要求

均值滤波
具体内容:利用 OpenCV 对灰度图像像素进行操作,分别利用算术均值滤波器. 几何均值滤波器. 谐波和逆谐波均值滤波器进行图像去噪。模板大小为5*5。(注:请分别为图像添加高斯噪声. 胡椒噪声. 盐噪声和椒盐噪声,并观察滤波效果)
中值滤波
具体内容:利用 OpenCV 对灰度图像像素进行操作,分别利用 5*5 和 9*9尺寸的模板对图像进行中值滤波。(注:请分别为图像添加胡椒噪声. 盐噪声和椒盐噪声,并观察滤波效果)
自适应均值滤波。
具体内容:利用 OpenCV 对灰度图像像素进行操作,设计自适应局部降低噪声滤波器去噪算法。模板大小 7*7 (对比该算法的效果和均值滤波器的效果)
自适应中值滤波
具体内容:利用 OpenCV 对灰度图像像素进行操作,设计自适应中值滤波算法对椒盐图像进行去噪。模板大小 7*7(对比中值滤波器的效果)
彩色图像均值滤波
具体内容:利用 OpenCV 对彩色图像 RGB 三个通道的像素进行操作,利用算术均值滤波器和几何均值滤波器进行彩色图像去噪。模板大小为 5*5。

过程

calcHist

使用的是


void cv::calcHist   (   const Mat *     images,
int     nimages,
const int *     channels,
InputArray  mask,
OutputArray     hist,
int     dims,
const int *     histSize,
const float **  ranges,
bool    uniform = true,
bool    accumulate = false
)

channels

当参数channels为1时，效果和np.histogram差不多。当channels为2时，效果和np.histogram2d差不多。

注意：当channels>1时，函数不是分别计算各个channel的一维直方图

ranges

range是个二维float数组。其中的各项（一维数组）指定了直方图各个维度的bin边界（取值范围）。当直方图中bin的边界是均匀分割得到的时候（unifor = true），对于每个维度i，只需要指定该维度的第一个bin（索引0）的下边界（包含在内）L0L_0L0和该维度的最后一个bin（索引为histSize[i]-1）的上边界UhistSize[i]−1U_{\texttt{histSize}[i]-1}UhistSize[i]−1（排除在外）。在这种情况下，ranges的各项（一维数组）只需要包含2个元素（float）就行了。在另外的情况下（uniform=false），ranges[i]包含histSize[i]+1个元素L0,U0=L1,U1=L2,...,UhistSize[i]−2=LhistSize[i]−1,UhistSize[i]−1L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}L0,U0=L1,U1=L2,...,UhistSize[i]−2=LhistSize[i]−1,UhistSize[i]−1。图片中没有被包含在L0L_0L0和UhistSize[i]−1U_{\texttt{histSize[i]}-1}UhistSize[i]−1之间的，不会被统计进直方图中。

STL和Mat

vector是深复制。

高效的使用STL 当对象很大时，建立指针的容器而不是对象的容器

带你深入理解STL之迭代器和Traits技法

cppMat中对应的是

template<typename _Tp>
class cv::DataType< _Tp >
Template "trait" class for OpenCV primitive data types.

static local var

使用静态局部变量实现带状态的函数

nth_element

STL中用来寻找在全排序中位列第n个元素，实际上并不需要全排序，局部排序即可。应该是借鉴快排的思路了。

ROI

locateROI的函数用来寻找ROI（如果ROI是从Mat中截取出来的）在原Mat中的位置。

adjustROI的函数用来调整ROI的边界。

结构设计

我不是太清楚这些设计的正确命名

调用处理框架

特定函数，首先实现其特定的小函数，然后将特定小函数作为回调函数传递给并调用公共的处理函数(类似于处理框架)。主体是特定函数

函数表驱动

将特定函数部分实现后放进函数表里。公共处理部分(框架)主动调用函数表里的函数。主体为公共处理部分

如果将公共部分抽象出来成函数，将特定的小函数和公共处理函数封装成函数，就成了实验一中的特定函数。估计在公共处理部分只是整个程序的一小部分时才会这么做吧，比如说库。opencv的特定滤波器好像就这么实现的

