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数字图像处理算法实现------------编程心得(1)

王朝other·作者佚名  2006-01-09
窄屏简体版  字體: |||超大  

数字图像处理算法实现

------------编程心得(1)

2001414班 朱伟 20014123

摘要: 关于空间域图像处理算法框架,直方图处理,空间域滤波器算法框架的编程心得,使用GDI+(C++)

一,图像文件的读取

初学数字图像处理时,图像文件的读取往往是一件麻烦的事情,我们要面对各种各样的图像文件格式,如果仅用C++的fstream库那就必须了解各种图像编码格式,这对于初学图像处理是不太现实的,需要一个能帮助轻松读取各类图像文件的库。在Win32平台上GDI+(C++)是不错的选择,不光使用上相对于Win32 GDI要容易得多,而且也容易移植到.Net平台上的GDI+。

Gdiplus::Bitmap类为我们提供了读取各类图像文件的接口,Bitmap::LockBits方法产生的BitmapData类也为我们提供了高速访问图像文件流的途径。这样我们就可以将精力集中于图像处理算法的实现,而不用关心各种图像编码。具体使用方式请参考MSDN中GDI+文档中关于Bitmap类和BitmapData类的说明。另外GDI+仅在Windows XP/2003上获得直接支持,对于Windows 2000必须安装相关DLL,或者安装有Office 2003,Visual Studio 2003 .Net等软件。

二,空间域图像处理算法框架

(1) 在空间域图像处理中,对于一个图像我们往往需要对其逐个像素的进行处理,对每个像素的处理使用相同的算法(或者是图像中的某个矩形部分)。即,对于图像f(x,y),其中0≤x≤M,0≤y≤N,图像为M*N大小,使用算法algo,则f(x,y) = algo(f(x,y))。事先实现一个算法框架,然后再以函数指针或函数对象(functor,即实现operator()的对象)传入算法,可以减轻编程的工作量。

如下代码便是一例:

#ifndef PROCESSALGO_H

#define PROCESSALGO_H

#include <windows.h>

#include <Gdiplus.h>

namespace nsimgtk

{

template <typename pixelType, Gdiplus::PixelFormat pixelFormat, class Processor>

bool ProcessPixelsOneByOne(Gdiplus::Bitmap* const p_bitmap, Processor processor, unsigned int x, unsigned int y,

unsigned int width, unsigned int height)

{

if (p_bitmap == NULL)

{

return false;

}

if ((width + x > p_bitmap->GetWidth()) || (height + y >p_bitmap->GetHeight()))

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(x, y, width,height);

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus::Ok)

{

return false;

}

pixelType *pixels = (pixelType*)bitmapData.Scan0;

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

processor(&pixels[col+row*bitmapData.Stride/sizeof(pixelType)]);

}

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

#endif

ProcessPixelsOneByOne函数可以对图像中从(x,y)位置起始,width*height大小的区域进行处理。模板参数pixelType用于指定像素大小,例如在Win32平台上传入unsigned char即为8位,用于8阶灰度图。模板参数Processor为图像处理算法实现,可以定义类实现void operator(pixelType *)函数,或者传入同样接口的函数指针。

如下便是一些算法示例(说明见具体注释):

#ifndef SPATIALDOMAIN_H

#define SPATIALDOMAIN_H

#include <cmath>

#include <string>

namespace nsimgtk

{

// 8阶灰度图的灰度反转算法

class NegativeGray8

{

public:

void operator()(unsigned char *const p_value)

{

*p_value ^= 0xff;

}

};

// 8阶灰度图的Gamma校正算法

class GammaCorrectGray8

{

private:

unsigned char d_s[256];

public:

GammaCorrectGray8::GammaCorrectGray8(double c, double gamma);

void operator()(unsigned char*const p_value)

{

*p_value = d_s[*p_value];

}

};

// 8阶灰度图的饱和度拉伸算法

class ContrastStretchingGray8

{

private:

unsigned char d_s[256];

public:

ContrastStretchingGray8::ContrastStretchingGray8(double a1, double b1, unsigned int x1,

double a2, double b2, unsigned int x2, double a3, double b3);

void operator()(unsigned char*const p_value)

