feat: make detectors work on windows

Author: developer239

.gitignore

@@ -11,3 +11,5 @@
 !/samples/game-1.jpg
 !/samples/game-2.jpg
 !/samples/game-3.jpg
+
+/externals/onnxruntime-win-x64-1.12.1

CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.23)
+cmake_minimum_required(VERSION 3.21) # use the lower version requirement
 
 set(APP_NAME yolo_opencv)
 project(${APP_NAME})
@@ -10,10 +10,17 @@ add_executable(${APP_NAME} src/main.cpp src/YOLODetector.h src/YOLOSegmentor.h)
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
 
 # OpenCV
-find_package(OpenCV REQUIRED)
-target_link_libraries(${APP_NAME} PUBLIC "-framework Carbon")
-target_link_libraries(${APP_NAME} PUBLIC ${OpenCV_LIBS})
+if(APPLE)
+    find_package(OpenCV REQUIRED)
+    target_link_libraries(${APP_NAME} PUBLIC "-framework Carbon")
+    target_link_libraries(${APP_NAME} PUBLIC ${OpenCV_LIBS})
+elseif(WIN32)
+    set(OpenCV_DIR "C:/tools/opencv/build/x64/vc15/lib")
+    find_package(OpenCV REQUIRED)
+    target_link_libraries(${APP_NAME} PUBLIC ${OpenCV_LIBS})
+endif()
 
+# Link Tesseract and Leptonica
 target_link_libraries(${APP_NAME} PUBLIC
         ${tesseract_lib}
         ${leptonica_lib}
@@ -23,15 +30,29 @@ target_link_libraries(${APP_NAME} PUBLIC
 
 target_include_directories(${APP_NAME} PUBLIC
         ${tesseract_include}
-        ${leptonica_include})
+        ${leptonica_include}
+        )
 
 # OnnxRuntime
-if(DEFINED ENV{ONNXRUNTIME_ROOT})
-    set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/externals/$ENV{ONNXRUNTIME_ROOT}")
-else()
-    set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/externals/onnxruntime-osx-arm64-1.12.1")
+if(APPLE)
+    if(DEFINED ENV{ONNXRUNTIME_ROOT})
+        set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/externals/$ENV{ONNXRUNTIME_ROOT}")
+    else()
+        set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/externals/onnxruntime-osx-arm64-1.12.1")
+    endif()
+elseif(WIN32)
+    set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/externals/onnxruntime-win-x64-1.12.1")
 endif()
 
 find_package(ONNXRUNTIME REQUIRED)
 target_link_libraries(${APP_NAME} PUBLIC ${ONNXRUNTIME_LIBRARY})
 target_include_directories(${APP_NAME} PUBLIC ${ONNXRUNTIME_INCLUDE_DIR})
+
+# Copy onnxruntime.dll on Windows
+if(WIN32)
+    add_custom_command(TARGET ${APP_NAME} POST_BUILD
+            COMMAND ${CMAKE_COMMAND} -E copy
+            "${CMAKE_CURRENT_SOURCE_DIR}/externals/onnxruntime-win-x64-1.12.1/lib/onnxruntime.dll"
+            "${CMAKE_CURRENT_BINARY_DIR}/onnxruntime.dll"
+            )
+endif()

README.md

@@ -8,7 +8,7 @@ by [hpc203/yolov7-opencv-onnxrun-cpp-py](https://github.com/hpc203/yolov7-opencv
 This repository serves mostly as documentation for my personal use, demonstrating how to use ONNX and OpenCV with CMake
 on MacOS.
 
-## How to run
+## MacOS installation
 
 1) Install dependencies:
 
