Full Code of Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI for AI

Repository: Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI
Branch: main
Commit: 2d1f448d75da
Files: 118
Total size: 95.5 MB

Directory structure:
gitextract_f02wz10a/

├── .github/
│   └── workflows/
│       ├── jekyll-gh-pages.yml
│       └── main.yml
├── .idea/
│   ├── Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI.iml
│   ├── inspectionProfiles/
│   │   └── profiles_settings.xml
│   ├── misc.xml
│   ├── modules.xml
│   ├── vcs.xml
│   └── workspace.xml
├── MouseLabel.py
├── README.md
├── apprcc.qrc
├── apprcc_rc.py
├── config/
│   ├── fold.json
│   ├── ip.json
│   └── setting.json
├── data/
│   ├── doc/
│   │   ├── LICENSE
│   │   ├── README_Parameter adjustment.md
│   │   └── README_cn.md
│   ├── regn_mysql.sql
│   ├── run/
│   │   └── exp52/
│   │       ├── hyp.yaml
│   │       └── opt.yaml
│   └── scripts/
│       ├── download_weights.sh
│       ├── get_coco.sh
│       └── get_coco128.sh
├── detect.py
├── dialog/
│   ├── rtsp_dialog.py
│   ├── rtsp_dialog.ui
│   └── rtsp_win.py
├── hubconf.py
├── login_lj.py
├── main.py
├── main_win/
│   ├── login.py
│   ├── login.ui
│   ├── win.py
│   └── win.ui
├── models/
│   ├── __init__.py
│   ├── common.py
│   ├── experimental.py
│   ├── hub/
│   │   ├── anchors.yaml
│   │   ├── yolov3-spp.yaml
│   │   ├── yolov3-tiny.yaml
│   │   ├── yolov3.yaml
│   │   ├── yolov5-bifpn.yaml
│   │   ├── yolov5-fpn.yaml
│   │   ├── yolov5-p2.yaml
│   │   ├── yolov5-p34.yaml
│   │   ├── yolov5-p6.yaml
│   │   ├── yolov5-p7.yaml
│   │   ├── yolov5-panet.yaml
│   │   ├── yolov5l6.yaml
│   │   ├── yolov5m6.yaml
│   │   ├── yolov5n6.yaml
│   │   ├── yolov5s-ghost.yaml
│   │   ├── yolov5s-transformer.yaml
│   │   ├── yolov5s6.yaml
│   │   └── yolov5x6.yaml
│   ├── tf.py
│   └── yolo.py
├── pt/
│   ├── best.engine
│   ├── best.onnx
│   ├── best.pt
│   └── yolov5s.pt
├── rc_apprcc.py
├── requirements.txt
├── setup-database.bat
└── utils/
    ├── CustomMessageBox.py
    ├── __init__.py
    ├── activations.py
    ├── augmentations.py
    ├── autoanchor.py
    ├── autobatch.py
    ├── aws/
    │   ├── __init__.py
    │   ├── mime.sh
    │   ├── resume.py
    │   └── userdata.sh
    ├── benchmarks.py
    ├── cal_fps.py
    ├── callbacks.py
    ├── capnums.py
    ├── datasets.py
    ├── downloads.py
    ├── flask_rest_api/
    │   ├── README.md
    │   ├── example_request.py
    │   └── restapi.py
    ├── general.py
    ├── google_app_engine/
    │   ├── Dockerfile
    │   ├── additional_requirements.txt
    │   └── app.yaml
    ├── google_utils.py
    ├── loggers/
    │   ├── __init__.py
    │   └── wandb/
    │       ├── README.md
    │       ├── __init__.py
    │       ├── log_dataset.py
    │       ├── sweep.py
    │       ├── sweep.yaml
    │       └── wandb_utils.py
    ├── loss.py
    ├── metrics.py
    ├── plots.py
    ├── torch_utils.py
    ├── tt100k_to_voc-main/
    │   ├── 1.py
    │   ├── 1_build_voc_dir.py
    │   ├── 2_json2xml.py
    │   ├── 3_delete_jpg_and_xml.py
    │   ├── 4_spilt_data.py
    │   ├── 5_label.py
    │   ├── Not_TT45_list_train.txt
    │   ├── Not_TT45_list_val.txt
    │   ├── README.md
    │   ├── TT100K_VOC_classes.json
    │   ├── __init__.py
    │   ├── annotations_all.json
    │   └── 新建 Internet 快捷方式.url
    └── wandb_logging/
        ├── __init__.py
        ├── log_dataset.py
        ├── sweep.py
        ├── sweep.yaml
        └── wandb_utils.py

================================================
FILE CONTENTS
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FILE: .github/workflows/jekyll-gh-pages.yml
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# Sample workflow for building and deploying a Jekyll site to GitHub Pages
name: Deploy Jekyll with GitHub Pages dependencies preinstalled

on:
  # Runs on pushes targeting the default branch
  push:
    branches: ["main"]

  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
  contents: read
  pages: write
  id-token: write

# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
concurrency:
  group: "pages"
  cancel-in-progress: false

jobs:
  # Build job
  build:
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        uses: actions/checkout@v3
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        uses: actions/configure-pages@v3
      - name: Build with Jekyll
        uses: actions/jekyll-build-pages@v1
        with:
          source: ./
          destination: ./_site
      - name: Upload artifact
        uses: actions/upload-pages-artifact@v1

  # Deployment job
  deploy:
    environment:
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      url: ${{ steps.deployment.outputs.page_url }}
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    needs: build
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================================================
FILE: .github/workflows/main.yml
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name: Sync with Gitee

on:
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jobs:
  sync:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout repository
      uses: actions/checkout@v2

    - name: Sync with Gitee
      run: |
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================================================
FILE: MouseLabel.py
================================================
from PyQt5.QtWidgets import QLabel
from PyQt5.QtCore import pyqtSignal


class LabelMouse(QLabel):
    double_clicked = pyqtSignal()

    # 鼠标双击事件
    def mouseDoubleClickEvent(self, event):
        self.double_clicked.emit()

    def mouseMoveEvent(self):
        """
        当鼠标划过标签label2时触发事件
        :return:
        """
        print('当鼠标划过标签label2时触发事件')


class Label_click_Mouse(QLabel):
    clicked = pyqtSignal()

    # 鼠标点击事件
    def mousePressEvent(self, event):
        self.clicked.emit()

================================================
FILE: README.md
================================================
<h1 align="center">Road Sign Recognition Project Based on YOLOv5 (YOLOv5 GUI)</h1>

<p align="center">
  <a href="README.md">English</a> |
  <a href="data/doc/README_cn.md">简体中文</a>
</p>

<p align="center">
  <a href="data/doc/README_Parameter adjustment.md">训练策略</a>
</p>


This system is a road sign recognition application leveraging YOLOv5🚀 😊. It employs a MySQL database 💽, PyQt5 for the interface design 🎨, PyTorch deep learning framework⚡. Additionally, it incorporates CSS styles 🌈.

The system comprises five key modules:

1. System Login Module 🔑: Responsible for user authentication.
2. Initialization Parameter Module 📋: Provides settings for initializing YOLOv5 model parameters.
3. Sign Recognition Module 🔍: The core functionality responsible for recognizing road signs and updating the database with the results.
4. Database Module 💾: Consists of two sub-modules - basic database operations and data analysis.
5. Image Processing Module 🖼️: Handles the processing of individual images and associated data.

The entire system is designed to support various data input methods and model switching. Additionally, it offers image enhancement techniques such as mosaic and mixup 📈.

![00013.jpg](data/doc/maxre1sdefault.jpg)
## Screenshots

* ### Sign Recognition Module
`The three checkboxes in the lower left corner are results save, start database entry, and model visual analysis.`

  ![img.png](data/doc/img.png)
* ### Image Processing and Data Augmentation Module

`The right column is a batch image data enhancement with custom parameters (using the checked data increment method for all images in a folder with a certain probability)`

![img_1.png](data/doc/img_1.png)
* ### Parameter Initialization Module
`Model basic parameters Select Configure`

  ![img_2.png](data/doc/img_2.png)
* ### Database Module
  ![img_3.png](data/doc/img_3.png)
* ### Data Analysis Module
  ![img_4.png](data/doc/img_4.png)
* ### Login Interface
  ![img_5.png](data/doc/img_5.png)
  






### Video Demo

[YOUTUBE DEMO: Road Sign Recognition System Based on YOLOV5](https://youtu.be/qoHaXvp_Gxk?si=xIAm1UXCLTjR8kUD)
[BiliBili Demo: Road Sign Recognition System Based on YOLOV5](https://www.bilibili.com/video/BV1Ck4y1Y7Bk/?spm_id_from=333.999.0.0&vd_source=40d9cda43378fbc89cd5184e09bf1272)

### Install Dependencies

To install the required dependencies, run:

```bash
pip install -r requirements.txt
```
---
## **Quick Start**

### 1. **Setting Up the Database**

To run the application, you need to set up your MySQL database. Follow these steps to prepare your database:

- **Automatic Database Creation (Optional)**:
    - If you prefer an automated setup, a batch script is provided. Run the **`setup_database.bat`** script to create the database. This requires MySQL to be installed and configured on your system.
- **Manual Database Creation**:
    - Alternatively, you can manually create the database in MySQL. Import and execute the **`data/regn_mysql.sql`** file in your MySQL environment to set up the necessary database and tables.

### 2. **Configuring Database Connection in Code**
After setting up the database, update the connection Settings in the code to change the authentication information for your local database (these four variables are at the beginning of the code, around line 59, as follows); P.S. These authentication messages are called twice in the code (around lines 111 and 1783)
```python
# Database connection settings as global variables
DB_HOST = 'localhost'    # Database host
DB_USER = 'root'         # Database user
DB_PASSWORD = '1234'     # Database password
DB_NAME = 'traffic_sign_recognition'  # Database name
```

### **Note on Cryptography Package**

If you encounter a **`RuntimeError: 'cryptography' package is required for sha256_password or caching_sha2_password auth methods`**, 
This is because the database authentication has gone wrong and the database needs to be properly created and the password entered.
This error will also be reported if you do not have the mysql service started locally, so make sure your mysql service is started.

### 3. Run `main.py`.

### 4. Enter your account and password to log in

Here are the default login credentials:

| Username | Password |
|----------|----------|
| admin    | 123456   |
| 1        | 2        |

Or modify the main function in main.py: remove the logon logic to enter the system directly without authentication.

---
## Project Structure

- `pt` folder: Contains the YOLOv5 model file `best.pt` for road sign recognition.
- `main_with` folder: Contains `login.py` for the login UI and `win.py` for the main UI.
- `dialog` folder: Contains the RTSP pop-up interface.
- `apprcc_rc.py`: The resource file for the project.
- `login_ji.py`: Implements the login logic for the UI.
- `data/run/run-exp52`: The YOLOv5 road sign recognition model trained for 300 epochs.
- `utils/tt100k_to_voc-main` folder: Tool for converting JSON annotations to YOLO format.
- `result`: Folder to save inference results.
- `run`: Folder to save training logs and outputs.
- Dataset: Download from [TT100k : Traffic-Sign Detection and Classification in the Wild](https://cg.cs.tsinghua.edu.cn/traffic-sign/).
- Database files: Located in the `data` folder, see `-regn_mysql.sql` for setup.

> Since this project was done while I was learning YOLOv5 (quite a while ago), the main logic is concentrated in the main.py file. In other words, I didn't modularize different functions, and I didn't have a clear division of module structure. Now I want to divide it into modules, but I'm too lazy,  ha ha :smile:. If you're interested, you can modularize it so it's clearer.
## Acknowledgements

- For converting the TT100K dataset to VOC format and selecting more than 100 images and XMLs for each category, see this [CSDN blog post](https://blog.csdn.net/Hankerchen/article/details/120727299?spm=1001.2014.3001.5502).
- The PyQt5-YOLOv5 integration was inspired by this [GitHub repository](https://github.com/Javacr/PyQt5-YOLOv5).