{

*p_value = d_s[*p_value];

}

};

// 8阶灰度图的位平面分割,构造函数指定位平面号

class BitPlaneSliceGray8

{

private:

unsigned char d_s[256];

public:

BitPlaneSliceGray8(unsigned char bitPlaneNum);

void operator()(unsigned char* const p_value)

{

*p_value = d_s[*p_value];

}

};

}

#endif

// 上述类中各构造函数的实现代码,应该分在另一个文件中,此处为说明方便,一并列出

#include "SpatialDomain/spatialDomain.h"

namespace nsimgtk

{

GammaCorrectGray8::GammaCorrectGray8(double c, double gamma)

{

double temp;

for (unsigned int i=0; i<256; ++i)

{

temp = ceil(c * 255.0 * pow(double(i)/255.0, gamma));

d_s[i] = unsigned char(temp);

}

}

ContrastStretchingGray8::ContrastStretchingGray8(double a1, double b1, unsigned int x1,

double a2, double b2, unsigned int x2, double a3, double b3)

{

if (x1 > 255 || x2 > 255 || x1 > x1)

{

for (unsigned int i=0; i<256; ++i)

d_s[i] = i;

}

else

{

double tmp;

for (unsigned int i=0; i<x1; ++i)

{

tmp = ceil(a1*double(i)+b1);

d_s[i] = (unsigned char)tmp;

}

for (unsigned int i=x1; i<x2; ++i)

{

tmp = ceil(a2*double(i)+b2);

d_s[i] = (unsigned char)tmp;

}

for (unsigned int i=x2; i<256; ++i)

{

tmp = ceil(a3*double(i)+b3);

d_s[i] = (unsigned char)tmp;

}

}

}

BitPlaneSliceGray8::BitPlaneSliceGray8(unsigned char bitPlaneNum)

{

unsigned char bitMaskArray[8] =

{

0x01, 0x02, 0x04, 0x08,

0x10, 0x20, 0x40, 0x80

};

for (unsigned int i=0; i<256; ++i)

{

unsigned char tmp = i;

tmp &= bitMaskArray[bitPlaneNum];

tmp = (tmp >> bitPlaneNum) * 255;

d_s[i] = tmp;

}

}

}

(2) 直方图在GDI+1.0中没有获得支持,我们必须自行实现。直方图相关的处理在数字图像处理中占有重要地位,可以通过它获取图像灰度级的统计信息,且可以通过直方图进行一些重要的图像增强技术,如直方图均衡化,直方图规定化,基本全局门限等。

下面是获取8阶图像直方图的算法实现:

namespace nsimgtk

{

bool GetHistogramNormalizeGray8(Gdiplus::Bitmap * const p_bitmap, float *histogramArray)

{

if (p_bitmap == NULL || histogramArray == NULL)

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(0, 0, p_bitmap->GetWidth(), p_bitmap->GetHeight());

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeRead, PixelFormat8bppIndexed, &bitmapData) != Gdiplus::Ok)

{

return false;

}

unsigned char *pixels = (unsigned char*)bitmapData.Scan0;

unsigned int histogram[256];

for (int i=0; i<256; ++i)

{

histogram[i] = 0;

}

for (unsigned int row=0; row<p_bitmap->GetWidth(); ++row)

{

for (unsigned int col=0; col<p_bitmap->GetHeight(); ++col)

{

++histogram[pixels[col+row*bitmapData.Stride]];

}

}

const unsigned int totalPixels = p_bitmap->GetWidth() * p_bitmap->GetHeight();

for (int i=0; i<256; ++i)

{

histogramArray[i] = float(histogram[i]) / float(totalPixels);

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

在获取直方图后(即上面算法的第二个参数),再将其作为参数传入下面的对象的构造函数,然后以该对象为仿函数传入ProcessPixelsOneByOne即可实现8阶图像直方图均衡化:

#ifndef SPATIALDOMAIN_H

#define SPATIALDOMAIN_H

#include <cmath>

#include <string>

namespace nsimgtk

{

// 8阶灰度图的直方图均衡化

class HistogramEqualizationGray8

{

private:

unsigned char d_s[256];

public:

