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修复cotton_color的bug 添加color_range的matrox实现

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zjc-zjc-123 2024-11-13 18:52:18 +08:00
parent ea72ee9310
commit af063ae8e0
4 changed files with 269 additions and 26 deletions
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13
CMakeLists.txt
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@ -31,6 +31,12 @@ set(CMAKE_AUTOMOC ON)
set(CMAKE_AUTOUIC ON)
set(CMAKE_AUTORCC ON)
#配置mil库
include_directories(E:/QTexamble/matrox/Include)
# 添加 MIL 库的库文件路径
link_directories(E:/QTexamble/matrox/LIB)
file(GLOB MIL_LIBS E:/QTexamble/matrox/LIB/*.lib)
# 添加可执行文件 cotton_color
add_executable(cotton_color cotton_color.cpp)
@ -40,16 +46,17 @@ target_link_libraries(cotton_color Qt6::Widgets ${OpenCV_LIBS} comdlg32)
# 添加可执行文件 cotton_color
add_executable(cotton_range Matrox/color_range.cpp)
# 链接 OpenCV 和 Qt 库
target_link_libraries(cotton_range Qt6::Widgets ${OpenCV_LIBS})
target_link_libraries(cotton_range Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
# 添加可执行文件 cotton_color2
add_executable(cotton_color2 cotton_color2.cpp)
# 链接 OpenCV 和 Qt 库
target_link_libraries(cotton_color2 Qt6::Widgets ${OpenCV_LIBS})
target_link_libraries(cotton_color2 Qt6::Widgets ${OpenCV_LIBS} Mil)
add_executable(color_matching Matrox/template_matching.cpp
Matrox/onnx_running.cpp
Matrox/onnx_running.h
Matrox/ui.cpp)
add_executable(ui Matrox/ui.cpp)
target_link_libraries(ui Qt6::Widgets)
target_link_libraries(ui Qt6::Widgets)

201
Matrox/color_range.cpp
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@ -1,3 +1,204 @@
//
// Created by zjc on 24-11-12.
//
#include <mil.h>
#include <iostream>
#include <chrono>
#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\cotton2.bmp")
// 全局变量，方便在各个函数中使用
MIL_ID MilApplication = M_NULL, MilSystem = M_NULL, MilDisplay = M_NULL;
// 时间测量模板函数
template <typename Func>
void measureExecutionTime(Func func) {
// 获取当前时间作为起点
auto start = std::chrono::high_resolution_clock::now();
// 执行传入的函数
func();
// 获取当前时间作为结束点
auto end = std::chrono::high_resolution_clock::now();
// 计算时间差并转换为毫秒
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
std::cout << "Function execution time: " << duration.count() << " milliseconds" << std::endl;
}
// LabProcess 函数，支持通过参数控制阈值范围，提供默认值
void LabProcess(MIL_ID& inputImage, MIL_ID& outputImageLab,
MIL_DOUBLE lowerL = 101.0, MIL_DOUBLE upperL = 135.0,
MIL_DOUBLE lowerA = 101.0, MIL_DOUBLE upperA = 120.0,
MIL_DOUBLE lowerB = 95.0, MIL_DOUBLE upperB = 134.0)
{
MIL_ID MilLabImage = M_NULL, MilLChannel = M_NULL, MilAChannel = M_NULL, MilBChannel = M_NULL;
MIL_ID MilBinaryL = M_NULL, MilBinaryA = M_NULL, MilBinaryB = M_NULL;
// 检查输入图像的通道数
MIL_INT NumBands = 0;
MbufInquire(inputImage, M_SIZE_BAND, &NumBands);
if (NumBands != 3)
{
printf("输入图像不是 3 通道图像，请提供彩色图像。\n");
return;
}
// 分配用于存储 Lab 图像的缓冲区
MbufAllocColor(MbufInquire(inputImage, M_OWNER_SYSTEM, M_NULL), 3,
MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL),
8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP,
&MilLabImage);
// 将图像从 sRGB 转换到 Lab
MimConvert(inputImage, MilLabImage, M_SRGB_TO_LAB);
// 创建 Lab 通道的子缓冲区
MbufChildColor(MilLabImage, 0, &MilLChannel);
MbufChildColor(MilLabImage, 1, &MilAChannel);
MbufChildColor(MilLabImage, 2, &MilBChannel);
// 分配二值图像缓冲区
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &MilBinaryL);