## Contact Us
> WeChat：AIGCSD
> 
> email: 2545197649@qq.com
> 
> Official Accounts：AI进修生

## Star History

Track the GitHub star history of this project:

![Star History Chart](https://api.star-history.com/svg?repos=Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI&type=Date)



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================================================
FILE: data/doc/LICENSE
================================================
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================================================
FILE: data/doc/README_Parameter adjustment.md
================================================
由于一些原因，后期再上传，同时包括（readme详细操作)


================================================
FILE: data/doc/README_cn.md
================================================
<h1 align="center">基于YOLOv5的道路标志识别项目（yolov5界面GUI）</h1>
<p align="center">
  <a href="../../README.md">English</a> |
  <a href="README_cn.md">简体中文</a>
</p>


这是一个关于yolov5的道路标志识别项目，使用Pyqt5开发界面，Yolov5训练模型，数据库Mysql，包含五个模块：初始化参数、标志识别、数据库、数据分析和图像处理。

## 软件截图

* ### 标志识别模块
`左下角三个勾选框分别是结果保存、启动数据库录入、以及模型可视化分析`

  ![img.png](img.png)

  
  
* ### 图像处理与数据增强模块
`右侧栏是自定义参数的批量图像数据增强（按一定概率对一个文件夹所有图片使用勾选的数据增加方法）`

  ![img_1.png](img_1.png)

* ### 初始化参数模块
`  模型基本参数勾选配置`

  ![img_2.png](img_2.png)


* ### 数据库模块
  ![img_3.png](img_3.png)
  
 
* ### 数据分析模块
  ![img_4.png](img_4.png)
* ### 登录界面
  ![img_5.png](img_5.png)

### 演示视频

[基于YOLOV5的道路标志识别系统](https://www.bilibili.com/video/BV1Ck4y1Y7Bk/?spm_id_from=333.999.0.0&vd_source=40d9cda43378fbc89cd5184e09bf1272)


### 安装依赖

pip install -r requirements.txt

---
## **快速开始**

### 1. **设置数据库**

为了运行应用程序，您需要设置您的 MySQL 数据库。按照以下步骤准备您的数据库：

- **自动数据库创建（可选）**：
    - 如果您喜欢自动设置，我们提供了一个批处理脚本。运行 **`setup_database.bat`** 脚本来创建数据库。这需要在您的系统上安装并配置 MySQL。
- **手动数据库创建**：
    - 或者，您可以在 MySQL 中手动创建数据库。在您的 MySQL 环境中导入并执行 **`data/regn_mysql.sql`** 文件，以设置必要的数据库和表。

### 2. **在代码中配置数据库连接**

设置数据库之后，请更新代码中的连接设置，请更改成你本地数据库的身份验证信息（这4个变量在代码的开头 , 大约在59行，具体如下）；附：这些身份验证信息在代码中被两次调用(大约第111行和第1783行)

```python
# 数据库连接设置作为全局变量
DB_HOST = 'localhost'    # 数据库主机
DB_USER = 'root'         # 数据库用户
DB_PASSWORD = '1234'     # 数据库密码
DB_NAME = 'traffic_sign_recognition'  # 数据库名
```

### **关于数据库链接的注意事项**

如果遇到 **`RuntimeError: 'cryptography' package is required for sha256_password or caching_sha2_password auth methods`** 错误，这是因为数据库身份验证出错了，需要正确地创建数据库并输入密码。
并且如果你本地没有启动mysql服务，也会报这个错，所以请确保你的mysql服务已经启动。
### 3. 运行 `main.py`。

### 4. 输入您的账号和密码以登录。

以下是默认的登录凭据：

| 用户名   | 密码     |
|----------|----------|
| admin    | 123456   |
| 1        | 2        |

或者修改`main.py`中的主函数：删除登陆逻辑，以直接进入系统而无需进行身份验证。

---

## 项目模块

- `pt`文件夹：存放模型(best.pt是道路标志识别模型)
- `main_with`文件夹：`login.py`(登陆ui)、`win.py`(主ui)
- `dialog`文件夹：rtsp弹出界面
- `apprcc_rc.py`：资源文件
- `login_ji.py`：界面登陆逻辑文件
- `data/run/run-exp52`：300轮训练后的道路标志识别模型
- `utils/tt100k_to_voc-main`文件夹：json转yolo格式
- `result`保存一些推理文件，`run`保存训练文件
- 数据集：[TT100k : Traffic-Sign Detection and Classification in the Wild](https://cg.cs.tsinghua.edu.cn/traffic-sign/)
- 数据库文件：`data`文件夹下`-regn_mysql.sql`


> 由于这个项目是在我学习YOLOv5时完成的（已经过了很长一段时间），因此主要的逻辑代码都集中在main.py文件中。换句话说，我没有将不同功能模块化，没有进行模块结构的清晰划分。虽然现在我想给它划分一下模块结构，但是我还是太懒了，嘻嘻 :smile:。如果您有兴趣，可以将其模块化，这样它就会更加清晰。



## 致谢

- [将TT100K数据集转成VOC格式，并且用Python脚本选出45类超过100张的图片和XML](https://blog.csdn.net/Hankerchen/article/details/120727299?spm=1001.2014.3001.5502)
- https://github.com/Javacr/PyQt5-YOLOv5

## 联系我们
> 微信：AIGC004
> 
> 邮箱: 2545197649@qq.com
> 
> 公众号：AI进修生


## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI&type=Date)](https://star-history.com/#Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI&Date)


================================================
FILE: data/regn_mysql.sql
================================================
/*
SQLyog Community v13.2.0 (64 bit)
MySQL - 8.0.32 : Database - traffic_sign_recognition
*********************************************************************
*/

/*!40101 SET NAMES utf8 */;

/*!40101 SET SQL_MODE=''*/;

/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
CREATE DATABASE /*!32312 IF NOT EXISTS*/`traffic_sign_recognition` /*!40100 DEFAULT CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci */ /*!80016 DEFAULT ENCRYPTION='N' */;

USE `traffic_sign_recognition`;

/*Table structure for table `detection_results` */

DROP TABLE IF EXISTS `detection_results`;

CREATE TABLE `detection_results` (
  `id` int NOT NULL AUTO_INCREMENT,
  `sign_type` varchar(255) NOT NULL,
  `sign_count` int NOT NULL,
  `detection_time` varchar(255) DEFAULT NULL,
  `additional_info` text,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=456 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;

/*Data for the table `detection_results` */

insert  into `detection_results`(`id`,`sign_type`,`sign_count`,`detection_time`,`additional_info`) values 
(1,'stop',5,'2022-10-02 15:30:00','Weather condition: sunny'),
(3,'pn',1,'2023-04-09 20:00:01','Confidence: 0.91'),
(4,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.93'),
(5,'i2',1,'2023-04-09 20:00:01','Confidence: 0.53'),
(6,'i4',1,'2023-04-09 20:00:01','Confidence: 0.88'),
(8,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.93'),
(14,'pn',1,'2023-04-09 20:00:01','Confidence: 0.90'),
(15,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.94'),
(16,'i2',1,'2023-04-09 20:00:01','Confidence: 0.95'),
(17,'i4',1,'2023-04-09 20:00:02','Confidence: 0.89'),
(18,'pn',1,'2023-04-09 20:00:02','Confidence: 0.90'),
(19,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),
(20,'i2',1,'2023-04-09 20:00:02','Confidence: 0.96'),
(21,'pn',1,'2023-04-09 20:00:02','Confidence: 0.89'),
(22,'i4',1,'2023-04-09 20:00:02','Confidence: 0.91'),
(23,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),
(24,'i2',1,'2023-04-09 20:00:02','Confidence: 0.94'),
(25,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.92'),
(26,'i4',1,'2023-04-09 20:00:02','Confidence: 0.92'),
(27,'pn',1,'2023-04-09 20:00:02','Confidence: 0.93'),
(28,'i2',1,'2023-04-09 20:00:02','Confidence: 0.96'),
(29,'pn',1,'2023-04-09 20:00:02','Confidence: 0.91'),
(30,'i4',1,'2023-04-09 20:00:02','Confidence: 0.92'),
(31,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),
(32,'i2',1,'2023-04-09 20:00:02','Confidence: 0.97'),
(33,'pn',1,'2023-04-09 20:00:03','Confidence: 0.92'),
(34,'i4',1,'2023-04-09 20:00:03','Confidence: 0.92'),
(35,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),
(36,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),
(37,'pn',1,'2023-04-09 20:00:03','Confidence: 0.89'),
(38,'i4',1,'2023-04-09 20:00:03','Confidence: 0.93'),
(39,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),
(40,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),
(41,'pn',1,'2023-04-09 20:00:03','Confidence: 0.88'),
(42,'i4',1,'2023-04-09 20:00:03','Confidence: 0.93'),
(43,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),
(44,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),
(45,'pn',1,'2023-04-09 20:00:03','Confidence: 0.90'),
(46,'i4',1,'2023-04-09 20:00:03','Confidence: 0.94'),
(47,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.95'),
(48,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),
(49,'pn',1,'2023-04-09 20:00:04','Confidence: 0.91'),
(50,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),
(51,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.94'),
(52,'i2',1,'2023-04-09 20:00:04','Confidence: 0.96'),
(53,'pn',1,'2023-04-09 20:00:04','Confidence: 0.92'),
(54,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),
(55,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.94'),
(56,'i2',1,'2023-04-09 20:00:04','Confidence: 0.95'),
(57,'pn',1,'2023-04-09 20:00:04','Confidence: 0.91'),
(58,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),
(59,'i2',1,'2023-04-09 20:00:04','Confidence: 0.93'),
(60,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.95'),
(61,'pn',1,'2023-04-09 20:00:04','Confidence: 0.93'),
(62,'i4',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(63,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.95'),
(64,'i2',1,'2023-04-09 20:00:05','Confidence: 0.96'),
(65,'i4',1,'2023-04-09 20:00:05','Confidence: 0.92'),
(66,'pn',1,'2023-04-09 20:00:05','Confidence: 0.92'),
(67,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(68,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(69,'pn',1,'2023-04-09 20:00:05','Confidence: 0.92'),
(70,'i4',1,'2023-04-09 20:00:05','Confidence: 0.93'),
(71,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.95'),
(72,'i2',1,'2023-04-09 20:00:05','Confidence: 0.95'),
(73,'pn',1,'2023-04-09 20:00:05','Confidence: 0.93'),
(74,'i4',1,'2023-04-09 20:00:05','Confidence: 0.93'),
(75,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(76,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(77,'i4',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(78,'pn',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(79,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),
(80,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.95'),
(81,'i4',1,'2023-04-09 20:00:06','Confidence: 0.92'),
(82,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(83,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.95'),
(84,'i2',1,'2023-04-09 20:00:06','Confidence: 0.95'),
(85,'i2',1,'2023-04-09 20:00:06','Confidence: 0.70'),
(86,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.91'),
(87,'i4',1,'2023-04-09 20:00:06','Confidence: 0.91'),
(88,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(89,'i2',2,'2023-04-09 20:00:06','Confidence: 0.97'),
(90,'p26',1,'2023-04-09 20:00:06','Confidence: 0.62'),
(91,'i4',1,'2023-04-09 20:00:06','Confidence: 0.93'),
(92,'i2',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(93,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(94,'i4',1,'2023-04-09 20:00:06','Confidence: 0.91'),
(95,'i2',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(96,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),
(97,'i2',1,'2023-04-09 20:00:06','Confidence: 0.92'),
(98,'pn',1,'2023-04-09 20:00:06','Confidence: 0.93'),
(99,'i4',1,'2023-04-09 20:00:06','Confidence: 0.93'),
(100,'i2',1,'2023-04-09 20:00:07','Confidence: 0.91'),
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(381,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.60'),
(382,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.55'),
(383,'p19',1,'2023-05-25 17:27:26','Confidence: 0.43'),
(384,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.43'),
(385,'p10',1,'2023-05-25 17:27:26','Confidence: 0.43'),
(386,'pl40',1,'2023-05-25 17:27:26','Confidence: 0.44'),
(387,'ph4',1,'2023-05-25 17:27:26','Confidence: 0.46'),
(388,'p6',1,'2023-05-25 17:27:26','Confidence: 0.48'),
(389,'p19',1,'2023-05-25 17:27:26','Confidence: 0.58'),
(390,'p10',1,'2023-05-25 17:27:27','Confidence: 0.44'),
(391,'ph4',1,'2023-05-25 17:27:27','Confidence: 0.60'),
(392,'pm20',1,'2023-05-25 17:27:27','Confidence: 0.71'),
(393,'p10',1,'2023-05-25 17:27:27','Confidence: 0.57'),
(394,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.80'),
(395,'p10',1,'2023-05-25 17:27:27','Confidence: 0.68'),
(396,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.81'),
(397,'w32',1,'2023-05-25 17:27:27','Confidence: 0.44'),
(398,'pl70',1,'2023-05-25 17:27:27','Confidence: 0.49'),
(399,'p10',1,'2023-05-25 17:27:27','Confidence: 0.55'),
(400,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.64'),
(401,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.81'),
(402,'p10',1,'2023-05-25 17:27:27','Confidence: 0.84'),
(403,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.88'),
(404,'w32',1,'2023-05-25 17:27:27','Confidence: 0.48'),
(405,'pl20',1,'2023-05-25 17:27:27','Confidence: 0.49'),
(406,'w32',1,'2023-05-25 17:27:27','Confidence: 0.43'),
(407,'p19',1,'2023-05-25 17:27:27','Confidence: 0.82'),
(408,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.92'),
(409,'p19',1,'2023-05-25 17:27:28','Confidence: 0.68'),
(410,'pl40',1,'2023-05-25 17:27:28','Confidence: 0.83'),
(411,'p10',1,'2023-05-25 17:27:28','Confidence: 0.75'),
(412,'pl60',1,'2023-05-25 17:27:28','Confidence: 0.86'),
(413,'w32',1,'2023-05-25 17:27:28','Confidence: 0.53'),
(414,'p10',1,'2023-05-25 17:27:28','Confidence: 0.53'),
(415,'p6',1,'2023-05-25 17:27:28','Confidence: 0.53'),
(416,'pm30',1,'2023-05-25 17:27:28','Confidence: 0.55'),
(417,'pl60',1,'2023-05-25 17:27:28','Confidence: 0.81'),
(418,'p6',1,'2023-05-25 17:27:35','Confidence: 0.43'),
(419,'w32',1,'2023-05-25 17:27:35','Confidence: 0.58'),
(420,'pl40',1,'2023-05-25 17:27:35','Confidence: 0.64'),
(421,'w32',1,'2023-05-25 17:27:35','Confidence: 0.51'),
(422,'pm20',1,'2023-05-25 17:27:36','Confidence: 0.82'),
(423,'p10',1,'2023-05-25 17:27:36','Confidence: 0.86'),
(424,'w32',1,'2023-05-25 17:27:36','Confidence: 0.41'),
(425,'pl60',1,'2023-05-25 17:27:36','Confidence: 0.66'),
(426,'p19',1,'2023-05-25 17:27:36','Confidence: 0.87'),
(427,'p10',1,'2023-05-25 17:27:36','Confidence: 0.43'),
(428,'w32',1,'2023-05-25 17:27:36','Confidence: 0.47'),
(429,'pl40',1,'2023-05-25 17:27:36','Confidence: 0.53'),
(430,'p6',1,'2023-05-25 17:27:36','Confidence: 0.69'),
(431,'pl40',1,'2023-05-25 17:27:36','Confidence: 0.43'),
(432,'p23',1,'2023-05-25 17:27:36','Confidence: 0.53'),
(433,'pm30',1,'2023-05-25 17:27:37','Confidence: 0.44'),
(434,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.88'),
(435,'p23',1,'2023-05-25 17:27:37','Confidence: 0.48'),
(436,'p19',1,'2023-05-25 17:27:37','Confidence: 0.61'),
(437,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.87'),
(438,'p19',1,'2023-05-25 17:27:37','Confidence: 0.89'),
(439,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.90'),
(440,'w32',1,'2023-05-25 17:27:37','Confidence: 0.49'),
(441,'p10',1,'2023-05-25 17:27:37','Confidence: 0.52'),
(442,'p23',1,'2023-05-25 17:27:37','Confidence: 0.61'),
(443,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.74'),
(444,'pm30',1,'2023-05-25 17:27:37','Confidence: 0.80'),
(445,'p23',1,'2023-05-25 17:27:37','Confidence: 0.55'),
(446,'w32',1,'2023-05-25 17:27:37','Confidence: 0.56'),
(447,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.75'),
(448,'p19',1,'2023-05-25 17:27:37','Confidence: 0.76'),
(449,'p19',1,'2023-05-25 17:27:38','Confidence: 0.59'),
(450,'pm20',1,'2023-05-25 17:27:38','Confidence: 0.78'),
(451,'p23',1,'2023-05-25 17:27:38','Confidence: 0.84'),
(452,'pl40',1,'2023-05-25 17:27:38','Confidence: 0.45'),
(453,'w32',1,'2023-05-25 17:27:38','Confidence: 0.50'),
(454,'pm20',1,'2023-05-25 17:27:38','Confidence: 0.85'),
(455,'p19',1,'2023-05-25 17:27:38','Confidence: 0.91');