HistogramEqualizationGray8(const float *const histogramArray);

void operator()(unsigned char *const p_value)

{

*p_value = d_s[*p_value];

}

};

}

#endif

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

#include "SpatialDomain/spatialDomain.h"

namespace nsimgtk

{

HistogramEqualizationGray8::HistogramEqualizationGray8(const float *const histogramArray)

{

if (histogramArray != NULL)

{

float sum = 0.0;

for (int i=0; i<256; ++i)

{

sum += histogramArray[i];

d_s[i] = unsigned char(sum * 255);

}

}

}

}

(3)空间域滤波器,滤波器是一个m*n大小的掩模,其中m,n均为大于1的奇数。滤波器逐像素地通过图像的全部或部分矩形区域,然后逐像素地对掩模覆盖下的像素使用滤波器算法获得响应,将响应赋值于当前像素即掩模中心像素,另外滤波器算法使用中将会涉及到图像边缘的问题,这可以对边缘部分掩模使用补零法补齐掩模下无像素值的区域,或者掩模的移动范围以不越出图像边缘的方式移动,当然这些处理方法都会给图像边缘部分带来不良效果,但是一般情况下,图像边缘部分往往不是我们关注的部分或者没有重要的信息。

下面的滤波器算法框架SpatialFilterAlgo即以补零法(zero-padding)实现:

#ifndef SPATIALFILTER_H

#define SPATIALFILTER_H

#include <vector>

#include <numeric>

#include <algorithm>

#include <gdiplus.h>

#include <fstream>

#include <cmath>

namespace nsimgtk

{

template <typename pixelType, Gdiplus::PixelFormat pixelFormat, class FilterMask>

bool SpatialFilterAlgo(Gdiplus::Bitmap* const p_bitmap, FilterMask filterMask, unsigned int x, unsigned int y,

unsigned int width, unsigned int height)

{

if (p_bitmap == NULL)

{

return false;

}

if ((width + x > p_bitmap->GetWidth()) || (height + y >p_bitmap->GetHeight()))

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(x, y, width,height);

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus::Ok)

{

return false;

}

pixelType *pixels = (pixelType*)bitmapData.Scan0;

const unsigned int m = filterMask.d_m; // mask's width

const unsigned int n = filterMask.d_n; // mask's height

std::vector<pixelType> tmpImage((m-1+width)*(n-1+height)); // extend image to use zero-padding

// copy original bitmap to extended image with zero-padding method

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

tmpImage[(col+m/2)+(row+n/2)*(bitmapData.Stride/sizeof(pixelType)+m-1)] =

pixels[col+row*bitmapData.Stride/sizeof(pixelType)];

}

}

// process every pixel with filterMask

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

// fill the "m*n" mask with the current pixel's neighborhood

for (unsigned int i=0; i<n; ++i)

{

for (unsigned int j=0; j<m; ++j)

{

filterMask.d_mask[i*m+j] = tmpImage[(col+j)+(row+i)*(bitmapData.Stride/sizeof(pixelType)+m-1)];

}

}

// replace the current pixel with filter mask's response

pixels[col+row*bitmapData.Stride/sizeof(pixelType)] = filterMask.response();

}

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

#endif

其中模板参数FilterMask即为滤波掩模算法。通常的滤波算法有均值滤波器,可以模糊化图像,去除图形中的细节部分,使得我们可以关注图像中较为明显的部分,均值滤波器用于周期性噪声。中值滤波器用于图像中存在椒盐噪声也即脉冲噪声的情况下。另外有基于一阶微分的Sobel梯度算子和基于两阶微分的拉普拉斯算子,它们往往被用于边缘检测中。

下面是一些滤波器算法的具体实现,所以滤波器算法都应该实现pixelType response()函数以及有d_mask,d_m,d_n成员,这可以通过继承__filteMask类获得(不需要付出虚函数代价)。

