C人脸识别

news/2024/12/2 21:27:39/

 1、原始图片：

 2、灰度化下：

3、均值滤波： 

4、 二值图加边缘检测

 

 5、生成积分图

6、把待检测的人脸区域划分为25个，因为是一个数组，这样分别统计每个区域的像素个数：

x0: 60, y0: 100, x1: 157, y1: 200    width: 228, height: 228
IGmap.data a4: 7979, a3: 4423, a2: 2130, a1: 1407 result: 2833（外层红色方框内像素个数）

x0: 60, y0: 100, x1: 79, y1: 120    width: 228, height: 228
IGmap.data a4: 2345, a3: 2002, a2: 1624, a1: 1407 result: 126（第一排左边第一个小格子）

x0: 79, y0: 100, x1: 98, y1: 120    width: 228, height: 228
IGmap.data a4: 3132, a3: 2598, a2: 2345, a1: 2002 result: 191（第一排左边第二个小格子）

x0: 98, y0: 100, x1: 117, y1: 120    width: 228, height: 228
IGmap.data a4: 3790, a3: 3137, a2: 3132, a1: 2598 result: 119

x0: 117, y0: 100, x1: 136, y1: 120    width: 228, height: 228
IGmap.data a4: 4551, a3: 3703, a2: 3790, a1: 3137 result: 195

x0: 136, y0: 100, x1: 155, y1: 120    width: 228, height: 228
IGmap.data a4: 5319, a3: 4350, a2: 4551, a1: 3703 result: 121

x0: 60, y0: 120, x1: 79, y1: 140    width: 228, height: 228
IGmap.data a4: 2549, a3: 2345, a2: 1800, a1: 1624 result: 28

x0: 79, y0: 120, x1: 98, y1: 140    width: 228, height: 228
IGmap.data a4: 3419, a3: 3132, a2: 2549, a1: 2345 result: 83

x0: 98, y0: 120, x1: 117, y1: 140    width: 228, height: 228
IGmap.data a4: 4106, a3: 3790, a2: 3419, a1: 3132 result: 29

x0: 117, y0: 120, x1: 136, y1: 140    width: 228, height: 228
IGmap.data a4: 4972, a3: 4551, a2: 4106, a1: 3790 result: 105

x0: 136, y0: 120, x1: 155, y1: 140    width: 228, height: 228
IGmap.data a4: 5757, a3: 5319, a2: 4972, a1: 4551 result: 17

x0: 60, y0: 140, x1: 79, y1: 160    width: 228, height: 228
IGmap.data a4: 2722, a3: 2549, a2: 1913, a1: 1800 result: 60

x0: 79, y0: 140, x1: 98, y1: 160    width: 228, height: 228
IGmap.data a4: 3688, a3: 3419, a2: 2722, a1: 2549 result: 96

x0: 98, y0: 140, x1: 117, y1: 160    width: 228, height: 228
IGmap.data a4: 4533, a3: 4106, a2: 3688, a1: 3419 result: 158

x0: 117, y0: 140, x1: 136, y1: 160    width: 228, height: 228
IGmap.data a4: 5482, a3: 4972, a2: 4533, a1: 4106 result: 83

x0: 136, y0: 140, x1: 155, y1: 160    width: 228, height: 228
IGmap.data a4: 6291, a3: 5757, a2: 5482, a1: 4972 result: 24

x0: 60, y0: 160, x1: 79, y1: 180    width: 228, height: 228
IGmap.data a4: 2874, a3: 2722, a2: 2008, a1: 1913 result: 57

x0: 79, y0: 160, x1: 98, y1: 180    width: 228, height: 228
IGmap.data a4: 4043, a3: 3688, a2: 2874, a1: 2722 result: 203

x0: 98, y0: 160, x1: 117, y1: 180    width: 228, height: 228
IGmap.data a4: 5052, a3: 4533, a2: 4043, a1: 3688 result: 164

x0: 117, y0: 160, x1: 136, y1: 180    width: 228, height: 228
IGmap.data a4: 6190, a3: 5482, a2: 5052, a1: 4533 result: 189

x0: 136, y0: 160, x1: 155, y1: 180    width: 228, height: 228
IGmap.data a4: 7121, a3: 6291, a2: 6190, a1: 5482 result: 122

x0: 60, y0: 180, x1: 79, y1: 200    width: 228, height: 228
IGmap.data a4: 3047, a3: 2874, a2: 2130, a1: 2008 result: 51

x0: 79, y0: 180, x1: 98, y1: 200    width: 228, height: 228
IGmap.data a4: 4405, a3: 4043, a2: 3047, a1: 2874 result: 189

x0: 98, y0: 180, x1: 117, y1: 200    width: 228, height: 228
IGmap.data a4: 5580, a3: 5052, a2: 4405, a1: 4043 result: 166

x0: 117, y0: 180, x1: 136, y1: 200    width: 228, height: 228
IGmap.data a4: 6851, a3: 6190, a2: 5580, a1: 5052 result: 133

x0: 136, y0: 180, x1: 155, y1: 200    width: 228, height: 228
IGmap.data a4: 7863, a3: 7121, a2: 6851, a1: 6190 result: 81