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &MilBinaryA);
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &MilBinaryB);
// 对每个通道进行阈值分割
MimBinarize(MilLChannel, MilBinaryL, M_IN_RANGE, lowerL, upperL);
MimBinarize(MilAChannel, MilBinaryA, M_IN_RANGE, lowerA, upperA);
MimBinarize(MilBChannel, MilBinaryB, M_IN_RANGE, lowerB, upperB);
// 分配输出图像缓冲区
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &outputImageLab);
// 将结果合并
MimArith(MilBinaryL, MilBinaryA, outputImageLab, M_AND);
MimArith(outputImageLab, MilBinaryB, outputImageLab, M_AND);
// 释放资源
MbufFree(MilBinaryL);
MbufFree(MilBinaryA);
MbufFree(MilBinaryB);
MbufFree(MilLChannel);
MbufFree(MilAChannel);
MbufFree(MilBChannel);
MbufFree(MilLabImage);
}
// HSVProcess 函数，支持通过参数控制饱和度阈值，提供默认值
void HSVProcess(MIL_ID& inputImage, MIL_ID& outputImageHSV, MIL_DOUBLE saturationThreshold = 120.0)
{
MIL_ID MilHSVImage = M_NULL, MilHChannel = M_NULL, MilSChannel = M_NULL, MilVChannel = M_NULL;
// 检查输入图像的通道数
MIL_INT NumBands = 0;
MbufInquire(inputImage, M_SIZE_BAND, &NumBands);
if (NumBands != 3)
{
printf("输入图像不是 3 通道图像，请提供彩色图像。\n");
return;
}
// 分配用于存储 HSV 图像的缓冲区
MbufAllocColor(MbufInquire(inputImage, M_OWNER_SYSTEM, M_NULL), 3,
MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL),
8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP,
&MilHSVImage);
// 将图像从 sRGB 转换到 HSV
MimConvert(inputImage, MilHSVImage, M_RGB_TO_HSV);
// 创建 HSV 通道的子缓冲区
MbufChildColor(MilHSVImage, 0, &MilHChannel);
MbufChildColor(MilHSVImage, 1, &MilSChannel);
MbufChildColor(MilHSVImage, 2, &MilVChannel);
// 分配输出图像缓冲区
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &outputImageHSV);
// 对 S 通道进行阈值分割
MimBinarize(MilSChannel, outputImageHSV, M_GREATER, saturationThreshold, M_NULL);
// 释放资源
MbufFree(MilHChannel);
MbufFree(MilSChannel);
MbufFree(MilVChannel);
MbufFree(MilHSVImage);
}
// 综合测试函数，调用 LabProcess 和 HSVProcess 并合并结果
void test_hsv(MIL_ID& inputImage,
MIL_DOUBLE lowerL = 101.0, MIL_DOUBLE upperL = 135.0,
MIL_DOUBLE lowerA = 101.0, MIL_DOUBLE upperA = 120.0,
MIL_DOUBLE lowerB = 95.0, MIL_DOUBLE upperB = 134.0,
MIL_DOUBLE saturationThreshold = 120.0)
{
MIL_ID MilResultLab = M_NULL, MilResultHSV = M_NULL, MilCombinedResult = M_NULL;
// 调用 LabProcess
LabProcess(inputImage, MilResultLab, lowerL, upperL, lowerA, upperA, lowerB, upperB);
// 调用 HSVProcess
HSVProcess(inputImage, MilResultHSV, saturationThreshold);
// 分配合并结果的缓冲区
MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
M_IMAGE + M_PROC + M_DISP, &MilCombinedResult);
// 合并 Lab 和 HSV 的结果（取“或”运算）
MimArith(MilResultLab, MilResultHSV, MilCombinedResult, M_OR);
//// 显示合并后的结果图像
MdispSelect(MilDisplay, MilCombinedResult);
//// 等待用户查看处理后的图像
printf("图像已处理并合并，按下 <Enter> 退出程序。\n");
getchar();
// 释放资源
MbufFree(MilResultLab);
MbufFree(MilResultHSV);
MbufFree(MilCombinedResult);
}
int main()
{
MIL_ID MilImage = M_NULL;
// 初始化 MIL 应用程序
MappAllocDefault(M_DEFAULT, &MilApplication, &MilSystem, &MilDisplay, M_NULL, M_NULL);
// 加载输入图像
MbufRestore(IMAGE_PATH, MilSystem, &MilImage);
// 使用 lambda 表达式测量 test_hsv() 的执行时间
measureExecutionTime([&]() {
test_hsv(MilImage);
});
// 释放资源
MbufFree(MilImage);
MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
return 0;
}

79
cotton_color.cpp
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@ -55,51 +55,86 @@ void vibrantColorDetection(const Mat& inputImage, Mat& outputImage, const map<st