#ifndef SPATIALFILTER_H

#define SPATIALFILTER_H

#include <vector>

#include <numeric>

#include <algorithm>

#include <gdiplus.h>

#include <fstream>

#include <cmath>

namespace nsimgtk

{

// 滤波器掩模的基类,提供掩模大小d_m,d_n,掩模覆盖下的m*n个像素值d_mask

// others filterMask should inherit it

template <typename pixelType>

struct __filterMask

{

const unsigned int d_m;

const unsigned int d_n;

// image's pixels under the m*n filter mask

std::vector<pixelType> d_mask;

// filter mask's width and heigh must be a odd, if not, it will plus one for the width or the height

__filterMask(unsigned int m, unsigned int n)

: d_m(m%2 ? m:m+1), d_n(n%2 ? n:n+1), d_mask(d_m*d_n)

{

}

};

// 掩模权值为全1的均值滤波器

template <typename pixelType>

class averagingFilterMaskSp

: public __filterMask<pixelType>

{

public:

averagingFilterMaskSp(unsigned int m, unsigned int n)

: __filterMask<pixelType>(m, n)

{ }

pixelType response()

{

return std::accumulate(d_mask.begin(), d_mask.end(), 0) / (d_m * d_n);

}

};

// 可自定义掩模权值的均值滤波器

template <typename pixelType>

class averagingFilterMask

: public __filterMask<pixelType>

{

private:

std::vector<pixelType> d_weight; // weights' vector(m*n)

int d_weight_sum; // all weights' sum

public:

averagingFilterMask(unsigned int m, unsigned int n, const std::vector<pixelType>& weightVec)

: __filterMask<pixelType>(m, n), d_weight(weightVec)

{

if (weightVec.size() != d_mask.size())

{

// if weight's size isn't equal to mask's size, it will change filter mask as a special filter mask

d_weight.resize(d_mask.size(), 1);

}

d_weight_sum = std::accumulate(d_weight.begin(), d_weight.end(), 0);

}

pixelType response()

{

return std::inner_product(d_mask.begin(), d_mask.end(), d_weight.begin(), 0) / d_weight_sum;

}

};

// 中值滤波器

template <typename pixelType>

class medianFilterMask

: public __filterMask<pixelType>

{

public:

medianFilterMask(unsigned int m, unsigned int n)

: __filterMask<pixelType>(m, n)

{ }

pixelType response()

{

std::sort(d_mask.begin(), d_mask.end());

return d_mask[d_mask.size()/2];

}

};

// 3*3拉普拉斯滤波器

// the mask is: [0 1 0 [0 -1 0

// 1 -5 1 or -1 5 -1

// 0 1 0] 0 -1 0]

// if pixel's brightness is less than min, set it to min

// if pixel's brightness is larger than max, set it to max

template <typename pixelType, pixelType min, pixelType max>

class laplacianFilter

: public __filterMask<pixelType>

{

public:

laplacianFilter()

: __filterMask<pixelType>(3, 3)

{ }

pixelType response()

{

int ret = (int)(5*(int)d_mask[4]) - ((int)d_mask[5]+d_mask[3]+d_mask[1]+d_mask[7]);

if (ret < min)

ret = min;

if (ret > max)

ret = max;

return ret;

}

};

// 3*3Sobel滤波器

// the mask is: [-1 -2 -1 [-1 0 1

// 0 0 0 and -2 0 2

// 1 2 1] -1 0 1]

// if pixel's brightness is larger than max, set it to max

template <typename pixelType, pixelType max>

class sobelFilter

: public __filterMask<pixelType>

{

public:

sobelFilter()

: __filterMask<pixelType>(3, 3)

{ }

pixelType response()

{

int ret = ::abs(d_mask[6]+2*d_mask[7]+d_mask[8]-d_mask[0]-2*d_mask[1]-d_mask[2])

+ ::abs(d_mask[2]+2*d_mask[5]+d_mask[8]-d_mask[0]-2*d_mask[3]-d_mask[6]);

if (ret > max)

ret = max;

return ret;

}

};

}

#endif

数字图像处理算法实现

------------编程心得(1)

