Smart target detection – PyQt5 builds target detection interface
Learn the preface
Based on the open source YoloV4-Pytorch source code of B guide, a face detection system for wearing a mask was developed (the undergraduate project completed in 21 years, it is relatively old, and can be replaced by the latest target detection algorithm by itself).
Source code download
https://github.com/Egrt/YOLO_PyQt5
You can order a star if you like it.
Support features
- Support reading local pictures
- Support reading local video
- Support open camera real-time detection
- Support multi-threading to prevent stuck
- Support recording when a face is detected without a mask, and a voice warning
Interface display
PyQt5
PyQt5 is a popular GUI (Graphical User Interface) development framework in the Python language. Based on the Qt GUI application development framework, it provides a powerful tool set for creating various desktop applications. PyQt5 can be used to develop desktop applications, web applications and mobile applications, with good cross-platform and rich functions.
Signals and slots
Signals and slots are an important concept in PyQt5 and are mechanisms for organizing and managing interactions between GUI elements. Signals are events or actions emitted by GUI elements, and slots are functions that handle signals. When a signal occurs, the slot associated with it will be called automatically.
Below is a simple example code demonstrating how to use signals and slots in PyQt5. This example creates a window that contains a button and a label. When the user clicks the button, the label’s text will change:
import sys
from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLabel
class MyWindow(QWidget):
def __init__(self):
super().__init__()
self.initUI()
def initUI(self):
self. setGeometry(300, 300, 300, 200)
self.setWindowTitle('Signal and Slot')
self.button = QPushButton('Click', self)
self. button. move(100, 100)
self.button.clicked.connect(self.changeText)
self.label = QLabel('Hello World', self)
self. label. move(110, 60)
def changeText(self):
self.label.setText('Button Clicked')
if __name__ == '__main__':
app = QApplication(sys. argv)
window = MyWindow()
window. show()
sys. exit(app. exec_())
In this sample code, we create a window class named MyWindow, which inherits from QWidget. In the MyWindow constructor, we create a button and a label, and use the clicked signal to connect the button’s click event to changeText slot function. When the button is clicked, the changeText slot function will be called, which will change the text of the label.
After running the code, you can see that there is a button and a label on the window, and the text of the label will change to “Button Clicked” after the button is clicked. This example demonstrates how to use signals and slots in PyQt5 to implement interactive GUI applications.
Function realization
Interface design
According to the task requirements, the interface can be divided into four parts:
- The button is placed on the top to realize the selection of reading pictures, videos, and opening the camera for real-time detection.
- A directory control is placed on the left to browse local files.
- The image processed by YOLO is shown in the middle.
- When processing video or reading camera detection in real time, if a face without a mask is continuously recognized in multiple frames, it will be recorded and a voice warning will be issued.
So write the code as follows:
class MyApp(QMainWindow):
def __init__(self):
super(MyApp, self).__init__()
self.cap = cv2.VideoCapture()
self.CAM_NUM = 0
self.thread_status = False # Determine whether the recognition thread is enabled
self.tool_bar = self.addToolBar('Toolbar')
self.action_right_rotate = QAction(
QIcon("icons/rotate right.png"), "rotate right 90", self)
self.action_left_rotate = QAction(
QIcon("icons/left rotation.png"), "rotate left 90°", self)
self.action_opencam = QAction(QIcon("icons/camera.png"), "open camera", self)
self.action_video = QAction(QIcon("icons/video.png"), "load video", self)
self.action_image = QAction(QIcon("icons/image.png"), "load image", self)
self.action_right_rotate.triggered.connect(self.right_rotate)
self.action_left_rotate.triggered.connect(self.left_rotate)
self.action_opencam.triggered.connect(self.opencam)
self.action_video.triggered.connect(self.openvideo)
self.action_image.triggered.connect(self.openimage)
self.tool_bar.addActions((self.action_left_rotate, self.action_right_rotate,
self.action_opencam, self.action_video, self.action_image))
self. stackedWidget = StackedWidget(self)
self. fileSystemTreeView = FileSystemTreeView(self)
self. graphicsView = GraphicsView(self)
self.dock_file = QDockWidget(self)
self.dock_file.setWidget(self.fileSystemTreeView)
self.dock_file.setTitleBarWidget(QLabel('directory'))
self.dock_file.setFeatures(QDockWidget.NoDockWidgetFeatures)
self.dock_attr = QDockWidget(self)
self.dock_attr.setWidget(self.stackedWidget)
self.dock_attr.setTitleBarWidget(QLabel('report data'))
self.dock_attr.setFeatures(QDockWidget.NoDockWidgetFeatures)
self.setCentralWidget(self.graphicsView)
self.addDockWidget(Qt.LeftDockWidgetArea, self.dock_file)
self.addDockWidget(Qt.RightDockWidgetArea, self.dock_attr)
self.setWindowTitle('mask wearing detection')
self.setWindowIcon(QIcon('icons/mask.png'))
self.src_img = None
self.cur_img = None
Slot function
Configure the interface of the window in the initialization and use connect to connect the signal and the slot function. When the signal occurs, the slot associated with it will be called automatically. The slot functions that control opening pictures, videos and local cameras are:
def openvideo(self):
print(self. thread_status)
if self. thread_status == False:
fileName, filetype = QFileDialog.getOpenFileName(
self, "Select Video", "D:/", "*.mp4;;*.flv;;All Files(*)")
flag = self.cap.open(fileName)
if flag == False:
msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"Please select a video file",
buttons=QtWidgets.QMessageBox.Ok,
defaultButton=QtWidgets.QMessageBox.Ok)
else:
self. detectThread = DetectThread(fileName)
self. detectThread. Send_signal. connect(self. Display)
self. detectThread. start()
self.action_video.setText('close video')
self. thread_status = True
elif self. thread_status == True:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
self.action_video.setText('open video')
self. thread_status = False
def openimage(self):
if self. thread_status == False:
fileName, filetype = QFileDialog.getOpenFileName(
self, "Select Image", "D:/", "*.jpg;;*.png;;All Files(*)")
if fileName != '':
src_img = Image.open(fileName)
r_image, predicted_class = yolo. detect_image(src_img)
r_image = np.array(r_image)
showImage = QtGui. QImage(
r_image.data, r_image.shape[1], r_image.shape[0], QtGui.QImage.Format_RGB888)
self.graphicsView.set_image(QtGui.QPixmap.fromImage(showImage))
def opencam(self):
if self. thread_status == False:
flag = self.cap.open(self.CAM_NUM)
if flag == False:
msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"Please check whether the camera is connected to the computer correctly",
buttons=QtWidgets.QMessageBox.Ok,
defaultButton=QtWidgets.QMessageBox.Ok)
else:
self. detectThread = DetectThread(self. CAM_NUM)
self. detectThread. Send_signal. connect(self. Display)
self. detectThread. start()
self.action_video.setText('close video')
self. thread_status = True
else:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
self.action_video.setText('open video')
self. thread_status = False
Multithreading
When reading video files or cameras, in order to avoid interface freezes, multi-threading is used for processing, and the thread needs to be closed to prevent the system from being stuck when the video file is finished processing, and self needs to be used when closing the camera. cap.release() releases the camera.
When processing continuous frames with multiple threads, the multi-threaded library QThread that comes with Qt is used:
class DetectThread(QThread):
Send_signal = pyqtSignal(np.ndarray, int)
def __init__(self, fileName):
super(DetectThread, self).__init__()
self.capture = cv2.VideoCapture(fileName)
self.count = 0
self.warn = False # Whether to send a warning signal
def run(self):
ret, self. frame = self. capture. read()
while ret:
ret, self. frame = self. capture. read()
self. detectCall()
def detectCall(self):
fps = 0.0
t1 = time. time()
# read a frame
frame = self.frame
# Format conversion, BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Convert to Image
frame = Image.fromarray(np.uint8(frame))
# check
frame_new, predicted_class = yolo. detect_image(frame)
frame = np.array(frame_new)
if predicted_class == "face":
self.count = self.count + 1
else:
self.count = 0
# RGBtoBGR meets the opencv display format
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
fps = (fps + (1./(time.time()-t1))) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (
fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
if self.count > 30:
self.count = 0
self. warn = True
else:
self. warn = False
# Send pyqt signal
self.Send_signal.emit(frame, self.warn)
Information record
If a face without a mask is recognized for 30 consecutive frames, a signal will be sent and displayed in the list on the right, and the current frame will be recorded:
def add_item(self, image):
# Total Widget
weight = QWidget()
# overall horizontal layout
layout_main = QHBoxLayout()
map_l = QLabel() # picture display
map_l. setFixedSize(60, 40)
map_l.setPixmap(image.scaled(60, 40))
# vertical layout on the right
layout_right = QVBoxLayout()
# Horizontal layout for bottom right
layout_right_down = QHBoxLayout() # The horizontal layout of the lower right
layout_right_down.addWidget(
QLabel(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
# Add according to the layout from left to right, top to bottom
layout_main.addWidget(map_l) # leftmost image
layout_right.addWidget(QLabel('Warning! No mask was detected')) # vertical layout on the right
layout_right.addLayout(layout_right_down) # Horizontal layout in the lower right corner
layout_main.addLayout(layout_right) # layout on the right
wight.setLayout(layout_main) # layout for weight
item = QListWidgetItem() # create QListWidgetItem object
item.setSizeHint(QSize(300, 80)) # Set the size of QListWidgetItem
self.stackedWidget.addItem(item) # add item
self.stackedWidget.setItemWidget(item, weight) # set widget for item
Shut down the system
When shutting down the system, you need to make sure that multi-threading is turned off, and the camera that has been opened is turned off, otherwise it will also cause a freeze when exiting:
def Display(self, frame, warn):
im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
showImage = QtGui. QImage(
im.data, im.shape[1], im.shape[0], QtGui.QImage.Format_RGB888)
self.graphicsView.set_image(QtGui.QPixmap.fromImage(showImage))
def closeEvent(self, event):
ok = QtWidgets. QPushButton()
cacel = QtWidgets. QPushButton()
msg = QtWidgets.QMessageBox(
QtWidgets.QMessageBox.Warning, u"Close", u"Are you sure to exit?")
