目标检测-yolov50-火灾识别可视化_yolov火灾

使用yolov5模型对利用开源火灾数据集进行训练模型，实现对照片、视频以及摄像头的火焰烟雾的检测。

详细的环境配置见：目标检测-YOLOV5-口罩检测

学习目标：

• 数据集的准备
• 预训练权重
• 训练模型
• 训练模型路径的修改
• 模型测试
• pyqt可视化

---

重要的事情，出错请去查看！！！

目标检测-YOLOV5-口罩检测

一、数据集的准备

1.数据集的下载

链接：https://pan.baidu.com/s/1LLfnSOEBdlgOnL_iEyMIsA
提取码：ghaq

2.划分数据集为训练集和验证集

该数据集的标签是xml格式，训练yolo模型需要txt类型的标签。需要先将xml类型的标签转换为txt类型，再划分数据集。

需要新建几个文件夹，具体如下：

划分数据集的py文件

split_train_val.py

# coding=utf-8
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile

classes = ["fire", "smoke"]
# classes=["ball"]

TRAIN_RATIO = 80  #训练集和验证集的比例，说明80%的训练集，20%的验证集


def clear_hidden_files(path):
    dir_list = os.listdir(path)
    for i in dir_list:
        abspath = os.path.join(os.path.abspath(path), i)
        if os.path.isfile(abspath):
            if i.startswith("._"):
                os.remove(abspath)
        else:
            clear_hidden_files(abspath)


def convert(size, box):
    dw = 1. / size[0]
    dh = 1. / size[1]
    x = (box[0] + box[1]) / 2.0
    y = (box[2] + box[3]) / 2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)


def convert_annotation(image_id):
    in_file = open('data-fire-smoke/fire/Annotation/%s.xml' % image_id,'rb')
    out_file = open('data-fire-smoke/fire/YOLOLabels/%s.txt' % image_id, 'w')
    tree = ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
             float(xmlbox.find('ymax').text))
        bb = convert((w, h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    in_file.close()
    out_file.close()


wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, "data-fire-smoke/")
if not os.path.isdir(data_base_dir):
    os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "fire/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "Annotation/")
if not os.path.isdir(annotation_dir):
    os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "Image/")
if not os.path.isdir(image_dir):
    os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):
    os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolov5_images_dir):
    os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolov5_labels_dir):
    os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
if not os.path.isdir(yolov5_images_train_dir):
    os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
if not os.path.isdir(yolov5_images_test_dir):
    os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
if not os.path.isdir(yolov5_labels_train_dir):
    os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
if not os.path.isdir(yolov5_labels_test_dir):
    os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)

train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir)  # list image files
prob = random.randint(1, 100)
print("Probability: %d" % prob)
for i in range(0, len(list_imgs)):
    path = os.path.join(image_dir, list_imgs[i])
    if os.path.isfile(path):
        image_path = image_dir + list_imgs[i]
        voc_path = list_imgs[i]
        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
        (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
        annotation_name = nameWithoutExtention + '.xml'
        annotation_path = os.path.join(annotation_dir, annotation_name)
        label_name = nameWithoutExtention + '.txt'
        label_path = os.path.join(yolo_labels_dir, label_name)
    prob = random.randint(1, 100)
    print("Probability: %d" % prob)
    if (prob < TRAIN_RATIO):  # train dataset
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention)  # convert label
            copyfile(image_path, yolov5_images_train_dir + voc_path)
            copyfile(label_path, yolov5_labels_train_dir + label_name)
    else:  # test dataset
        if os.path.exists(annotation_path):
            test_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention)  # convert label
            copyfile(image_path, yolov5_images_test_dir + voc_path)
            copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()

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运行成功后会出现下图的文件夹

二、预训练权重

预训练权重下载 本实验使用的是 yolov5s.pt

预训练权重：预先设置的训练权重，可以缩短训练时间。

将yolov5s.pt放置到weights文件夹中

三、训练模型

训练自己的数据集需要更改数据配置文件（data中的yaml）和模型配置文件（midels中的yaml文件）

1.更改数据配置文件

复制data中的voc.yaml命名为fire.yaml

fire.yaml

# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Train command: python train.py --data voc.yaml
# Default dataset location is next to /yolov5:
#   /parent_folder
#     /VOC
#     /yolov5