// 对饱和度图像应用阈值处理
threshold(saturation, outputImage, saturationThreshold, 255, THRESH_BINARY);
}
string openFileDialog() {
std::wstring openFileDialog() {
// 初始化文件选择对话框
OPENFILENAME ofn; // 文件对话框结构
wchar_t szFile[260]; // 存储选择的文件路径
OPENFILENAMEW ofn; // 使用宽字符版本的结构
wchar_t szFile[260] = {0}; // 存储选择的文件路径
// 设置 OPENFILENAME 结构的默认值
// 设置 OPENFILENAMEW 结构的默认值
ZeroMemory(&ofn, sizeof(ofn));
ofn.lStructSize = sizeof(ofn);
ofn.hwndOwner = NULL;
ofn.lpstrFile = szFile;
ofn.lpstrFile = szFile; // 设置文件路径缓冲区
ofn.nMaxFile = sizeof(szFile) / sizeof(szFile[0]);
ofn.lpstrFilter = L"Image Files\0*.BMP;*.JPG;*.JPEG;*.PNG;*.GIF\0All Files\0*.*\0";
ofn.nFilterIndex = 1;
ofn.lpstrFileTitle = NULL;
ofn.lpstrFileTitle = NULL; // 不需要单独的文件名
ofn.nMaxFileTitle = 0;
ofn.lpstrInitialDir = NULL;
ofn.lpstrTitle = L"Select an image file";
ofn.lpstrInitialDir = NULL; // 使用默认初始目录
ofn.lpstrTitle = L"Select an image file"; // 对话框标题
ofn.Flags = OFN_PATHMUSTEXIST | OFN_FILEMUSTEXIST;
// 打开文件选择对话框
if (GetOpenFileName(&ofn) == TRUE) {
// 将 wchar_t 转换为 string
wstring ws(szFile);
string filePath(ws.begin(), ws.end());
return filePath;
if (GetOpenFileNameW(&ofn) == TRUE) {
return szFile; // 返回选中的文件路径
}
return ""; // 如果用户取消，返回空字符串
return L""; // 如果用户取消，返回空字符串
}
/**
* @brief 读取图像文件，支持 Unicode 路径。
*
* @return 加载的图像，类型为 cv::Mat。如果加载失败，返回空的 Mat。
*/
Mat readImage() {
// 读取输入图像
string imagePath = openFileDialog();
// 读取输入图像路径
std::wstring imagePath = openFileDialog();
if (imagePath.empty()) {
cout << "No file selected or user cancelled." << endl;
wcout << L"No file selected or user cancelled." << endl;
return Mat();
}
// 使用 OpenCV 读取选中的图片
Mat image = imread(imagePath);
// 使用 Windows API 打开文件
HANDLE hFile = CreateFileW(imagePath.c_str(), GENERIC_READ, FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL);
if (hFile == INVALID_HANDLE_VALUE) {
wcout << L"Error: Could not open file." << endl;
return Mat();
}
// 获取文件大小
LARGE_INTEGER fileSize;
if (!GetFileSizeEx(hFile, &fileSize)) {
wcout << L"Error: Could not get file size." << endl;
CloseHandle(hFile);
return Mat();
}
if (fileSize.QuadPart > MAXDWORD) {
wcout << L"Error: File size too large." << endl;
CloseHandle(hFile);
return Mat();
}
DWORD dwFileSize = static_cast<DWORD>(fileSize.QuadPart);
// 读取文件内容到缓冲区
std::vector<BYTE> buffer(dwFileSize);
DWORD bytesRead = 0;
if (!ReadFile(hFile, buffer.data(), dwFileSize, &bytesRead, NULL) || bytesRead != dwFileSize) {
wcout << L"Error: Could not read file." << endl;
CloseHandle(hFile);
return Mat();
}
CloseHandle(hFile);
// 使用 OpenCV 从内存缓冲区读取图像
Mat image = imdecode(buffer, IMREAD_COLOR);
if (image.empty()) {
cout << "Error: Could not load image." << endl;
wcout << L"Error: Could not decode image." << endl;
return Mat();
}
@ -139,7 +174,7 @@ int main() {
vibrantGreenDetection(inputImage, outputImage, params);
// 定义缩放因子，1.0 表示原始大小，>1.0 表示放大，<1.0 表示缩小
double scaleFactor = 1.5; // 将图像放大1.5倍
double scaleFactor = 0.6; // 将图像放大1.5倍
// 显示原图和检测到的绿色区域，使用缩放因子
showImage("Original Image", inputImage, scaleFactor);

2
cotton_color2.cpp
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@ -142,7 +142,7 @@ int main() {
// 使用 map 模拟参数传递
map<string, int> params;
params["saturationThreshold"] = 100; // 设置饱和度阈值为 100
params["saturationThreshold"] = 134; // 设置饱和度阈值为 100
// 调用鲜艳颜色检测函数
vibrantColorDetection(inputImage, outputImage, params);