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================================================
FILE: data/run/exp52/hyp.yaml
================================================
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0


================================================
FILE: data/run/exp52/opt.yaml
================================================
weights: runs\train\exp51\weights\best.pt
cfg: ''
data: E:\Desktop\yolov5-6.0\data\1.yaml
hyp: data\hyps\hyp.scratch-low.yaml
epochs: 20
batch_size: 8
imgsz: 1280
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
evolve: null
bucket: ''
cache: null
image_weights: true
device: ''
multi_scale: false
single_cls: false
adam: true
sync_bn: false
workers: 4
project: runs\train
name: exp
exist_ok: false
quad: false
linear_lr: false
label_smoothing: 0.0
patience: 100
freeze: 0
save_period: -1
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs\train\exp52


================================================
FILE: data/scripts/download_weights.sh
================================================
#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download latest models from https://github.com/ultralytics/yolov5/releases
# Example usage: bash path/to/download_weights.sh
# parent
# └── yolov5
#     ├── yolov5s.pt  ← downloads here
#     ├── yolov5m.pt
#     └── ...

python - <<EOF
from utils.downloads import attempt_download

models = ['n', 's', 'm', 'l', 'x']
models.extend([x + '6' for x in models])  # add P6 models

for x in models:
    attempt_download(f'yolov5{x}.pt')

EOF


================================================
FILE: data/scripts/get_coco.sh
================================================
#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download COCO 2017 dataset http://cocodataset.org
# Example usage: bash data/scripts/get_coco.sh
# parent
# ├── yolov5
# └── datasets
#     └── coco  ← downloads here

# Download/unzip labels
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &

# Download/unzip images
d='../datasets/coco/images' # unzip directory
url=http://images.cocodataset.org/zips/
f1='train2017.zip' # 19G, 118k images
f2='val2017.zip'   # 1G, 5k images
f3='test2017.zip'  # 7G, 41k images (optional)
for f in $f1 $f2; do
  echo 'Downloading' $url$f '...'
  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
done
wait # finish background tasks


================================================
FILE: data/scripts/get_coco128.sh
================================================
#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: bash data/scripts/get_coco128.sh
# parent
# ├── yolov5
# └── datasets
#     └── coco128  ← downloads here

# Download/unzip images and labels
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &

wait # finish background tasks


================================================
FILE: detect.py
================================================
"""Run inference with a YOLOv5 model on images, videos, directories, streams

Usage:
    $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""

import argparse
import sys
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn

FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
    apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_sync

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

@torch.no_grad()
def run(weights='yolov5s.pt',  # model.pt path(s)
        source='data/images',  # file/dir/URL/glob, 0 for webcam
        imgsz=640,  # inference size (pixels)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project='runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        ):
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet50', n=2)  # initialize
        modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if 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
        t1 = time_sync()
        pred = model(img,
                     augment=augment,
                     visualize=increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False)[0]

        # Apply NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        t2 = time_sync()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")

    if update:
        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)

    print(f'Done. ({time.time() - t0:.3f}s)')


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='imgs/GUI_new.png', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    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='show 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('--save-crop', action='store_true', help='save cropped prediction boxes')
    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('--visualize', action='store_true', help='visualize features')
    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')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    opt = parser.parse_args()
    return opt


def main(opt):
    print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)


================================================
FILE: dialog/rtsp_dialog.py
================================================
# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file 'rtsp_dialog.ui'
#
# Created by: PyQt5 UI code generator 5.15.2
#
# 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_Form(object):
    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(783, 40)
        Form.setMinimumSize(QtCore.QSize(0, 40))
        Form.setMaximumSize(QtCore.QSize(16777215, 41))
        icon = QtGui.QIcon()
        icon.addPixmap(QtGui.QPixmap(":/img/icon/实时视频流解析.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
        Form.setWindowIcon(icon)
        Form.setStyleSheet("#Form{background:rgba(120,120,120,255)}")
        self.horizontalLayout = QtWidgets.QHBoxLayout(Form)
        self.horizontalLayout.setContentsMargins(-1, 5, -1, 5)
        self.horizontalLayout.setObjectName("horizontalLayout")
        self.label = QtWidgets.QLabel(Form)
        self.label.setMinimumSize(QtCore.QSize(0, 30))
        self.label.setMaximumSize(QtCore.QSize(16777215, 30))
        self.label.setStyleSheet("QLabel{font-family: \"Microsoft YaHei\";\n"
"font-size: 18px;\n"
"font-weight: bold;\n"
"color:white;}")
        self.label.setObjectName("label")
        self.horizontalLayout.addWidget(self.label)
        self.rtspEdit = QtWidgets.QLineEdit(Form)
        self.rtspEdit.setMinimumSize(QtCore.QSize(0, 31))
        self.rtspEdit.setStyleSheet("background-color: rgb(207, 207, 207);")
        self.rtspEdit.setObjectName("rtspEdit")
        self.horizontalLayout.addWidget(self.rtspEdit)
        self.rtspButton = QtWidgets.QPushButton(Form)
        self.rtspButton.setStyleSheet("QPushButton{font-family: \"Microsoft YaHei\";\n"
"font-size: 18px;\n"
"font-weight: bold;\n"
"color:white;\n"
"text-align: center center;\n"
"padding-left: 5px;\n"
"padding-right: 5px;\n"
"padding-top: 4px;\n"
"padding-bottom: 4px;\n"
"border-style: solid;\n"
"border-width: 0px;\n"
"border-color: rgba(255, 255, 255, 255);\n"
"border-radius: 3px;\n"
"background-color: rgba(255,255,255,30);}\n"
"\n"
"QPushButton:focus{outline: none;}\n"
"\n"
"QPushButton::pressed{font-family: \"Microsoft YaHei\";\n"
"                     font-size: 16px;\n"
"                     font-weight: bold;\n"
"                     color:rgb(200,200,200);\n"
"                     text-align: center center;\n"
"                     padding-left: 5px;\n"
"                     padding-right: 5px;\n"
"                     padding-top: 4px;\n"
"                     padding-bottom: 4px;\n"
"                     border-style: solid;\n"
"                     border-width: 0px;\n"
"                     border-color: rgba(255, 255, 255, 255);\n"
"                     border-radius: 3px;\n"
"                     background-color:  rgba(255,255,255,150);}\n"
"\n"
"QPushButton::hover {\n"
"border-style: solid;\n"
"border-width: 0px;\n"
"border-radius: 0px;\n"
"background-color: rgba(255,255,255,50);}")
        self.rtspButton.setObjectName("rtspButton")
        self.horizontalLayout.addWidget(self.rtspButton)

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

    def retranslateUi(self, Form):
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "Form"))
        self.label.setText(_translate("Form", "rtsp address:"))
        self.rtspButton.setText(_translate("Form", "confirm"))
import apprcc_rc


================================================
FILE: dialog/rtsp_dialog.ui
================================================
<?xml version="1.0" encoding="UTF-8"?>
<ui version="4.0">
 <class>Form</class>
 <widget class="QWidget" name="Form">
  <property name="geometry">
   <rect>
    <x>0</x>
    <y>0</y>
    <width>783</width>
    <height>40</height>
   </rect>
  </property>
  <property name="minimumSize">
   <size>
    <width>0</width>
    <height>40</height>
   </size>
  </property>
  <property name="maximumSize">
   <size>
    <width>16777215</width>
    <height>41</height>
   </size>
  </property>
  <property name="windowTitle">
   <string>Form</string>
  </property>
  <property name="windowIcon">
   <iconset resource="../apprcc.qrc">
    <normaloff>:/img/icon/实时视频流解析.png</normaloff>:/img/icon/实时视频流解析.png</iconset>
  </property>
  <property name="styleSheet">
   <string notr="true">#Form{background:rgba(120,120,120,255)}</string>
  </property>
  <layout class="QHBoxLayout" name="horizontalLayout">
   <property name="topMargin">
    <number>5</number>
   </property>
   <property name="bottomMargin">
    <number>5</number>
   </property>
   <item>
    <widget class="QLabel" name="label">
     <property name="minimumSize">
      <size>
       <width>0</width>
       <height>30</height>
      </size>
     </property>
     <property name="maximumSize">
      <size>
       <width>16777215</width>
       <height>30</height>
      </size>
     </property>
     <property name="styleSheet">
      <string notr="true">QLabel{font-family: &quot;Microsoft YaHei&quot;;
font-size: 18px;
font-weight: bold;
color:white;}</string>
     </property>
     <property name="text">
      <string>rtsp address:</string>
     </property>
    </widget>
   </item>
   <item>
    <widget class="QLineEdit" name="rtspEdit">
     <property name="minimumSize">
      <size>
       <width>0</width>
       <height>31</height>
      </size>
     </property>
     <property name="styleSheet">
      <string notr="true">background-color: rgb(207, 207, 207);</string>
     </property>
    </widget>
   </item>
   <item>
    <widget class="QPushButton" name="rtspButton">
     <property name="styleSheet">
      <string notr="true">QPushButton{font-family: &quot;Microsoft YaHei&quot;;
font-size: 18px;
font-weight: bold;
color:white;
text-align: center center;
padding-left: 5px;
padding-right: 5px;
padding-top: 4px;
padding-bottom: 4px;
border-style: solid;
border-width: 0px;
border-color: rgba(255, 255, 255, 255);
border-radius: 3px;
background-color: rgba(255,255,255,30);}