msg.addButton(ok, QtWidgets.QMessageBox.ActionRole)
msg.addButton(cacel, QtWidgets.QMessageBox.RejectRole)
ok.setText(u'OK')
cacel.setText(u'Cancel')
if msg.exec_() == QtWidgets.QMessageBox.RejectRole:
event. ignore()
else:
if self. thread_status == True:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
event. accept()
The final complete code is as follows:
import ctypes
import sys
import time
import cv2
import numpy as np
import qdarkstyle
from PIL import Image
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.Qt import QThread
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from custom.graphicsView import GraphicsView
from custom.listWidgets import *
from custom.stackedWidget import *
from custom. treeView import FileSystemTreeView
from yolo import YOLO
ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID("myappid")
# Multi-thread real-time detection
class DetectThread(QThread):
Send_signal = pyqtSignal(np.ndarray, int)
def __init__(self, fileName):
super(DetectThread, self).__init__()
self.capture = cv2.VideoCapture(fileName)
self.count = 0
self.warn = False # Whether to send a warning signal
def run(self):
ret, self. frame = self. capture. read()
while ret:
ret, self. frame = self. capture. read()
self. detectCall()
def detectCall(self):
fps = 0.0
t1 = time. time()
# read a frame
frame = self.frame
# Format conversion, BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Convert to Image
frame = Image.fromarray(np.uint8(frame))
# check
frame_new, predicted_class = yolo. detect_image(frame)
frame = np.array(frame_new)
if predicted_class == "face":
self.count = self.count + 1
else:
self.count = 0
# RGBtoBGR meets the opencv display format
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
fps = (fps + (1./(time.time()-t1))) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (
fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
if self.count > 30:
self.count = 0
self. warn = True
else:
self. warn = False
# Send pyqt signal
self.Send_signal.emit(frame, self.warn)
class MyApp(QMainWindow):
def __init__(self):
super(MyApp, self).__init__()
self.cap = cv2.VideoCapture()
self.CAM_NUM = 0
self.thread_status = False # Determine whether the recognition thread is enabled
self.tool_bar = self.addToolBar('Toolbar')
self.action_right_rotate = QAction(
QIcon("icons/rotate right.png"), "rotate right 90", self)
self.action_left_rotate = QAction(
QIcon("icons/left rotation.png"), "rotate left 90°", self)
self.action_opencam = QAction(QIcon("icons/camera.png"), "open camera", self)
self.action_video = QAction(QIcon("icons/video.png"), "load video", self)
self.action_image = QAction(QIcon("icons/image.png"), "load image", self)
self.action_right_rotate.triggered.connect(self.right_rotate)
self.action_left_rotate.triggered.connect(self.left_rotate)
self.action_opencam.triggered.connect(self.opencam)
self.action_video.triggered.connect(self.openvideo)
self.action_image.triggered.connect(self.openimage)
self.tool_bar.addActions((self.action_left_rotate, self.action_right_rotate,
self.action_opencam, self.action_video, self.action_image))
self. stackedWidget = StackedWidget(self)
self. fileSystemTreeView = FileSystemTreeView(self)
self. graphicsView = GraphicsView(self)
self.dock_file = QDockWidget(self)
self.dock_file.setWidget(self.fileSystemTreeView)
self.dock_file.setTitleBarWidget(QLabel('directory'))
self.dock_file.setFeatures(QDockWidget.NoDockWidgetFeatures)
self.dock_attr = QDockWidget(self)
self.dock_attr.setWidget(self.stackedWidget)
self.dock_attr.setTitleBarWidget(QLabel('report data'))
self.dock_attr.setFeatures(QDockWidget.NoDockWidgetFeatures)
self.setCentralWidget(self.graphicsView)
self.addDockWidget(Qt.LeftDockWidgetArea, self.dock_file)
self.addDockWidget(Qt.RightDockWidgetArea, self.dock_attr)
self.setWindowTitle('mask wearing detection')
self.setWindowIcon(QIcon('icons/mask.png'))
self.src_img = None
self.cur_img = None
def update_image(self):
if self.src_img is None:
return
img = self. process_image()
self.cur_img = img
self.graphicsView.update_image(img)
def change_image(self, img):
self.src_img = img
img = self. process_image()
self.cur_img = img
self.graphicsView.change_image(img)
def process_image(self):