运行效果图：

 

比如，这里是左边眼睛跟人中的比值(d1/d2)，和左边眼睛跟人中像素的比值(d3/d2)，正常人脸，眼睛区域比人中像素会多些 （如上图所示）

	//2、两只眼睛，与人中的比例，正常来说，眼睛比人中的像素点多些double d1 = (double)(Areas[0] + Areas[1] + Areas[5] + Areas[6]);        double d2 = (double)(Areas[2] + Areas[7]);  double d3 = (double)(Areas[3] + Areas[4] + Areas[8] + Areas[9]);DEBUG_PRINT_WITH_TIME("if: Step 2: %f,  %f", d1 / d2 ,  d3 / d2);if (d1 / d2 < 2.6 || d3 / d2 < 2.6){DEBUG_PRINT_WITH_TIME("1");return 1;}

当前还不能在图片中滑动窗口，检测窗口不断变化，这个其实也是需要测试参数的，因为图片中人脸大小是不固定的。

代码（从这里借鉴了不少，但发现原博客有些小问题，测试了一两天）：

#include<stdio.h>
#include<stdlib.h>
#include<Windows.h>
#include<windef.h>
#include<math.h>
#include<string.h>#include <graphics.h>
#include "Global.h"// 获取文件的后缀名
char* GetFlieExta(char* filename)
{int fileLen = strlen(filename);int exLen = 0;char *fileExta = (char *)malloc(255);memset(fileExta, 0, 255);for (int i = fileLen-1; i > 0; i--)if (filename[i] == '.'){exLen = fileLen - i;break;}strncpy(fileExta, filename + fileLen - exLen, exLen);return fileExta;
}// BGRA颜色结构体
typedef struct tagBGRA
{unsigned char blue;          // 该颜色的蓝色分量  (值范围为0-255)unsigned char green;         // 该颜色的绿色分量  (值范围为0-255)unsigned char red;           // 该颜色的红色分量  (值范围为0-255)unsigned char transparency;  // 透明度，在bmp中是保留值，无实际效果
}BGRA, * PBGRA;// 图像结构体
typedef struct tagIMAGE_SELF
{unsigned int width;unsigned int height;BGRA* data;
}IMAGE_SELF, * PIMAGE_SELF;// BMP文件的处理// BMP文件头结构体
typedef struct tagBITMAP_HEAD_INFO
{/* bmp文件头的信息，有#的是重点!!*/// bmp文件头unsigned short  bfType;             // 0x424D，即BM字符串，表明是bmp格式文件unsigned int    bfSize;             // ###总的bmp文件大小 以字节为单位     unsigned short  bfReserved1;        // 保留，必须设置为0                     unsigned short  bfReserved2;        // 保留，必须设置为0 unsigned int    bfOffBits;          // ###总的bmp头部的大小（包括位图信息头），即到像素数据的偏移  // 位图信息头unsigned int    biSize;             // 位图信息头的大小unsigned int    biWidth;            // ###图像的宽  unsigned int    biHeight;           // ###图像的高  unsigned short  biPlanes;           // 颜色平面数，即调色盘数，恒等于1 unsigned short  biBitCount;         // ###图片颜色的位数,一般为32unsigned int    biCompression;      // 说明图象数据压缩的类型，0为不压缩unsigned int    biSizeImage;        // 像素数据所占大小,因为使用BI_RGB，所以设置为0unsigned int    biXPelsPerMeter;    // 说明水平分辨率，缺省为0unsigned int    biYPelsPerMeter;    // 说明垂直分辨率，缺省为0unsigned int    biClrUsed;          // 说明本位图实际使用调色盘的颜色索引数，0表示全部unsigned int    biClrImportant;     // 说明本位图重要调色盘的颜色索引数，0表示全都重要
}BITMAP_HEAD_INFO,*PBITMAP_HEAD_INFO;// 加载BMP图片
IMAGE_SELF Image_bmp_load(char* filename)
{IMAGE_SELF imageOld;BITMAP_HEAD_INFO bmpHeadInfo;FILE* fp;DEBUG_PRINT_WITH_TIME("filename: %s", filename);if ((fp = fopen(filename, "rb")) == NULL){printf("打开%s文件失败！