QPushButton:focus{outline: none;}

QPushButton::pressed{font-family: &quot;Microsoft YaHei&quot;;
                     font-size: 16px;
                     font-weight: bold;
                     color:rgb(200,200,200);
                     text-align: center center;
                     padding-left: 5px;
                     padding-right: 5px;
                     padding-top: 4px;
                     padding-bottom: 4px;
                     border-style: solid;
                     border-width: 0px;
                     border-color: rgba(255, 255, 255, 255);
                     border-radius: 3px;
                     background-color:  rgba(255,255,255,150);}

QPushButton::hover {
border-style: solid;
border-width: 0px;
border-radius: 0px;
background-color: rgba(255,255,255,50);}</string>
     </property>
     <property name="text">
      <string>confirm</string>
     </property>
    </widget>
   </item>
  </layout>
 </widget>
 <resources>
  <include location="../apprcc.qrc"/>
 </resources>
 <connections/>
</ui>


================================================
FILE: dialog/rtsp_win.py
================================================
import sys
from PyQt5.QtWidgets import QApplication, QWidget
from dialog.rtsp_dialog import Ui_Form


class Window(QWidget, Ui_Form):
    def __init__(self):
        super(Window, self).__init__()
        self.setupUi(self)


if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = Window()
    window.show()
    sys.exit(app.exec_())


================================================
FILE: hubconf.py
================================================
"""YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/

Usage:
    import torch
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
"""

import torch


def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    """Creates a specified YOLOv5 model

    Arguments:
        name (str): name of model, i.e. 'yolov5s'
        pretrained (bool): load pretrained weights into the model
        channels (int): number of input channels
        classes (int): number of model classes
        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
        verbose (bool): print all information to screen
        device (str, torch.device, None): device to use for model parameters

    Returns:
        YOLOv5 pytorch model
    """
    from pathlib import Path

    from models.yolo import Model, attempt_load
    from utils.general import check_requirements, set_logging
    from utils.google_utils import attempt_download
    from utils.torch_utils import select_device

    file = Path(__file__).absolute()
    check_requirements(requirements=file.parent / 'requirements.txt', exclude=('tensorboard', 'thop', 'opencv-python'))
    set_logging(verbose=verbose)

    save_dir = Path('') if str(name).endswith('.pt') else file.parent
    path = (save_dir / name).with_suffix('.pt')  # checkpoint path
    try:
        device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)

        if pretrained and channels == 3 and classes == 80:
            model = attempt_load(path, map_location=device)  # download/load FP32 model
        else:
            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
            model = Model(cfg, channels, classes)  # create model
            if pretrained:
                ckpt = torch.load(attempt_download(path), map_location=device)  # load
                msd = model.state_dict()  # model state_dict
                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
                csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
                model.load_state_dict(csd, strict=False)  # load
                if len(ckpt['model'].names) == classes:
                    model.names = ckpt['model'].names  # set class names attribute
        if autoshape:
            model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
        return model.to(device)

    except Exception as e:
        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
        s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
        raise Exception(s) from e


def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
    # YOLOv5 custom or local model
    return _create(path, autoshape=autoshape, verbose=verbose, device=device)


def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-small model https://github.com/ultralytics/yolov5
    return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)


def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-medium model https://github.com/ultralytics/yolov5
    return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)


def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-large model https://github.com/ultralytics/yolov5
    return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)


def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
    return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)


def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)


if __name__ == '__main__':
    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
    # model = custom(path='path/to/model.pt')  # custom

    # Verify inference
    import cv2
    import numpy as np
    from PIL import Image

    imgs = ['data/images/zidane.jpg',  # filename
            'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg',  # URI
            cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
            Image.open('data/images/bus.jpg'),  # PIL
            np.zeros((320, 640, 3))]  # numpy

    results = model(imgs)  # batched inference
    results.print()
    results.save()


================================================
FILE: login_lj.py
================================================
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import QMessageBox

from main_win.login import Ui_Dialog


class Login(QtWidgets.QDialog, Ui_Dialog):
    def __init__(self):
        super(Login, self).__init__()
        self.setupUi(self)

        # 加载保存的用户名和密码
        self.load_credentials()

        # 将登录方法与登录按钮关联
        self.pushButton.clicked.connect(self.login)
        self.accept()

    def login(self):
        # 获取输入框中的用户名和密码
        username = self.lineEdit.text()
        password = self.lineEdit_2.text()

        # 判断用户名和密码是否正确
        if (username == "admin" and password == "123456") or \
                (username == "1" and password == "2") or \
                (username == "user2" and password == "password2"):
            # 登录成功，弹出提示框
            QMessageBox.information(self, "提示", "登录成功！")
            self.save_credentials()  # 保存用户名和密码
            self.accept()  # 关闭登录界面
        else:
            # 登录失败，弹出提示框
            QMessageBox.warning(self, "警告", "用户名或密码错误！")

    def save_credentials(self):
        settings = QtCore.QSettings()
        if self.checkBox.isChecked():
            settings.setValue("username", self.lineEdit.text())
            settings.setValue("password", self.lineEdit_2.text())
        else:
            settings.setValue("username", "")
            settings.setValue("password", "")

    def load_credentials(self):
        settings = QtCore.QSettings()
        username = settings.value("username", "")
        password = settings.value("password", "")
        self.lineEdit.setText(username)
        self.lineEdit_2.setText(password)
        self.checkBox.setChecked(bool(username and password))


if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    Dialog = Login()
    if Dialog.exec_() == QtWidgets.QDialog.Accepted:
        print("登录成功！")
    else:
        print("登录失败！")
    sys.exit(app.exec_())


================================================
FILE: main.py
================================================
import sys
import json
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import os
import time
from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadWebcam
from utils.CustomMessageBox import MessageBox
from utils.general import check_img_size, check_imshow, non_max_suppression, \
    scale_coords, increment_path
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device
from utils.capnums import Camera
from dialog.rtsp_win import Window
import datetime
from pathlib import Path
from PyQt5.QtGui import QTransform
from PyQt5.QtWidgets import QHeaderView
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib.pyplot as plt
from PyQt5.QtWidgets import QSizePolicy
from PyQt5.QtWidgets import QMessageBox
import pymysql
from PyQt5.QtCore import  QAbstractTableModel
from PyQt5 import QtWidgets
from PyQt5.QtWidgets import QFileDialog, QMenu
from main_win.win import Ui_mainWindow
from PyQt5.QtCore import Qt, QPoint, QTimer, QThread, pyqtSignal
from PyQt5.QtCore import pyqtSlot
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QLineEdit, QVBoxLayout, QLabel, QDialogButtonBox
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import QInputDialog
from PyQt5.QtWidgets import QApplication, QMainWindow, QDialog
import seaborn as sns

sns.set(style='darkgrid', palette='pastel')
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  #

visualize = False
databases = False
agnostic_nms=False
half=False # use FP16 half-precision inference
imgsz=1280
save_crop = False
augment = False
classes = None
project = ROOT / 'result' # save results to project/name
hide_labels=False  # hide labels
hide_conf=False  # hide confidences
line_thickness=3  # bounding box thickness (pixels)

# Database connection settings as global variables
DB_HOST = 'localhost'
DB_USER = 'root'
DB_PASSWORD = '1234'
DB_NAME = 'traffic_sign_recognition'


#检测线程
class DetThread(QThread):
    """这段代码定义了6个信号，用于在PyQt程序中进行通信。
send_img信号用于发送图像数组，
send_raw信号用于发送原始数组，
send_statistic信号用于发送统计字典数据，
send_msg信号用于发送分析(检测)的状态消息，
send_percent信号用于发送分析(检测)进度，
最后send_fps用于发送帧率数"""
    send_img = pyqtSignal(np.ndarray)
    send_raw = pyqtSignal(np.ndarray)
    send_statistic = pyqtSignal(dict)
    # emit：detecting/pause/stop/finished/error msg
    send_msg = pyqtSignal(str)
    send_percent = pyqtSignal(int)
    send_fps = pyqtSignal(str)

    def __init__(self):
        super(DetThread, self).__init__()
        self.weights = './yolov5s.pt'# 设置权重
        self.current_weight = './yolov5s.pt'# 当前权重
        self.source = '0'# 视频源
        self.conf_thres = 0.25 # 置信度
        self.iou_thres = 0.45# iou
        self.jump_out = False                   # jump out of the loop # 跳出循环
        self.is_continue = True                 # continue/pause# 进度条
        self.percent_length = 1000              # progress bar# 是否启用延时
        self.rate_check = True                  # Whether to enable delay# 延时HZ
        self.rate = 100
        self.save_fold = './result' # 保存文件夹


#数据库录入
    def get_db_connection(self, db_host=DB_HOST, db_user=DB_USER, db_password=DB_PASSWORD, db_name=DB_NAME):
        return pymysql.connect(
            host=DB_HOST,
            user=DB_USER,
            password=DB_PASSWORD,
            database=DB_NAME
        )
    def insert_detection_result_to_database(self, sign_type, sign_count, additional_info):
        with self.get_db_connection() as connection:
            with connection.cursor() as cursor:
                current_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                sql = "INSERT INTO detection_results (sign_type, sign_count, detection_time, additional_info) VALUES (%s, %s, %s, %s)"
                cursor.execute(sql, (sign_type, sign_count, current_time, additional_info))
                connection.commit()


    @torch.no_grad()
    def run(self,
            # imgsz=1280,  # inference size (pixels)
            max_det=1000,  # maximum detections per image
            device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
            view_img=True,  # show results
            # save_crop=False,  # save cropped prediction boxes
            # augment=False,  # augmented inference
            # visualize=True,  # visualize features
            # classes=None,  # filter by class: --class 0, or --class 0 2 3
            # agnostic_nms=False,  # class-agnostic NMS
            # project=ROOT / 'result',  # save results to project/name
            name='exp',  # save results to project/name
            exist_ok=False,  # existing project/name ok, do not increment， False是自增，true是不自增覆盖
            # line_thickness=3,  # bounding box thickness (pixels)
            # hide_labels=False,  # hide labels
            # hide_conf=False,  # hide confidences
            # half=False,  # use FP16 half-precision inference
            save_txt=False,  # save results to *.txt
            save_conf=False,  # save confidences in --save-txt labels
            update=False,  # update all models
            nosave=False,  # do not save images/videos
            prune_model =True
            ):

        global half
        global imgsz
        # Initialize
        try:
            device = select_device(device)
            print("half:",half)
            half &= device.type != 'cpu'  # half precision only supported on CUDA

            # Load model这段代码中，num_params变量用于计算模型中参数的总个数。在 for 循环中，对每一个 param，累加其 numel() 方法返回的参数总数。
            model = attempt_load(self.weights, map_location=device)  # load FP32 model

            num_params = 0
            for param in model.parameters():
                num_params += param.numel()
            stride = int(model.stride.max())  # model stride
            print("imgsz:",imgsz)
            imgsz = check_img_size(imgsz, s=stride)  # check image size
            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
            if half:
                model.half()  # to FP16




            # Dataloader
            if self.source.isnumeric() or self.source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')):
                view_img = check_imshow()
                cudnn.benchmark = True  # set True to speed up constant image size inference
                dataset = LoadWebcam(self.source, img_size=imgsz, stride=stride)
                # bs = len(dataset)  # batch_size
            else:
                dataset = LoadImages(self.source, img_size=imgsz, stride=stride)




            # Run inference
            if device.type != 'cpu':
                model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
            count = 0
            # 跳帧检测
            jump_count = 0
            start_time = time.time()
            dataset = iter(dataset)

            global save_crop
            print("save_crop", save_crop)
            global visualize
            global augment
            print("augment", augment)

            global agnostic_nms
            global classes
            print("agnostic_nms:", agnostic_nms)
            print("classes:", classes)
            global hide_labels
            print("hide_labels:",hide_labels)
            global hide_conf
            print("hide_conf:",hide_conf)
            global line_thickness
            print("line_thickness:",line_thickness)


            while True:
                # 手动停止
                if self.jump_out:
                    self.vid_cap.release()
                    self.send_percent.emit(0)
                    self.send_msg.emit('Stop')
                    if hasattr(self, 'out'):
                        self.out.release()
                    break



                # 临时更换模型
                if self.current_weight != self.weights:
                    # Load model
                    model = attempt_load(self.weights, map_location=device)  # load FP32 model

                    num_params = 0
                    for param in model.parameters():
                        num_params += param.numel()
                    stride = int(model.stride.max())  # model stride
                    imgsz = check_img_size(imgsz, s=stride)  # check image size
                    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
                    if half:
                        model.half()  # to FP16
                    # Run inference
                    if device.type != 'cpu':
                        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
                    self.current_weight = self.weights


