pythonpyqtpyqt4
how to fit a windowed output to a widget
I am trying to fit a separate dialog window into my PyQT GUI. I have the camera feed in a separate window, but the stats pop up in another separate window. I wish to get it into my application next to the camera feed so it is all in one. It is the section labeled emotional probabilities.
here is the GUI application main window:
Here is my code that I have tried thus far with no success (ImgWidget_3 is the Emotional probability group box/container in the pyqt designer .ui file ):
from keras.preprocessing.image import img_to_array
from keras.models import load_model
# parameters for loading data and images
detection_model_path = '/xxxxxxx/haarcascade_frontalface_default.xml'
emotion_model_path = '/xxxxxxx/_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 StatImageWidget(QtGui.QWidget):
def __init__(self, parent=None):
super(StatImageWidget, 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.ImgWidget_3 = StatImageWidget(self.ImgWidget_3)
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(img, 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(img, label, (fX, fY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
cv2.rectangle(img, (fX, fY), (fX + fW, fY + fH),
(0, 0, 255), 2)
cv2.imshow("Emotional Probabilities", canvas)
cv2.waitKey(1) & 0xFF == ord('q')
self.ImgWidget_3.canvas(imshow)
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('Test app')
w.show()
app.exec_()
How can I make this successfully work?
Solution
The idea in this case is to convert the numpy array to QImage and place it in the widget, on the other hand it is not necessary to have a custom widget, you can use a QLabel so change the .ui. And finally your implementation froze the GUI which is not pleasant for the user so improve your implementation by sending the information through signals and using QThread.
simple.ui
<?xml version="1.0" encoding="UTF-8"?>
<ui version="4.0">
<class>MainWindow</class>
<widget class="QMainWindow" name="MainWindow">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>1000</width>
<height>610</height>
</rect>
</property>
<property name="windowTitle">
<string>MainWindow</string>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<widget class="QWidget" name="centralwidget">
<layout class="QGridLayout" name="gridLayout">
<item row="0" column="2">
<widget class="QGroupBox" name="groupBox_2">
<property name="title">
<string>Emotion Probabilities</string>
</property>
<layout class="QVBoxLayout" name="verticalLayout_2">
<item>
<widget class="QLabel" name="emotional_label">
<property name="text">
<string/>
</property>
</widget>
</item>
</layout>
</widget>
</item>
<item row="1" column="0">
<widget class="QPushButton" name="startButton">
<property name="minimumSize">
<size>
<width>0</width>
<height>50</height>
</size>
</property>
<property name="text">
<string>Start</string>
</property>
</widget>
</item>
<item row="0" column="0">
<widget class="QGroupBox" name="groupBox">
<property name="title">
<string>Video</string>
</property>
<layout class="QVBoxLayout" name="verticalLayout">
<item>
<widget class="QLabel" name="video_label">
<property name="text">
<string/>
</property>
</widget>
</item>
</layout>
</widget>
</item>
</layout>
</widget>
<widget class="QMenuBar" name="menubar">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>1000</width>
<height>25</height>
</rect>
</property>
</widget>
<widget class="QStatusBar" name="statusbar"/>
</widget>
<resources/>
<connections/>
</ui>
simpleMultifaceGUI_v01.py
# -*- coding: utf-8 -*-
import os
import cv2
import numpy as np
from PyQt4 import QtCore, QtGui, uic
from keras.engine.saving import load_model
from keras_preprocessing.image import img_to_array
__author__ = "Ismail ibn Thomas-Benge"
__copyright__ = "Copyright 2018, blackstone.software"
__version__ = "0.1"
__license__ = "GPL"
# parameters for loading data and images
dir_path = os.path.dirname(os.path.realpath(__file__))