\n", filename);exit(0);}    // 读取bmp头部// bmp文件头fread(&bmpHeadInfo.bfType, 1, sizeof(bmpHeadInfo.bfType), fp);fread(&bmpHeadInfo.bfSize, 1, sizeof(bmpHeadInfo.bfSize), fp);fread(&bmpHeadInfo.bfReserved1, 1, sizeof(bmpHeadInfo.bfReserved1), fp);fread(&bmpHeadInfo.bfReserved2, 1, sizeof(bmpHeadInfo.bfReserved2), fp);fread(&bmpHeadInfo.bfOffBits, 1, sizeof(bmpHeadInfo.bfOffBits), fp);// 位图信息头fread(&bmpHeadInfo.biSize, 1, sizeof(bmpHeadInfo.biSize), fp);fread(&bmpHeadInfo.biWidth, 1, sizeof(bmpHeadInfo.biWidth), fp);fread(&bmpHeadInfo.biHeight, 1, sizeof(bmpHeadInfo.biHeight), fp);fread(&bmpHeadInfo.biPlanes, 1, sizeof(bmpHeadInfo.biPlanes), fp);fread(&bmpHeadInfo.biBitCount, 1, sizeof(bmpHeadInfo.biBitCount), fp);fread(&bmpHeadInfo.biCompression, 1, sizeof(bmpHeadInfo.biCompression), fp);fread(&bmpHeadInfo.biSizeImage, 1, sizeof(bmpHeadInfo.biSizeImage), fp);fread(&bmpHeadInfo.biXPelsPerMeter, 1, sizeof(bmpHeadInfo.biXPelsPerMeter), fp);fread(&bmpHeadInfo.biYPelsPerMeter, 1, sizeof(bmpHeadInfo.biYPelsPerMeter), fp);fread(&bmpHeadInfo.biClrUsed, 1, sizeof(bmpHeadInfo.biClrUsed), fp);fread(&bmpHeadInfo.biClrImportant, 1, sizeof(bmpHeadInfo.biClrImportant), fp);// 读取bmp位图数据BGRA* bgra = (BGRA*)malloc(sizeof(BGRA) * (bmpHeadInfo.biWidth * bmpHeadInfo.biHeight));fseek(fp, bmpHeadInfo.bfOffBits, SEEK_SET);if (bmpHeadInfo.biBitCount == 32){for (unsigned int i = 0; i < bmpHeadInfo.biWidth * bmpHeadInfo.biHeight; i++)fread(&bgra[i], 1, sizeof(BGRA), fp);}else if (bmpHeadInfo.biBitCount == 24){// 计算每行补几个字节零int k = 4 * (3 * bmpHeadInfo.biWidth / 4 + 1) - 3 * bmpHeadInfo.biWidth;for (unsigned int i = 0; i < bmpHeadInfo.biWidth * bmpHeadInfo.biHeight; i++){if (k != 4 && (ftell(fp)- 54 + k ) % (3 * bmpHeadInfo.biWidth + k)==0)fseek(fp, ftell(fp) + k, SEEK_SET);fread(&bgra[i].blue, 1, sizeof(unsigned char), fp);fread(&bgra[i].green, 1, sizeof(unsigned char), fp);fread(&bgra[i].red, 1, sizeof(unsigned char), fp);bgra[i].transparency = (unsigned char)0xFF;}}imageOld.data = bgra;imageOld.width = bmpHeadInfo.biWidth;imageOld.height = bmpHeadInfo.biHeight;fclose(fp);return imageOld;
}// 保存BMP图片
void Image_bmp_save(char* filename,IMAGE_SELF imageOld)
{FILE* fp = fopen(filename, "wb");unsigned short  bfType = 0x4D42;                // 0x424D，即BM字符串，表明是bmp格式文件unsigned int    bfSize = imageOld.width * imageOld.height * 4 + 54;  // ###总的bmp文件大小 以字节为单位     unsigned short  bfReserved1 = 0;                // 保留，必须设置为0                     unsigned short  bfReserved2 = 0;                // 保留，必须设置为0 unsigned int    bfOffBits = 54;                 // ###总的bmp头部的大小（包括位图信息头），即到像素数据的偏移  unsigned int    biSize = 40;                    // 位图信息头的大小unsigned int    biWidth = imageOld.width;                 // ###图像的宽  unsigned int    biHeight = imageOld.height;                // ###图像的高  unsigned short  biPlanes = 1;                   // 颜色平面数，即调色盘数，恒等于1 unsigned short  biBitCount = 32;                // ###图片颜色的位数,一般为32unsigned int    biCompression = 0;              // 说明图象数据压缩的类型，0为不压缩unsigned int    biSizeImage = 0;                // 像素数据所占大小,因为使用BI_RGB，所以设置为0unsigned int    biXPelsPerMeter = 0;            // 说明水平分辨率，缺省为0unsigned int    biYPelsPerMeter = 0;            // 说明垂直分辨率，缺省为0unsigned int    biClrUsed = 