                # 暂停开关,这个就是主程序
                if self.is_continue:
                    path, img, im0s, self.vid_cap = next(dataset)
                    # jump_count += 1
                    # if jump_count % 5 != 0:
                    #     continue
                    count += 1
                    # 每三十帧刷新一次输出帧率
                    if count % 30 == 0 and count >= 30:
                        fps = int(30/(time.time()-start_time))
                        self.send_fps.emit('fps：'+str(fps))
                        start_time = time.time()
                    if self.vid_cap:
                        percent = int(count/self.vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)*self.percent_length)
                        self.send_percent.emit(percent)
                    else:
                        percent = self.percent_length

                    statistic_dic = {name: 0 for name in names}
                    img = torch.from_numpy(img).to(device)
                    img = img.half() if 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)

                    #1. pred = model(img, augment=augment)[0]

                    # 2.save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
                    # (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
                    #  pred = model(img,
                    #              augment=augment,
                    #              visualize=increment_path(save_dir / Path(path).stem,
                    #                                       mkdir=True) if visualize else False)[0]

                    # 3.global visualize
                    # vis_path = False
                    # print("visualize:", visualize)
                    # if visualize :
                    #     vis_path = increment_path(save_dir / Path(path).stem, mkdir=True)
                    # print("vis_path:", vis_path)
                    # pred = model(img, augment=augment, visualize=vis_path)[0]
#特征提取参数
                    global project
                    print("project:", project)
                    if visualize:
                        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
                        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
                        vis_path = increment_path(save_dir / Path(path).stem, mkdir=True)
                        pred = model(img, augment=augment, visualize=vis_path)[0]
                    else:
                        pred = model(img, augment=augment)[0]


                    # Apply NMS
                    pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, classes, agnostic_nms, max_det=max_det)
                    # Process detections
                    for i, det in enumerate(pred):  # detections per image
                        im0 = im0s.copy()
                        annotator = Annotator(im0, line_width=line_thickness, example=str(names))
                        if len(det):
                            # Rescale boxes from img_size to im0 size
                            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                            # Write results
                            for *xyxy, conf, cls in reversed(det):
                                c = int(cls)  # integer class
                                statistic_dic[names[c]] += 1
                                label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                                annotator.box_label(xyxy, label, color=colors(c, True))
#数据库录入
                                if databases==True:
                                    # Insert the detection result into the database
                                    print("该检测值已录入数据库")
                                    sign_type = names[c]
                                    sign_count = statistic_dic[names[c]]
                                    additional_info = f"Confidence: {conf:.2f}"
                                    self.insert_detection_result_to_database(sign_type, sign_count, additional_info)


                                im0 = im0.copy() if save_crop else im0  # for save_crop
                                if save_crop:
                                        file_path ='result'
                                        save_dir = increment_path(Path(project) / name,
                                                                  exist_ok=True)  # increment run
                                        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
                                        save_one_box(xyxy, im0, file=save_dir / 'crops' / names[c] / f'{file_path}.jpg',
                                                     BGR=True)
                                        print("crop已保存")
                                        # save_one_box(xyxy, im0, file=save_dir / 'crops' / names[c] / 'file_path.jpg', BGR=True)


                    # 控制视频发送频率
                    if self.rate_check:
                        time.sleep(1/self.rate)
                    im0 = annotator.result()
                    self.send_img.emit(im0)
                    self.send_raw.emit(im0s if isinstance(im0s, np.ndarray) else im0s[0])
                    self.send_statistic.emit(statistic_dic)

                    # 如果自动录制
                    if self.save_fold:
                        os.makedirs(self.save_fold, exist_ok=True)
                        if self.vid_cap is None:
                            save_path = os.path.join(self.save_fold,
                                                     time.strftime('%Y_%m_%d_%H_%M_%S',
                                                                   time.localtime()) + '.jpg')
                            cv2.imwrite(save_path, im0)
                        else:
                            if count == 1: # 第一帧时初始化录制
                                # 以视频原始帧率进行录制
                                ori_fps = int(self.vid_cap.get(cv2.CAP_PROP_FPS))
                                if ori_fps == 0:
                                    ori_fps = 25
                                # width = int(self.vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                # height = int(self.vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                                width, height = im0.shape[1], im0.shape[0]
                                save_path = os.path.join(self.save_fold, time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime()) + '.mp4')
                                self.out = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), ori_fps,
                                                           (width, height))
                            self.out.write(im0)
                    if percent == self.percent_length:
                        # print(count)
                        self.send_percent.emit(0)
                        self.send_msg.emit('finished')
                        if hasattr(self, 'out'):
                            self.out.release()
                        # 正常跳出循环
                        break

        except Exception as e:
            self.send_msg.emit('%s' % e)

#插入弹窗数据库
class AddRecordDialog(QtWidgets.QDialog):
    def __init__(self, parent=None):
        super(AddRecordDialog, self).__init__(parent)

        self.sign_type_line_edit = QtWidgets.QLineEdit()
        self.sign_count_line_edit = QtWidgets.QLineEdit()
        self.additional_info_line_edit = QtWidgets.QLineEdit()

        button_box = QtWidgets.QDialogButtonBox(QtWidgets.QDialogButtonBox.Ok | QtWidgets.QDialogButtonBox.Cancel)
        button_box.accepted.connect(self.accept)
        button_box.rejected.connect(self.reject)

        layout = QtWidgets.QFormLayout()
        layout.addRow("Sign Type:", self.sign_type_line_edit)
        layout.addRow("Sign Count:", self.sign_count_line_edit)
        layout.addRow("Additional Info:", self.additional_info_line_edit)
        layout.addWidget(button_box)

        self.setLayout(layout)

    def get_record_data(self):
        sign_type = self.sign_type_line_edit.text()
        sign_count = self.sign_count_line_edit.text()
        additional_info = self.additional_info_line_edit.text()

        return sign_type, sign_count, additional_info
#总体数据库
class PymysqlTableModel(QAbstractTableModel):
    def __init__(self, data, headers):
        super().__init__()
        self._data = data
        self._headers = headers

    def rowCount(self, parent=None):
        return len(self._data)

    def columnCount(self, parent=None):
        return len(self._data[0])

    def data(self, index, role=Qt.DisplayRole):
        if role == Qt.DisplayRole:
            return self._data[index.row()][index.column()]
        return None

    def headerData(self, section, orientation, role=Qt.DisplayRole):
        if role == Qt.DisplayRole and orientation == Qt.Horizontal:
            return self._headers[section]
        return None
    def filter_data(self, filter_function):
        self._filtered_data = list(filter(filter_function, self._data))
        self.layoutChanged.emit()

#图像增强线程
import os
import cv2
import random
from typing import List, Tuple
import numpy as np
from typing import Optional

class ImageAugmentor(QObject):
    progress_updated = pyqtSignal(int)
    def __init__(self):
        super().__init__()
        self.operations = [
            self.flip, self.random_crop, self.random_scale, self.random_rotate,
            self.random_brightness_contrast_saturation_hue, self.random_erase,
            self.random_noise, self.cutout, self.mixup, self.mosaic
        ]
        self.input_folder = None  # 添加此行



    def process_images(self, input_folder: str, output_folder: str, probabilities: List[float]) -> Tuple[int, int, int]:
        self.input_folder = input_folder
        success_count = 0
        failure_count = 0
        image_count = sum(1 for file in os.listdir(input_folder) if file.lower().endswith(('.png', '.jpg', '.jpeg')))


        for index, file in enumerate(os.listdir(input_folder)):
            filepath = os.path.join(input_folder, file)
            if os.path.isfile(filepath) and file.lower().endswith(('.png', '.jpg', '.jpeg')):
                # 读取图像
                image = cv2.imdecode(np.fromfile(filepath, dtype=np.uint8), cv2.IMREAD_COLOR)

                # 检查图像是否为空
                if image is None:
                    print(f"Error loading image: {filepath}")
                    failure_count += 1
                    continue

                for i, probability in enumerate(probabilities):
                    if random.random() < probability:
                        if i in {8, 9}:  # Mixup and Mosaic operations
                            extra_image = self.load_random_image(input_folder)
                            image = self.apply_operation(image, i, extra_image)
                        else:
                            image = self.apply_operation(image, i)

                output_path = os.path.join(output_folder, file)

                # 保存图像
                encoded_image = cv2.imencode('.jpg', image)[1]
                if encoded_image is not None:
                    encoded_image.tofile(output_path)
                    success_count += 1
                else:
                    failure_count += 1
            self.progress_updated.emit(success_count + failure_count)

        return success_count, failure_count, image_count

    def load_random_image(self, input_folder: str) -> np.ndarray:
        files = [file for file in os.listdir(input_folder) if file.lower().endswith(('.png', '.jpg', '.jpeg'))]
        random_file = random.choice(files)
        filepath = os.path.join(input_folder, random_file)
        return cv2.imdecode(np.fromfile(filepath, dtype=np.uint8), cv2.IMREAD_COLOR)

    def apply_operation(self, image: np.ndarray, operation_index: int,
                        extra_image: Optional[np.ndarray] = None) -> np.ndarray:
        if operation_index == 8:  # Mixup
            if extra_image is not None:
                return self.mixup(image, extra_image)
            else:
                return image
        elif operation_index == 9:  # Mosaic
            if extra_image is not None:
                return self.mosaic(image, extra_image)
            else:
                return image
        else:
            return self.operations[operation_index](image)

    # 1. 图像翻转
    def flip(self, image):
        mode = random.choice(['horizontal', 'vertical'])
        print("flip启用")
        return cv2.flip(image, 1 if mode == 'horizontal' else 0)

    # 2. 随机裁剪
    def random_crop(self, image):
        height, width, _ = image.shape
        crop_ratio = random.uniform(0.5, 1.0)
        new_height, new_width = int(height * crop_ratio), int(width * crop_ratio)

        y = random.randint(0, height - new_height)
        x = random.randint(0, width - new_width)
        print("random_crop启用")
        return image[y:y + new_height, x:x + new_width]

    # 3. 随机缩放
    def random_scale(self, image):
        scale_factor = random.uniform(0.5, 2.0)
        print("random_scale启用")
        return cv2.resize(image, None, fx=scale_factor, fy=scale_factor)

    # 4. 随机旋转
    def random_rotate(self, image):
        angle = random.randint(-180, 180)
        height, width = image.shape[:2]
        matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
        print("random_rotate启用")
        return cv2.warpAffine(image, matrix, (width, height))

    # 5. 随机亮度、对比度、饱和度、色调调整
    def random_brightness_contrast_saturation_hue(self, image):
        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

        brightness = random.uniform(0.5, 1.5)
        hsv[:, :, 2] = np.clip(hsv[:, :, 2] * brightness, 0, 255)

        contrast = random.uniform(0.5, 1.5)
        image = np.clip((image - 128) * contrast + 128, 0, 255)

        saturation = random.uniform(0.5, 1.5)
        hsv[:, :, 1] = np.clip(hsv[:, :, 1] * saturation, 0, 255)

        hue_shift = random.uniform(-30, 30)
        hsv[:, :, 0] = np.clip(hsv[:, :, 0] + hue_shift, 0, 180)
        print("random_brightness_contrast_saturation_hue启用")
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    # 6. 随机擦除
    def random_erase(self, image):
        height, width, _ = image.shape
        erase_size = random.randint(10, min(height, width) // 2)

        y = random.randint(0, height - erase_size)
        x = random.randint(0, width - erase_size)

        image[y:y + erase_size, x:x + erase_size, :] = 0
        print("random_erase启用")
        return image

    # 7. 随机加噪
    def random_noise(self, image):
        row, col, ch = image.shape
        mean = 0
        sigma = random.uniform(10, 50)
        gauss = np.random.normal(mean, sigma, (row, col, ch))
        gauss = gauss.reshape(row, col, ch)
        noisy = image + gauss
        print("random_noise启用")
        return np.clip(noisy, 0, 255).astype(np.uint8)

    # 8. Mixup
    def mixup(self, image):
        # 在这个示例中，我们将随机选择另一张图像进行混合。您可以根据需要自定义图像选择方法。
        image_list = [f for f in os.listdir(self.input_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
        random_image_path = os.path.join(self.input_folder, random.choice(image_list))
        mixup_image = cv2.imread(random_image_path)

        height, width, _ = image.shape
        mixup_image = cv2.resize(mixup_image, (width, height))

        alpha = random.uniform(0, 1)
        print("mixup启用")
        return cv2.addWeighted(image, alpha, mixup_image, 1 - alpha, 0)

    # 9. CutOut
    def cutout(self, image):
        height, width, _ = image.shape
        cutout_size = random.randint(10, min(height, width) // 2)

        y = random.randint(0, height - cutout_size)
        x = random.randint(0, width - cutout_size)

        image[y:y + cutout_size, x:x + cutout_size, :] = 0
        print("cutout启用")
        return image

