用 pyqt5给深度学习目标检测yolov3搭建界面(1)
最终效果图:注意一点:在窗口初始化中建立yolov3模型并进行相应的初始化，直接将预测处理的函数加到槽函数中。不这样做，会使得检测速度十分的慢。代码如下:import sysfrom PyQt5 import QtCore, QtGui, QtWidgetsfrom PyQt5.QtWidgets import *from pyqt5.yolov3.util import *impo...
·
最终效果图:
注意一点:在窗口初始化中建立yolov3模型并进行相应的初始化，直接将预测处理的函数加到槽函数中。不这样做，会使得检测速度十分的慢。
代码如下:
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import *
from pyqt5.yolov3.util import *
import torch
import time
from pyqt5.yolov3.cam_demo import write, prep_image, arg_parse
from pyqt5.yolov3.darknet import Darknet
from pyqt5.yolov3.preprocess import prep_image
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(1042, 921)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
# 显示摄像头画面
self.cam_frame = QtWidgets.QFrame(self.centralwidget)
self.cam_frame.setGeometry(QtCore.QRect(10, 110, 521, 571))
self.cam_frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.cam_frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.cam_frame.setObjectName("cam_frame")
self.label_img_show = QtWidgets.QLabel(self.cam_frame)
self.label_img_show.setGeometry(QtCore.QRect(10, 10, 501, 551))
self.label_img_show.setObjectName("label_img_show")
# self.label_img_show.setStyleSheet(("border:2px solid red"))
# 显示检测画面
self.detect_frame = QtWidgets.QFrame(self.centralwidget)
self.detect_frame.setGeometry(QtCore.QRect(540, 110, 491, 571))
self.detect_frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.detect_frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.detect_frame.setObjectName("detect_frame")
self.label_detect_show = QtWidgets.QLabel(self.detect_frame)
self.label_detect_show.setGeometry(QtCore.QRect(10, 10, 481, 551))
self.label_detect_show.setObjectName("label_detect_show")
# self.label_detect_show.setStyleSheet(("border:2px solid green"))
# 按钮框架
self.btn_frame = QtWidgets.QFrame(self.centralwidget)
self.btn_frame.setGeometry(QtCore.QRect(10, 20, 1021, 80))
self.btn_frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.btn_frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.btn_frame.setObjectName("frame_3")
# 按钮水平布局
self.widget = QtWidgets.QWidget(self.btn_frame)
self.widget.setGeometry(QtCore.QRect(20, 10, 501, 60))
self.widget.setObjectName("widget")
self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget)
self.horizontalLayout.setContentsMargins(0, 20, 20, 20)
self.horizontalLayout.setSpacing(20)
self.horizontalLayout.setObjectName("horizontalLayout")
# 打开摄像头
self.btn_opencam = QtWidgets.QPushButton(self.widget)
self.btn_opencam.setObjectName("btn_opencam")
self.horizontalLayout.addWidget(self.btn_opencam)
# 加载模型文件
self.btn_model_add_file = QtWidgets.QPushButton(self.widget)
self.btn_model_add_file.setObjectName("btn_model_add_file")
self.horizontalLayout.addWidget(self.btn_model_add_file)
# 加载cfg文件
self.btn_cfg_add_file = QtWidgets.QPushButton(self.widget)
self.btn_cfg_add_file.setObjectName("btn_cfg_add_file")
self.horizontalLayout.addWidget(self.btn_cfg_add_file)
# 开始检测
self.btn_detect = QtWidgets.QPushButton(self.widget)
self.btn_detect.setObjectName("btn_detect")
self.horizontalLayout.addWidget(self.btn_detect)
# 退出
self.btn_exit = QtWidgets.QPushButton(self.widget)
self.btn_exit.setObjectName("btn_exit")
self.horizontalLayout.addWidget(self.btn_exit)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 1042, 17))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
# 这里将按钮和定义的动作相连，通过click信号连接openfile槽？
# 加载模型文件
self.btn_model_add_file.clicked.connect(self.open_model)
# 加载cfg文件
self.btn_cfg_add_file.clicked.connect(self.open_cfg)
