开发环境：Ubuntu 18.04 LTS + ROS Melodic + ViSP 3.3.1
文章内容主要参考ViSP官方教学文档：https://visp-doc.inria.fr/doxygen/visp-daily/tutorial_mainpage.html
本文主要介绍了如何使用ViSP来调整图像的明度和对比度，主要涉及四种内置的调整算法：线性调整，伽马修正，正方图均衡和Retinex算法。本文主要参考了imgproc中的 tutorial-brightness-adjustment.cpp 例程。首先还是下载和编译例程
svn export https://github.com/lagadic/visp.git/trunk/tutorial/imgproccd imgproc/brightnessmkdir buildcd build cmake .. -DCMAKE_BUILD_TYPE=Release -DVISP_DIR=$VISP_WS/visp-buildmake
执行例程，查看效果
./tutorial-brightness-adjustment
左图是原图，右图是执行线性调整后的结果
伽马修正的结果
直方图均衡后的结果
Retinex调整后的结果
我们来看一下程序的实现过程
#include <cstdlib>#include <iostream>#include <visp3/core/vpImage.h>#include <visp3/gui/vpDisplayGDI.h>#include <visp3/gui/vpDisplayOpenCV.h>#include <visp3/gui/vpDisplayX.h>#include <visp3/io/vpImageIo.h>
#if defined(VISP_HAVE_MODULE_IMGPROC)//! [Include]#include <visp3/imgproc/vpImgproc.h>//! [Include]#endif
int main(int argc, const char **argv){//! [Macro defined]#if defined(VISP_HAVE_MODULE_IMGPROC) && \(defined(VISP_HAVE_X11) || defined(VISP_HAVE_GDI) || defined(VISP_HAVE_OPENCV)) && \(defined(VISP_HAVE_PNG) || defined(VISP_HAVE_OPENCV))//! [Macro defined]//!std::string input_filename = "Sample_low_brightness.png"; //设置默认输入图像的名称double alpha = 10.0, beta = 50.0; // 设置线性调整的默认参数double gamma = 3.5; //设置伽马修正的默认参数int scale = 240, scaleDiv = 3, level = 0, kernelSize = -1; //设置Retinex算法的默认参数double dynamic = 3.0; //设置Retinex算法的默认参数
for (int i = 1; i < argc; i++) {if (std::string(argv[i]) == "--input" && i + 1 < argc) {input_filename = std::string(argv[i + 1]); //通过--input指令来设置输入图像的名称} else if (std::string(argv[i]) == "--alpha" && i + 1 < argc) {alpha = atof(argv[i + 1]); //设置线性调整参数} else if (std::string(argv[i]) == "--beta" && i + 1 < argc) {beta = atof(argv[i + 1]); //设置线性调整参数} else if (std::string(argv[i]) == "--gamma" && i + 1 < argc) {gamma = atof(argv[i + 1]); //设置伽马修正参数} else if (std::string(argv[i]) == "--scale" && i + 1 < argc) {scale = atoi(argv[i + 1]); //设置Retinex算法参数} else if (std::string(argv[i]) == "--scaleDiv" && i + 1 < argc) {scaleDiv = atoi(argv[i + 1]); //设置Retinex算法参数} else if (std::string(argv[i]) == "--level" && i + 1 < argc) {level = atoi(argv[i + 1]); //设置Retinex算法参数} else if (std::string(argv[i]) == "--kernelSize" && i + 1 < argc) {kernelSize = atoi(argv[i + 1]); //设置Retinex算法参数} else if (std::string(argv[i]) == "--dynamic" && i + 1 < argc) {dynamic = atof(argv[i + 1]); //设置Retinex算法参数} else if (std::string(argv[i]) == "--help" || std::string(argv[i]) == "-h") {std::cout << "Usage: " << argv[0]<< " [--input <input image>]"" [--alpha <alpha for vp::adjust()>] [--beta <beta for ""vp::adjust()>]"" [--gamma <gamma for vp::gammaCorrection()>]"" [--scale <scale for vp::retinex()> [--scaleDiv for ""vp::retinex()]"" [--level <level for vp::retinex()> [--kernelSize ""<kernelSize for vp::retinex()>]"" [--dynamic <dynamic for vp::retinex()>] [--help]"<< std::endl;return EXIT_SUCCESS;}}
