lesson2 hometask

HarrisCornerDetector/HarrisCornerDetector.cpp

@@ -0,0 +1,148 @@
+///@File: HarrisCornerDetector.cpp
+///@Brief: Contains implementation of HarrisCornerDetector class
+///@Author: Stepan Sidorov
+///@Date: 05 October 2015
+
+#include "stdafx.h"
+#include "HarrisCornerDetector.h"
+
+
+HarrisCornerDetector::HarrisCornerDetector()
+{
+	static const Params defaultParams = {
+		5, 5,
+		1,
+		1,
+		"New window"
+	};
+
+	m_param = defaultParams;
+}
+
+void HarrisCornerDetector::Show(const cv::String& imgPath)
+{
+	// Load an image
+	m_param.srcImage = cv::imread(imgPath);
+	if (!m_param.srcImage.data)
+	{
+		return;
+	}
+
+	// Create window with original image
+	cv::namedWindow(imgPath, CV_WINDOW_AUTOSIZE);
+	cv::imshow(imgPath, m_param.srcImage);
+
+	// Create DEMO window
+	m_param.windowName = imgPath + " " + ReplyName();
+	cv::namedWindow(m_param.windowName, CV_WINDOW_AUTOSIZE);
+
+	cv::createTrackbar("WinSize",
+		m_param.windowName, &m_param.windowSize, 7, detectFeatures, static_cast<void*>(&m_param));
+	cv::createTrackbar("GSigma",
+		m_param.windowName, &m_param.gaussianSigma, 15, detectFeatures, static_cast<void*>(&m_param));
+	cv::createTrackbar("K*100",
+		m_param.windowName, &m_param.parameterK, 15, detectFeatures, static_cast<void*>(&m_param));
+	cv::createTrackbar("Thresh",
+		m_param.windowName, &m_param.threshold, 25, detectFeatures, static_cast<void*>(&m_param));
+
+	cv::waitKey(0);
+}
+
+void HarrisCornerDetector::detectFeatures(int pos, void* data)
+{
+	const Params& userData = *static_cast<Params*>(data);
+
+	if (userData.windowSize % 2 == 0)
+	{
+		// skip invalid values
+		return;
+	}
+
+	// Calculate weight matrix
+	cv::Mat weightMatrix;
+	if (userData.gaussianSigma == 0)
+	{
+		// Use weighted average window
+		weightMatrix = cv::Mat::ones(userData.windowSize, userData.windowSize, CV_32FC1) / userData.windowSize / userData.windowSize;
+	}
+	else
+	{
+		// Use gaussian window
+		cv::Mat gaussianKernel = cv::getGaussianKernel(userData.windowSize, userData.gaussianSigma, CV_32FC1);
+		cv::mulTransposed(gaussianKernel, weightMatrix, false);
+	}
+
+	// Convert to grayscale
+	cv::Mat gray;
+	cvtColor(userData.srcImage, gray, CV_RGB2GRAY);
+
+	// Remove noise by blurring with a Gaussian filter
+	cv::Mat bluredImg;
+	cv::GaussianBlur(gray, bluredImg, cv::Size(3, 3), 1);
+
+	// Filtering blured image with Sobel masks
+	cv::Mat IxxMat, IxyMat, IyyMat;
+	cv::Sobel(bluredImg, IxxMat, CV_32FC1, 2, 0);
+	cv::Sobel(bluredImg, IxyMat, CV_32FC1, 1, 1);
+	cv::Sobel(bluredImg, IyyMat, CV_32FC1, 0, 2);
+
+	cv::Mat result = cv::Mat::zeros(userData.srcImage.rows, userData.srcImage.cols, CV_32FC1);
+
+	// Compute result matrix
+	for (int i = (userData.windowSize - 1) / 2; i < userData.srcImage.rows - (userData.windowSize - 1) / 2; i++)
+	{
+		for (int j = (userData.windowSize - 1) / 2; j < userData.srcImage.cols - (userData.windowSize - 1) / 2; j++)
+		{
+			float Ixx = 0, Ixy = 0, Iyy = 0;
+			for (int p = 0; p < userData.windowSize; p++)
+			{
+				for (int q = 0; q < userData.windowSize; q++)
+				{
+					int x = j - (userData.windowSize - 1) / 2 + q;
+					int y = i - (userData.windowSize - 1) / 2 + p;
+					Ixx += weightMatrix.at<float>(p, q) * IxxMat.at<float>(y, x);
+					Ixy += weightMatrix.at<float>(p, q) * IxyMat.at<float>(y, x);
+					Iyy += weightMatrix.at<float>(p, q) * IyyMat.at<float>(y, x);
+				}
+			}
+
+			// Use original Harris corner measure
+			result.at<float>(i, j) = (Ixx * Iyy - Ixy*Ixy) * static_cast<float>(userData.parameterK) / 100 * ((Ixx + Iyy) * (Ixx + Iyy));
+		}
+	}
+
+	cv::Mat dst = userData.srcImage.clone();
+	// Threshold filtering and extracting local maxima
+	for (int i = 1; i < userData.srcImage.rows - 1; i++)
+	{
+		for (int j = 1; j < userData.srcImage.cols - 1; j++)
+		{
+			// Threshold filtering
+			if (result.at<float>(i, j) < static_cast<float>(userData.threshold))
+			{
+				continue;
+			}
+
+			// Extract local maxima
+			bool isFeature = true;
+			for (int p = i - 1; i < p + 1; i++)
+			{
+				for (int q = j - 1; j < q + 1; j++)
+				{
+					if (result.at<float>(p, q) > result.at<float>(i, j))
+					{
+						isFeature = false;
+						break;
+					}
+				}
+			}
+			if (isFeature)
+			{
+				cv::circle(dst, cv::Point(j, i), 2, cv::Scalar(255, 255, 0));
+			}
+		}
+	}
+
+	// Show result
+	cv::imshow(userData.windowName, dst);
+}

HarrisCornerDetector/HarrisCornerDetector.h

@@ -0,0 +1,48 @@
+///@File: HarrisCornerDetector.h
+///@Brief: Contains definition of HarrisCornerDetector class
+///@Author: Stepan Sidorov
+///@Date: 05 October 2015
+
+#pragma once
+
+#include "stdafx.h"
+
+///@class HarrisCornerDetector
+///@brief Demonstrates the Harris corner detector
+class HarrisCornerDetector
+{
+public:
+	///@brief ctor
+	HarrisCornerDetector();
+
+	///@brief Launch demonstration for passed image
+	///@param imgPath, in - the path to the image to be processed
+	void Show(const cv::String& imgPath);
+
+	///@brief Returns the string with full name of this detector
+	static cv::String ReplyName()
+	{
+		return "Harris Corner Detector";
+	}
+
+private:
+	///@brief applies algorithm according to the passed data
+	///@see cv::TrackbarCallback
+	static void detectFeatures(int pos, void* data);
+
+private:
+	///@brief Contains main configurable parametrs for demo
+	struct Params
+	{
+		int windowSize;				// size of window
+		int gaussianSigma;			// sigma of Gaussian
+		int parameterK;				// value of K*100 (because of too small values for integer type)
+		int threshold;				// threshold value
+
+		cv::String windowName;		// name of the window to be used for showing result image
+		cv::Mat srcImage;			// source image
+	};
+
+	///@brief current parameters
+	Params m_param;
+};

HarrisCornerDetector/HarrisCornerDetector.vcxproj

@@ -0,0 +1,168 @@
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