代码

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>#include <numeric>
#include <iostream>
#include <vector>
#include <map>
#include <functional>
#include <string>using namespace cv;
using namespace std;
using namespace std::placeholders;float gauss_mean = 0, gauss_stdev = 15, pulse_prcnt = 0.01;// line's point's x-aixs gap is depend on point number and image's width.
void plot_poly(Mat &imgIO, Mat &pts, const Scalar color, bool reset_scale=true)
{static float scale = 1.;Mat points;pts.convertTo(points, CV_32F);int width = imgIO.cols, height = imgIO.rows, pts_num = points.total();if (reset_scale) {double max_val;minMaxLoc(points, NULL, &max_val);scale = (double)height / max_val;}Mat tmpM(imgIO.size(), imgIO.type(), Scalar::all(0));for (int i = 1; i < pts_num; ++i)//Round operator should be done at last.line(tmpM,Point(cvRound((float)(i-1) / pts_num * width), height - cvRound(points.at<float>(i-1) * scale)),Point(cvRound((float)(i) / pts_num * width), height - cvRound(points.at<float>(i) * scale)),color, 2, LINE_AA, 0);imgIO += tmpM;
}void add_gaussian_noise(const Mat & imgI, Mat & imgO, float mean, float stdev)
{cout << "Add gussian noise.\tmean:" << mean << "\tstdev:" << stdev << endl;Mat noise(imgI.size(), imgI.type());randn(noise, Scalar::all(mean), Scalar::all(stdev));add(imgI, noise, imgO);
}void add_pulse_noise(const Mat &imgI, Mat &imgO, float percent, uchar value)
{CV_Assert(imgI.type() == CV_8U);cout << "Add pulse noise.\tpulse value:" << (int)value << endl;RNG rng(getTickCount());imgO = imgI.clone();int rows = imgI.rows, cols = imgI.cols;int counts = rows * cols * percent;for (int i = 0; i < counts; ++i)imgO.at<uchar>(rng.uniform(0, rows), rng.uniform(0, cols)) = value;
}void filter2Dfun(const Mat &imgI, Mat &imgO, function<uchar(const Mat&)> pf, int m=3, int n=0)
{if (!n) n = m;cout << "\n\tsize(mxn):" << m << "x" << n;CV_Assert(m % 2 == 1);CV_Assert(n % 2 == 1);imgO = imgI.clone();// imgI.convertTo(imgO, CV_64F);int cols = imgI.cols, rows = imgI.rows;// top == (n - 1) / 2 == n / 2;  left == (m - 1) / 2 == m / 2;int top = n / 2, bottom = rows - top, left = m / 2, right = cols - left;for (int y = top; y < bottom; ++y) {for (int x = left; x < right; ++x) {Mat roi(imgI, Rect(x - left, y - top, m, n));imgO.at<uchar>(Point(x, y)) = pf(roi);}}
}void apply_arithmetic_mean_filter(const Mat &imgI, Mat &imgO, int m=3, int n=0)
{if (!n) n = m;cout << "apply arithmetic mean filter.";auto fun = [=](const Mat &roi) -> uchar { return mean(roi)[0]; };filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_geometric_mean_filter(const Mat &imgI, Mat &imgO, int m=3, int n=0)
{if (!n) n = m;cout << "apply geometric mean filter.";auto fun = [=](const Mat &roi) -> uchar {Mat tmpM;roi.convertTo(tmpM, CV_64F);// cv::pow(tmpM, (double)1/(m*n), tmpM);// TODO: split 2-D into 1-D's may be betterauto product = std::accumulate(tmpM.begin<double>(), tmpM.end<double>(), (double)1., [](double lhs, double rhs) -> double {if (!lhs) lhs = 1;if (!rhs) rhs = 1;return lhs * rhs;});product = pow(product, 1./m/n);// auto product = std::accumulate(tmpM.begin<double>(), tmpM.end<double>(), (double)1., multiplies<double>());return saturate_cast<uchar>(product);};filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_harmonic_mean_filter(const Mat &imgI, Mat &imgO, int m=3, int n=0)