2001414班 朱伟 20014123

摘要: 关于空间域图像处理算法框架,直方图处理,空间域滤波器算法框架的编程心得,使用GDI+(C++)

一,图像文件的读取

初学数字图像处理时,图像文件的读取往往是一件麻烦的事情,我们要面对各种各样的图像文件格式,如果仅用C++的fstream库那就必须了解各种图像编码格式,这对于初学图像处理是不太现实的,需要一个能帮助轻松读取各类图像文件的库。在Win32平台上GDI+(C++)是不错的选择,不光使用上相对于Win32 GDI要容易得多,而且也容易移植到.Net平台上的GDI+。

Gdiplus::Bitmap类为我们提供了读取各类图像文件的接口,Bitmap::LockBits方法产生的BitmapData类也为我们提供了高速访问图像文件流的途径。这样我们就可以将精力集中于图像处理算法的实现,而不用关心各种图像编码。具体使用方式请参考MSDN中GDI+文档中关于Bitmap类和BitmapData类的说明。另外GDI+仅在Windows XP/2003上获得直接支持,对于Windows 2000必须安装相关DLL,或者安装有Office 2003,Visual Studio 2003 .Net等软件。

二,空间域图像处理算法框架

(1) 在空间域图像处理中,对于一个图像我们往往需要对其逐个像素的进行处理,对每个像素的处理使用相同的算法(或者是图像中的某个矩形部分)。即,对于图像f(x,y),其中0≤x≤M,0≤y≤N,图像为M*N大小,使用算法algo,则f(x,y) = algo(f(x,y))。事先实现一个算法框架,然后再以函数指针或函数对象(functor,即实现operator()的对象)传入算法,可以减轻编程的工作量。

如下代码便是一例:

#ifndef PROCESSALGO_H

#define PROCESSALGO_H

#include <windows.h>

#include <Gdiplus.h>

namespace nsimgtk

{

template <typename pixelType, Gdiplus::PixelFormat pixelFormat, class Processor>

bool ProcessPixelsOneByOne(Gdiplus::Bitmap* const p_bitmap, Processor processor, unsigned int x, unsigned int y,

unsigned int width, unsigned int height)

{

if (p_bitmap == NULL)

{

return false;

}

if ((width + x > p_bitmap->GetWidth()) || (height + y >p_bitmap->GetHeight()))

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(x, y, width,height);

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus::Ok)

{

return false;

}

pixelType *pixels = (pixelType*)bitmapData.Scan0;

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

processor(&pixels[col+row*bitmapData.Stride/sizeof(pixelType)]);

}

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

#endif

ProcessPixelsOneByOne函数可以对图像中从(x,y)位置起始,width*height大小的区域进行处理。模板参数pixelType用于指定像素大小,例如在Win32平台上传入unsigned char即为8位,用于8阶灰度图。模板参数Processor为图像处理算法实现,可以定义类实现void operator(pixelType *)函数,或者传入同样接口的函数指针。

如下便是一些算法示例(说明见具体注释):

#ifndef SPATIALDOMAIN_H

#define SPATIALDOMAIN_H

#include <cmath>

#include <string>

namespace nsimgtk

{

// 8阶灰度图的灰度反转算法

class NegativeGray8

{

public:

void operator()(unsigned char *const p_value)

{

*p_value ^= 0xff;

}

};

// 8阶灰度图的Gamma校正算法

class GammaCorrectGray8

{

private:

unsigned char d_s[256];

public:

GammaCorrectGray8::GammaCorrectGray8(double c, double gamma);

void operator()(unsigned char*const p_value)

{

*p_value = d_s[*p_value];

}

};

// 8阶灰度图的饱和度拉伸算法

class ContrastStretchingGray8

{

private:

unsigned char d_s[256];

public:

ContrastStretchingGray8::ContrastStretchingGray8(double a1, double b1, unsigned int x1,

double a2, double b2, unsigned int x2, double a3, double b3);

void operator()(unsigned char*const p_value)

{

*p_value = d_s[*p_value];

}

};

// 8阶灰度图的位平面分割,构造函数指定位平面号

class BitPlaneSliceGray8

{

private:

unsigned char d_s[256];

public:

BitPlaneSliceGray8(unsigned char bitPlaneNum);

void operator()(unsigned char* const p_value)

{

*p_value = d_s[*p_value];

}

};

}

#endif

// 上述类中各构造函数的实现代码,应该分在另一个文件中,此处为说明方便,一并列出

#include "SpatialDomain/spatialDomain.h"

namespace nsimgtk

{

GammaCorrectGray8::GammaCorrectGray8(double c, double gamma)

{

double temp;

for (unsigned int i=0; i<256; ++i)

{

temp = ceil(c * 255.0 * pow(double(i)/255.0, gamma));

d_s[i] = unsigned char(temp);

}

}

ContrastStretchingGray8::ContrastStretchingGray8(double a1, double b1, unsigned int x1,

double a2, double b2, unsigned int x2, double a3, double b3)

{

if (x1 > 255 || x2 > 255 || x1 > x1)

{

for (unsigned int i=0; i<256; ++i)

d_s[i] = i;

}

else

{

double tmp;

for (unsigned int i=0; i<x1; ++i)

{

tmp = ceil(a1*double(i)+b1);

d_s[i] = (unsigned char)tmp;

}

for (unsigned int i=x1; i<x2; ++i)

{

tmp = ceil(a2*double(i)+b2);

d_s[i] = (unsigned char)tmp;

}

for (unsigned int i=x2; i<256; ++i)

{

tmp = ceil(a3*double(i)+b3);

d_s[i] = (unsigned char)tmp;

}

}

}

BitPlaneSliceGray8::BitPlaneSliceGray8(unsigned char bitPlaneNum)

{

unsigned char bitMaskArray[8] =

{

0x01, 0x02, 0x04, 0x08,

0x10, 0x20, 0x40, 0x80

};

for (unsigned int i=0; i<256; ++i)

{

unsigned char tmp = i;

tmp &= bitMaskArray[bitPlaneNum];

tmp = (tmp >> bitPlaneNum) * 255;

d_s[i] = tmp;

}

}

}

(2) 直方图在GDI+1.0中没有获得支持,我们必须自行实现。直方图相关的处理在数字图像处理中占有重要地位,可以通过它获取图像灰度级的统计信息,且可以通过直方图进行一些重要的图像增强技术,如直方图均衡化,直方图规定化,基本全局门限等。

下面是获取8阶图像直方图的算法实现:

namespace nsimgtk

{

bool GetHistogramNormalizeGray8(Gdiplus::Bitmap * const p_bitmap, float *histogramArray)

{

if (p_bitmap == NULL || histogramArray == NULL)

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(0, 0, p_bitmap->GetWidth(), p_bitmap->GetHeight());

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeRead, PixelFormat8bppIndexed, &bitmapData) != Gdiplus::Ok)

{

return false;

}

unsigned char *pixels = (unsigned char*)bitmapData.Scan0;

unsigned int histogram[256];

for (int i=0; i<256; ++i)

{

histogram[i] = 0;

}

for (unsigned int row=0; row<p_bitmap->GetWidth(); ++row)

{

for (unsigned int col=0; col<p_bitmap->GetHeight(); ++col)

{

++histogram[pixels[col+row*bitmapData.Stride]];

}

}

const unsigned int totalPixels = p_bitmap->GetWidth() * p_bitmap->GetHeight();

for (int i=0; i<256; ++i)

{

histogramArray[i] = float(histogram[i]) / float(totalPixels);

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

在获取直方图后(即上面算法的第二个参数),再将其作为参数传入下面的对象的构造函数,然后以该对象为仿函数传入ProcessPixelsOneByOne即可实现8阶图像直方图均衡化:

#ifndef SPATIALDOMAIN_H

#define SPATIALDOMAIN_H

#include <cmath>

#include <string>

namespace nsimgtk

{

// 8阶灰度图的直方图均衡化

class HistogramEqualizationGray8

{

private:

unsigned char d_s[256];

public:

HistogramEqualizationGray8(const float *const histogramArray);

void operator()(unsigned char *const p_value)

{

*p_value = d_s[*p_value];

}

};