    # 10. Mosaic
    def mosaic(self, image: np.ndarray, extra_image: np.ndarray) -> np.ndarray:
        image_list = [os.path.join(self.input_folder, f) for f in os.listdir(self.input_folder) if
                      f.lower().endswith(('.png', '.jpg', '.jpeg'))]
        random_images = [cv2.imread(random.choice(image_list)) for _ in range(3)]

        height, width, _ = image.shape
        height = height - height % 2  # Make height an even number
        width = width - width % 2  # Make width an even number

        # Resize the input image
        image = cv2.resize(image, (width, height))

        for i in range(3):
            random_images[i] = cv2.resize(random_images[i], (width // 2, height // 2))

        mosaic_image = np.zeros((height, width, 3), dtype=np.uint8)

        mosaic_image[:height // 2, :width // 2] = image[:height // 2, :width // 2]
        mosaic_image[height // 2:, :width // 2] = random_images[0][:height // 2, :width // 2]
        mosaic_image[:height // 2, width // 2:] = random_images[1][:height // 2, :width // 2]
        mosaic_image[height // 2:, width // 2:] = random_images[2][:height // 2, :width // 2]
        print("mosaic启用")
        return mosaic_image


#主UI线程
class MainWindow(QMainWindow, Ui_mainWindow):
    def __init__(self, parent=None):
        super(MainWindow, self).__init__(parent)
        self.setupUi(self)
        self.m_flag = False

        # style 1: window can be stretched
        # self.setWindowFlags(Qt.CustomizeWindowHint | Qt.WindowStaysOnTopHint)
        self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)
        # self.setWindowFlags(Qt.CustomizeWindowHint | Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)

        # style 2: window can not be stretched
        # self.setWindowFlags(Qt.Window | Qt.FramelessWindowHint
        #                     | Qt.WindowSystemMenuHint | Qt.WindowMinimizeButtonHint | Qt.WindowMaximizeButtonHint)
        # self.setWindowOpacity(0.85)  # Transparency of window

        # 自定义标题栏按钮
        self.minButton.clicked.connect(self.showMinimized)
        self.maxButton.clicked.connect(self.max_or_restore)
        # show Maximized window
        # self.maxButton.animateClick(10)
        self.closeButton.clicked.connect(self.close)

        # 定时清空自定义状态栏上的文字
        self.qtimer = QTimer(self)
        self.qtimer.setSingleShot(True)
        self.qtimer.timeout.connect(lambda: self.statistic_label.clear())

        # 自动搜索模型
        self.comboBox.clear()
        self.pt_list = os.listdir('./pt')
        self.pt_list = [file for file in self.pt_list if file.endswith('.pt')]
        self.pt_list.sort(key=lambda x: os.path.getsize('./pt/'+x))
        self.comboBox.clear()
        self.comboBox.addItems(self.pt_list)
        self.qtimer_search = QTimer(self)
        self.qtimer_search.timeout.connect(lambda: self.search_pt())
        self.qtimer_search.start(2000)

        #  yolov5线程
        self.det_thread = DetThread()
        self.model_type = self.comboBox.currentText()
        self.det_thread.weights = "./pt/%s" % self.model_type # 权重
        self.det_thread.source = '0'     # 默认打开本机摄像头，无需保存到配置文件
        self.det_thread.percent_length = self.progressBar.maximum()
        self.det_thread.send_raw.connect(lambda x: self.show_image(x, self.raw_video))
        self.det_thread.send_img.connect(lambda x: self.show_image(x, self.out_video))
        self.det_thread.send_statistic.connect(self.show_statistic)
        self.det_thread.send_msg.connect(lambda x: self.show_msg(x))
        self.det_thread.send_percent.connect(lambda x: self.progressBar.setValue(x))
        self.det_thread.send_fps.connect(lambda x: self.fps_label.setText(x))


        self.fileButton.clicked.connect(self.open_file)
        self.cameraButton.clicked.connect(self.chose_cam)
        self.rtspButton.clicked.connect(self.chose_rtsp)

        self.runButton.clicked.connect(self.run_or_continue)
        self.stopButton.clicked.connect(self.stop)

        self.comboBox.currentTextChanged.connect(self.change_model)
        self.confSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'confSpinBox'))
        self.confSlider.valueChanged.connect(lambda x: self.change_val(x, 'confSlider'))
        self.iouSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'iouSpinBox'))
        self.iouSlider.valueChanged.connect(lambda x: self.change_val(x, 'iouSlider'))
        self.rateSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'rateSpinBox'))
        self.rateSlider.valueChanged.connect(lambda x: self.change_val(x, 'rateSlider'))

        self.checkBox.clicked.connect(self.checkrate)
        self.saveCheckBox.clicked.connect(self.is_save)
        self.load_setting()

# 在您的窗口类的 __init__ 方法中，连接按钮的 clicked 信号到相应的槽函数
        self.pushButton.clicked.connect(lambda: self.open_stackedWidget(2))
        self.pushButton_4.clicked.connect(lambda: self.open_stackedWidget(0))
        self.refreshButton_2.clicked.connect(lambda: self.open_stackedWidget(1))
        self.pushButton_2.clicked.connect(lambda: self.open_stackedWidget(3))
        self.pushButton_5.clicked.connect(lambda: self.open_stackedWidget(4))
#数据库大模块
        self.tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)
        self.table_model = self.fetch_data_from_database()
        if self.table_model:
            self.tableView.setModel(self.table_model)
        # Assuming the button is named refreshButton
        self.refreshButton.clicked.connect(self.fetch_data_from_database)
# Connect the delete button to the on_delete_button_clicked slotgraphicsView
        self.deleteButton_2.clicked.connect(self.on_delete_button_clicked)
        self.insertButton_2.clicked.connect(self.on_add_button_clicked)
#排序
        # print("开始排序111！！！")
        self.sortcomboBox_3.currentIndexChanged.connect(self.on_sort_changed)
        self.sortcomboBox_2.currentIndexChanged.connect(self.on_sort_changed)
#过滤
        self.filterButton_3.clicked.connect(self.apply_filter)

#数据分析
        # Add the following lines in the __init__ method
        self.figure, self.ax = plt.subplots()
        self.canvas = FigureCanvas(self.figure)
        self.plotLayout = QVBoxLayout(self.graphicsView)
        self.plotLayout.addWidget(self.canvas)

        self.refreshButton_3.clicked.connect(self.on_analyze_button_clicked)

#高参数大模块
        self.visualize_checkbox1.stateChanged.connect(self.on_visualize_checkbox_stateChanged)
        self.SavedatabaseCheckBox.stateChanged.connect(self.on_databases_checkbox_stateChanged)
        self.agnostic_nmscheckbox.stateChanged.connect(self.on_agnostic_nmscheckbox_checkbox_stateChanged)
        self.halfcheckbox_2.stateChanged.connect(self.on_halfcheckbox_2_nmscheckbox_checkbox_stateChanged)
        self.save_cropcheckbox_2.stateChanged.connect(self.on_save_crop_nmscheckbox_checkbox_stateChanged)
        self.augmentedcheckbox_3.stateChanged.connect(self.on_augment_nmscheckbox_checkbox_stateChanged)
        self.hide_labelscheckbox_4.stateChanged.connect(self.on_hide_labelscheckbox_checkbox_checkbox_stateChanged)
        self.hide_confscheckbox_6.stateChanged.connect(self.on_hide_confcheckbox_checkbox_checkbox_stateChanged)

        self.imgsz_spinbox12.valueChanged.connect(self.on_imgsz_spinbox_valueChanged)
        self.line_thickness_spinbox12_2.valueChanged.connect(self.on_line_thickness_spinbox_valueChanged)
        self.lineEdit.textChanged.connect(self.update_classes)
        self.browse_result_folder_button.clicked.connect(self.on_browse_result_folder_button_clicked)





#批处理图像增强大模块
        # 创建一个 ImageAugmentor 实例
        self.image_augmentor = ImageAugmentor()

        # 将 UI 控件连接到相关方法
        self.inputFolderButton.clicked.connect(self.select_input_folder)
        self.outputFolderButton.clicked.connect(self.select_output_folder)
        self.startProcessingButton.clicked.connect(self.start_processing)

        self.init_progress_bar()
        # 在MainWindow类的__init__方法中为每个复选框添加事件处理程序
        for checkbox in [self.enhanceMethod1Checkbox, self.enhanceMethod2Checkbox, self.enhanceMethod3Checkbox,
                         self.enhanceMethod4Checkbox, self.enhanceMethod5Checkbox, self.enhanceMethod6Checkbox,
                         self.enhanceMethod7Checkbox, self.enhanceMethod8Checkbox, self.enhanceMethod9Checkbox,
                         self.enhanceMethod10Checkbox]:
            checkbox.clicked.connect(self.checkbox_clicked)

        self.helpButton.clicked.connect(self.show_help)
        # 高级参数设置
        self.advancedSettingsButton.clicked.connect(self.open_advanced_settings)
        # 设置默认值
        self.enhanceMethod2Parameter = QLineEdit("0.8, 1.0")  # 示例：设置增强方法2的默认参数值




























# 单个图像处理大模块
        # 加载缩放
        self.loadImageButton.clicked.connect(self.load_image1)
        self.scaleSlider.valueChanged.connect(self.scale_image)
        # # 翻转
        # self.comboBox_3.currentTextChanged.connect(self.on_comboBox_3_currentIndexChanged)
        # 旋转
        self.rotationSlider.valueChanged.connect(self.rotate_image)

# 正常显示和预览
        self.imageEnhanceButton.clicked.connect(lambda: self.apply_image_enhance(preview=False))
        self.previewEnhanceButton.clicked.connect(lambda: self.apply_image_enhance(preview=True))

        self.imageFilterButton.clicked.connect(lambda: self.apply_image_filter(preview=False))
        self.previewFilterButton.clicked.connect(lambda: self.apply_image_filter(preview=True))

        self.morphologyButton.clicked.connect(lambda: self.apply_morphology(preview=False))
        self.previewmorphologyButton.clicked.connect(lambda: self.apply_morphology(preview=True))

        self.histogramButton.clicked.connect(lambda: self.apply_histogram(preview=False))
        self.previewhistogramButton.clicked.connect(lambda: self.apply_histogram(preview=True))

        #打开一个文件夹选择上一个下一个
        self.image_list = []
        self.current_image_index = -1
        self.save_folder = ""
        #文件按钮
        self.openFolderButton.clicked.connect(self.open_folder)
        self.saveFolderButton.clicked.connect(self.select_save_folder)
        self.nextImageButton.clicked.connect(self.next_image)
        self.previousImageButton.clicked.connect(self.previous_image)
        self.saveButton.clicked.connect(self.save_image)


# 自定义参数
        self.slider_adjust_contrast.valueChanged.connect(self.slider_adjustment)
        self.apply_custom_adjustments_imageEnhance.clicked.connect(self.apply_custom_adjustments_with_value)

#这里只提供图像增强的滑条和设置具体数值的按钮，其他的自行按照这种模式添加
    def slider_adjustment(self, value):
        operation = self.imageEnhanceComboBox.currentText()
        value = value / 10.0
        if operation == '对比度增强':
            self.adjust_contrast(alpha=value, preview=False)
        elif operation == '饱和度调整':
            self.adjust_saturation(value=value, preview=False)
        elif operation == '调整色相':
            self.adjust_hue(value=value, preview=False)
        elif operation == '调整亮度':
            self.adjust_brightness1(value=value, preview=False)
        elif operation == 'Gamma 校正':
            self.adjust_gamma(gamma=value, preview=False)
        elif operation == '二值化':
            self.binarize_image(threshold=value, preview=False)

    def apply_custom_adjustments_with_value(self):
        operation = self.imageEnhanceComboBox.currentText()
        value, ok = QInputDialog.getInt(self, "输入参数", f"设置{operation}的值：", min=0, max=100)

        if ok:
            if operation == '对比度增强':
                self.adjust_contrast(alpha=value, preview=False)
            elif operation == '饱和度调整':
                self.adjust_saturation(value=value, preview=False)
            elif operation == '调整色相':
                self.adjust_hue(value=value, preview=False)
            elif operation == '调整亮度':
                self.adjust_brightness1(value=value, preview=False)
            elif operation == 'Gamma 校正':
                self.adjust_gamma(gamma=value, preview=False)
            elif operation == '二值化':
                self.binarize_image(threshold=value, preview=False)

   # 二值化
    def binarize_image(self, threshold=128, preview=True):

        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            if original_image is None:
                QMessageBox.warning(self, "提示", "无法处理此图片")
                return

            gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
            _, result_image = cv2.threshold(gray_image, threshold, 255, cv2.THRESH_BINARY)

            pixmap = self.cv2_image_to_pixmap(result_image)