0;                  // 说明本位图实际使用调色盘的颜色索引数，0表示全部unsigned int    biClrImportant = 0;             // 说明本位图重要调色盘的颜色索引数，0表示全都重要fwrite(&bfType, 2, 1, fp);fwrite(&bfSize, 4, 1, fp);fwrite(&bfReserved1, 2, 1, fp);fwrite(&bfReserved2, 2, 1, fp);fwrite(&bfOffBits, 4, 1, fp);fwrite(&biSize, 4, 1, fp);fwrite(&biWidth, 4, 1, fp);fwrite(&biHeight, 4, 1, fp);fwrite(&biPlanes, 2, 1, fp);fwrite(&biBitCount, 2, 1, fp);fwrite(&biCompression, 4, 1, fp);fwrite(&biSizeImage, 4, 1, fp);fwrite(&biXPelsPerMeter, 4, 1, fp);fwrite(&biYPelsPerMeter, 4, 1, fp);fwrite(&biClrUsed, 4, 1, fp);fwrite(&biClrImportant, 4, 1, fp);fwrite(imageOld.data, sizeof(BGRA) * imageOld.width * imageOld.height, 1, fp);fclose(fp);
}// 加载图片
IMAGE_SELF Image_load(char* filename)
{IMAGE_SELF im;//char* fileEx= GetFlieExta(filename);//DEBUG_PRINT_WITH_TIME("fileEx: %s", fileEx);//if (strcmp(fileEx, ".bmp") == 0)im = Image_bmp_load(filename);return im;
}// 保存图片
void Image_save(char* filename, IMAGE_SELF imageOld)
{char* fileEx = GetFlieExta(filename);if (strcmp(fileEx, ".bmp") == 0)Image_bmp_save(filename, imageOld);
}// 释放图像结构体
void Image_free(IMAGE_SELF imageOld)
{free(imageOld.data);
}#define UPTURN_MODE_HORIZONTAL 0    // 水平翻转
#define UPTURN_MODE_VERTICAL 1      // 垂直翻转#define GRAY_MODE_WEIGHT 1           // 加权法(推荐使用)
#define GRAY_MODE_BEST 2             // 最值法
#define GRAY_MODE_AVERAGE 3          // 均值法
#define GRAY_MODE_PART_RED 4         // 分量法_RED
#define GRAY_MODE_PART_GREEN 5       // 分量法_GREEN
#define GRAY_MODE_PART_BLUE 6        // 分量法_BLUE// 彩色图转灰度图
IMAGE_SELF Transform_color_grayscale(IMAGE_SELF imageOld, int grayscale_mode)
{int color = 0;IMAGE_SELF imageNew;imageNew.width = imageOld.width;imageNew.height = imageOld.height;imageNew.data = (BGRA*)malloc(sizeof(BGRA) * imageOld.width * imageOld.height);switch (grayscale_mode){case GRAY_MODE_WEIGHT:{for (unsigned int i = 0; i < imageOld.width * imageOld.height; i++){color = (imageOld.data[i].blue * 114 + imageOld.data[i].green * 587 + imageOld.data[i].red * 299) / 1000;imageNew.data[i].blue = color;imageNew.data[i].green = color;imageNew.data[i].red = color;}break;}default: DEBUG_PRINT_WITH_TIME("error:  switch default branch....");}return imageNew;
}// 二值图(自适应阈值法，areaSize=25较合适，当图片线条多且密时，不推荐使用)
IMAGE_SELF Transform_color_BW_Adaptive(IMAGE_SELF imageOld, int areaSize)
{IMAGE_SELF imageNew;imageNew.width = imageOld.width;imageNew.height = imageOld.height;// areaSize为区域的大小，区域越大，效果图的细节越好，areaSize=25较合适BGRA* bgra = (BGRA*)malloc(sizeof(BGRA) * imageOld.width * imageOld.height);int* p = (int*)malloc(sizeof(int) * areaSize); // p->position 位置坐标int k = (int)(sqrt((double)areaSize)) / 2;  // 重合区域边长的一半for (unsigned int i = 0; i < imageOld.width * imageOld.height; i++){// 计算与卷积和对应重合区域的坐标int t = 0; // 记录p的下标for (int n = k; n >= -k; n--)for (int m = -k; m <= k; m++){p[t] = ((i % imageOld.width) + m) + (i / imageOld.width + n) * imageOld.width;t++;}// 判断是否越界for (int j = 0; j < areaSize; j++)if (p[j] < 0 || p[j] >= imageOld.width * imageOld.height)p[j] = i;unsigned int color = 0;for (int j = 0; j < areaSize; j++)color += imageOld.data[p[j]].blue;color /= areaSize;if (imageOld.data[i].blue >= color)bgra[i].blue = 255;elsebgra[i].blue = 0;bgra[i].green = bgra[i].blue;bgra[i].red = bgra[i].blue;}free(p);imageNew.data = bgra;return imageNew;
