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ccerhanccerhan
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‎CMakeLists.txt‎

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cmake_minimum_required(VERSION 3.7)
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project(PALM)
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set(CMAKE_CXX_STANDARD 11)
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# Import OpenCV
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find_package(OpenCV)
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if (OpenCV_FOUND)
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include_directories(${OpenCV_INCLUDE_DIRS})
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endif ()
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include_directories(PALM)
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set(SOURCES
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PALM/HistogramBuilder.cpp
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PALM/HistogramBuilder.h
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PALM/IlluminationFilter.cpp
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PALM/IlluminationFilter.h
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PALM/PALM.cpp
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PALM/PALM.h
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PALM/PatternImageExtractor.cpp
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PALM/PatternImageExtractor.h
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PALM/ZernikeBaseGenerator.cpp
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PALM/ZernikeBaseGenerator.h
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)
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add_executable(PALM main.cpp ${SOURCES})
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target_link_libraries(PALM ${OpenCV_LIBS})

‎PALM/HistogramBuilder.cpp‎

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#include "HistogramBuilder.h"
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using namespace palm;
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HistogramBuilder::HistogramBuilder(cv::Size gridSize, int binCount, bool applyInsidePartitioning)
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{
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setGridSize(gridSize);
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setBinCount(binCount);
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setApplyInsidePartitioning(applyInsidePartitioning);
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}
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void HistogramBuilder::setGridSize(cv::Size gridSize)
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{
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CV_Assert(gridSize.height > 0 && gridSize.width > 0);
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_gridSize = gridSize;
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}
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void HistogramBuilder::setApplyInsidePartitioning(bool applyInsidePartitioning)
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{
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_applyInsidePartitioning = applyInsidePartitioning;
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}
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void HistogramBuilder::setBinCount(int binCount)
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{
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CV_Assert(binCount > 1);
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_binCount = binCount;
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}
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cv::Mat HistogramBuilder::build(const cv::Mat &image)
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{
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return compute(image, getGridSize(), getBinCount(), isInsidePartitioningApplied());
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}
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cv::Mat HistogramBuilder::getGaussianKernel(cv::Size size, double sigma) const
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{
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CV_Assert(sigma > 0);
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int kernelSize = (size.height % 2 != 0) ? size.height : size.height + 1;
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cv::Mat kernel = cv::getGaussianKernel(kernelSize, sigma);
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kernel = kernel * kernel.t();
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if (kernel.size() != size)
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{
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cv::resize(kernel, kernel, size);
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}
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return kernel;
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}
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cv::Mat HistogramBuilder::getRegionHistogram(const cv::Mat &region, int binCount, const cv::Mat &gaussianKernel)
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{
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cv::Mat histogram = cv::Mat::zeros(1, binCount, CV_64F);
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for (int i = 0; i < region.rows; i++)
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{
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for (int j = 0; j < region.cols; j++)
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{
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int bin = region.at<uchar>(i, j);
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histogram.at<double>(0, bin) += gaussianKernel.at<double>(i, j);
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}
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}
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histogram = histogram / (cv::norm(histogram, cv::NORM_L2) + std::numeric_limits<double>::epsilon());
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return histogram;
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}
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int HistogramBuilder::histogramLength()
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{
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cv::Size gridSize = getGridSize();
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int binCount = getBinCount();
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int length = gridSize.area() * binCount;
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if (isInsidePartitioningApplied())
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{
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length += (gridSize.height - 1) * (gridSize.width - 1) * binCount;
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}
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return length;
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}
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cv::Mat HistogramBuilder::compute(const cv::Mat &image, cv::Size gridSize, int binCount, bool applySlidedGrid)
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{
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CV_Assert(!image.empty() && image.rows > 0 && image.cols > 0);
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CV_Assert(gridSize.width > 0 && gridSize.height > 0);
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cv::Size regionSize = cv::Size(image.cols / gridSize.width, image.rows / gridSize.height);
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cv::Mat gaussianKernel = getGaussianKernel(regionSize, 8);
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int descriptorSize = histogramLength();
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cv::Mat histogram(1, descriptorSize, CV_64F);
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// Compute the histograms for the complete grid
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for (int i = 0; i < gridSize.width; i++)
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{
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for (int j = 0; j < gridSize.height; j++)
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{
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cv::Rect roi = cv::Rect(i * regionSize.width, j * regionSize.height, regionSize.width, regionSize.height);
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cv::Mat region = image(roi);
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cv::Mat regionHistogram = getRegionHistogram(region, binCount, gaussianKernel);
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cv::Rect targetLocation = cv::Rect(binCount * (j * gridSize.width + i), 0, binCount, 1);
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cv::Mat targetRegion = histogram(targetLocation);
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regionHistogram.copyTo(targetRegion);
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}
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}
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if (applySlidedGrid)
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{
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// Compute the histograms for the slided grid
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int numRegionsX = gridSize.width - 1;
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int numRegionsY = gridSize.height - 1;
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int numRegions = gridSize.width * gridSize.height;
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for (uint i = 0; i < numRegionsX; i++)
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{
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for (uint j = 0; j < numRegionsY; j++)
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{
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cv::Range rowRange = cv::Range(j * regionSize.height + regionSize.height / 2,
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(j + 1) * regionSize.height + regionSize.height / 2);
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cv::Range colRange = cv::Range(i * regionSize.width + regionSize.width / 2,
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(i + 1) * regionSize.width + regionSize.width / 2);
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cv::Mat region = image(rowRange, colRange).clone();
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cv::Mat regionHistogram = getRegionHistogram(region, binCount, gaussianKernel);
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cv::Rect targetLocation = cv::Rect(binCount * (numRegions + j * numRegionsX + i), 0, binCount, 1);
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cv::Mat targetRegion = histogram(targetLocation);
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regionHistogram.copyTo(targetRegion);
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}
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}
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}
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return histogram;
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}

