我正在尝试将一个单独的对话框窗口放入我的 PyQT GUI 中。我在一个单独的窗口中显示摄像头信息，但统计数据在另一个单独的窗口中弹出。我希望将其放入我的应用程序中的相机源旁边，这样它就合二为一了。这是标记为情绪概率的部分。
这是 GUI 应用程序主窗口:
这是我迄今为止尝试过但没有成功的代码(ImgWidget_3是pyqt设计器.ui文件中的情感概率组框/容器):
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_()
如何才能成功实现此目的？
最佳答案
本例的想法是将 numpy 数组转换为 QImage 并将其放置在小部件中，另一方面，不需要自定义小部件，您可以使用 QLabel，因此更改 .ui。最后，您的实现卡住了 GUI，这对用户来说并不愉快，因此通过信号发送信息并使用 QThread 来改进您的实现。
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_())
关于python - 如何使窗口输出适合小部件，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53756796/