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OpencvVision\n\n## 是什么？\n\n这是一个使用.net 6基于OpencvSharp、ReactiveUI开发的自用工具，主要用来做ReactiveUI与OpencvSharp学习过程中的尝试；根据个人需求不定期更新添加功能，欢迎讨论交流。\n\n### ！其他信息请参考项目OpenCVVision.View内AppBootstrapper.cs文件里的相关说明\n\n## 引用\n\n| 功能          | 引用包                                                                                                |\n| ----------- | -------------------------------------------------------------------------------------------------- |\n| OpencvSharp | [OpencvSharp](https://github.com/shimat/opencvsharp_samples)                                       |\n| MVVMk框架     | [ReactiveUI](https://github.com/reactiveui/ReactiveUI)                                             |\n| 界面控件        | [MaterialDesignInXamlToolkit](https://github.com/MaterialDesignInXAML/MaterialDesignInXamlToolkit) |\n| 标题栏         | [MahApps.Metro](https://github.com/MahApps/MahApps.Metro)                                          |\n| 响应式集合       | [DynamicData](https://github.com/reactivemarbles/DynamicData)                                      |\n| 返回结果包装      | [FluentResults](https://github.com/altmann/FluentResults)                                          |\n\n## 推荐\n\n\u003e 其它项目： \n\u003e [OpenCVSharpHelper](https://gitee.com/tfarcraw/opencvsharphelper)\n\n## 部分功能截图\n\n### 22/09/08更新2\n\n从朋友处了解到基恩士的浓淡补正算法，感觉挺有意思，查找了下相关的资料，尝试简单的实现一下明暗局部的筛选。思路是选取一个卷积核，进行滑移，规则是先排列挑出核内部的中值，在通过设置删除亮暗比例设置中值附近的阈值，来筛选出局部过亮或过暗的部分；可以通过卷积核长宽设置方向，来实现特定方向的筛选，示意如下图：\n筛选出不均匀的边缘\n![筛选边缘](Img/23.png)\n筛选出中间的过亮部分和过暗部分\n![筛选暗点亮点](Img/24.png)\n沿着Y方向，筛选出过亮部分，因为选定了方向，条纹不会被选出,仅选出非条纹的亮线\n![Y方向筛选](Img/25.png)\n\n### 22/09/08更新1\n\n调整解决方案项目目录，新增服务子项目\n添加海康，大恒相机采集图像。需要自行安装对应品牌SDK。由以往项目中使用程序汇总而来，暂时不具备测试条件，等待后续测试硬件。\n添加信息栏，根据日志等级变换背景颜色\n![信息栏默认](Img/20.png)\n![信息栏错误](Img/21.png)\n添加日志面板，点击信息栏右侧按钮展开，点击其它空白区域丢失焦点关闭\n![日志面板](Img/22.png)\n\n### 22/06/21更新\n\n微信二维码检测解码\n![18](Img/18.jpg)\n\n![19](Img/19.jpg)\n\n### 22/06/20更新\n\nPaddleX图像分类推理\n![16](Img/16.jpg)\nPaddleX目标检测推理\n![17](Img/17.jpg)\n\n### 功能\n\n首页\n![00](Img/00.jpg)\n夜间模式添加图片\n![02](Img/02.jpg)\nROI\n![03](Img/03.jpg)\n色彩空间\n![04](Img/04.jpg)\n滤波\n![05](Img/05.jpg)\n二值化\n![06](Img/06.jpg)\n形态学\n![07](Img/07.jpg)\n连通域\n![08](Img/08.jpg)\n轮廓\n![09](Img/09.jpg)\n格雷码条纹结构光\n![10](Img/10.jpg)\n灰度质心线激光\n![11](Img/11.jpg)\n光度立体法求表面法线与深度图\n![12](Img/12.jpg)\nYolo识别\n![13](Img/13.jpg)\n特征点匹配\n![14](Img/14.jpg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flolo77777%2Fopencvvision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flolo77777%2Fopencvvision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flolo77777%2Fopencvvision/lists"}