python 深度学习 pytorch Yolov5(v5.0) + pyqt5界面设计

1.下载安装pyqt5工具包以及配置ui界面开发环境

pip install PyQt5

pip install PyQt5-tools

2.点击File->Settings->External Tools进行工具添加，依次进行Qt Designer、PyUIC环境配置.

2.1 添加QtDesigner

Qt Designer 是通过拖拽的方式放置控件，并实时查看控件效果进行快速UI设计

位置内容name可以随便命名，只要便于记忆就可以，本次采取通用命名：Qt DesignerProgramdesigner.exe路径,一般在python中.\Library\bin\designer.exeArguments固定格式，直接复制也可：$FileDir$\$FileName$Working directory固定格式，直接复制也可：$FileDir$

2.2 添加PyUIC

PyUIC主要是把Qt Designer生成的.ui文件换成.py文件

位置内容name可以随便命名，只要便于记忆就可以，本次采取通用命名：PyUiCProgrampython.exe路径,一般在python安装根目录中Arguments固定格式，直接复制也可：-m PyQt5.uic.pyuic $FileName$ -o $FileNameWithoutExtension$.pyWorking directory固定格式，直接复制也可：$FileDir$

3. QtDesigner建立图形化窗口界面

3.1 在根目录下新建UI文件夹进行UI文件的专门存储，点击Tools->External Tools->Qt Designer进行图形界面创建.

3.2 创建一个Main Window窗口

3.3 完成基本界面开发后，保存其为Detect.ui，放置在UI文件夹下，利用PyUic工具将其转化为Detect.py文件。

转换完成后，进行相应的槽函数的建立与修改，此处建议直接看我后面给出的demo。

4. demo

使用时只需将parser.add_argument中的'--weights'设为响应权重即可。

# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file '.\project.ui'

# Created by: PyQt5 UI code generator 5.9.2

# WARNING! All changes made in this file will be lost!

import sys

import cv2

import argparse

import random

import torch

import numpy as np

import torch.backends.cudnn as cudnn

from PyQt5 import QtCore, QtGui, QtWidgets

from utils.torch_utils import select_device

from models.experimental import attempt_load

from utils.general import check_img_size, non_max_suppression, scale_coords

from utils.datasets import letterbox

from utils.plots import plot_one_box

class Ui_MainWindow(QtWidgets.QMainWindow):

def __init__(self, parent=None):

super(Ui_MainWindow, self).__init__(parent)

self.timer_video = QtCore.QTimer()

self.setupUi(self)

self.init_logo()

self.init_slots()

self.cap = cv2.VideoCapture()

self.out = None

# self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480))

parser = argparse.ArgumentParser()

parser.add_argument('--weights', nargs='+', type=str,

default='weights/best.pt', help='model.pt path(s)')

# file/folder, 0 for webcam

parser.add_argument('--source', type=str,

default='data/images', help='source')

parser.add_argument('--img-size', type=int,

default=640, help='inference size (pixels)')

parser.add_argument('--conf-thres', type=float,

default=0.25, help='object confidence threshold')

parser.add_argument('--iou-thres', type=float,

default=0.45, help='IOU threshold for NMS')

parser.add_argument('--device', default='',

help='cuda device, i.e. 0 or 0,1,2,3 or cpu')

parser.add_argument(

'--view-img', action='store_true', help='display results')

parser.add_argument('--save-txt', action='store_true',

help='save results to *.txt')

parser.add_argument('--save-conf', action='store_true',

help='save confidences in --save-txt labels')

parser.add_argument('--nosave', action='store_true',

help='do not save images/videos')

parser.add_argument('--classes', nargs='+', type=int,

help='filter by class: --class 0, or --class 0 2 3')

parser.add_argument(

'--agnostic-nms', action='store_true', help='class-agnostic NMS')

parser.add_argument('--augment', action='store_true',

help='augmented inference')

parser.add_argument('--update', action='store_true',

help='update all models')

parser.add_argument('--project', default='runs/detect',

help='save results to project/name')

parser.add_argument('--name', default='exp',

help='save results to project/name')

parser.add_argument('--exist-ok', action='store_true',

help='existing project/name ok, do not increment')

self.opt = parser.parse_args()

print(self.opt)

source, weights, view_img, save_txt, imgsz = self.opt.source, self.opt.weights, self.opt.view_img, self.opt.save_txt, self.opt.img_size

self.device = select_device(self.opt.device)

self.half = self.device.type != 'cpu' # half precision only supported on CUDA

cudnn.benchmark = True

# Load model

self.model = attempt_load(

weights, map_location=self.device) # load FP32 model

stride = int(self.model.stride.max()) # model stride

self.imgsz = check_img_size(imgsz, s=stride) # check img_size

if self.half:

self.model.half() # to FP16

# Get names and colors

self.names = self.model.module.names if hasattr(

self.model, 'module') else self.model.names

self.colors = [[random.randint(0, 255)

for _ in range(3)] for _ in self.names]

def setupUi(self, MainWindow):