detection_model_path = os.path.join("haarcascade_files/haarcascade_frontalface_default.xml")
emotion_model_path = os.path.join("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"]
emotion_classifier._make_predict_function()
running = False
capture_thread = None
form_class, _ = uic.loadUiType("simple.ui")
def NumpyToQImage(img):
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
qimg = QtGui.QImage(rgb.data, rgb.shape[1], rgb.shape[0], QtGui.QImage.Format_RGB888)
return qimg
class CaptureWorker(QtCore.QObject):
imageChanged = QtCore.pyqtSignal(np.ndarray)
def __init__(self, properties, parent=None):
super(CaptureWorker, self).__init__(parent)
self._running = False
self._capture = None
self._properties = properties
@QtCore.pyqtSlot()
def start(self):
if self._capture is None:
self._capture = cv2.VideoCapture(self._properties["index"])
self._capture.set(cv2.CAP_PROP_FRAME_WIDTH, self._properties["width"])
self._capture.set(cv2.CAP_PROP_FRAME_HEIGHT, self._properties["height"])
self._capture.set(cv2.CAP_PROP_FPS, self._properties["fps"])
self._running = True
self.doWork()
@QtCore.pyqtSlot()
def stop(self):
self._running = False
def doWork(self):
while self._running:
self._capture.grab()
ret, img = self._capture.retrieve(0)
if ret:
self.imageChanged.emit(img)
self._capture.release()
self._capture = None
class ProcessWorker(QtCore.QObject):
resultsChanged = QtCore.pyqtSignal(np.ndarray)
imageChanged = QtCore.pyqtSignal(np.ndarray)
@QtCore.pyqtSlot(np.ndarray)
def process_image(self, img):
gray = cv2.cvtColor(img, 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")
if len(faces) > 0:
face = sorted(faces, reverse=True, key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
(fX, fY, fW, fH) = face
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]
label = EMOTIONS[preds.argmax()]
cv2.putText(img, label, (fX, fY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
cv2.rectangle(img, (fX, fY), (fX+fW, fY+fH), (255, 0, 0), 2)
self.imageChanged.emit(img)
for i, (emotion, prob) in enumerate(zip(EMOTIONS, preds)):
text = "{}: {:.2f}%".format(emotion, prob * 100)
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(img, label, (fX, fY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
cv2.rectangle(img, (fX, fY), (fX + fW, fY + fH),
(0, 0, 255), 2)
self.resultsChanged.emit(canvas)
class MyWindowClass(QtGui.QMainWindow, form_class):
def __init__(self, parent=None):
super(MyWindowClass, self).__init__(parent)
self.setupUi(self)
self._thread = QtCore.QThread(self)
self._thread.start()
self._capture_obj = CaptureWorker({"index": 0, "width": 640, "height": 480, "fps": 30})
self._process_obj = ProcessWorker()
self._capture_obj.moveToThread(self._thread)
self._process_obj.moveToThread(self._thread)
self._capture_obj.imageChanged.connect(self._process_obj.process_image)
self._process_obj.imageChanged.connect(self.on_video_changed)
self._process_obj.resultsChanged.connect(self.on_emotional_changed)
self.startButton.clicked.connect(self.start_clicked)
@QtCore.pyqtSlot()
def start_clicked(self):
QtCore.QMetaObject.invokeMethod(self._capture_obj, "start", QtCore.Qt.QueuedConnection)
self.startButton.setEnabled(False)
self.startButton.setText('Starting...')
@QtCore.pyqtSlot(np.ndarray)
def on_emotional_changed(self, im):
img = NumpyToQImage(im)
pix = QtGui.QPixmap.fromImage(img)
self.emotional_label.setFixedSize(pix.size())
self.emotional_label.setPixmap(pix)
@QtCore.pyqtSlot(np.ndarray)
def on_video_changed(self, im):
img = NumpyToQImage(im)
pix = QtGui.QPixmap.fromImage(img)
self.video_label.setPixmap(pix.scaled(self.video_label.size()))
def closeEvent(self, event):
self._capture_obj.stop()
self._thread.quit()
self._thread.wait()
super(MyWindowClass, self).closeEvent(event)
if __name__ == '__main__':
import sys
app = QtGui.QApplication(sys.argv)
w = MyWindowClass()
w.show()
sys.exit(app.exec_())