mirror of
https://github.com/Karllzy/cotton_color.git
synced 2025-11-08 18:53:53 +00:00
修改了阈值调整函数
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621213a6c1
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@ -43,7 +43,8 @@ add_executable(cotton_color cotton_color.cpp)
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target_link_libraries(cotton_color Qt6::Widgets ${OpenCV_LIBS} comdlg32)
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# 添加可执行文件 cotton_color
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add_executable(cotton_range Matrox/color_range.cpp)
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add_executable(cotton_range Matrox/color_range.cpp
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Matrox/color_range.h Matrox/utils.h Matrox/utils.cpp)
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# 链接 OpenCV 和 Qt 库
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target_link_libraries(cotton_range Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
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@ -52,11 +53,12 @@ add_executable(cotton_color2 cotton_color2.cpp)
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# 链接 OpenCV 和 Qt 库
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target_link_libraries(cotton_color2 Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
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add_executable(color_matching Matrox/template_matching.cpp)
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add_executable(color_matching Matrox/template_matching.cpp
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Matrox/utils.cpp
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Matrox/utils.h)
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target_link_libraries(color_matching Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
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add_executable(ui Matrox/ui.cpp)
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target_link_libraries(ui Qt6::Widgets)
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@ -1,44 +1,28 @@
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//
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// Created by zjc on 24-11-12.
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//
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#include <mil.h>
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#include <iostream>
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#include <chrono>
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#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\cotton2.bmp")
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#include <map>
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#include <mil.h>
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#include <string>
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#include "utils.h"
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// 全局变量，方便在各个函数中使用
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#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\cotton_image\\174.bmp")
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#define SAVE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\diguandai.png")
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// Define LabRange structure
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struct LabRange {
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std::pair<MIL_DOUBLE, MIL_DOUBLE> rangeL;
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std::pair<MIL_DOUBLE, MIL_DOUBLE> rangeA;
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std::pair<MIL_DOUBLE, MIL_DOUBLE> rangeB;
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};
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// Global variables
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MIL_ID MilApplication = M_NULL, MilSystem = M_NULL, MilDisplay = M_NULL;
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// 时间测量模板函数
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template <typename Func>
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void measureExecutionTime(Func func) {
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// 获取当前时间作为起点
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auto start = std::chrono::high_resolution_clock::now();
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// 执行传入的函数
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func();
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// 获取当前时间作为结束点
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auto end = std::chrono::high_resolution_clock::now();
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// 计算时间差并转换为毫秒
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auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
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std::cout << "Function execution time: " << duration.count() << " milliseconds" << std::endl;
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}
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// LabProcess 函数，支持通过参数控制阈值范围，提供默认值
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void LabProcess(MIL_ID& inputImage, MIL_ID& outputImageLab,
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MIL_DOUBLE lowerL = 101.0, MIL_DOUBLE upperL = 135.0,
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MIL_DOUBLE lowerA = 101.0, MIL_DOUBLE upperA = 120.0,
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MIL_DOUBLE lowerB = 95.0, MIL_DOUBLE upperB = 134.0)
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// Optimized LabProcess function
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void lab_process(MIL_ID& inputImage, MIL_ID& outputImageLab, const std::map<std::string, int>& params)
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{
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MIL_ID MilLabImage = M_NULL, MilLChannel = M_NULL, MilAChannel = M_NULL, MilBChannel = M_NULL;
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MIL_ID MilBinaryL = M_NULL, MilBinaryA = M_NULL, MilBinaryB = M_NULL;
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// 检查输入图像的通道数
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// Check number of bands
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MIL_INT NumBands = 0;
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MbufInquire(inputImage, M_SIZE_BAND, &NumBands);
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if (NumBands != 3)
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@ -47,62 +31,84 @@ void LabProcess(MIL_ID& inputImage, MIL_ID& outputImageLab,
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return;
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}
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// 分配用于存储 Lab 图像的缓冲区
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MbufAllocColor(MbufInquire(inputImage, M_OWNER_SYSTEM, M_NULL), 3,
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MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL),
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8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP,
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&MilLabImage);
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// Inquire image properties once
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MIL_ID MilSystem = MbufInquire(inputImage, M_OWNER_SYSTEM, M_NULL);
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MIL_INT SizeX = MbufInquire(inputImage, M_SIZE_X, M_NULL);