}// 判断像素值的范围
unsigned char Tool_RBG(int BRRA)
{if (BRRA > 255)return (unsigned char)255;else if (BRRA < 0)return (unsigned char)0;elsereturn (unsigned char)BRRA;
}// 卷积操作（自定义）
IMAGE_SELF Kernels_use_DIY(IMAGE_SELF imageOld, double* kernels, int areaSize, double modulus)
{IMAGE_SELF imageNew;imageNew.width = imageOld.width;imageNew.height = imageOld.height;imageNew.data = (BGRA*)malloc(sizeof(BGRA) * imageOld.width * imageOld.height);	memcpy(imageNew.data, imageOld.data, sizeof(BGRA) * imageOld.width * imageOld.height);// kernels卷积核// areaSize区域的大小// modulus最后乘的系数BGRA* bgra = (BGRA*)malloc(sizeof(BGRA) * imageOld.width * imageOld.height);int* p = (int*)malloc(sizeof(int) * areaSize); // p->position 位置坐标int k = (int)(sqrt((double)areaSize)) / 2;  // 重合区域边长的一半for (unsigned int i = 0; i < imageOld.width * imageOld.height; i++){// 计算与卷积和对应重合区域的坐标int t = 0; // 记录p的下标for(int n = k; n >= -k; n--)for (int m = -k; m <= k; m++)p[t] = ((i % imageOld.width) + m) + (i / imageOld.width + n) * imageOld.width, t++;// 判断是否越界for (int j = 0; j < areaSize; j++) if (p[j] < 0 || p[j] >= imageOld.width * imageOld.height)p[j] = i;// 相乘相加int blue = 0, green = 0, red = 0;for (int j = 0; j < areaSize; j++){blue += imageOld.data[p[j]].blue * kernels[j];green += imageOld.data[p[j]].green * kernels[j];red += imageOld.data[p[j]].red * kernels[j];}bgra[i].blue = Tool_RBG(blue * modulus);bgra[i].green = Tool_RBG(green * modulus);bgra[i].red = Tool_RBG(red * modulus);}free(p);imageNew.data = bgra;return imageNew;
}// 均值滤波卷积核
double KERNELS_Wave_Average[25] =
{1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1
};// 均值滤波
IMAGE_SELF Wavefiltering_Average(IMAGE_SELF imageOld)
{return Kernels_use_DIY(imageOld, KERNELS_Wave_Average, 25, 1.0 / 25);
}// 积分图结构体
typedef struct tagIGIMAGE_SELF
{unsigned int width;unsigned int height;unsigned int *data;
}IGIMAGE_SELF, *PIGIMAGE_SELF;// 获得积分图（在此之前要保证图片是“白底黑字”）
IGIMAGE_SELF IntegralImage_get(IMAGE_SELF imageOld)
{IGIMAGE_SELF IGmap;unsigned int* array = (unsigned int *)malloc(sizeof(unsigned int) * imageOld.width * imageOld.height);memset(array, 0, sizeof(int) * imageOld.width * imageOld.height);int k = 0; // 用于统计每一行的像素个数for(int height = imageOld.height; height > 0; height--){k = 0;for(int width = 0; width < imageOld.width; width++){int temp = imageOld.data[(height - 1) * imageOld.width + width].blue;if(temp == 0){k++;}if (temp == 0){//printf("y:");}else{//printf("n:");}int heightTemp = imageOld.height - height;if (height == imageOld.height){array[width] = k;}else{array[heightTemp * imageOld.width + width] =  array[(heightTemp - 1) * imageOld.width + width] + k;//printf("%3d ", array[heightTemp * imageOld.width + width]);}}//printf("\n");//Sleep(1000);//pause();//DEBUG_PRINT_WITH_TIME("height: %d, k: %d", height, k);}IGmap.data = array;IGmap.width = imageOld.width;IGmap.height = imageOld.height;return IGmap;