{if (!n) n = m;cout << "apply harmonic mean filter.";auto fun = [=](const Mat &roi) -> uchar {Mat tmpM;roi.convertTo(tmpM, CV_64F);divide(1, tmpM, tmpM);return saturate_cast<uchar>((double)m * n / sum(tmpM)[0]);};filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_inverse_harmonic_mean_filter(const Mat &imgI, Mat &imgO, int Q, int m=3, int n=0)
{if (!n) n = m;cout << "apply inverse harmonic mean filter.\tQ:" << Q;auto fun = [=](const Mat &roi) -> uchar {Mat tmpM, tmpM_Qp1, tmpM_Q;roi.convertTo(tmpM, CV_64F);pow(tmpM, Q, tmpM_Q);pow(tmpM, Q+1, tmpM_Qp1);return saturate_cast<uchar>(sum(tmpM_Qp1)[0] / sum(tmpM_Q)[0]);};filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_median_filter(const Mat &imgI, Mat &imgO, int m=3, int n=0)
{if (!n) n = m;cout << "apply median filter.";auto fun = [=](const Mat &roi) -> uchar {Mat tmpM = roi.clone();nth_element(tmpM.begin<uchar>(), tmpM.begin<uchar>() + tmpM.total()/2, tmpM.end<uchar>());return tmpM.at<uchar>(tmpM.total()/2);};filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_adaptive_mean_filtering(const Mat &imgI, Mat &imgO, int m=3, int n=0)
{if (!n) n = m;cout << "apply adaptive mean filter.";float var_g = gauss_stdev;auto fun = [=](const Mat &roi) -> uchar {Mat mean_l, stdev_l;meanStdDev(roi, mean_l, stdev_l);double var_l = stdev_l.at<double>(0) * stdev_l.at<double>(0);if (var_g > var_l) {return saturate_cast<uchar>(mean_l.at<double>(0));} else {uchar val = roi.at<uchar>(roi.total()/2);return saturate_cast<uchar>(val - var_g / var_l * (val - mean_l.at<double>(0)));}};filter2Dfun(imgI, imgO, fun, m, n);cout << endl;
}void apply_adaptive_median_filtering(const Mat &imgI, Mat &imgO, int S_max=9)
{int m = 3, r_max = (sqrt(S_max)-1)/2;cout << "apply adaptive median filter.";auto fun = [=](const Mat &roi0) -> uchar {uchar z_med, z_min, z_max, z_xy = roi0.at<uchar>(roi0.total()/2);Size sz;Point ofs;roi0.locateROI(sz, ofs);int w = sz.width, h = sz.height, cx = ofs.x + roi0.cols/2, cy = ofs.y + roi0.rows/2;Mat roi = roi0;// cout << endl << "cx:" << cx << "\tcy:" << cy << endl;for (int r = 1; r<=cx && r<=cy && r<w-cx && r<h-cy && r<=r_max; ++r) {auto z_minmax = minmax_element(roi.begin<uchar>(),roi.end<uchar>());z_min = *z_minmax.first, z_max = *z_minmax.second;Mat tmpM = roi.clone();nth_element(tmpM.begin<uchar>(), tmpM.begin<uchar>() + tmpM.total()/2, tmpM.end<uchar>());z_med =  tmpM.at<uchar>(tmpM.total()/2);if (z_min < z_med && z_med < z_max) {// cout << "return form program B." << endl;return (z_min < z_xy  && z_xy < z_max) ? z_xy : z_med;} else {roi.adjustROI(1, 1, 1, 1);// cout << "roi.size():" << roi.size() << endl;}}// cout << "size can't be bigger.return from program A." << endl;return z_med;};filter2Dfun(imgI, imgO, fun, m);cout << "~" << r_max *2 + 1 << "x" << r_max * 2 + 1 << endl;
}void update_display(vector<string> wndwnms, vector<Mat*> images, string hist_wn="hist_img")
{namedWindow(hist_wn);Mat hist_img(images[0]->rows, images[0]->cols, CV_8UC3, Scalar::all(0));// origin image pdf, noise image pdf, processed image pdfvector<Mat> pdfs(3);float range[] = {0, 256};const float * hist_range = {range};int hist_size = 256;// cout << images[1] << endl;for (int i = 0; i < 3; ++i) {calcHist(images[i], 1, 0, Mat(), pdfs[i], 1, &hist_size, &hist_range);// plot_poly(hist_img,pdfs[i],Scalar(255*(0==i),255*(1==i),255*(2==i)));plot_poly(hist_img, pdfs[i], Scalar(255*(0==i),255*(1==i),255*(2==i)), i==0);imshow(wndwnms[i], *images[i]);}imshow(hist_wn, hist_img);waitKey();destroyWindow(hist_wn);
