src不是numpy数组或scaler-python人脸检测

但是,显示人脸检测和情感识别的帧没有显示出来。我在控制台中遇到以下错误:

灰色=cv2.CVT颜色(边框,cv2.COLOR\u BGR2GRAY) TypeError:src不是numpy数组,也不是标量

from PyQt4 import QtCore, QtGui, uic
import sys
import cv2
import numpy as np
import threading
import time
import Queue
import imutils

from keras.preprocessing.image import img_to_array
from keras.models import load_model

# parameters for loading data and images
detection_model_path = '/xxxxxxxx/haarcascade_files/haarcascade_frontalface_default.xml'
emotion_model_path = '/xxxxxxxx//models/_mini_XCEPTION.102-0.66.hdf5'

# hyper-parameters for bounding boxes shape
# loading models
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
 "neutral"]


running = False
capture_thread = None
form_class = uic.loadUiType("simple.ui")[0]
q = Queue.Queue()


def grab(cam, queue, width, height, fps):
    global running
    capture = cv2.VideoCapture(cam)
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    capture.set(cv2.CAP_PROP_FPS, fps)

    while(running):
        frame = {}        
        capture.grab()
        retval, img = capture.retrieve(0)
        frame["img"] = img

        if queue.qsize() < 10:
            queue.put(frame)
        else:
            print queue.qsize()

class OwnImageWidget(QtGui.QWidget):
    def __init__(self, parent=None):
        super(OwnImageWidget, self).__init__(parent)
        self.image = None

    def setImage(self, image):
        self.image = image
        sz = image.size()
        self.setMinimumSize(sz)
        self.update()

    def paintEvent(self, event):
        qp = QtGui.QPainter()
        qp.begin(self)
        if self.image:
            qp.drawImage(QtCore.QPoint(0, 0), self.image)
        qp.end()



class MyWindowClass(QtGui.QMainWindow, form_class):
    def __init__(self, parent=None):
        QtGui.QMainWindow.__init__(self, parent)
        self.setupUi(self)

        self.startButton.clicked.connect(self.start_clicked)

        self.window_width = self.ImgWidget.frameSize().width()
        self.window_height = self.ImgWidget.frameSize().height()
        self.ImgWidget = OwnImageWidget(self.ImgWidget)       

        self.timer = QtCore.QTimer(self)
        self.timer.timeout.connect(self.update_frame)
        self.timer.start(1)


    def start_clicked(self):
        global running
        running = True
        capture_thread.start()
        self.startButton.setEnabled(False)
        self.startButton.setText('Starting...')


    def update_frame(self):
        if not q.empty():
            self.startButton.setText('Camera is live')
            frame = q.get()
            img = frame["img"]

            img_height, img_width, img_colors = img.shape
            scale_w = float(self.window_width) / float(img_width)
            scale_h = float(self.window_height) / float(img_height)
            scale = min([scale_w, scale_h])

            if scale == 0:
                scale = 1

            img = cv2.resize(img, None, fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            height, width, bpc = img.shape
            bpl = bpc * width
            image = QtGui.QImage(img.data, width, height, bpl, QtGui.QImage.Format_RGB888)
            self.ImgWidget.setImage(image)

            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)

            canvas = np.zeros((250, 300, 3), dtype="uint8")
            frameClone = frame.copy()
            if len(faces) > 0:
                faces = sorted(faces, reverse=True,
                key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
                (fX, fY, fW, fH) = faces
                            # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
                    # the ROI for classification via the CNN
                roi = gray[fY:fY + fH, fX:fX + fW]
                roi = cv2.resize(roi, (64, 64))
                roi = roi.astype("float") / 255.0
                roi = img_to_array(roi)
                roi = np.expand_dims(roi, axis=0)


                preds = emotion_classifier.predict(roi)[0]
                emotion_probability = np.max(preds)
                label = EMOTIONS[preds.argmax()]


                for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
                        # construct the label text
                        text = "{}: {:.2f}%".format(emotion, prob * 100)

                        # draw the label + probability bar on the canvas
                       # emoji_face = feelings_faces[np.argmax(preds)]


                        w = int(prob * 300)
                        cv2.rectangle(canvas, (7, (i * 35) + 5),
                        (w, (i * 35) + 35), (0, 0, 255), -1)
                        cv2.putText(canvas, text, (10, (i * 35) + 23),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45,
                        (255, 255, 255), 2)
                        cv2.putText(frameClone, label, (fX, fY - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
                        cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
                                      (0, 0, 255), 2)

    def closeEvent(self, event):
        global running
        running = False



capture_thread = threading.Thread(target=grab, args = (0, q, 1920, 1080, 30))

app = QtGui.QApplication(sys.argv)
w = MyWindowClass(None)
w.setWindowTitle('window #1')
w.show()
app.exec_()

我哪里出错了?