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BJUT数字图像处理作业

2017-01-10 14:45 916 查看

一、

1) 将宽为2n的正方形图像,用FFT算法从空域变换到频域,并用频域图像的模来进行显示。

  2) 使图像能量中心,对应到几何中心,并用频域图像的模来进行显示。

  3)将频域图象,通过FFT逆变换到空域,并显示。

#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;

int main()
{

//以灰度模式读取原始图像并显示
Mat srcImage = imread("lena.png", 0);
if (srcImage.empty())
{
cout << "打开图像失败!" << endl;
return -1;
}
imshow("原始图像", srcImage);

//将输入图像延扩到最佳的尺寸,边界用0补充
int m = getOptimalDFTSize(srcImage.rows);
int n = getOptimalDFTSize(srcImage.cols);
//将添加的像素初始化为0.
Mat padded;
copyMakeBorder(srcImage, padded, 0, m - srcImage.rows, 0, n - srcImage.cols, BORDER_CONSTANT, Scalar::all(0));

//为傅立叶变换的结果(实部和虚部)分配存储空间。
//将planes数组组合合并成一个多通道的数组complexI
Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);

//进行就地离散傅里叶变换
dft(complexI, complexI);

//将复数转换为幅值,即=> log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
split(complexI, planes); // 将多通道数组complexI分离成几个单通道数组,planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude
Mat magnitudeImage = planes[0];

//进行对数尺度(logarithmic scale)缩放
magnitudeImage += Scalar::all(1);
log(magnitudeImage, magnitudeImage);//求自然对数

//剪切和重分布幅度图象限
//若有奇数行或奇数列,进行频谱裁剪
magnitudeImage = magnitudeImage(Rect(0, 0, magnitudeImage.cols & -2, magnitudeImage.rows & -2));
//重新排列傅立叶图像中的象限,使得原点位于图像中心
int cx = magnitudeImage.cols / 2;
int cy = magnitudeImage.rows / 2;
Mat q0(magnitudeImage, Rect(0, 0, cx, cy));   // ROI区域的左上
Mat q1(magnitudeImage, Rect(cx, 0, cx, cy));  // ROI区域的右上
Mat q2(magnitudeImage, Rect(0, cy, cx, cy));  // ROI区域的左下
Mat q3(magnitudeImage, Rect(cx, cy, cx, cy)); // ROI区域的右下
//交换象限(左上与右下进行交换)
Mat tmp;
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
//交换象限(右上与左下进行交换)
q1.copyTo(tmp);
q2.copyTo(q1);
tmp.copyTo(q2);

//归一化,用0到1之间的浮点值将矩阵变换为可视的图像格式
normalize(magnitudeImage, magnitudeImage, 0, 1, CV_MINMAX);

//显示效果图
imshow("频域", magnitudeImage);

//频域-->空域
Mat inversed;
dft(complexI, inversed, DFT_INVERSE | DFT_REAL_OUTPUT);
normalize(inversed, inversed, 0, 1, CV_MINMAX);
imshow("空域", inversed);

waitKey();

return 0;
}


二、

对于下面这幅图像,请问可以通过那些图像增强的手段,达到改善视觉效果的目的?请显示处理结果,并附简要处理流程说明。



#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int ContrastValue; //对比度值
int BrightValue;  //亮度值
Mat src, dst;
//改变图像对比度和亮度值的回调函数
static void ContrastAndBright(int, void *)
{
//创建窗口
namedWindow("【原始图窗口】", WINDOW_AUTOSIZE);
for (int y = 0; y < src.rows; y++)
{
for (int x = 0; x < src.cols; x++)
{
for (int c = 0; c < 3; c++)
{
dst.at<Vec3b>(y, x)[c] = saturate_cast<uchar>((ContrastValue * 0.01)*(src.at<Vec3b>(y, x)[c]) + BrightValue);
}
}
}
//显示图像
imshow("【原始图窗口】", src);
imshow("【效果图窗口】", dst);
}

int main(int argc, char *argv[])
{
//打开图像
src = imread("two.png");
if (src.empty())
{
cout << "打开图像失败!" << endl;
return -1;
}
//中值滤波去噪
medianBlur(src, src, 3);
dst = Mat::zeros(src.size(), src.type());
//设定对比度和亮度的初值
ContrastValue = 80;
BrightValue = 80;
//创建窗口
namedWindow("【效果图窗口】", WINDOW_AUTOSIZE);
//创建轨迹条
createTrackbar("对比度:", "【效果图窗口】", &ContrastValue, 300, ContrastAndBright);
createTrackbar("亮   度:", "【效果图窗口】", &BrightValue, 200, ContrastAndBright);
//调用回调函数
ContrastAndBright(ContrastValue, 0);
ContrastAndBright(BrightValue, 0);
//等待用户按键,起到暂停的作用
waitKey();
return 0;
}