}// 计算积分区域像素个数
//int IntegralImage_count(IGIMAGE_SELF IGmap, int x0, int y0, int x1, int y1)
long IntegralImage_count(IGIMAGE_SELF IGmap, int x0, int y0, int x1, int y1)
{long a1, a2, a3, a4;//int leftBottom = x0 + y1 *IGmap.width;//int rightTop = x1 + y0 *IGmap.width;if(x0 > IGmap.width || x1 > IGmap.width || y1 > IGmap.height || y0 > IGmap.height){DEBUG_PRINT_WITH_TIME("x0: %ld, y0: %ld, x1: %ld, y1: %ld    width: %d, height: %d", x0, y0, x1, y1, IGmap.width, IGmap.height)return -1;}//DEBUG_PRINT_WITH_TIME("x0: %ld, y0: %ld, x1: %ld, y1: %ld    width: %d, height: %d", x0, y0, x1, y1, IGmap.width, IGmap.height)a1 = y0 * IGmap.width + x0;a2 = y1 * IGmap.width + x0;a3 = y0 * IGmap.width + x1;a4 = y1 * IGmap.width + x1;//DEBUG_PRINT_WITH_TIME("a4: %u, a3: %u, a2: %u, a1: %u", a4, a3, a2, a1)//  判断是否越界if (a1 < 0)a1 = 0;if (a2 < 0)a2 = 0;if (a3 < 0)a3 = 0;if (a3 > IGmap.width * IGmap.height - 1)a3 = a4;long result = IGmap.data[a4] + IGmap.data[a1] - IGmap.data[a3]  - IGmap.data[a2];//DEBUG_PRINT_WITH_TIME("IGmap.data a4: %u, a3: %u, a2: %u, a1: %u result: %u\n", IGmap.data[a4], IGmap.data[a3], IGmap.data[a2], IGmap.data[a1], result)// 计算区域中的像素数return  result;
}// 释放积分图结构体
void IntegralImage_free(IGIMAGE_SELF IGimage)
{free(IGimage.data);
}// 单分支决策树分类器
double Classifier_decisionStump(IGIMAGE_SELF IGmap, int x0, int y0, int x1, int y1)
{   int areaW = abs(x0-x1);int areaH = abs(y0-y1);// 计算25个区域的像素个数int w_all = IntegralImage_count(IGmap, x0, y0, x1, y1);int xStep = abs(x0-x1)/5;int yStep = abs(y0-y1)/5;long Areas[25] = {0};for(int j = 0; j < 5; j++){for(int i = 0; i < 5; i++){Areas[j * 5 + i] = IntegralImage_count(IGmap, x0 + i * xStep, y0 + yStep * j, x0 + (i + 1) * xStep, y0 + yStep * (j + 1));//DEBUG_PRINT_WITH_TIME("j: %d, i: %d, num: %d\n", j, i, num);}}DEBUG_PRINT_WITH_TIME("if: Step 1: %f", (double)w_all / (areaW * areaH));for(int i=0;i<25;i++){//printf("%d: %d, ", i, Areas[i]);}// 1、判断是否为人脸，整个区域有的像素个数占整个区域面积的比例if ((double)w_all / (areaW * areaH) < 0.19){DEBUG_PRINT_WITH_TIME("1");return 0;}//2、两只眼睛，与人中的比例，正常来说，眼睛比人中的像素点多些double d1 = (double)(Areas[0] + Areas[1] + Areas[5] + Areas[6]);        double d2 = (double)(Areas[2] + Areas[7]);  double d3 = (double)(Areas[3] + Areas[4] + Areas[8] + Areas[9]);DEBUG_PRINT_WITH_TIME("if: Step 2: %f,  %f", d1 / d2 ,  d3 / d2);if (d1 / d2 < 2.6 || d3 / d2 < 2.6){DEBUG_PRINT_WITH_TIME("1");return 0;}//3、鼻子的像素正常比鼻子两边像素多些d1 = (double)(Areas[12] + Areas[17]);d2 = (double)(Areas[11] + Areas[16]);double d4 = (double)(Areas[13] + Areas[18]);DEBUG_PRINT_WITH_TIME("if: Step 3: %f,  %f",  d1 / d2 , d3 / d4);if (d1 / d2 < 1 || d1 / d4 < 1){DEBUG_PRINT_WITH_TIME("1");return 