/*############################################################################# * 文件名：imageenhance.c * 功能：  實(shí)現(xiàn)了圖像增強(qiáng)算法 * modified by  PRTsinghua@hotmail.com#############################################################################*/#include <math.h>#include <stdio.h>#include <stdlib.h>#include <time.h>#include <string.h>#include "imagemanip.h"/******************************************************************************** 圖像增強(qiáng)部分** ** 該增強(qiáng)算法針對(duì)指紋圖像設(shè)計(jì)，它標(biāo)記了指紋圖像中沒有使用的區(qū)域，而其它的區(qū)域** 在增強(qiáng)后，脊線可以被清晰的分離出來（使用一個(gè)閾值）。** ** 該算法生成了一個(gè)脊線方向圖，一個(gè)掩碼圖。**** 可參考如下兩篇文章：** 1 - Fingerprint Enhancement: Lin Hong, Anil Jain, Sharathcha Pankanti,**     and Ruud Bolle. [Hong96]** 2 - Fingerprint Image Enhancement, Algorithm and Performance Evaluation:**     Lin Hong, Yifei Wan and Anil Jain. [Hong98]**** 增強(qiáng)算法使用了 文獻(xiàn)(2) 中的幾個(gè)步驟：**  A - 歸一化**  B - 計(jì)算方向圖**  C - 計(jì)算頻率**  D - 計(jì)算區(qū)域掩碼**  E - 濾波********************************************************************************/#define P(x,y)      ((int32_t)p[(x)+(y)*pitch])/******************************************************************************** 采用了Gabor方向?yàn)V波器，如下：****                    / 1|x'     y'  |\** h(x,y:phi,f) = exp|- -|--- + ---| |.cos(2.PI.f.x')**                    \ 2|dx     dy  |/**** x' =  x.cos(phi) + y.sin(phi)** y' = -x.sin(phi) + y.cos(phi)**** 定義如下：**  G 歸一化后的圖像**  O 方向圖**  F 頻率圖**  R 掩碼圖像**  E 增強(qiáng)后的圖像**  Wg Gabor濾波器窗口大小****          / 255                                          if R(i,j) = 0**         |**         |  Wg/2    Wg/2 **         |  ---     ---** E(i,j)= |  \       \**         |   --      --  h(u,v:O(i,j),F(i,j)).G(i-u,j-v) otherwise**         |  /       /**          \ ---     ---**            u=-Wg/2 v=-Wg/2********************************************************************************/inline FvsFloat_t EnhanceGabor(FvsFloat_t x, FvsFloat_t y, FvsFloat_t phi, 								FvsFloat_t f, FvsFloat_t r2){    FvsFloat_t dy2 = 1.0/r2;    FvsFloat_t dx2 = 1.0/r2;    FvsFloat_t x2, y2;    phi += M_PI/2;    x2 = -x*sin(phi) + y*cos(phi);    y2 =  x*cos(phi) + y*sin(phi);    return exp(-0.5*(x2*x2*dx2 + y2*y2*dy2))*cos(2*M_PI*x2*f);}static FvsError_t ImageEnhanceFilter    (    FvsImage_t        normalized,    const FvsImage_t  mask,    const FvsFloat_t* orientation,    const FvsFloat_t* frequence,    FvsFloat_t        radius    ){    FvsInt_t Wg2 = 8;    FvsInt_t i,j, u,v;    FvsError_t nRet  = FvsOK;    FvsImage_t enhanced = NULL;    FvsInt_t w        = ImageGetWidth (normalized);    FvsInt_t h        = ImageGetHeight(normalized);    FvsInt_t pitchG   = ImageGetPitch (normalized);    FvsByte_t* pG     = ImageGetBuffer(normalized);    FvsFloat_t sum, f, o;    /* 平方 */    radius = radius*radius;    enhanced = ImageCreate();    if (enhanced==NULL || pG==NULL)        return FvsMemory;    if (nRet==FvsOK)        nRet = ImageSetSize(enhanced, w, h);    if (nRet==FvsOK)    {        FvsInt_t pitchE  = ImageGetPitch (enhanced);        FvsByte_t* pE    = ImageGetBuffer(enhanced);        if (pE==NULL)            return FvsMemory;        (void)ImageClear(enhanced);        for (j = Wg2; j < h-Wg2; j++)        for (i = Wg2; i < w-Wg2; i++)        {            if (mask==NULL || ImageGetPixel(mask, i, j)!=0)            {                sum = 0.0;                o = orientation[i+j*w];                f = frequence[i+j*w];                for (v = -Wg2; v <= Wg2; v++)                for (u = -Wg2; u <= Wg2; u++)                {                    sum += EnhanceGabor		        			(		                		(FvsFloat_t)u,								(FvsFloat_t)v,								o,f,radius							)							* pG[(i-u)+(j-v)*pitchG];                }                if (sum>255.0)                 	sum = 255.0;                if (sum<0.0)                   	sum = 0.0;                pE[i+j*pitchE] = (uint8_t)sum;            }        }        nRet = ImageCopy(normalized, enhanced);    }    (void)ImageDestroy(enhanced);    return nRet;}/* }}} *//******************************************************************************  * 功能：指紋圖像增強(qiáng)算法  *       該算法描述起來比較復(fù)雜，其后處理的部分是基于Gabor濾波器的，          參數(shù)動(dòng)態(tài)計(jì)算。圖像處理時(shí)參數(shù)依次改變，所以要做一個(gè)原圖的備份。  * 參數(shù)：image        指紋圖像  *       direction    脊線方向，需要事先計(jì)算  *       frequency    脊線頻率，需要事先計(jì)算  *       mask         指示指紋的有效區(qū)域  *       radius       濾波器半徑，大多數(shù)情況下，4.0即可。                       值越大，噪聲可以受到更大抑制，但會(huì)產(chǎn)生更多的偽特征。  * 返回：錯(cuò)誤編號(hào)******************************************************************************/FvsError_t ImageEnhanceGabor(FvsImage_t image, const FvsFloatField_t direction,            const FvsFloatField_t frequency, const FvsImage_t mask,             const FvsFloat_t radius){    FvsError_t nRet = FvsOK;    FvsFloat_t * image_orientation = FloatFieldGetBuffer(direction);    FvsFloat_t * image_frequence   = FloatFieldGetBuffer(frequency);    if (image_orientation==NULL || image_frequence==NULL)        return FvsMemory;    nRet = ImageEnhanceFilter(image, mask, image_orientation,     						image_frequence, radius);    return nRet;}