# 打开摄像头
self.btn_opencam.clicked.connect(self.opencam)
# 开始识别
self.btn_detect.clicked.connect(self.detect)
# 这里是将btn_exit按钮和Form窗口相连，点击按钮发送关闭窗口命令
self.btn_exit.clicked.connect(MainWindow.close)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "目标检测"))
self.label_img_show.setText(_translate("MainWindow", "摄像头原始画面"))
self.label_detect_show.setText(_translate("MainWindow", "实时检测效果"))
self.btn_opencam.setText(_translate("MainWindow", "打开摄像头"))
self.btn_model_add_file.setText(_translate("MainWindow", "加载模型文件"))
self.btn_cfg_add_file.setText(_translate("MainWindow", "加载cfg文件"))
self.btn_detect.setText(_translate("MainWindow", "开始检测"))
self.btn_exit.setText(_translate("MainWindow", "退出"))
def open_model(self):
global openfile_name_mdoel
openfile_name_mdoel, _ = QFileDialog.getOpenFileName(self.btn_model_add_file, '选择模型文件',
'/home/ljw/桌面/demo/project_demo/pyqt5/yolov3/')
print('加载模型文件地址为：' + str(openfile_name_mdoel))
def open_cfg(self):
global openfile_name_cfg
openfile_name_cfg, _ = QFileDialog.getOpenFileName(self.btn_cfg_add_file, '选择cfg文件',
'/home/ljw/桌面/demo/project_demo/pyqt5/yolov3/')
print('加载cfg文件地址为：' + str(openfile_name_cfg))
def opencam(self):
self.camcapture = cv2.VideoCapture(0)
self.timer = QtCore.QTimer()
self.timer.start()
self.timer.setInterval(3) # 0.1s刷新一次
self.timer.timeout.connect(self.camshow)
def camshow(self):
# global self.camimg
_, self.camimg = self.camcapture.read()
camimg = cv2.cvtColor(self.camimg, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(camimg.data, camimg.shape[1], camimg.shape[0], QtGui.QImage.Format_RGB888)
self.label_img_show.setPixmap(QtGui.QPixmap.fromImage(showImage))
def detect(self):
self.frames = 0
self.start = time.time()
cfgfile = openfile_name_cfg
weightsfile = openfile_name_mdoel
self.num_classes = 80
args = arg_parse()
self.confidence = float(args.confidence)
self.nms_thesh = float(args.nms_thresh)
self.CUDA = torch.cuda.is_available()
self.model = Darknet(cfgfile)
self.model.load_weights(weightsfile)
self.model.net_info["height"] = args.reso
self.inp_dim = int(self.model.net_info["height"])
assert self.inp_dim % 32 == 0
assert self.inp_dim > 32
self.timerdec = QtCore.QTimer()
self.timerdec.start()
self.timerdec.setInterval(3) # 0.1s刷新一次
self.timerdec.timeout.connect(self.object_detection)
def object_detection(self):
if self.CUDA:
self.model.cuda()
self.model.eval()
img, orig_im, dim = prep_image(self.camimg, self.inp_dim)
output = self.model(Variable(img), self.CUDA)
output = write_results(output, self.confidence, self.num_classes, nms=True, nms_conf=self.nms_thesh)
output[:, 1:5] = torch.clamp(output[:, 1:5], 0.0, float(self.inp_dim)) / self.inp_dim
output[:, [1, 3]] *= self.camimg.shape[1]
output[:, [2, 4]] *= self.camimg.shape[0]
list(map(lambda x: write(x, orig_im), output))
self.frames += 1
print("FPS of the video is {:5.2f}".format(self.frames / (time.time() - self.start)))
camimg = cv2.cvtColor(self.camimg, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(camimg.data, camimg.shape[1], camimg.shape[0], QtGui.QImage.Format_RGB888)
self.label_detect_show.setPixmap(QtGui.QPixmap.fromImage(showImage))
QApplication.processEvents()
if __name__ == '__main__':
app = QApplication(sys.argv)
mainWindow = QMainWindow()
ui = Ui_MainWindow()
# 向主窗口上添加控件
ui.setupUi(mainWindow)
mainWindow.show()
sys.exit(app.exec_())
注意修改自己模型与cfg文件的路径，然后就是注意调用yolov3的包的头文件。
我这里使用的yolov3的代码为
yolov3的代码
后续将对这个界面进行改进，加入一个文本显示框用来显示帧率以及检测的内容。
CSDN联合极客时间，共同打造面向开发者的精品内容学习社区，助力成长！
更多推荐
13
0- 0
已为社区贡献1条内容
所有评论(0)