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MIL_INT SizeY = MbufInquire(inputImage, M_SIZE_Y, M_NULL);
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// 将图像从 sRGB 转换到 Lab
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// Allocate buffer for Lab image
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MbufAllocColor(MilSystem, 3, SizeX, SizeY, 8 + M_UNSIGNED, M_IMAGE + M_PROC, &MilLabImage);
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// Convert image from sRGB to Lab
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MimConvert(inputImage, MilLabImage, M_SRGB_TO_LAB);
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// 创建 Lab 通道的子缓冲区
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// Create child buffers for L, a, b channels
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MbufChildColor(MilLabImage, 0, &MilLChannel);
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MbufChildColor(MilLabImage, 1, &MilAChannel);
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MbufChildColor(MilLabImage, 2, &MilBChannel);
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// 分配二值图像缓冲区
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MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP, &MilBinaryL);
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MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP, &MilBinaryA);
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MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP, &MilBinaryB);
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// Allocate output image as 1-bit image
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MbufAlloc2d(MilSystem, SizeX, SizeY, 1 + M_UNSIGNED, M_IMAGE + M_PROC, &outputImageLab);
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MbufClear(outputImageLab, 0); // Initialize to 0
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// 对每个通道进行阈值分割
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MimBinarize(MilLChannel, MilBinaryL, M_IN_RANGE, lowerL, upperL);
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MimBinarize(MilAChannel, MilBinaryA, M_IN_RANGE, lowerA, upperA);
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MimBinarize(MilBChannel, MilBinaryB, M_IN_RANGE, lowerB, upperB);
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// Pre-allocate binary buffers as 1-bit images
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MIL_ID MilBinaryL = M_NULL, MilBinaryA = M_NULL, MilBinaryB = M_NULL, MilResultLab = M_NULL;
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MbufAlloc2d(MilSystem, SizeX, SizeY, 1 + M_UNSIGNED, M_IMAGE + M_PROC, &MilBinaryL);
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MbufAlloc2d(MilSystem, SizeX, SizeY, 1 + M_UNSIGNED, M_IMAGE + M_PROC, &MilBinaryA);
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MbufAlloc2d(MilSystem, SizeX, SizeY, 1 + M_UNSIGNED, M_IMAGE + M_PROC, &MilBinaryB);
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MbufAlloc2d(MilSystem, SizeX, SizeY, 1 + M_UNSIGNED, M_IMAGE + M_PROC, &MilResultLab);
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// 分配输出图像缓冲区
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MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP, &outputImageLab);
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const std::vector<std::string> colors = {"green", "blue", "orange", "black", "red", "purple"};
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// 将结果合并
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MimArith(MilBinaryL, MilBinaryA, outputImageLab, M_AND);
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MimArith(outputImageLab, MilBinaryB, outputImageLab, M_AND);
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// Iterate over colors
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// 遍历颜色
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for (const auto& color : colors) {
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// 构建参数键
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std::string L_min_key = color + "_L_min";
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std::string L_max_key = color + "_L_max";
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std::string a_min_key = color + "_a_min";
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std::string a_max_key = color + "_a_max";
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std::string b_min_key = color + "_b_min";
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std::string b_max_key = color + "_b_max";
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// 释放资源
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// 获取参数值
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int L_min = params.at(L_min_key);
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int L_max = params.at(L_max_key);
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int a_min = params.at(a_min_key);
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int a_max = params.at(a_max_key);
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int b_min = params.at(b_min_key);
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int b_max = params.at(b_max_key);
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// 对每个通道进行二值化
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MimBinarize(MilLChannel, MilBinaryL, M_IN_RANGE, L_min, L_max);
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MimBinarize(MilAChannel, MilBinaryA, M_IN_RANGE, a_min, a_max);
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MimBinarize(MilBChannel, MilBinaryB, M_IN_RANGE, b_min, b_max);
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// 合并阈值结果
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MimArith(MilBinaryL, MilBinaryA, MilResultLab, M_AND);
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MimArith(MilResultLab, MilBinaryB, MilResultLab, M_AND);
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// 与输出图像合并
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MimArith(outputImageLab, MilResultLab, outputImageLab, M_OR);
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}
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// Free binary buffers
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MbufFree(MilBinaryL);
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MbufFree(MilBinaryA);
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MbufFree(MilBinaryB);
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MbufFree(MilResultLab);
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// Free resources
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MbufFree(MilLChannel);
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MbufFree(MilAChannel);
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MbufFree(MilBChannel);
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MbufFree(MilLabImage);
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}
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// HSVProcess 函数，支持通过参数控制饱和度阈值，提供默认值
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void HSVProcess(MIL_ID& inputImage, MIL_ID& outputImageHSV, MIL_DOUBLE saturationThreshold = 120.0)
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void hsv_process(MIL_ID& inputImage, MIL_ID& outputImageHSV, const std::map<std::string, int>& params)
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{
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MIL_ID MilHSVImage = M_NULL, MilHChannel = M_NULL, MilSChannel = M_NULL, MilVChannel = M_NULL;