0;}//4、眼睛的像素比眼睛下边的脸像素少些，两边都是，因为考虑到还有胡子d1 = (double)(Areas[0] + Areas[1] + Areas[5] + Areas[6]);d2 = (double)(Areas[10] + Areas[11] + Areas[15]);d3 = (double)(Areas[3] + Areas[4] + Areas[8] + Areas[9]);d4 = (double)(Areas[13] + Areas[14] + Areas[19]);   DEBUG_PRINT_WITH_TIME("if: Step 4: %f,  %f",  d1 / d2 , d3 / d4);if (d1 / d2 < 1.3 || d3 / d4 < 1.3){DEBUG_PRINT_WITH_TIME("1");return 0;}//5、上边脸跟嘴和下巴像素的比值d1 = (double)(Areas[0] + Areas[1] + Areas[2] + Areas[3] + Areas[4] + Areas[5] + Areas[6] + Areas[7] + Areas[8] + Areas[9]);d2 = (double)(Areas[15] + Areas[16] + Areas[17] + Areas[18] + Areas[19] + Areas[20] + Areas[21] + Areas[22] + Areas[23] + Areas[24]);DEBUG_PRINT_WITH_TIME("if: Step 5: %f", d1 / d2);if ( d1 / d2 > 2){DEBUG_PRINT_WITH_TIME("1");return 0;}//6、眼睛和嘴巴所占的像素比值不能太小d1 = (double)(Areas[0] + Areas[1] + Areas[3] + Areas[4] + Areas[5] + Areas[6] + Areas[8] + Areas[9] + Areas[12] + Areas[16] + Areas[17] + Areas[18] + Areas[22]);DEBUG_PRINT_WITH_TIME("if: Step 6: %f", d1 / w_all);if (d1/ w_all < 0.6){DEBUG_PRINT_WITH_TIME("1");return 0;}//7、脸左边跟脸右边的比值不能差太多d1 = (double)(Areas[0] + Areas[1] + Areas[5] + Areas[6] + Areas[10] + Areas[11]  + Areas[15] + Areas[16]  + Areas[20] + Areas[21]);d2 = (double)(Areas[3] + Areas[4] + Areas[8] + Areas[9] + Areas[13] + Areas[14] + Areas[18] + Areas[19] + Areas[23] + Areas[24]);double PCT_1 = (double)min(d1, d2)/ max(d1, d2);PCT_1 = exp(-3.125 * (PCT_1 - 1) * (PCT_1 - 1)) * 100;//8、两只眼睛的比值不能差太多d1 = (double)(Areas[0] + Areas[1] + Areas[5] + Areas[6]);d2 = (double)(Areas[3] + Areas[4] + Areas[8] + Areas[9]);double PCT_2 = (double)min(d1, d2) / max(d1, d2);PCT_2 = exp(-3.125 * (PCT_2 - 1) * (PCT_2 - 1)) * 100;//9、两腮的比值不能差太多d1 = (double)(Areas[15] + Areas[20]);d2 = (double)(Areas[19] + Areas[24]);double PCT_3 = (double)min(d1, d2) / max(d1, d2);PCT_3 = exp(-3.125 * (PCT_3 - 1) * (PCT_3 - 1)) * 100;// 计算总的概率double PCT_all = (PCT_1 + PCT_2 + PCT_3) / 3;DEBUG_PRINT_WITH_TIME("PCT_all: %f, PCT_1: %f, PCT_2: %f, PCT_3: %f", PCT_all, PCT_1, PCT_2, PCT_3)if (PCT_all > 60)return PCT_all;
}// 人脸数据结构体
typedef struct tagFACEDATE
{int x0;int y0;int x1;int y1;double confidence;
}FACEDATE;//滑动窗口区域（训练用）
FACEDATE MoveWindowArea(IMAGE_SELF imageOld, IGIMAGE_SELF IGmap)
{FACEDATE maxFaceDate = { 0, 0, 0 };       // 保存概率最大的人脸区域double confidence = 0;              // 置信度int minSide = min(imageOld.width, imageOld.height) / 4;  // 最小区域int step = 5;                  // 区域每次的增加量DEBUG_PRINT_WITH_TIME("minSide: %d", minSide)setinitmode(0);initgraph(imageOld.width, imageOld.height);int heightStep = imageOld.height / 10;int widthStep = imageOld.width / 10;setcolor(RED);int miniStep = 3;setlinewidth(2);int FaceWidth = 97;int FaceHeight = 100;int MoveStepSize = 15;int x0 = 0;int y0 = 0;for(int i = 0; i * MoveStepSize < imageOld.width - FaceWidth; i++) //height{x0 = i * MoveStepSize;for(int j = 0 ; j * MoveStepSize < imageOld.height - FaceHeight; j++){y0 = j * MoveStepSize;for(int k = 0; k <= 20; k++){int x1 = x0 + 97;int y1 = y0 + 100;if(x1 > imageOld.width || y1 > imageOld.height){continue;	//TODO}for(unsigned int i = imageOld.height - 1; i > 0 ; i--){for(unsigned int j = 0; j < imageOld.width; j++){unsigned int point = (imageOld.height - i) * imageOld.width + j; putpixel(j, i, EGERGB(imageOld.data[point].red, imageOld.data[point].green, imageOld.data[point].blue));}}rectangle(x0, y0, x1, y1);int xStep = abs(x0-x1)/5;int yStep = abs(y0-y1)/5;for(int j = 1; j < 5; j++){line(x0 + xStep * j, y0, x0 + xStep * j, y1 );line(x0, y0 + yStep * j, x1, y0 + yStep * j );}double confidenceTemp = Classifier_decisionStump(IGmap, x0, y0, x1, y1);printf("confidenceTemp: %lf\n\n", confidenceTemp);/*DEBUG_PRINT_WITH_TIME("confidenceTemp: %lf", confidenceTemp);if ((confidence = confidenceTemp) > 1 && confidence > maxFaceDate.confidence){maxFaceDate.confidence = confidence;maxFaceDate.x0 = x0;maxFaceDate.y0 = y0;maxFaceDate.x1 = x1;maxFaceDate.y1 = y1;}*/Sleep(100);}		}Sleep(100);}// 窗口区域的取值范围getch();closegraph();return maxFaceDate;}// 画出人框
void Image_draw(IMAGE_SELF imageOld ,FACEDATE faceDate)
{}int main()
{DEBUG_PRINT_WITH_TIME("main2 start....");char loadFilename[300] = "InputTest_01.bmp";char saveFilename[300] = "456.bmp";// 用于处理IMAGE_SELF image1 = Image_load(loadFilename);// 用于保存IMAGE_SELF image2 = Image_load(loadFilename);Image_save("test_image2.bmp", image2);// 灰度图IMAGE_SELF image3 = Transform_color_grayscale(image1, GRAY_MODE_WEIGHT);Image_save("test_image3.bmp", image3);// 均值滤波IMAGE_SELF image4 = Wavefiltering_Average(image3);Image_save("test_image4.bmp", image4);// 二值图加边缘检测IMAGE_SELF image5 = Transform_color_BW_Adaptive(image4, 25);Image_save("test_image5.bmp", image5);// 积分图IGIMAGE_SELF IGmap1 = IntegralImage_get(image5);//return 0;IMAGE_SELF image36;image36.width = IGmap1.width;image36.height = IGmap1.height;image36.data = (BGRA *)malloc(sizeof(BGRA) * IGmap1.width * IGmap1.height);for (unsigned int i = 0; i < IGmap1.width * IGmap1.height; i++){image36.data[i].red = IGmap1.data[i] % 256;image36.data[i].green = IGmap1.data[i] / 256 % 256;image36.data[i].blue = IGmap1.data[i] / 256 / 256 % 256;image36.data[i].transparency = 0;//if (i %1000 == 0)//	DEBUG_PRINT_WITH_TIME("w: %d, h: %d, i: %d", image35.width, image35.h, i);}Image_save("test_image6.bmp", image36);	unsigned int *p = IGmap1.data;DEBUG_PRINT_WITH_TIME("1")for(unsigned int i = 0; i < IGmap1.width * IGmap1.height; i++){//printf("%d ", p[i]);if((i + 1) % IGmap1.width == 0 ){//putchar('\n');}}// 滑动窗口FACEDATE faceDate1 = MoveWindowArea(image5, IGmap1);//DEBUG_PRINT_WITH_TIME("%d, %d, %f", faceDate1.leftBottom, faceDate1.rightTop, faceDate1.confidence);//return 0;// 画出人脸框//Image_draw(image2, faceDate1);// 保存图片Image_save(saveFilename, image2);// 释放积分图IntegralImage_free(IGmap1);// 释放图片资源 Image_free(image1);Image_free(image2);//Image_show(saveFilename);return 0;
}


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