details[0]['index'], X_data1)
                    interpreter.invoke()
                    net_out_value = interpreter.get_tensor(output_details[0]['index'])
                    pred_texts = decode_batch(net_out_value)
                    if a != pred_texts:
                        a = pred_texts
                conv_to_qt = QTextLine(a)
                convert_to_qt_format = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888)
                self.ImageUpdate.emit(convert_to_qt_format)
                self.NuberUpdate.emit(conv_to_qt)
# Class Main
self.PredictNumber.NuberUpdate.connect(self.number_update_slot)
    def number_update_slot(self, number):
        self.ui.predicted_number.setText(number)
python многопоточность

1 个回答

  1. Best Answer

    您不能与额外线程上的小部件进行交互。

    我无法测试您的代码，因为您没有提供最低限度可重现的示例。
    尝试进行一些更改并检查。

    class PredictNumber(QThread):
# -> v ?
#    ImageUpdate = pyqtSignal(QImage)
#    NuberUpdate = pyqtSignal(QTextLine)

    imageUpdate = pyqtSignal(QImage) 
# -------------------------> vvv    
    nuberUpdate = pyqtSignal(str)    

    def run(self):
        self.ThreadActive = True
        capture = cv2.VideoCapture(0)
        a = None
        while self.ThreadActive:
            face_cascade = cv2.CascadeClassifier('cascade/haarcascade_russian_plate_number.xml')
            ret, frame = capture.read()
            if ret:
                image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                with_cascade = face_cascade.detectMultiScale(image, 1.3, 7)
                for i, (x, y, w, h) in enumerate(with_cascade):
                    roi_color = image[y:y + h, x:x + w]
                    r = 300.0 / roi_color.shape[1]
                    dim = (400, int(roi_color.shape[0] * r))
                    resized = cv2.resize(roi_color, dim, interpolation=cv2.INTER_AREA)
                    w_resized = resized.shape[0]
                    h_resized = resized.shape[1]
                    image[380:380 + w_resized, 235:235 + h_resized] = resized
                    letters = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'E', 'H', 'K', 'M', 'O',
                               'P', 'T', 'X', 'Y']

                    def decode_batch(out):
                        ret = []
                        for j in range(out.shape[0]):
                            out_best = list(np.argmax(out[j, 2:], 1))
                            out_best = [k for k, g in itertools.groupby(out_best)]
                            outstr = ''
                            for c in out_best:
                                if c < len(letters):
                                    outstr += letters[c]
                            ret.append(outstr)
                        return ret

                    paths = 'model1_nomer.tflite'
                    interpreter = tf.lite.Interpreter(model_path=paths)
                    interpreter.allocate_tensors()
                    # Get input and output tensors.
                    input_details = interpreter.get_input_details()
                    output_details = interpreter.get_output_details()
                    img = resized
                    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                    img = cv2.resize(img, (128, 64))
                    img = img.astype(np.float32)
                    img /= 255
                    img1 = img.T
                    img1.shape
                    X_data1 = np.float32(img1.reshape(1, 128, 64, 1))
                    input_index = (interpreter.get_input_details()[0]['index'])
                    interpreter.set_tensor(input_details[0]['index'], X_data1)
                    interpreter.invoke()
                    net_out_value = interpreter.get_tensor(output_details[0]['index'])
                    pred_texts = decode_batch(net_out_value)
                    if a != pred_texts:
                        a = pred_texts
                        
# ???                 conv_to_qt = QTextLine(a)
# !!! +++ 
                conv_to_qt = str(a)                                       # !!! +++ 
                
                convert_to_qt_format = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888)

                self.imageUpdate.emit(convert_to_qt_format)
                self.nuberUpdate.emit(conv_to_qt)
                
                self.msleep(10)                              # какая-то пауза видимо нужна ?

# Class Main
        ...
        self.predictNumber = PredictNumber()                                 # +++
# ---------> v^v^v^v^v^v^v      
        self.predictNumber.nuberUpdate.connect(self.number_update_slot)
        
    def number_update_slot(self, number):
        self.ui.predicted_number.setText(number)
    • 1

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