            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)

        except Exception as e:
            print(f"Error in binarize_image: {e}")




    def adjust_gamma(self, gamma=1.0, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            if original_image is None:
                QMessageBox.warning(self, "提示", "无法处理此图片")
                return

            result_image = np.power(original_image, gamma)

            pixmap = self.cv2_image_to_pixmap(result_image)

            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_gamma: {e}")


    #调整亮度
    def adjust_brightness1(self, value=1.5, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            if original_image is None:
                QMessageBox.warning(self, "提示", "无法处理此图片")
                return

            hsv_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV)

            brightness_multiplier = np.ones(hsv_image.shape[:2], dtype=np.float32) * value
            hsv_image[..., 2] = cv2.add(hsv_image[..., 2], brightness_multiplier, dtype=cv2.CV_32F)

            hsv_image[..., 2] = cv2.convertScaleAbs(hsv_image[..., 2])

            result_image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)

            pixmap = self.cv2_image_to_pixmap(result_image)

            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_brightness1: {e}")


        # ...
#调整色相
    def adjust_hue(self, value=1.5, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            if original_image is None:
                QMessageBox.warning(self, "提示", "无法处理此图片")
                return

            hsv_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV)

            hue_multiplier = np.ones(hsv_image.shape[:2], dtype=np.float32) * value
            hsv_image[..., 0] = cv2.add(hsv_image[..., 0], hue_multiplier, dtype=cv2.CV_32F)

            hsv_image[..., 0] = cv2.convertScaleAbs(hsv_image[..., 0])

            result_image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)

            pixmap = self.cv2_image_to_pixmap(result_image)

            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_hue: {e}")


    def adjust_saturation(self, value=1.5, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

                # 获取原始图像
            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            if original_image is None:
                QMessageBox.warning(self, "提示", "无法处理此图片")
                return

            # 转换为 HSV 颜色空间
            hsv_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV)

            # 增加饱和度
            saturation_multiplier = np.ones(hsv_image.shape[:2], dtype=np.float32) * value
            hsv_image[..., 1] = cv2.multiply(hsv_image[..., 1], saturation_multiplier, dtype=cv2.CV_32F)

            hsv_image[..., 1] = cv2.convertScaleAbs(hsv_image[..., 1])

            # 转换回 BGR 颜色空间
            result_image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)

            # 将结果显示在界面上
            pixmap = self.cv2_image_to_pixmap(result_image)

            # Scale the pixmap to fit the scrollArea
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_saturation: {e}")




    def adjust_contrast(self, alpha=1.5, preview=True):
        try :
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            original_image = original_image.astype(np.float64)
            contrast_multiplier = np.ones(original_image.shape, dtype=np.float64) * alpha
            contrast_image = cv2.multiply(original_image, contrast_multiplier)
            contrast_image = cv2.convertScaleAbs(contrast_image)
            pixmap = self.cv2_image_to_pixmap(contrast_image)

            # Scale the pixmap to fit the scrollArea
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)

            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_contrast: {e}")


    def adjust_brightness(self, beta=50, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            brightness_image = cv2.add(original_image, np.full(original_image.shape, beta, dtype=np.uint8))

            pixmap = self.cv2_image_to_pixmap(brightness_image)
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in adjust_brightness: {e}")

    def sharpen_image(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
            sharpened_image = cv2.filter2D(original_image, -1, kernel)
            pixmap = self.cv2_image_to_pixmap(sharpened_image)
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in sharpen_image: {e}")

    def smooth_image(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            smooth_image = cv2.GaussianBlur(original_image, (7, 7), 0)
            pixmap = self.cv2_image_to_pixmap(smooth_image)
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in smooth_image: {e}")

    def flip_image(self,  orientation='水平翻转',preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())

            if orientation == '水平翻转':
                flipped_image = cv2.flip(original_image, 1)
            elif orientation == '垂直翻转':
                flipped_image = cv2.flip(original_image, 0)
            elif orientation == '水平垂直翻转':
                flipped_image = cv2.flip(original_image, -1)

            pixmap = self.cv2_image_to_pixmap(flipped_image)
            # scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
                # self.imageLabel_2.setPixmap(scaled_pixmap)
                # self.imageLabel_2.setScaledContents(False)
                # self.imageLabel_2.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
        except Exception as e:
            print(f"Error in flip_image: {e}")
    def histogram_equalization(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
            equalized_image = cv2.equalizeHist(gray_image)
            pixmap = self.cv2_image_to_pixmap(equalized_image)
            scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in histogram_equalization: {e}")

    def pixmap_to_cv2_image(self, pixmap):
        try:
            qimage = pixmap.toImage()
            if not qimage.constBits():
                return None
            qimage = qimage.convertToFormat(QImage.Format_ARGB32)
            buffer = qimage.constBits().asarray(qimage.byteCount())
            image = np.frombuffer(buffer, dtype=np.uint8).reshape((qimage.height(), qimage.width(), 4))
            image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)
            return image
        except Exception as e:
            print(f"Error in pixmap_to_cv2_image: {e}")

    def cv2_image_to_pixmap(self, image):
        try:
            qimage = QImage(image.data, image.shape[1], image.shape[0], QImage.Format.Format_RGB888).rgbSwapped()
            pixmap = QPixmap.fromImage(qimage)
            return pixmap
        except Exception as e:
            print(f"Error in cv2_image_to_pixmap: {e}")

    def morphology_operation(self, operation, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
            _, binary_image = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY)

            kernel = np.ones((5, 5), np.uint8)

            if operation == 'dilate':
                result_image = cv2.dilate(binary_image, kernel, iterations=1)
            elif operation == 'erode':
                result_image = cv2.erode(binary_image, kernel, iterations=1)
            elif operation == 'opening':
                result_image = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel)
            elif operation == 'closing':
                result_image = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel)

            pixmap = self.cv2_image_to_pixmap(result_image)
            scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in morphology_operation: {e}")
    def mean_filter(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            mean_filtered_image = cv2.blur(original_image, (5, 5))
            pixmap = self.cv2_image_to_pixmap(mean_filtered_image)
            scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in mean_filter: {e}")

    def gaussian_filter(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            gaussian_filtered_image = cv2.GaussianBlur(original_image, (5, 5), 0)
            pixmap = self.cv2_image_to_pixmap(gaussian_filtered_image)
            scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in gaussian_filter: {e}")

    def median_filter(self, preview=True):
        try:
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_image = self.pixmap_to_cv2_image(self.imageLabel.pixmap())
            median_filtered_image = cv2.medianBlur(original_image, 5)
            pixmap = self.cv2_image_to_pixmap(median_filtered_image)
            scaled_pixmap = pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation)
            if preview:
                label_size = self.previewLabel.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.previewLabel.setPixmap(scaled_pixmap)

            else:
                label_size = self.imageLabel_2.size()
                scaled_pixmap = pixmap.scaled(label_size, Qt.KeepAspectRatio, Qt.SmoothTransformation)
                self.imageLabel_2.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in median_filter: {e}")

    def load_image1(self, file_name):
        options = QFileDialog.Options()
        options |= QFileDialog.ReadOnly
        file_name, _ = QFileDialog.getOpenFileName(self, "Load Image", "",
                                                   "Images (*.png *.xpm *.jpg *.bmp);;All Files (*)", options=options)

        if file_name:
            # Load and display the image
            pixmap = QPixmap(file_name)
            self.imageLabel.setPixmap(
                pixmap.scaled(self.imageLabel.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
            self.imageLabel.setScaledContents(False)
            self.imageLabel.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)

            # 将图片添加到self.image_list并设置self.current_image_index为0
            self.image_list = [file_name]
            self.current_image_index = 0

    def load_image(self, file_name):
        if file_name:
            pixmap = QPixmap(file_name)
            self.imageLabel.setPixmap(
                pixmap.scaled(self.imageLabel.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
            self.imageLabel.setScaledContents(False)
            self.imageLabel.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)

    def open_folder(self):
        folder_name = QFileDialog.getExistingDirectory(self, "打开文件夹")
        if folder_name:
            self.image_list = [os.path.join(folder_name, f) for f in os.listdir(folder_name)
                               if os.path.splitext(f)[-1].lower() in ['.png', '.jpg', '.bmp', '.xpm']]
            self.current_image_index = 0
            self.load_image(self.image_list[self.current_image_index])

    def select_save_folder(self):
        self.save_folder = QFileDialog.getExistingDirectory(self, "选择保存文件夹")

    def next_image(self):
        if not self.image_list:
            QMessageBox.warning(self, "提示", "请先打开一个文件夹")
            return

        self.current_image_index = (self.current_image_index + 1) % len(self.image_list)
        self.load_image(self.image_list[self.current_image_index])

    def previous_image(self):
        if not self.image_list:
            QMessageBox.warning(self, "提示", "请先打开一个文件夹")
            return

        self.current_image_index = (self.current_image_index - 1) % len(self.image_list)
        self.load_image(self.image_list[self.current_image_index])

    def save_image(self):
        if not self.imageLabel_2.pixmap():
            QMessageBox.warning(self, "提示", "没有处理后的图片可供保存")
            return

        if not self.save_folder:
            QMessageBox.warning(self, "提示", "请先选择保存文件夹")
            return

        if not self.image_list or self.current_image_index >= len(self.image_list):
            QMessageBox.warning(self, "提示", "当前没有图像可供保存")
            return

        file_name = os.path.basename(self.image_list[self.current_image_index])
        save_path = os.path.join(self.save_folder, "processed_" + file_name)
        self.imageLabel_2.pixmap().save(save_path)
        QMessageBox.information(self, "提示", f"图片已成功保存到：{save_path}")




    def apply_image_enhance(self, preview):
        operation = self.imageEnhanceComboBox.currentText()

        if operation == '对比度增强':
            self.adjust_contrast(preview=preview)
        # ... and so on for other operations

        elif operation == '亮度增强':
            self.adjust_brightness(preview=preview)
        elif operation == '锐化':
            self.sharpen_image(preview=preview)
        elif operation == '平滑':
            self.smooth_image(preview=preview)
        else:
            self.flip_image(operation,preview=preview)


    def apply_image_filter(self,preview):
        operation = self.imageFilterComboBox.currentText()

        if operation == '均值滤波':
            self.mean_filter(preview=preview)
        elif operation == '高斯滤波':
            self.gaussian_filter(preview=preview)
        elif operation == '中值滤波':
            self.median_filter(preview=preview)

    def apply_morphology(self,preview):
        operation = self.morphologyComboBox.currentText()

        if operation == '膨胀':
            self.morphology_operation('dilate', preview=preview)
        elif operation == '腐蚀':
            self.morphology_operation('erode', preview=preview)
        elif operation == '开运算':
            self.morphology_operation('opening', preview=preview)
        elif operation == '闭运算':
            self.morphology_operation('closing', preview=preview)

    def apply_histogram(self,preview):
        operation = self.histogramComboBox.currentText()

        if operation == '直方图均衡化':
            self.histogram_equalization(preview=preview)