@@ -18,27 +18,33 @@ $ brew install onnxruntime
 $ brew install opencv
 ```
 
-2) If you are on M1 then you are good to go 🎉
+2) If you are on ARM then you are good to go 🎉
 
+3) **If you are not using an ARM processor**, [download correct runtime 1.12.1 header files from the official GitHub repository](https://github.com/microsoft/onnxruntime/releases/tag/v1.12.1).
 
-3) **If you are not using an M1 processor**, download ONNX Runtime.
+4) Put onnxruntime files to `external/`
 
-- Either select the correct version from the releases available [here](github.com/microsoft/onnxruntime/releases)
-- If you are unsure which version to download, you can use the official optimize inferencing
-  picker [here](https://onnxruntime.ai)
+5) Set `ONNXRUNTIME_ROOT` env variable to your onnxruntime folder name
 
-4) Put ONNX runtime files to `external/`
+## Windows installation
 
+1) Install dependencies:
+
+```bash
+$ choco install cmake --pre
+$ choco install opencv
+```
 
-5) Set `ONNXRUNTIME_ROOT` .env variable to your onnxruntime folder name
+2) Download [ONNX Runtime 1.12.1](https://github.com/microsoft/onnxruntime/releases/download/v1.12.1/onnxruntime-win-x64-1.12.1.zip)
+3) Extract and put onnxruntime folder to `external/onnxruntime-win-x64-1.12.1`
 
 ## Model
 
 - This example utilizes a custom-trained model. If you wish to train your own model, you can create a dataset
   using [Roboflow](https://roboflow.com/#annotate) and then use one of
   their [official notebooks](https://github.com/roboflow/notebooks) to perform the training. There are also many
   comprehensive tutorials in the official [ultralytics/yolov5 repository](https://github.com/ultralytics/yolov5)
-- You are free to use any model you prefer, but be sure to update the `class.names` file accordingly. Additionally, for instance segmentation, it is important to pass `SegNetConfig` with proper parameters. 
+- You are free to use any model you prefer, but be sure to update the `class.names` file accordingly. Additionally, for instance segmentation, it is important to pass `SegNetConfig` with proper parameters.
 
 ## Preview
 

src/YOLODetector.h

@@ -1,5 +1,9 @@
 #pragma once
 
+#ifdef _WIN32
+#include <Windows.h>
+#endif
+
 #include <fstream>
 #include <iostream>
 #include <opencv2/highgui.hpp>
@@ -38,9 +42,17 @@ class YOLODetector {
     // OrtStatus* status =
     // OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0);
     sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
-    ortSession =
-        Ort::Session(ortEnv, config.pathToModel.c_str(), sessionOptions);
 
+#ifdef _WIN32
+    int size_needed = MultiByteToWideChar(CP_UTF8, 0, config.pathToModel.c_str(), (int)config.pathToModel.size(), NULL, 0);
+    std::wstring wstrPathToModel(size_needed, 0);
+    MultiByteToWideChar(CP_UTF8, 0, config.pathToModel.c_str(), (int)config.pathToModel.size(), &wstrPathToModel[0], size_needed);
+    ORTCHAR_T* path = const_cast<ORTCHAR_T*>(wstrPathToModel.c_str());
+#else
+    ORTCHAR_T* path = const_cast<ORTCHAR_T*>(config.pathToModel.c_str());
+#endif
+
+    ortSession = Ort::Session(ortEnv, path, sessionOptions);
     LoadTypeInfo();
     LoadClasses(config.pathToClasses);
   }

src/YOLOSegmentor.h

@@ -1,5 +1,9 @@
 #pragma once
 
+#ifdef _WIN32
+#include <Windows.h>
+#endif
+
 #include <fstream>
 #include <iostream>
 #include <memory>
@@ -51,7 +55,16 @@ class YOLOSegmentor {
         GraphOptimizationLevel::ORT_ENABLE_EXTENDED
     );
 
-    session = Ort::Session(env, config.pathToModel.c_str(), sessionOptions);
+    #ifdef _WIN32
+    int size_needed = MultiByteToWideChar(CP_UTF8, 0, config.pathToModel.c_str(), (int)config.pathToModel.size(), NULL, 0);
+    std::wstring wstrPathToModel(size_needed, 0);
+    MultiByteToWideChar(CP_UTF8, 0, config.pathToModel.c_str(), (int)config.pathToModel.size(), &wstrPathToModel[0], size_needed);
+    ORTCHAR_T* path = const_cast<ORTCHAR_T*>(wstrPathToModel.c_str());
+#else
+    ORTCHAR_T* path = const_cast<ORTCHAR_T*>(config.pathToModel.c_str());
+#endif
+
+    session = Ort::Session(env, path, sessionOptions);
 
     LoadTypeInfo();
     LoadClasses(config.pathToClasses);