I am trying to figure out what this error is. I am still relatively new to Python.
So my application is a face detection and emotion detection with a PyQT GUI interface
The camera does come up and capture the image (video).
However, the frame to show the face detection and the emotion recognition does not show up. I get the following error in the console:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) TypeError: src is not a numpy array, neither a scalar
Here is my code:
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_()
Where have I gone wrong?
