Create Exercises_7.cpp

Exercises at end of Chapter 7

Exercises_7.cpp

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+//Exercises at end of Chapter 7
+// 1-4
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace std;
+
+
+void help(const char **argv) {
+	cout << "\n\n"
+		<< "This program solves the Exercises at the end of Chapter 7\n"
+		<< endl;
+}
+
+
+int main( int argc, const char** argv )
+{
+	help(argv);
+
+	/************************************************************************/
+	/* 1.  Using the cv::RNG random number generator:
+	a.  Generate and print three floating-point numbers, each drawn from a uniform
+	distribution from 0.0 to 1.0.
+	b.  Generate  and  print  three  double-precision  numbers,  each  drawn  from  a
+	Gaussian distribution centered at 0.0 and with a standard deviation of 1.0.
+	c.  Generate and print three unsigned bytes, each drawn from a uniform distri‐
+	bution from 0 to 255.                                                                     
+	/************************************************************************/
+	RNG rng = theRNG();
+	// a
+	float f1 = rng.uniform(0.f,1.f);
+	float f2 = rng.uniform(0.f,1.f);
+	float f3 = rng.uniform(0.f,1.f);
+	cout<<" f1 " << f1 <<" f2 "<<f2<<" f3 "<<f3<<endl;
+	// b
+	Vec3d vec3d;
+    rng.fill(vec3d,RNG::NORMAL,0.,1.);
+	cout<<" d1 "<<vec3d[0]<<" d2 "<<vec3d[1]<<" d3 "<<vec3d[2]<<endl;
+	// c
+	unsigned byte1 = rng.uniform(0,255);
+	unsigned byte2 = rng.uniform(0,255);
+	unsigned byte3 = rng.uniform(0,255);
+	cout<<" byte1 " << byte1 <<" byte2 "<<byte2<<" byte3 "<<byte3<<endl;
+
+	/************************************************************************/
+	/* 2.  Using  the  fill()  method  of  the  cv::RNG  random  number  generator,  create  an
+	array of:
+	a.  20 floating-point numbers with a uniform distribution from 0.0 to 1.0.
+	b.  20  floating-point  numbers  with  a  Gaussian  distribution  centered  at  0.0  and
+	with a standard deviation of 1.0.
+	c.  20 unsigned bytes with a uniform distribution from 0 to 255.
+	d.  20 color triplets, each of three bytes with a uniform distribution from 0 to 255.                                                                     */
+	/************************************************************************/
+	// a
+	Mat matFloat20 = Mat(20,1,CV_32FC1,Scalar(0));
+	rng.fill(matFloat20,RNG::UNIFORM,0.f,1.f);
+	// b
+	rng.fill(matFloat20,RNG::NORMAL,0.f,1.f);
+	// c
+	Mat matUbyte20 = Mat(20,1,CV_8UC1,Scalar(0));
+	rng.fill(matUbyte20,RNG::UNIFORM,0,255);
+	// d
+	Mat matColor20 = Mat(20,1,CV_8UC3,Scalar(0));
+	rng.fill(matColor20,RNG::UNIFORM,0,255);
+
+	/************************************************************************/
+	/* 3.  Using the cv::RNG random number generator, create an array of 100 three-byte
+	objects such that:
+	a.  The first and second dimensions have a Gaussian distribution, centered at 64
+	and 192, respectively, each with a variance of 10.
+	b.  The third dimension has a Gaussian distribution, centered at 128 and with a
+	variance of 2.
+	c.  Using the cv::PCA object, compute a projection for which maxComponents=2.
+	d.  Compute the mean in both dimensions of the projection; explain the result.                                                                     */
+	/************************************************************************/
+	Mat matInt100 = Mat(100,1,CV_32FC3,Scalar(0));
+	// a
+	vector<Mat> planes;
+	split(matInt100,planes);
+	rng.fill(planes[0],RNG::NORMAL,64,10);
+	rng.fill(planes[1],RNG::NORMAL,192,10);
+	// b
+	rng.fill(planes[2],RNG::NORMAL,128,2);
+	// c 
+	PCA pca(planes[0],Mat(),CV_PCA_DATA_AS_ROW,2);
+	planes[0] = pca.project(planes[0]);
+	pca(planes[1],Mat(),CV_PCA_DATA_AS_ROW,2);
+	planes[1] = pca.project(planes[1]);
+	pca(planes[2],Mat(),CV_PCA_DATA_AS_ROW,2);
+	planes[2] = pca.project(planes[2]);
+	//d
+	f1 = 0;
+	f2 = 0;
+	f3 = 0;
+	for (int i = 0;i<100;i++)
+	{
+		f1 += planes[0].at<float>(i,0);
+		f2 += planes[1].at<float>(i,0);
+		f3 += planes[2].at<float>(i,0);
+	}
+	f1 = f1/100;
+	f2 = f2/100;
+	f3 = f3/100;
+	/************************************************************************/
+	/* 4.  page 206 at 《leanring Opencv 3.0》                                                            
+	/************************************************************************/
+	Matx32d AX(1, 1,
+		       0, 1,
+		      -1 ,1); 
+	Mat A = static_cast<Mat>(AX);
+	Mat U, W, V;
+	SVD::compute(A, W, U, V);
+
+	waitKey();
+	getchar();
+	return 0;
+
+}