# download command/URL (optional)
download: bash data/scripts/get_voc.sh

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: data-fire-smoke/images/train/  # 划分后训练集的照片路径
val: data-fire-smoke/images/val/  # 划分后验证集的照片路径

# number of classes
nc: 2   #标签的类别书

# class names
names: [ 'fire', 'smoke']   #标签

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2.更改模型的配置文件

预训练权重选择的是ylov5s.pt
所以models模型应该选择yolov5s.yaml（不同的与训练权重对应不同的网络层数，用错会报错）

复制models中的yolov5.yaml命名为fire.yaml

只需要修改一处地方：

四、训练模型路径的修改

打开train.py文件（train.py是训练自己数据集的函数）

需要修改

大概在train.py文件的458行左右

• 修改预训练权重

parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='initial weights path')
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• 修改模型路径

parser.add_argument('--cfg', type=str, default='models/fire.yaml', help='model.yaml path')
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• 修改数据路径

parser.add_argument('--data', type=str, default='data/fire.yaml', help='data.yaml path')
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训练结束会出现runs文件夹

在测试的时候我们选取最好的权重

五、模型测试

将前面训练得到的最好的权重（best.pt）进行测试（detect.py测试文件）

1.换权重文件

2.照片测试

• 输入测试照片的路径

• 运行detect.py文件

3.摄像头测试

• 将刚才照片的路径改为0即可
  
  测试完成，下面开始可视化部分！

六、Pyqt可视化

1.安装Pyqt

pip3 install PyQt5
pip install PyQt5-tools 
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2.登录界面

新建两个py文件

login.py

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

# Form implementation generated from reading ui file 'login.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again.  Do not edit this file unless you know what you are doing.


from PyQt5 import QtCore, QtGui, QtWidgets


class Ui_login_MainWindow(object):
    def setupUi(self, login_MainWindow):
        login_MainWindow.setObjectName("login_MainWindow")
        login_MainWindow.setEnabled(True)
        login_MainWindow.resize(575, 392)
        login_MainWindow.setAnimated(True)
        self.centralwidget = QtWidgets.QWidget(login_MainWindow)
        self.centralwidget.setEnabled(True)
        self.centralwidget.setObjectName("centralwidget")
        self.label_3 = QtWidgets.QLabel(self.centralwidget)
        self.label_3.setGeometry(QtCore.QRect(90, 30, 800, 79))
        font = QtGui.QFont()
        font.setPointSize(30)
        font.setBold(True)
        font.setItalic(True)
        font.setWeight(75)
        self.label_3.setFont(font)
        self.label_3.setObjectName("label_3")
        self.label_2 = QtWidgets.QLabel(self.centralwidget)
        self.label_2.setGeometry(QtCore.QRect(100, 200, 71, 41))
        font = QtGui.QFont()
        font.setPointSize(15)
        self.label_2.setFont(font)
        self.label_2.setObjectName("label_2")
        self.label = QtWidgets.QLabel(self.centralwidget)
        self.label.setGeometry(QtCore.QRect(100, 150, 81, 41))
        font = QtGui.QFont()
        font.setPointSize(15)
        self.label.setFont(font)
        self.label.setObjectName("label")
        self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)
        self.lineEdit.setGeometry(QtCore.QRect(180, 200, 221, 41))
        font = QtGui.QFont()
        font.setPointSize(15)
        self.lineEdit.setFont(font)
        self.lineEdit.setText("")
        self.lineEdit.setEchoMode(QtWidgets.QLineEdit.Password)
        self.lineEdit.setObjectName("lineEdit")
        self.lineEdit_2 = QtWidgets.QLineEdit(self.centralwidget)
        self.lineEdit_2.setGeometry(QtCore.QRect(180, 150, 221, 41))
        font = QtGui.QFont()
        font.setPointSize(15)
        self.lineEdit_2.setFont(font)
        self.lineEdit_2.setObjectName("lineEdit_2")
        self.pushButton = QtWidgets.QPushButton(self.centralwidget)
        self.pushButton.setGeometry(QtCore.QRect(180, 280, 171, 51))
        font = QtGui.QFont()
        font.setPointSize(20)
        font.setBold(True)
        font.setWeight(75)
        self.pushButton.setFont(font)
        self.pushButton.setObjectName("pushButton")
        login_MainWindow.setCentralWidget(self.centralwidget)
        self.menubar = QtWidgets.QMenuBar(login_MainWindow)
        self.menubar.setGeometry(QtCore.QRect(0, 0, 575, 22))
        self.menubar.setObjectName("menubar")
        login_MainWindow.setMenuBar(self.menubar)