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int saturationThreshold = params.at("saturation_threshold");
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// 检查输入图像的通道数
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MIL_INT NumBands = 0;
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MbufInquire(inputImage, M_SIZE_BAND, &NumBands);
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@ -134,7 +140,8 @@ void HSVProcess(MIL_ID& inputImage, MIL_ID& outputImageHSV, MIL_DOUBLE saturatio
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M_IMAGE + M_PROC + M_DISP, &outputImageHSV);
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// 对 S 通道进行阈值分割
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MimBinarize(MilSChannel, outputImageHSV, M_GREATER, saturationThreshold, M_NULL);
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MimBinarize(MilSChannel, outputImageHSV, M_GREATER,
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saturationThreshold, M_NULL);
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// 释放资源
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MbufFree(MilHChannel);
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@ -143,62 +150,95 @@ void HSVProcess(MIL_ID& inputImage, MIL_ID& outputImageHSV, MIL_DOUBLE saturatio
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MbufFree(MilHSVImage);
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}
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// 综合测试函数，调用 LabProcess 和 HSVProcess 并合并结果
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void test_hsv(MIL_ID& inputImage,
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MIL_DOUBLE lowerL = 101.0, MIL_DOUBLE upperL = 135.0,
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MIL_DOUBLE lowerA = 101.0, MIL_DOUBLE upperA = 120.0,
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MIL_DOUBLE lowerB = 95.0, MIL_DOUBLE upperB = 134.0,
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MIL_DOUBLE saturationThreshold = 120.0)
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{
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MIL_ID MilResultLab = M_NULL, MilResultHSV = M_NULL, MilCombinedResult = M_NULL;
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void high_sat_detect(MIL_ID& inputImage, MIL_ID& outputImage, const std::map<std::string, int>& params) {
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MIL_ID output_hsv=M_NULL, output_lab=M_NULL;
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// 调用 LabProcess
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LabProcess(inputImage, MilResultLab, lowerL, upperL, lowerA, upperA, lowerB, upperB);
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hsv_process(inputImage, output_hsv, params);
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lab_process(inputImage, output_lab, params);
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// 调用 HSVProcess
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HSVProcess(inputImage, MilResultHSV, saturationThreshold);
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// 分配合并结果的缓冲区
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MbufAlloc2d(MilSystem, MbufInquire(inputImage, M_SIZE_X, M_NULL),
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 8 + M_UNSIGNED,
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M_IMAGE + M_PROC + M_DISP, &MilCombinedResult);
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MbufInquire(inputImage, M_SIZE_Y, M_NULL), 1 + M_UNSIGNED,
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M_IMAGE + M_PROC, &outputImage);
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// 合并 Lab 和 HSV 的结果（取“或”运算）
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MimArith(MilResultLab, MilResultHSV, MilCombinedResult, M_OR);
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MimArith(output_hsv, output_lab, outputImage, M_OR);
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//// 显示合并后的结果图像
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MdispSelect(MilDisplay, MilCombinedResult);
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//// 等待用户查看处理后的图像
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printf("图像已处理并合并，按下 <Enter> 退出程序。\n");
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getchar();
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// 释放资源
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MbufFree(MilResultLab);
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MbufFree(MilResultHSV);
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MbufFree(MilCombinedResult);
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MbufFree(output_lab);
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MbufFree(output_hsv);
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}
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int main()
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{
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MIL_ID MilImage = M_NULL;
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// 初始化 MIL 应用程序
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// Initialize MIL application
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MappAllocDefault(M_DEFAULT, &MilApplication, &MilSystem, &MilDisplay, M_NULL, M_NULL);
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// 加载输入图像
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// Load input image
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MIL_ID MilImage = M_NULL;
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MbufRestore(IMAGE_PATH, MilSystem, &MilImage);
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// 使用 lambda 表达式测量 test_hsv() 的执行时间
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measureExecutionTime([&]() {
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test_hsv(MilImage);
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});
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// Define color ranges
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std::map<std::string, int> params;
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params["green_L_min"] = 68;
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params["green_L_max"] = 125;
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params["green_a_min"] = 101;
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params["green_a_max"] = 120;
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params["green_b_min"] = 130;
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params["green_b_max"] = 140;
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params["blue_L_min"] = 45;
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params["blue_L_max"] = 66;
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params["blue_a_min"] = 130;
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params["blue_a_max"] = 145;
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params["blue_b_min"] = 95;
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params["blue_b_max"] = 105;
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// 释放资源
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params["orange_L_min"] = 166;
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params["orange_L_max"] = 191;
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params["orange_a_min"] = 135;
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params["orange_a_max"] = 142;
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params["orange_b_min"] = 160;