img = self.src_img.copy()
for i in range(self. useListWidget. count()):
img = self.useListWidget.item(i)(img)
return img
def right_rotate(self):
self. graphicsView. rotate(90)
def left_rotate(self):
self.graphicsView.rotate(-90)
def add_item(self, image):
# Total Widget
weight = QWidget()
# overall horizontal layout
layout_main = QHBoxLayout()
map_l = QLabel() # picture display
map_l. setFixedSize(60, 40)
map_l.setPixmap(image.scaled(60, 40))
# vertical layout on the right
layout_right = QVBoxLayout()
# Horizontal layout for bottom right
layout_right_down = QHBoxLayout() # The horizontal layout of the lower right
layout_right_down.addWidget(
QLabel(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
# Add according to the layout from left to right, top to bottom
layout_main.addWidget(map_l) # leftmost image
layout_right.addWidget(QLabel('Warning! No mask was detected')) # vertical layout on the right
layout_right.addLayout(layout_right_down) # Horizontal layout in the lower right corner
layout_main.addLayout(layout_right) # layout on the right
wight.setLayout(layout_main) # layout for weight
item = QListWidgetItem() # create QListWidgetItem object
item.setSizeHint(QSize(300, 80)) # Set the size of QListWidgetItem
self.stackedWidget.addItem(item) # add item
self.stackedWidget.setItemWidget(item, weight) # set widget for item
def openvideo(self):
print(self. thread_status)
if self. thread_status == False:
fileName, filetype = QFileDialog.getOpenFileName(
self, "Select Video", "D:/", "*.mp4;;*.flv;;All Files(*)")
flag = self.cap.open(fileName)
if flag == False:
msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"Please select a video file",
buttons=QtWidgets.QMessageBox.Ok,
defaultButton=QtWidgets.QMessageBox.Ok)
else:
self. detectThread = DetectThread(fileName)
self. detectThread. Send_signal. connect(self. Display)
self. detectThread. start()
self.action_video.setText('close video')
self. thread_status = True
elif self. thread_status == True:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
self.action_video.setText('open video')
self. thread_status = False
def openimage(self):
if self. thread_status == False:
fileName, filetype = QFileDialog.getOpenFileName(
self, "Select Image", "D:/", "*.jpg;;*.png;;All Files(*)")
if fileName != '':
src_img = Image.open(fileName)
r_image, predicted_class = yolo. detect_image(src_img)
r_image = np.array(r_image)
showImage = QtGui. QImage(
r_image.data, r_image.shape[1], r_image.shape[0], QtGui.QImage.Format_RGB888)
self.graphicsView.set_image(QtGui.QPixmap.fromImage(showImage))
def opencam(self):
if self. thread_status == False:
flag = self.cap.open(self.CAM_NUM)
if flag == False:
msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"Please check whether the camera is connected to the computer correctly",
buttons=QtWidgets.QMessageBox.Ok,
defaultButton=QtWidgets.QMessageBox.Ok)
else:
self. detectThread = DetectThread(self. CAM_NUM)
self. detectThread. Send_signal. connect(self. Display)
self. detectThread. start()
self.action_video.setText('close video')
self. thread_status = True
else:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
self.action_video.setText('open video')
self. thread_status = False
def Display(self, frame, warn):
im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
showImage = QtGui. QImage(
im.data, im.shape[1], im.shape[0], QtGui.QImage.Format_RGB888)
self.graphicsView.set_image(QtGui.QPixmap.fromImage(showImage))
def closeEvent(self, event):
ok = QtWidgets. QPushButton()
cacel = QtWidgets. QPushButton()
msg = QtWidgets.QMessageBox(
QtWidgets.QMessageBox.Warning, u"Close", u"Are you sure to exit?")
msg.addButton(ok, QtWidgets.QMessageBox.ActionRole)
msg.addButton(cacel, QtWidgets.QMessageBox.RejectRole)
ok.setText(u'OK')
cacel.setText(u'Cancel')
if msg.exec_() == QtWidgets.QMessageBox.RejectRole:
event. ignore()
else:
if self. thread_status == True:
self. detectThread. terminate()
if self.cap.isOpened():
self.cap.release()
event. accept()
if __name__ == "__main__":
# Initialize the yolo model
yolo = YOLO()
app = QApplication(sys. argv)
app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())
window = MyApp()
window. show()
sys. exit(app. exec_())