三、

对于下面这幅图像,编程实现染色体计数,并附简要处理流程说明。



#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <vector>
using namespace std;
using namespace cv;
int main(int argc, char **argv)
{
Mat gray, src, dst;
//打开图像
src = imread("image.png");
if (src.empty())
{
cout << "打开图像失败!" << endl;
return -1;
}
cout << "rows = " << src.rows << endl;
cout << "cols = " << src.cols << endl;
//转换为灰度图
cvtColor(src, gray, CV_BGR2GRAY);
//中值滤波
medianBlur(gray, gray, 7);
//图像二值化
threshold(gray, dst, 170, 255, THRESH_BINARY);
//腐蚀,默认内核3*3
erode(dst, dst, Mat());
//erode(dst, dst, Mat());

Mat canny_output;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
//画轮廓线
Canny(dst, canny_output, 100, 100 * 2, 3);
imwrite("data.png", dst);

//检测轮廓
findContours(dst, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
cout << "一共检测到染色体数目:" << contours.size() - 1 << endl;
/*
for (int i = 0; i < contours.size(); i++)
{
for (int j = 0; j < contours[i].size(); j++)
{
cout << contours[i][j] << " ";
}
cout << ";" << endl;
}
*/

//显示图片
imshow("src", src);
imshow("canny_output", canny_output);

//将图片保存到文件
imwrite("dst.png", canny_output);
//等待用户输入
waitKey();
return 0;
}

//高斯滤波
//GaussianBlur(gray, gray, Size(5, 5), 0, 0);

//双边滤波
//bilateralFilter(gray, gray, 5, 10.0, 2.0);

//中值滤波
//medianBlur(gray, gray, 3);


四、

对MNIST手写数字数据库(可在网上搜索下载),编程实现来提取其链码。

#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <vector>
#include <string>
#include <fstream>

using namespace std;
using namespace cv;

//格式转换
int ReverseInt(int i)
{
unsigned char ch1, ch2, ch3, ch4;
ch1 = i & 255;
ch2 = (i >> 8) & 255;
ch3 = (i >> 16) & 255;
ch4 = (i >> 24) & 255;
return((int)ch1 << 24) + ((int)ch2 << 16) + ((int)ch3 << 8) + ch4;
}

/**
*  将Mnist数据库读取到OpenCV::Mat格式中
*  格式:
*  magic number
*  number of images
*  rows
*  cols
*  a very very long vector contains all digits
*/
void read_Mnist(string filename, vector<Mat> &vec)
{
ifstream file(filename, ios::binary);
if (file.is_open())
{
int magic_number = 0;
int number_of_images = 0;
int n_rows = 0;
int n_cols = 0;
file.read((char*)&magic_number, sizeof(magic_number));
magic_number = ReverseInt(magic_number);

file.read((char*)&number_of_images, sizeof(number_of_images));
number_of_images = ReverseInt(number_of_images);

file.read((char*)&n_rows, sizeof(n_rows));
n_rows = ReverseInt(n_rows);

file.read((char*)&n_cols, sizeof(n_cols));
n_cols = ReverseInt(n_cols);

for (int i = 0; i < number_of_images; ++i)
{
cv::Mat tp = Mat::zeros(n_rows, n_cols, CV_8UC1);
for (int r = 0; r < n_rows; ++r)
{
for (int c = 0; c < n_cols; ++c)
{
unsigned char temp = 0;
file.read((char*)&temp, sizeof(temp));
tp.at<uchar>(r, c) = (int)temp;
}
}
vec.push_back(tp);
}
}//if
}

int main(int argc, char **argv)
{
int count = 1;
//存储Mnist字库
vector<Mat> vec;
//将Mnist字库读取到vector中
read_Mnist("t10k-images.idx3-ubyte", vec);
cout << "共含有:" << vec.size() << "幅图片" << endl;

for (auto iter = vec.begin(); iter != vec.end(); iter++)
{
cout << "第" << count++ << "幅图片..." << endl;
//显示Mnist字库
imshow("Mnist", *iter);
vector<vector<Point> > contours;

//读取轮廓
findContours(*iter, contours, CV_RETR_EXTERNAL, CV_CHAIN_CODE);
//输出链码
for (int i = 0; i < contours.size(); i++)
{
for (int j = 0; j < contours[i].size(); j++)
{
cout << contours[i][j];
}
cout << endl;
}
contours.clear();
waitKey(1000);
}
waitKey();
return 0;
}


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http://download.csdn.net/detail/lgh1992314/9733718
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