vpImage<vpRGBa> I_color; //新建彩色图像容器，用于储存输入图像vpImageIo::read(I_color, input_filename); //读取输入图像
vpImage<vpRGBa> I_color_res(I_color.getHeight(), 2 * I_color.getWidth());//新建一个彩色图像容器，宽度为两个输入图像的宽度，用于储存输入图像和处理后的图像I_color_res.insert(I_color, vpImagePoint()); //把输入图像插入到图像容器中#ifdef VISP_HAVE_X11vpDisplayX d(I_color_res);#elif defined(VISP_HAVE_GDI)vpDisplayGDI d(I_color_res);#elif defined(VISP_HAVE_OPENCV)vpDisplayOpenCV d(I_color_res);#endif
//! [Brightness contrast adjustment]vpImage<vpRGBa> I_color_adjust; //新建彩色图像容器，用于储存处理后的图像vp::adjust(I_color, I_color_adjust, alpha, beta); //使用线性调整算法处理输入图像//! [Brightness contrast adjustment]I_color_res.insert(I_color_adjust, vpImagePoint(0, I_color.getWidth())); //将调整后的图像插入到图像容器中std::stringstream ss;ss << "Sample_low_brightness_alpha=" << alpha << "_beta=" << beta << ".png";vpImageIo::write(I_color_res, ss.str()); //保存图像
vpDisplay::display(I_color_res); //显示图像vpDisplay::displayText(I_color_res, 20, 20, "Brightness and contrast adjustment. Click to continue.", vpColor::red);vpDisplay::flush(I_color_res);vpDisplay::getClick(I_color_res);
//! [Gamma correction]vpImage<vpRGBa> I_color_gamma_correction;vp::gammaCorrection(I_color, I_color_gamma_correction, gamma); //使用伽马修正算法处理图像//! [Gamma correction]I_color_res.insert(I_color_gamma_correction, vpImagePoint(0, I_color.getWidth()));ss.str("");ss << "Sample_low_brightness_gamma=" << gamma << ".png";vpImageIo::write(I_color_res, ss.str());
vpDisplay::display(I_color_res);vpDisplay::displayText(I_color_res, 20, 20, "Gamma correction. Click to continue.", vpColor::red);vpDisplay::flush(I_color_res);vpDisplay::getClick(I_color_res);
//! [Histogram equalization]vpImage<vpRGBa> I_color_equalize_histogram;vp::equalizeHistogram(I_color, I_color_equalize_histogram); //使用直方图均衡算法处理图像//! [Histogram equalization]I_color_res.insert(I_color_equalize_histogram, vpImagePoint(0, I_color.getWidth()));ss.str("");ss << "Sample_low_brightness_eqHist.png";vpImageIo::write(I_color_res, ss.str());
vpDisplay::display(I_color_res);vpDisplay::displayText(I_color_res, 20, 20, "Histogram equalization. Click to continue.", vpColor::red);vpDisplay::flush(I_color_res);vpDisplay::getClick(I_color_res);
//! [Retinex]vpImage<vpRGBa> I_color_retinex;vp::retinex(I_color, I_color_retinex, scale, scaleDiv, level, dynamic, kernelSize);//! [Retinex]使用Retinex算法处理图像I_color_res.insert(I_color_retinex, vpImagePoint(0, I_color.getWidth()));
ss.str("");ss << "Sample_low_brightness_scale=" << scale << "_scaleDiv=" << scaleDiv << "_level=" << level<< "_dynamic=" << dynamic << "_kernelSize=" << kernelSize << ".png";vpImageIo::write(I_color_res, ss.str());
vpDisplay::display(I_color_res);vpDisplay::displayText(I_color_res, 20, 20, "Retinex. Click to quit.", vpColor::red);vpDisplay::flush(I_color_res);vpDisplay::getClick(I_color_res);
return EXIT_SUCCESS;#else(void)argc;(void)argv;return 0;#endif}
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