    # def on_comboBox_3_currentIndexChanged(self):
    #     orientation = self.comboBox_3.currentText()
    #     self.flip_image(orientation)
    # def flip_image(self, orientation):
    #     if not self.imageLabel.pixmap():
    #         QMessageBox.warning(self, "提示", "请先加载一张图片")
    #         return
    #
    #     original_pixmap = self.imageLabel.pixmap()
    #
    #     if orientation == '水平翻转':
    #         flipped_pixmap = original_pixmap.transformed(QTransform().scale(-1, 1), mode=Qt.SmoothTransformation)
    #     elif orientation == '垂直翻转':
    #         flipped_pixmap = original_pixmap.transformed(QTransform().scale(1, -1), mode=Qt.SmoothTransformation)
    #     elif orientation == '无翻转':
    #         pass
    #
    #     self.imageLabel_2.setPixmap(
    #         flipped_pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
    #     self.imageLabel_2.setScaledContents(False)
    #     self.imageLabel_2.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
    def scale_image(self):
        try:
            # 获取滑块的值（将滑块的值除以100，使其表示为百分比，然后用200减去当前值实现反转）
            scale_value = (200 - self.scaleSlider.value()) / 100.0
            # 检查原始图像是否已加载
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return
            # 获取原始图像
            original_pixmap = self.imageLabel.pixmap()
            original_size = original_pixmap.size()
            # 根据缩放比例计算新的图像尺寸
            new_size = original_size * scale_value
            # 对原始图像进行缩放
            scaled_pixmap = original_pixmap.scaled(new_size, Qt.KeepAspectRatio)
            # 在 imageLabel 上显示缩放后的图像
            self.imageLabel.setPixmap(scaled_pixmap)
        except Exception as e:
            print(f"Error in scale_image: {e}")

    def rotate_image(self):
        try:
            rotation_angle = self.rotationSlider.value()
            if not self.imageLabel.pixmap():
                QMessageBox.warning(self, "提示", "请先加载一张图片")
                return

            original_pixmap = self.imageLabel.pixmap()
            original_size = original_pixmap.size()

            transform = QTransform().rotate(rotation_angle)
            rotated_pixmap = original_pixmap.transformed(transform, mode=Qt.SmoothTransformation)

            self.imageLabel_2.setPixmap(
                rotated_pixmap.scaled(self.scrollArea.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
            self.imageLabel_2.setScaledContents(False)
            self.imageLabel_2.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
        except Exception as e:
            print(f"Error in rotate_image: {e}")

























    def show_help(self):
        help_text = (
            "<h3>图像增强操作详细信息：</h3>"
            "<ul>"
            "<li><b>翻转</b>：水平或垂直翻转图像。</li>"
            "<li><b>随机裁剪</b>：从图像中随机裁剪一部分。</li>"
            "<li><b>随机缩放</b>：对图像进行随机缩放。</li>"
            "<li><b>随机旋转</b>：在一定范围内随机旋转图像。</li>"
            "<li><b>随机亮度/对比度/饱和度/色调</b>：调整图像的亮度、对比度、饱和度和色调。</li>"
            "<li><b>随机擦除</b>：在图像上随机擦除一个矩形区域。</li>"
            "<li><b>随机噪声</b>：在图像上添加随机噪声。</li>"
            "<li><b>剪切</b>：在图像上随机剪切一个矩形区域。</li>"
            "<li><b>Mixup</b>：将两个图像按一定比例混合在一起。</li>"
            "<li><b>Mosaic</b>：将四个图像组合成一个马赛克图像。</li>"
            "</ul>"
            "<p><b>注意：</b>Mixup 和 Mosaic 操作不能与其他操作同时使用。</p>"
            "<h3>建议的图像增强组合：</h3>"
            "<ol>"
            "<li>翻转 + 随机裁剪 + 随机缩放 + 随机旋转</li>"
            "<li>随机亮度/对比度/饱和度/色调 + 随机擦除 + 随机噪声 + 剪切</li>"
            "<li>使用 Mixup 或 Mosaic 作为单独操作。</li>"
            "</ol>"
            "<p><a href='https://www.bilibili.com/video/BV1h84y1G72P/?spm_id_from=333.337.search-card.all.click'>查看视频教程</a></p>"
        )

        style_sheet = """
            QMessageBox {
                background-color: rgba(0, 0, 0, 50%);
                color: white;
                font-size: 14px;
                font-weight: bold;
                border-radius: 10px;
            }

            QMessageBox QLabel {
                color: white;
                font-size: 14px;
            }

            QPushButton {
                background-color: qlineargradient(x1:0, y1:0, x2:1, y2:1, stop:0 rgb(3, 85, 160), stop:1 rgb(13, 206, 197));
                color: white;
                font-size: 14px;
                font-weight: bold;
                border-radius: 5px;
                padding: 5px;
                min-width: 100px;
            }

            QPushButton:hover {
                background-color: qlineargradient(x1:0, y1:0, x2:1, y2:1, stop:0 rgb(13, 206, 197), stop:1 rgb(3, 85, 160));
            }

            QPushButton:pressed {
                background-color: rgb(20, 120, 100);
            }
        """

        # 创建自定义 QMessageBox
        message_box = QMessageBox(self)
        message_box.setWindowTitle("帮助")
        message_box.setText(help_text)
        message_box.setIcon(QMessageBox.Information)


        message_box.setStyleSheet(style_sheet)

        message_box.exec_()

    # 添加新的 checkbox_clicked 方法
    def checkbox_clicked(self):
            incompatible_operations = {8, 9}  # Mixup 和 Mosaic 的索引

            # 获取当前已选中的复选框索引
            checked_indexes = [i for i, checkbox in enumerate(
                [self.enhanceMethod1Checkbox, self.enhanceMethod2Checkbox, self.enhanceMethod3Checkbox,
                 self.enhanceMethod4Checkbox, self.enhanceMethod5Checkbox, self.enhanceMethod6Checkbox,
                 self.enhanceMethod7Checkbox, self.enhanceMethod8Checkbox, self.enhanceMethod9Checkbox,
                 self.enhanceMethod10Checkbox]) if checkbox.isChecked()]

            # 检查是否选中了不兼容的操作
            if any(index in checked_indexes for index in incompatible_operations) and len(checked_indexes) > 1:
                QMessageBox.warning(self, "警告", "Mixup 或 Mosaic 操作不能与其他操作同时使用，请取消选择不兼容的操作。")


    def init_progress_bar(self):
        self.image_augmentor.progress_updated.connect(self.update_progress)
        self.progressBar_2.setValue(0)  # 假设 progressBar 是您在 Qt Designer 中添加的 QProgressBar 对象的名称

    @pyqtSlot(int)
    def update_progress(self, value):
        self.progressBar_2.setValue(self.progressBar_2.value() + value)






    # 在这里添加 select_input_folder、select_output_folder 和start_processing 槽函数的实现。
    def select_input_folder(self):
        folder_path = QFileDialog.getExistingDirectory()
        self.inputFolderLabel.setText(folder_path)

    def select_output_folder(self):
        folder_path = QFileDialog.getExistingDirectory()
        self.outputFolderLabel.setText(folder_path)

    def start_processing(self):
        input_folder = self.inputFolderLabel.text()
        output_folder = self.outputFolderLabel.text()
        probabilities = [float(self.enhanceProbabilityInput.text()) if checkbox.isChecked() else 0 for checkbox in
                         [self.enhanceMethod1Checkbox, self.enhanceMethod2Checkbox, self.enhanceMethod3Checkbox,
                          self.enhanceMethod4Checkbox, self.enhanceMethod5Checkbox, self.enhanceMethod6Checkbox,
                          self.enhanceMethod7Checkbox, self.enhanceMethod8Checkbox, self.enhanceMethod9Checkbox,
                          self.enhanceMethod10Checkbox]]

        success_count, failure_count, image_count = self.image_augmentor.process_images(input_folder, output_folder,
                                                                                        probabilities)

        # 设置进度条的最大值
        self.progressBar_2.setMaximum(image_count)

        # 显示弹窗
        QMessageBox.information(self, "处理完成", f"成功处理 {success_count} 个图像，失败 {failure_count} 个")

        self.progressBar_2.setValue(0)

    def open_advanced_settings(self):
        dialog = QDialog(self)
        dialog.setWindowTitle("高级参数设置")

        layout = QVBoxLayout()

        # 为每个增强方法添加一个 QLineEdit，用于输入自定义参数
        # 添加默认值和输入规范的 QLabel
        enhance_method2_parameter = QLineEdit(self.enhanceMethod2Parameter.text())  # 使用默认值作为 QLineEdit 的初始值
        enhance_method2_label = QLabel("scale_range (tuple): (min, max), e.g., (0.8, 1.0)")
        layout.addWidget(enhance_method2_label)
        layout.addWidget(enhance_method2_parameter)

        # ...在此处添加更多的增强方法参数和 QLabel...

        # 添加确认和取消按钮
        buttons = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)
        buttons.accepted.connect(dialog.accept)
        buttons.rejected.connect(dialog.reject)
        layout.addWidget(buttons)

        dialog.setLayout(layout)

        # 如果用户点击“确认”，请使用新的参数值更新增强方法
        if dialog.exec() == QDialog.Accepted:
            self.enhanceMethod2Parameter.setText(enhance_method2_parameter.text())
            # ...在此处更新更多增强方法参数值...










#检测线程的参数
    def on_visualize_checkbox_stateChanged(self, state):
        global visualize
        visualize = state == 2
        print(f"Visualize is now: {visualize}")
    def on_databases_checkbox_stateChanged(self, state):
        global databases
        databases = state == 2
        print(f"databases is now: {databases}")
    def on_agnostic_nmscheckbox_checkbox_stateChanged(self, state):
        global agnostic_nms
        agnostic_nms = state == 2
        print(f"agnostic_nmscheckbox is now: {agnostic_nms}")
    def on_halfcheckbox_2_nmscheckbox_checkbox_stateChanged(self, state):
        global half
        half = state == 2
        print(f"agnostic_nmscheckbox is now: {half}")
    def on_save_crop_nmscheckbox_checkbox_stateChanged(self, state):
        global save_crop
        save_crop = state == 2
        print(f"on_save_crop_nmscheckbox is now: {save_crop}")
    def on_augment_nmscheckbox_checkbox_stateChanged(self, state):
        global augment
        augment = state == 2
        print(f"augmentedcheckbox_3 is now: {augment}")
    def on_imgsz_spinbox_valueChanged(self, value):
        global imgsz
        imgsz = value
        print(f"imgsz is now: {imgsz}")
    def on_line_thickness_spinbox_valueChanged(self, value):
        global line_thickness
        line_thickness = value
        print(f"line_thickness is now: {line_thickness}")
    def update_classes(self, text):
        global classes
        if text.strip():
            classes = [int(c) for c in text.split()]
        else:
            classes = None
        print(f"Classes are now: {classes}")
    def on_hide_labelscheckbox_checkbox_checkbox_stateChanged(self, state):
        global hide_labels
        hide_labels = state == 2
        print(f"hide_labels is now: {hide_labels}")
    def on_hide_confcheckbox_checkbox_checkbox_stateChanged(self, state):
        global hide_conf
        hide_conf = state == 2
        print(f"hide_conf is now: {hide_conf}")
    def on_browse_result_folder_button_clicked(self):
        # print("Function on_browse_result_folder_button_clicked called")
        global project
        # Get the button that triggered the slot
        button = self.sender()
        # Disconnect the button's clicked signal
        button.clicked.disconnect()
        # Browse the folder
        folder_path = QFileDialog.getExistingDirectory(self, "Select Result Folder", str(Path.home()))
        if folder_path:
            project = Path(folder_path)
            self.result_folder_lineEdit.setText(folder_path)
        # Reconnect the button's clicked signal
        button.clicked.connect(self.on_browse_result_folder_button_clicked)
        # print("on_browse_result_folder_button_clickedproject:",project)

    def get_db_connection( self, host=DB_HOST, user=DB_USER, password=DB_PASSWORD, database=DB_NAME):
        return pymysql.connect(
            host=DB_HOST,
            user=DB_USER,
            password=DB_PASSWORD,
            database=DB_NAME
        )

    def draw_bar_chart(self, data):
        sns.set(style='darkgrid', palette='pastel')

        # Extract sign_type and sign_count from data
        sign_types = [row[1] for row in data]
        sign_counts = [row[2] for row in data]

        # Clear the previous plot
        self.ax.clear()

        # Create the bar chart
        self.ax.bar(sign_types, sign_counts)

        # Set plot labels and title
        self.ax.set_xlabel('Sign Types')
        self.ax.set_ylabel('Detection Counts')
        self.ax.set_title('Detection Counts by Sign Type')

        # Draw the plot
        self.canvas.draw()

    def draw_line_chart(self, data):
        sns.set(style='darkgrid', palette='pastel')

        # Extract detection_time and sign_count from data
        detection_times = [row[3] for row in data]
        sign_counts = [row[2] for row in data]

        # Clear the previous plot
        self.ax.clear()

        # Create the line chart
        self.ax.plot(detection_times, sign_counts)

        # Set plot labels and title
        self.ax.set_xlabel('Detection Time')
        self.ax.set_ylabel('Detection Counts')
        self.ax.set_title('Detection Counts by Time')

        # Draw the plot
        self.canvas.draw()

    def draw_pie_chart(self, data, threshold=5):
        sns.set(style='darkgrid', palette='pastel')

        # Extract sign_type and sign_count from data
        sign_types = [row[1] for row in data]
        sign_counts = [row[2] for row in data]

        # Filter out data with counts below the threshold
        filtered_sign_types = []
        filtered_sign_counts = []
        other_count = 0
        for i in range(len(sign_types)):
            if sign_counts[i] >= threshold:
                filtered_sign_types.append(sign_types[i])
                filtered_sign_counts.append(sign_counts[i])
            else:
                other_count += sign_counts[i]
        if other_count > 0:
            filtered_sign_types.append('Other')
            filtered_sign_counts.append(other_count)

        # Clear the previous plot
        self.ax.clear()

        # Create the pie chart
        explode = [0.05] * len(filtered_sign_types)
        self.ax.pie(filtered_sign_counts, labels=filtered_sign_types, explode=explode,
                    autopct='%1.1f%%', shadow=True, startangle=90)

        # Set plot title
        self.ax.set_title('Detection Counts by Sign Type')

        # Draw the plot
        self.canvas.draw()

    def draw_box_plot(self, data):
        sns.set(style='darkgrid', palette='pastel')

        # Extract sign_type and additional_info (confidence) from data