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params["orange_b_max"] = 174;
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params["black_L_min"] = 0;
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params["black_L_max"] = 21;
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params["black_a_min"] = 127;
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params["black_a_max"] = 133;
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params["black_b_min"] = 126;
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params["black_b_max"] = 134;
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params["red_L_min"] = 71;
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params["red_L_max"] = 97;
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params["red_a_min"] = 143;
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params["red_a_max"] = 153;
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params["red_b_min"] = 33;
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params["red_b_max"] = 154;
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params["purple_L_min"] = 171;
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params["purple_L_max"] = 197;
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params["purple_a_min"] = 131;
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params["purple_a_max"] = 141;
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params["purple_b_min"] = 108;
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params["purple_b_max"] = 123;
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params["saturation_threshold"] = 150;
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// Initialize combined result
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MIL_ID detection_result = M_NULL;
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// Measure execution time
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measure_execution_time([&]() {
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high_sat_detect(MilImage, detection_result, params);
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});
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MbufSave(SAVE_PATH, detection_result);
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// Display result
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std::cout << "所有颜色检测已完成并合并。按 <Enter> 退出。" << std::endl;
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getchar();
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// Free resources
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MbufFree(detection_result);
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MbufFree(MilImage);
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MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
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return 0;
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}
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}
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@ -5,4 +5,62 @@
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#ifndef COLOR_RANGE_H
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#define COLOR_RANGE_H
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#include <mil.h>
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#include <map>
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#include <string>
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void lab_process(MIL_ID& inputImage, MIL_ID& outputImageLab, const std::map<std::string, int>& params);
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// 用法
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// std::map<std::string, int> params;
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// params["saturation_threshold"] = 150;
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void hsv_process(MIL_ID& inputImage, MIL_ID& outputImageHSV, const std::map<std::string, int>& params);
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// 用法
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// std::map<std::string, int> params;
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// params["green_L_min"] = 68;
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// params["green_L_max"] = 125;
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// params["green_a_min"] = 101;
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// params["green_a_max"] = 120;
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// params["green_b_min"] = 130;
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// params["green_b_max"] = 140;
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//
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// params["blue_L_min"] = 45;
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// params["blue_L_max"] = 66;
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// params["blue_a_min"] = 130;
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// params["blue_a_max"] = 145;
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// params["blue_b_min"] = 95;
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// params["blue_b_max"] = 105;
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//
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// params["orange_L_min"] = 166;
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// params["orange_L_max"] = 191;
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// params["orange_a_min"] = 135;
|
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// params["orange_a_max"] = 142;
|
||||
// params["orange_b_min"] = 160;
|
||||
// params["orange_b_max"] = 174;
|
||||
//
|
||||
// params["black_L_min"] = 0;
|
||||
// params["black_L_max"] = 21;
|
||||
// params["black_a_min"] = 127;
|
||||
// params["black_a_max"] = 133;
|
||||
// params["black_b_min"] = 126;
|
||||
// params["black_b_max"] = 134;
|
||||
//
|
||||
// params["red_L_min"] = 71;
|
||||
// params["red_L_max"] = 97;
|
||||
// params["red_a_min"] = 143;
|
||||
// params["red_a_max"] = 153;
|
||||
// params["red_b_min"] = 33;
|
||||
// params["red_b_max"] = 154;
|
||||
//
|
||||
// params["purple_L_min"] = 171;
|
||||
// params["purple_L_max"] = 197;
|
||||
// params["purple_a_min"] = 131;
|
||||
// params["purple_a_max"] = 141;
|
||||
// params["purple_b_min"] = 108;
|
||||
// params["purple_b_max"] = 123;
|
||||
//
|
||||
|
||||
|
||||
|
||||
|
||||
#endif //COLOR_RANGE_H
|
||||
|
||||
9
Matrox/utils.cpp
Normal file
9
Matrox/utils.cpp
Normal file
@ -0,0 +1,9 @@
|
||||
//
|
||||
// Created by zjc on 24-11-18.
|
||||
//
|
||||
|
||||
#include "utils.h"
|
||||
|
||||
|
||||
|
||||
// 图片转换函数，输入4096*1024*3的图片，输出为(4096 / n_valves) * (1024 / n_merge_vertical) * 1
|
||||
21
Matrox/utils.h
Normal file
21
Matrox/utils.h
Normal file
@ -0,0 +1,21 @@
|
||||
//
|
||||
// Created by zjc on 24-11-18.
|
||||
//
|
||||
|
||||
#ifndef UTILS_H
|
||||
#define UTILS_H
|
||||
|
||||
#include <chrono>
|
||||
#include <iostream>
|
||||
template <typename Func>
|
||||
// Time measurement function
|
||||
void measure_execution_time(Func func) {
|
||||
std::chrono::time_point<std::chrono::steady_clock> start;
|
||||
start = std::chrono::steady_clock::now();
|
||||
func();
|
||||
auto end = std::chrono::steady_clock::now();
|
||||
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
|
||||
std::cout << "Function execution time: " << duration.count() << " milliseconds" << std::endl;
|
||||
}
|
||||
|
||||
#endif //UTILS_H
|
||||
Loading…
Reference in New Issue
Block a user