}int main(int argc, char const *argv[])
{const char *image_path = (argc > 1) ? argv[1] : "img_test.jpg";Mat img, image, image_color, image_gray, image_noise, image_dst;img = imread(image_path, IMREAD_COLOR);if (img.empty()) {cout << "Cannot read image: " << image_path << std::endl;return -1;}image_color = img;cvtColor(image_color, image_gray, CV_BGR2GRAY);image = image_gray;vector<Mat*> imgs {&image, &image_noise, &image_dst};vector<string> wndnms {"image_src", "image_noise", "image_processed"};for (auto it : wndnms)namedWindow(it, CV_WINDOW_AUTOSIZE);vector<function<void(const Mat&, Mat&)>> add_noise_funs {bind(add_gaussian_noise, _1, _2, gauss_mean, gauss_stdev), bind(add_pulse_noise, _1, _2, pulse_prcnt, 0), bind(add_pulse_noise, _1, _2, pulse_prcnt, 255), // TODO: [add_pulse_noise][&](const Mat& imgI, Mat& imgO) -> void {add_pulse_noise(imgI, imgO, pulse_prcnt, 0); add_pulse_noise(imgO, imgO, pulse_prcnt, 255);}};vector<function<void(const Mat&, Mat&)>> apply_mean_filters {bind(apply_arithmetic_mean_filter, _1, _2, 5, 5),bind(apply_geometric_mean_filter, _1, _2, 5, 5),bind(apply_harmonic_mean_filter, _1, _2, 5, 5),bind(apply_inverse_harmonic_mean_filter, _1, _2, 1, 5, 5)};for (auto apply_mean_filter : apply_mean_filters) {for (auto add_noise : add_noise_funs) {add_noise(image, image_noise);apply_mean_filter(image_noise, image_dst);update_display(wndnms, imgs);cout << endl;}}for (size_t i = 1; i < add_noise_funs.size(); ++i) {add_noise_funs[i](image, image_noise);apply_median_filter(image_noise, image_dst, 5);update_display(wndnms, imgs);cout << endl;apply_median_filter(image_noise, image_dst, 9);update_display(wndnms, imgs);cout << endl;}add_gaussian_noise(image, image_noise, gauss_mean, gauss_stdev);apply_arithmetic_mean_filter(image_noise, image_dst);update_display(wndnms, imgs);apply_adaptive_mean_filtering(image_noise, image_dst, 7);update_display(wndnms, imgs);cout << endl;add_pulse_noise(image, image_noise, pulse_prcnt, 255);add_pulse_noise(image_noise, image_noise, pulse_prcnt, 0);apply_median_filter(image_noise, image_dst, 7);update_display(wndnms, imgs);apply_adaptive_median_filtering(image_noise, image_dst, 7*7);update_display(wndnms, imgs);cout << endl;image = image_color;vector<Mat> bgr_planes;split(image, bgr_planes);for (auto &plane : bgr_planes) {add_gaussian_noise(plane, plane, gauss_mean, gauss_stdev);add_pulse_noise(plane, plane, pulse_prcnt, 0);add_pulse_noise(plane, plane, pulse_prcnt, 255);}merge(bgr_planes, image_noise);split(image_noise, bgr_planes);for(auto &plane : bgr_planes) {apply_arithmetic_mean_filter(plane, plane, 5);}merge(bgr_planes, image_dst);for (size_t i = 0; i < imgs.size(); ++i)imshow(wndnms[i], *imgs[i]);waitKey();split(image_noise, bgr_planes);for(auto &plane : bgr_planes) {apply_geometric_mean_filter(plane, plane, 5);}merge(bgr_planes, image_dst);for (size_t i = 0; i < imgs.size(); ++i)imshow(wndnms[i], *imgs[i]);waitKey();return 0;
}

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图像处理实验4:图像去噪

要求

过程