        self.retranslateUi(login_MainWindow)
        QtCore.QMetaObject.connectSlotsByName(login_MainWindow)

    def retranslateUi(self, login_MainWindow):
        _translate = QtCore.QCoreApplication.translate
        login_MainWindow.setWindowTitle(_translate("login_MainWindow", "火灾火焰烟雾识别系统"))
        self.label_3.setText(_translate("login_MainWindow", "欢迎使用火灾火焰烟雾识别系统"))
        self.label_2.setText(_translate("login_MainWindow", "密 码"))
        self.label.setText(_translate("login_MainWindow", "管理员"))
        self.lineEdit_2.setText(_translate("login_MainWindow", ""))
        self.pushButton.setText(_translate("login_MainWindow", "登录"))
        # self.pushButton.setShortcut(_translate("login_MainWindow", "Enter"))  # 设置快捷键
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login_mian.py

# self.pushButton.setShortcut(_translate("MainWindow", "enter")) #设置快捷键
import sys

from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import QApplication, QMainWindow
from PyQt5.QtCore import QTimer
from PyQt5.QtGui import QImage, QPixmap, QKeyEvent

from PyQt5.QtWidgets import QMessageBox
from login import Ui_login_MainWindow
from main import *
from PyQt5.QtCore import Qt


class login_window(QtWidgets.QMainWindow, Ui_login_MainWindow):
    def __init__(self):
        super(login_window, self).__init__()
        self.setupUi(self)  # 创建窗体对象
        self.init()
        self.admin = "美女"
        self.Password = "123456"

    def init(self):
        self.pushButton.clicked.connect(self.login_button)  # 连接槽

    def login_button(self):
        if self.lineEdit.text() == "":
            QMessageBox.warning(self, '警告', '密码不能为空，请输入！')
            return None

        # if  self.password == self.lineEdit.text():
        if (self.lineEdit.text() == self.Password) and self.lineEdit_2.text() == self.admin:
            # Ui_Main = Open_Camera()  # 生成主窗口的实例
            # 1打开新窗口
            Ui_Main.show()
            # 2关闭本窗口
            self.close()
        else:
            QMessageBox.critical(self, '错误', '密码错误！')
            self.lineEdit.clear()
            return None


if __name__ == '__main__':
    from PyQt5 import QtCore

    QtCore.QCoreApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling)  # 自适应分辨率

    app = QtWidgets.QApplication(sys.argv)
    window = login_window()
    Ui_Main = Ui_MainWindow()  # 生成主窗口的实例
    window.show()

    sys.exit(app.exec_())
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运行login_mian.py文件会出现登陆界面，
登录成功会跳转主界面
管理员：美女
密码：123456