MainWindow.setObjectName("MainWindow")

MainWindow.resize(800, 600)

self.centralwidget = QtWidgets.QWidget(MainWindow)

self.centralwidget.setObjectName("centralwidget")

self.pushButton = QtWidgets.QPushButton(self.centralwidget)

self.pushButton.setGeometry(QtCore.QRect(20, 130, 112, 34))

self.pushButton.setObjectName("pushButton")

self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget)

self.pushButton_2.setGeometry(QtCore.QRect(20, 220, 112, 34))

self.pushButton_2.setObjectName("pushButton_2")

self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget)

self.pushButton_3.setGeometry(QtCore.QRect(20, 300, 112, 34))

self.pushButton_3.setObjectName("pushButton_3")

self.groupBox = QtWidgets.QGroupBox(self.centralwidget)

self.groupBox.setGeometry(QtCore.QRect(160, 90, 611, 411))

self.groupBox.setObjectName("groupBox")

self.label = QtWidgets.QLabel(self.groupBox)

self.label.setGeometry(QtCore.QRect(10, 40, 561, 331))

self.label.setObjectName("label")

self.textEdit = QtWidgets.QTextEdit(self.centralwidget)

self.textEdit.setGeometry(QtCore.QRect(150, 10, 471, 51))

self.textEdit.setObjectName("textEdit")

MainWindow.setCentralWidget(self.centralwidget)

self.menubar = QtWidgets.QMenuBar(MainWindow)

self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 30))

self.menubar.setObjectName("menubar")

MainWindow.setMenuBar(self.menubar)

self.statusbar = QtWidgets.QStatusBar(MainWindow)

self.statusbar.setObjectName("statusbar")

MainWindow.setStatusBar(self.statusbar)

self.retranslateUi(MainWindow)

QtCore.QMetaObject.connectSlotsByName(MainWindow)

def retranslateUi(self, MainWindow):

_translate = QtCore.QCoreApplication.translate

MainWindow.setWindowTitle(_translate("MainWindow", "演示系统"))

self.pushButton.setText(_translate("MainWindow", "图片检测"))

self.pushButton_2.setText(_translate("MainWindow", "摄像头检测"))

self.pushButton_3.setText(_translate("MainWindow", "视频检测"))

self.groupBox.setTitle(_translate("MainWindow", "检测结果"))

self.label.setText(_translate("MainWindow", "TextLabel"))

self.textEdit.setHtml(_translate("MainWindow",

"\n"

演示系统

"))

def init_slots(self):

self.pushButton.clicked.connect(self.button_image_open)

self.pushButton_3.clicked.connect(self.button_video_open)

self.pushButton_2.clicked.connect(self.button_camera_open)

self.timer_video.timeout.connect(self.show_video_frame)

def init_logo(self):

pix = QtGui.QPixmap('wechat.jpg')

self.label.setScaledContents(True)

self.label.setPixmap(pix)

def button_image_open(self):

print('button_image_open')

name_list = []

img_name, _ = QtWidgets.QFileDialog.getOpenFileName(

self, "打开图片", "", "*.jpg;;*.png;;All Files(*)")

if not img_name:

return

img = cv2.imread(img_name)

print(img_name)

showimg = img

with torch.no_grad():

img = letterbox(img, new_shape=self.opt.img_size)[0]

# Convert

# BGR to RGB, to 3x416x416

img = img[:, :, ::-1].transpose(2, 0, 1)

img = np.ascontiguousarray(img)

img = torch.from_numpy(img).to(self.device)

img = img.half() if self.half else img.float() # uint8 to fp16/32

img /= 255.0 # 0 - 255 to 0.0 - 1.0

if img.ndimension() == 3:

img = img.unsqueeze(0)

# Inference

pred = self.model(img, augment=self.opt.augment)[0]

# Apply NMS

pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,

agnostic=self.opt.agnostic_nms)

print(pred)

# Process detections

for i, det in enumerate(pred):

if det is not None and len(det):

# Rescale boxes from img_size to im0 size

det[:, :4] = scale_coords(

img.shape[2:], det[:, :4], showimg.shape).round()

for *xyxy, conf, cls in reversed(det):

label = '%s %.2f' % (self.names[int(cls)], conf)

name_list.append(self.names[int(cls)])

plot_one_box(xyxy, showimg, label=label,

color=self.colors[int(cls)], line_thickness=2)

cv2.imwrite('prediction.jpg', showimg)

self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA)

self.result = cv2.resize(

self.result, (640, 480), interpolation=cv2.INTER_AREA)

self.QtImg = QtGui.QImage(

self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32)

self.label.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))

def button_video_open(self):

video_name, _ = QtWidgets.QFileDialog.getOpenFileName(

self, "打开视频", "", "*.mp4;;*.avi;;All Files(*)")

if not video_name:

return

flag = self.cap.open(video_name)

if flag == False:

QtWidgets.QMessageBox.warning(

self, u"Warning", u"打开视频失败", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok)

else:

self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(

*'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))

self.timer_video.start(30)

self.pushButton_3.setDisabled(True)

self.pushButton.setDisabled(True)

self.pushButton_2.setDisabled(True)

def button_camera_open(self):

if not self.timer_video.isActive():

# 默认使用第一个本地camera

flag = self.cap.open(0)

if flag == False:

QtWidgets.QMessageBox.warning(

self, u"Warning", u"打开摄像头失败", buttons=QtWidgets.QMessageBox.Ok,

defaultButton=QtWidgets.QMessageBox.Ok)

else:

self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(

*'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))

self.timer_video.start(30)

self.pushButton_3.setDisabled(True)

self.pushButton.setDisabled(True)

self.pushButton_2.setText(u"关闭摄像头")

else:

self.timer_video.stop()

self.cap.release()

self.out.release()

self.label.clear()

self.init_logo()

self.pushButton_3.setDisabled(False)

self.pushButton.setDisabled(False)

self.pushButton_2.setText(u"摄像头检测")

def show_video_frame(self):

name_list = []

flag, img = self.cap.read()

if img is not None:

showimg = img

with torch.no_grad():

img = letterbox(img, new_shape=self.opt.img_size)[0]

# Convert

# BGR to RGB, to 3x416x416

img = img[:, :, ::-1].transpose(2, 0, 1)

img = np.ascontiguousarray(img)

img = torch.from_numpy(img).to(self.device)

img = img.half() if self.half else img.float() # uint8 to fp16/32

img /= 255.0 # 0 - 255 to 0.0 - 1.0

if img.ndimension() == 3:

img = img.unsqueeze(0)

# Inference

pred = self.model(img, augment=self.opt.augment)[0]

# Apply NMS

pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,

agnostic=self.opt.agnostic_nms)

# Process detections

for i, det in enumerate(pred): # detections per image

if det is not None and len(det):

# Rescale boxes from img_size to im0 size

det[:, :4] = scale_coords(

img.shape[2:], det[:, :4], showimg.shape).round()

# Write results

for *xyxy, conf, cls in reversed(det):

label = '%s %.2f' % (self.names[int(cls)], conf)

name_list.append(self.names[int(cls)])

print(label)

plot_one_box(

xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2)

self.out.write(showimg)

show = cv2.resize(showimg, (640, 480))

self.result = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)

showImage = QtGui.QImage(self.result.data, self.result.shape[1], self.result.shape[0],

QtGui.QImage.Format_RGB888)

self.label.setPixmap(QtGui.QPixmap.fromImage(showImage))

else:

self.timer_video.stop()

self.cap.release()

self.out.release()

self.label.clear()

self.pushButton_3.setDisabled(False)

self.pushButton.setDisabled(False)

self.pushButton_2.setDisabled(False)

self.init_logo()

if __name__ == '__main__':

app = QtWidgets.QApplication(sys.argv)

ui = Ui_MainWindow()

ui.show()

sys.exit(app.exec_())

5.添加背景图片

将demo中最后一段代码改为如下，其中background-image为背景图片地址。

if __name__ == '__main__':

stylesheet = """

Ui_MainWindow {

background-image: url("4K.jpg");

background-repeat: no-repeat;

background-position: center;

}

"""

app = QtWidgets.QApplication(sys.argv)

app.setStyleSheet(stylesheet)

ui = Ui_MainWindow()

ui.show()

sys.exit(app.exec_())

6.reference

http://t.csdn.cn/ZVtSKhttp://t.csdn.cn/ZVtSKPyQt5系列教程(三)利用QtDesigner设计UI界面 - 迷途小书童的Note迷途小书童的Note (xugaoxiang.com)https://xugaoxiang.com/2019/12/04/pyqt5-3-qtdesigner/

金钥匙

python 深度学习 pytorch Yolov5(v5.0) + pyqt5界面设计

算法人工智能 python 《动手学深度学习(PyTorch版)》笔记8.4

人工智能 pytorch+LSTM实现使用单参数预测，以及多参数预测（代码注释版）

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金钥匙

python 深度学习 pytorch Yolov5(v5.0) + pyqt5界面设计

算法 人工智能 python 《动手学深度学习(PyTorch版)》笔记8.4

人工智能 pytorch+LSTM实现使用单参数预测，以及多参数预测（代码注释版）

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算法人工智能 python 《动手学深度学习(PyTorch版)》笔记8.4