‎PALM/HistogramBuilder.h‎

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#ifndef PALM_HISTOGRAMBUILDER_H
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#define PALM_HISTOGRAMBUILDER_H
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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namespace palm
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{
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class HistogramBuilder
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{
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public:
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HistogramBuilder(cv::Size gridSize, int binCount, bool applyInsidePartitioning);
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virtual ~HistogramBuilder() { };
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cv::Size getGridSize() const { return _gridSize; }
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void setGridSize(cv::Size gridSize);
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bool isInsidePartitioningApplied() const { return _applyInsidePartitioning; }
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void setApplyInsidePartitioning(bool applyInsidePartitioning);
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int getBinCount() const { return _binCount; }
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void setBinCount(int binCount);
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virtual int histogramLength();
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virtual cv::Mat build(const cv::Mat &image);
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protected:
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cv::Mat getGaussianKernel(cv::Size size, double sigma) const;
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cv::Mat getRegionHistogram(const cv::Mat &region, int binCount, const cv::Mat &gaussianKernel);
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cv::Mat compute(const cv::Mat &image, cv::Size gridSize, int binCount, bool applySlidedGrid);
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private:
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cv::Size _gridSize;
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bool _applyInsidePartitioning;
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int _binCount;
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};
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}
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#endif //PALM_HISTOGRAMBUILDER_H

‎PALM/IlluminationFilter.cpp‎

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#include "IlluminationFilter.h"
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palm::IlluminationFilter::IlluminationFilter(double alpha, int returnType)
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{
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setAlpha(alpha);
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setReturnType(returnType);
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}
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void palm::IlluminationFilter::setAlpha(double alpha)
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{
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CV_Assert(alpha > 0 && alpha < 1);
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_alpha = alpha;
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}
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void palm::IlluminationFilter::setReturnType(int returnType)
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{
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CV_Assert(returnType == CV_8U || returnType == CV_64F);
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_returnType = returnType;
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}
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cv::Mat palm::IlluminationFilter::apply(const cv::Mat &image)
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{
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CV_Assert(!image.empty() && image.type() == CV_8UC3);
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cv::Mat out = cv::Mat::zeros(image.rows, image.cols, _returnType);
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for (int i = 0; i < out.rows; i++)
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{
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for (int j = 0; j < out.cols; j++)
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{
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cv::Vec3b value = image.at<cv::Vec3b>(i, j);
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double b = value.val[0] / 255.0;
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double g = value.val[1] / 255.0;
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double r = value.val[2] / 255.0;
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double temp = 0.5 + std::log(g) - _alpha * std::log(b) - (1 - _alpha) * std::log(r);
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if (temp > 1) temp = 1;
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if (temp < 0) temp = 0;
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if (_returnType == CV_8U)
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{
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out.at<uchar>(i, j) = (uchar) (temp * 255.0);
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}
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else if (_returnType == CV_64F)
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{
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out.at<double>(i, j) = temp;
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}
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}
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}
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return out;
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}

‎PALM/IlluminationFilter.h‎

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#ifndef PALM_FILTER_H
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#define PALM_FILTER_H
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#include <opencv2/core.hpp>
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namespace palm
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{
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class IlluminationFilter
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{
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public:
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IlluminationFilter(double alpha = 0.3, int returnType = CV_8U);
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virtual ~IlluminationFilter() { }
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double getAlpha() { return _alpha; }
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void setAlpha(double alpha);
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int getReturnType() { return _returnType; }
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void setReturnType(int returnType);
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cv::Mat apply(const cv::Mat &image);
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protected:
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private:
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double _alpha;
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int _returnType;
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};
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}
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#endif //PALM_FILTER_H

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