}

#endif

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

#include "SpatialDomain/spatialDomain.h"

namespace nsimgtk

{

HistogramEqualizationGray8::HistogramEqualizationGray8(const float *const histogramArray)

{

if (histogramArray != NULL)

{

float sum = 0.0;

for (int i=0; i<256; ++i)

{

sum += histogramArray[i];

d_s[i] = unsigned char(sum * 255);

}

}

}

}

(3)空间域滤波器,滤波器是一个m*n大小的掩模,其中m,n均为大于1的奇数。滤波器逐像素地通过图像的全部或部分矩形区域,然后逐像素地对掩模覆盖下的像素使用滤波器算法获得响应,将响应赋值于当前像素即掩模中心像素,另外滤波器算法使用中将会涉及到图像边缘的问题,这可以对边缘部分掩模使用补零法补齐掩模下无像素值的区域,或者掩模的移动范围以不越出图像边缘的方式移动,当然这些处理方法都会给图像边缘部分带来不良效果,但是一般情况下,图像边缘部分往往不是我们关注的部分或者没有重要的信息。

下面的滤波器算法框架SpatialFilterAlgo即以补零法(zero-padding)实现:

#ifndef SPATIALFILTER_H

#define SPATIALFILTER_H

#include <vector>

#include <numeric>

#include <algorithm>

#include <gdiplus.h>

#include <fstream>

#include <cmath>

namespace nsimgtk

{

template <typename pixelType, Gdiplus::PixelFormat pixelFormat, class FilterMask>

bool SpatialFilterAlgo(Gdiplus::Bitmap* const p_bitmap, FilterMask filterMask, unsigned int x, unsigned int y,

unsigned int width, unsigned int height)

{

if (p_bitmap == NULL)

{

return false;

}

if ((width + x > p_bitmap->GetWidth()) || (height + y >p_bitmap->GetHeight()))

{

return false;

}

Gdiplus::BitmapData bitmapData;

Gdiplus::Rect rect(x, y, width,height);

if (p_bitmap->LockBits(&rect, Gdiplus::ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus::Ok)

{

return false;

}

pixelType *pixels = (pixelType*)bitmapData.Scan0;

const unsigned int m = filterMask.d_m; // mask's width

const unsigned int n = filterMask.d_n; // mask's height

std::vector<pixelType> tmpImage((m-1+width)*(n-1+height)); // extend image to use zero-padding

// copy original bitmap to extended image with zero-padding method

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

tmpImage[(col+m/2)+(row+n/2)*(bitmapData.Stride/sizeof(pixelType)+m-1)] =

pixels[col+row*bitmapData.Stride/sizeof(pixelType)];

}

}

// process every pixel with filterMask

for (unsigned int row=0; row<height; ++row)

{

for (unsigned int col=0; col<width; ++col)

{

// fill the "m*n" mask with the current pixel's neighborhood

for (unsigned int i=0; i<n; ++i)

{

for (unsigned int j=0; j<m; ++j)

{

filterMask.d_mask[i*m+j] = tmpImage[(col+j)+(row+i)*(bitmapData.Stride/sizeof(pixelType)+m-1)];

}

}

// replace the current pixel with filter mask's response

pixels[col+row*bitmapData.Stride/sizeof(pixelType)] = filterMask.response();

}

}

if (p_bitmap->UnlockBits(&bitmapData) != Gdiplus::Ok)

{

return false;

}

return true;

}

}

#endif

其中模板参数FilterMask即为滤波掩模算法。通常的滤波算法有均值滤波器,可以模糊化图像,去除图形中的细节部分,使得我们可以关注图像中较为明显的部分,均值滤波器用于周期性噪声。中值滤波器用于图像中存在椒盐噪声也即脉冲噪声的情况下。另外有基于一阶微分的Sobel梯度算子和基于两阶微分的拉普拉斯算子,它们往往被用于边缘检测中。

下面是一些滤波器算法的具体实现,所以滤波器算法都应该实现pixelType response()函数以及有d_mask,d_m,d_n成员,这可以通过继承__filteMask类获得(不需要付出虚函数代价)。

