# image_processing_platform

**Repository Path**: shaoeric/image_processing_platform

## Basic Information

- **Project Name**: image_processing_platform
- **Description**: c++ qt5 opencv实现的图像处理与标注系统
- **Primary Language**: C++
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 15
- **Forks**: 8
- **Created**: 2019-12-25
- **Last Updated**: 2024-09-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

### 图像处理与标注集成系统 Integrated system of image processing and annotation
#### 项目需求
- 实验室需要一个图像处理平台来对实验的图片做处理，并希望该平台可以将实验室前期的图像算法成果集成在内
- 期望有图像标注系统

#### 项目说明
- 在CSDN上发现一个效果相对满足需求的图像处理软件，原作者为笔尖，[原作的github链接](https://github.com/HuangBingjian/ImageProcessing)，在此项目基础上做了大量的修改，在修改之前，也征得了原作者的同意。
- 本项目使用C++ Qt5实现客户端服务。


#### 界面示意图
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CvL1YmPZOXow57eukqtu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%0A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whur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%0AoBAAAAAACEEhAAAAABCCQgAAAAAgBIUAAAAAQAg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#### 主要改进
- 添加了通道直方图效果，可以根据显示的图片实时完成效果显示，并自动根据通道数量调整曲线数量。还可以手动点选legend来控制对应的曲线是否显示
- 从json文件中读取并载入配置
- 修改了原历史存储方案，设置一个历史存储器最大存储量
- 添加了深度学习方法对图像处理的接口，通过C/S模式完成交互
- 添加了图像级标签标注系统，可以通过快捷键方式快速完成标注图像类别


#### 项目扩展的方法
由于原始项目代码结构很乱，也不方便修改。此处做个总结记录，方便他人快速上手。用“深度处理->目标检测->SSD模型”做例子
##### 菜单项扩展
   - 首先要定义菜单项，即“SSD模型”项
```cpp
QAction *Act_Detect_SSD = new QAction(tr("SSD模型"), this); // 菜单项
connect(Act_Detect_SSD, SIGNAL(triggered()), I_MainWindow, SLOT(deep_detect_ssd())); // 绑定点击菜单项的信号槽， deep_detect_ssd函数就是点击该项去执行的槽函数
```
   - 然后定义“目标检测”，这里定义该项为一级菜单
   ```cpp
// 菜单项添加到一级菜单中
menu_deep_detect = new QMenu();  // 此处为头文件中定义的指针，用来添加最后一级的菜单项，比如“SSD模型”、“YOLO模型”等
QAction *Act_detects = new QAction(tr("目标检测"), this);
//action添加menu 当且仅当菜单项有子选项时，才需要setMenu
Act_detects->setMenu(menu_deep_detect);
// “目标检测”将一级菜单项“SSD模型”添加进来
menu_deep_detect->addAction(Act_Detect_SSD);
```
   - 最后将菜单项“深度处理”添加到菜单栏中
```cpp
QMenu *deep = addMenu(tr("深度处理"));
deep->addAction(Act_detects);
```
#####  深度处理接口扩展
   - 仍以ssd为例，在MainWindow.cpp文件中定义了deep_detect_ssd()方法，即上述中所指的槽函数
   - 首先实例化深度对象DeepProcess，该对象实现了客户端与服务器之间的交互，通过post方式将图片base64编码的json字符串数据包提交给服务器，服务器响应base64，客户端解析即可
```cpp
DeepProcess *ssd;
ssd = new DeepProcess("ssd", "detect/ssd", this);// detect/ssd为服务器端的ssd方法的url
```
   - 然后获取当前图片有两种方法，一种是从历史记录中选取，historyClass->getCurrentImage()，另一种是直接选择label中显示的图片，imgLabel->getImage()。在设计的时候主要是遇到了问题，对图片的几何放缩特别消耗资源，所以让几何变换的结果不存入历史记录，如果有必要使其存储，可以点击“记录图像”按钮。后续对其扩展开发需要注意。
```cpp
QImage current = imgLabel->getImage(); // 此处使用此方法获取当前图像
```
   - 最后要绑定深度方法返回的图像结果的信号槽，绑定后执行post方法进行交互
```cpp
connect(ssd, SIGNAL(responseImage(QImage, QString)), this, SLOT(process_response(QImage, QString)));
ssd->post(current, "hello"); // "hello"是方法提供的额外参数，可以扩展为 向服务器发送的客户机id、密钥等信息，供服务器验证
```

##### 深度处理的服务器接口说明
- 使用的python3的flask框架搭建服务器，主要是使用两个文件app.py和utils.py
- utils.py主要做了图像编解码的工作
```python
import cv2
import base64
import numpy as np

def cv2_base64(image):
    base64_str = cv2.imencode('.jpg', image)[1].tostring()
    base64_str = base64.b64encode(base64_str)
    return str(base64_str).lstrip("b'")


##base64转cv2
def base64_cv2(base64_str):
    imgString = base64.b64decode(base64_str)
    nparr = np.fromstring(imgString, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return image
```
- app.py 就是服务器的入口，需要定义深度模型的接口，下面对ssd模型举例
```python
from flask import Flask, jsonify, request
import utils
import cv2

app = Flask(__name__)
@app.route("/detect/ssd", methods=['POST'])
def ssd():
    data = request.json
    img = data['image'] # 获取post过来的图像和msg信息
    msg = data['msg']
    
    try:
        # 将图像转化
        image = utils.base64_cv2(img)
        # 模型处理
        image = ssd(image)    # 返回的应该是cv2 ndarrray的图像
        # 将结果返回
        return jsonify({"image": utils.cv2_base64(image), "msg": "OK", "code": 200})
    except Exception as e:
        print(e)
        return jsonify({"code": 500, "msg": str(e)})

if __name__ == '__main__':
    app.run(debug=True)
```

#### 标注系统的相关说明
- 相关的组件都是在ui文件中进行绘制
- 相关的快捷键在菜单栏帮助-快捷键说明中可以查找
- 设置了一个标注缓存的存储区可存储的最大数，存储区满就会将存储的标注写入文件，避免对文件频繁写入
- 标注系统的主题与主界面的主题保持一致
- 可以动态的在类别表格中添加行数
- 类别可多选

#### todo
- 继续丰富深度处理的接口，使其功能更加完善
- 标注系统的标注最大存储数应从配置文件中读取，并能修改
- 可以进一步集成视频处理及标注系统
- 资源使用方面需进一步优化，可从对ImageProcessing类的简化工作开始