3.主界面

新建main.py文件

main.py

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)')
        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.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.centralwidget)
        self.horizontalLayout_2.setObjectName("horizontalLayout_2")
        self.horizontalLayout = QtWidgets.QHBoxLayout()
        self.horizontalLayout.setSizeConstraint(
            QtWidgets.QLayout.SetNoConstraint)
        self.horizontalLayout.setObjectName("horizontalLayout")
        self.verticalLayout = QtWidgets.QVBoxLayout()
        self.verticalLayout.setContentsMargins(-1, -1, 0, -1)
        self.verticalLayout.setSpacing(80)
        self.verticalLayout.setObjectName("verticalLayout")
        self.pushButton_img = QtWidgets.QPushButton(self.centralwidget)
        sizePolicy = QtWidgets.QSizePolicy(
            QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.MinimumExpanding)
        sizePolicy.setHorizontalStretch(0)
        sizePolicy.setVerticalStretch(0)
        sizePolicy.setHeightForWidth(
            self.pushButton_img.sizePolicy().hasHeightForWidth())
        self.pushButton_img.setSizePolicy(sizePolicy)
        self.pushButton_img.setMinimumSize(QtCore.QSize(150, 100))
        self.pushButton_img.setMaximumSize(QtCore.QSize(150, 100))
        font = QtGui.QFont()
        font.setFamily("Agency FB")
        font.setPointSize(12)
        self.pushButton_img.setFont(font)
        self.pushButton_img.setObjectName("pushButton_img")
        self.verticalLayout.addWidget(
            self.pushButton_img, 0, QtCore.Qt.AlignHCenter)
        self.pushButton_camera = QtWidgets.QPushButton(self.centralwidget)
        sizePolicy = QtWidgets.QSizePolicy(
            QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)
        sizePolicy.setHorizontalStretch(0)
        sizePolicy.setVerticalStretch(0)
        sizePolicy.setHeightForWidth(
            self.pushButton_camera.sizePolicy().hasHeightForWidth())
        self.pushButton_camera.setSizePolicy(sizePolicy)
        self.pushButton_camera.setMinimumSize(QtCore.QSize(150, 100))
        self.pushButton_camera.setMaximumSize(QtCore.QSize(150, 100))
        font = QtGui.QFont()
        font.setFamily("Agency FB")
        font.setPointSize(12)
        self.pushButton_camera.setFont(font)
        self.pushButton_camera.setObjectName("pushButton_camera")
        self.verticalLayout.addWidget(
            self.pushButton_camera, 0, QtCore.Qt.AlignHCenter)
        self.pushButton_video = QtWidgets.QPushButton(self.centralwidget)
        sizePolicy = QtWidgets.QSizePolicy(
            QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)
        sizePolicy.setHorizontalStretch(0)
        sizePolicy.setVerticalStretch(0)
        sizePolicy.setHeightForWidth(
            self.pushButton_video.sizePolicy().hasHeightForWidth())
        self.pushButton_video.setSizePolicy(sizePolicy)
        self.pushButton_video.setMinimumSize(QtCore.QSize(150, 100))
        self.pushButton_video.setMaximumSize(QtCore.QSize(150, 100))
        font = QtGui.QFont()
        font.setFamily("Agency FB")
        font.setPointSize(12)
        self.pushButton_video.setFont(font)
        self.pushButton_video.setObjectName("pushButton_video")
        self.verticalLayout.addWidget(
            self.pushButton_video, 0, QtCore.Qt.AlignHCenter)
        self.verticalLayout.setStretch(2, 1)
        self.horizontalLayout.addLayout(self.verticalLayout)
        self.label = QtWidgets.QLabel(self.centralwidget)
        self.label.setObjectName("label")
        self.horizontalLayout.addWidget(self.label)
        self.horizontalLayout.setStretch(0, 1)
        self.horizontalLayout.setStretch(1, 3)
        self.horizontalLayout_2.addLayout(self.horizontalLayout)
        MainWindow.setCentralWidget(self.centralwidget)
        self.menubar = QtWidgets.QMenuBar(MainWindow)
        self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 23))
        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", "火灾火焰烟雾识别系统"))
        self.pushButton_img.setText(_translate("MainWindow", "图片检测"))
        self.pushButton_camera.setText(_translate("MainWindow", "摄像头检测"))
        self.pushButton_video.setText(_translate("MainWindow", "视频检测"))
        self.label.setText(_translate("MainWindow", "TextLabel"))

    def init_slots(self):
        self.pushButton_img.clicked.connect(self.button_image_open)
        self.pushButton_video.clicked.connect(self.button_video_open)
        self.pushButton_camera.clicked.connect(self.button_camera_open)
        self.timer_video.timeout.connect(self.show_video_frame)

    def init_logo(self):
        pix = QtGui.QPixmap('R-C.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_video.setDisabled(True)
            self.pushButton_img.setDisabled(True)
            self.pushButton_camera.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_video.setDisabled(True)
                self.pushButton_img.setDisabled(True)
                self.pushButton_camera.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_video.setDisabled(False)
            self.pushButton_img.setDisabled(False)
            self.pushButton_camera.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_video.setDisabled(False)
            self.pushButton_img.setDisabled(False)
            self.pushButton_camera.setDisabled(False)
            self.init_logo()


if __name__ == '__main__':
    app = QtWidgets.QApplication(sys.argv)
    ui = Ui_MainWindow()
    ui.show()
    sys.exit(app.exec_())

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4.照片检测

选择本地图片进行识别

5.视频检测

选择本地的视频进行识别

识别结果

6.摄像头检测

可以成功调用摄像头，这里就不再进行展示！