#ifndef SPATIALFILTER_H

#define SPATIALFILTER_H

#include <vector>

#include <numeric>

#include <algorithm>

#include <gdiplus.h>

#include <fstream>

#include <cmath>

namespace nsimgtk

{

// 滤波器掩模的基类,提供掩模大小d_m,d_n,掩模覆盖下的m*n个像素值d_mask

// others filterMask should inherit it

template <typename pixelType>

struct __filterMask

{

const unsigned int d_m;

const unsigned int d_n;

// image's pixels under the m*n filter mask

std::vector<pixelType> d_mask;

// filter mask's width and heigh must be a odd, if not, it will plus one for the width or the height

__filterMask(unsigned int m, unsigned int n)

: d_m(m%2 ? m:m+1), d_n(n%2 ? n:n+1), d_mask(d_m*d_n)

{

}

};

// 掩模权值为全1的均值滤波器

template <typename pixelType>

class averagingFilterMaskSp

: public __filterMask<pixelType>

{

public:

averagingFilterMaskSp(unsigned int m, unsigned int n)

: __filterMask<pixelType>(m, n)

{ }

pixelType response()

{

return std::accumulate(d_mask.begin(), d_mask.end(), 0) / (d_m * d_n);

}

};

// 可自定义掩模权值的均值滤波器

template <typename pixelType>

class averagingFilterMask

: public __filterMask<pixelType>

{

private:

std::vector<pixelType> d_weight; // weights' vector(m*n)

int d_weight_sum; // all weights' sum

public:

averagingFilterMask(unsigned int m, unsigned int n, const std::vector<pixelType>& weightVec)

: __filterMask<pixelType>(m, n), d_weight(weightVec)

{

if (weightVec.size() != d_mask.size())

{

// if weight's size isn't equal to mask's size, it will change filter mask as a special filter mask

d_weight.resize(d_mask.size(), 1);

}

d_weight_sum = std::accumulate(d_weight.begin(), d_weight.end(), 0);

}

pixelType response()

{

return std::inner_product(d_mask.begin(), d_mask.end(), d_weight.begin(), 0) / d_weight_sum;

}

};

// 中值滤波器

template <typename pixelType>

class medianFilterMask

: public __filterMask<pixelType>

{

public:

medianFilterMask(unsigned int m, unsigned int n)

: __filterMask<pixelType>(m, n)

{ }

pixelType response()

{

std::sort(d_mask.begin(), d_mask.end());

return d_mask[d_mask.size()/2];

}

};

// 3*3拉普拉斯滤波器

// the mask is: [0 1 0 [0 -1 0

// 1 -5 1 or -1 5 -1

// 0 1 0] 0 -1 0]

// if pixel's brightness is less than min, set it to min

// if pixel's brightness is larger than max, set it to max

template <typename pixelType, pixelType min, pixelType max>

class laplacianFilter

: public __filterMask<pixelType>

{

public:

laplacianFilter()

: __filterMask<pixelType>(3, 3)

{ }

pixelType response()

{

int ret = (int)(5*(int)d_mask[4]) - ((int)d_mask[5]+d_mask[3]+d_mask[1]+d_mask[7]);

if (ret < min)

ret = min;

if (ret > max)

ret = max;

return ret;

}

};

// 3*3Sobel滤波器

// the mask is: [-1 -2 -1 [-1 0 1

// 0 0 0 and -2 0 2

// 1 2 1] -1 0 1]

// if pixel's brightness is larger than max, set it to max

template <typename pixelType, pixelType max>

class sobelFilter

: public __filterMask<pixelType>

{

public:

sobelFilter()

: __filterMask<pixelType>(3, 3)

{ }

pixelType response()

{

int ret = ::abs(d_mask[6]+2*d_mask[7]+d_mask[8]-d_mask[0]-2*d_mask[1]-d_mask[2])

+ ::abs(d_mask[2]+2*d_mask[5]+d_mask[8]-d_mask[0]-2*d_mask[3]-d_mask[6]);

if (ret > max)

ret = max;

return ret;

}

};

}

#endif

 
 
 
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