车牌识别--倾斜矫正

在车牌识别系统中， 车牌字符能够正确分割的前提是车牌图像能够水平，以至于水平投影和垂直投影能够正常进行。如果车牌倾斜没有矫正，那么水平投影和垂直投影，甚至铆钉都无法正常处理。所以，当车辆信息中获取车牌的第一步，应该是检查倾斜角度，做倾斜矫正。

倾斜矫正，这里使用的算法：

1、倾斜角度检测： 霍夫变换

关于hough变换，可以参考前面图像处理博文：

http://blog.csdn.net/liujia2100/article/details/6989693   直线检测

http://blog.csdn.net/liujia2100/article/details/6989688   文本图像倾斜矫正

2、倾斜矫正： 图像旋转

下面详细说明倾斜矫正过程：

原车牌图像为（从车牌图像中，可以看到车牌有倾斜角度）：

1、 获取车牌在车辆中的粗略位置（可以用多种方法，这里暂不分析）

2、提取车牌整体图片数据， 根据第一步结果，提取出，车牌在辆大体位置信息。

关于车牌定位，我使用两部，第一步粗略定位，然后做一些预处理，比如倾斜矫正，然后第二部才是精确定位，只提取车牌的位置信息图像

3、利用HSV颜色空间转换，获取车牌背景蓝色区域位置，获取车牌粗略信息图像后，由于车牌背景颜色与周围颜色有很明显的区别，这里采用HSV颜色过滤的方法，过滤绿色背景图像

4、水平膨胀， 水平膨胀的目的，是为了边缘检测，只要求检测边缘，尽量除去字符信息，也可以降低hough变换的运算量

5、水平差分运算，相当于 边缘检测，经过上面的处理后，才进行边缘检测

6、这个时候就可以利用hough变换检测直线了。

由于hough变换运算量十分大，所以，尽量减少图像中的白点，来降低计算量，因此前面才做了这么多步骤。

请看下图的红线，就是检测出来的角度，为177度（Hough代码在下面）。

7、利用旋转算法，旋转刚才粗略提取的车牌位置（旋转代码在下面），尽管旋转后的车牌有些锯齿，但是已经能够保证水平，就可以使用水平投影和垂直投影了

这是旋转后的车牌，有些锯齿出现，由于图像分辨率较低，就没有用差值运算。

8、精确提取车牌

9、正常分割字符

10、识别结果

由于正弦余弦运算，计算量比较大，这里进行一部分优化，就是正弦余弦计算用数组代替。

生成正弦，余弦数组的的代码如下：

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>

int main(void)
{
	char buf[20];
	int i;
	float p;
	float k;
	FILE *fcos;
	FILE *fsin;
	
	fcos = fopen(".\\cos.txt", "wb");
	fsin = fopen(".\\sin.txt", "wb");
	
	if(fcos == NULL || fsin == NULL)
	{
		printf("open error\n");
		exit(-1);
	}


	i = 0;

	for(i = 0; i <= 180; i++)
	{
		k = 3.1415926 * i / 180.0;
		p = cos(k);
		
				
		if((i%16 == 0))
			fwrite("\n",strlen("\n"),1,fcos);
		
		sprintf(buf,"%f, ", p);
		fwrite(buf,strlen(buf),1,fcos);

	}

	for(i = 0; i <= 180; i++)
	{
		k = 3.1415926 * i / 180.0;
		p = sin(k);
		
				
		if((i%16 == 0))
			fwrite("\n",strlen("\n"),1,fsin);
		
		sprintf(buf,"%f, ", p);
		fwrite(buf,strlen(buf),1,fsin);

	}
	fclose(fcos);
	fclose(fsin);
	return 0;
}

生成数组为：

float carCos[] = {
1.000000, 0.999848, 0.999391, 0.998630, 0.997564, 0.996195, 0.994522, 0.992546, 0.990268, 0.987688, 0.984808, 0.981627, 0.978148, 0.974370, 0.970296, 0.965926, 
0.961262, 0.956305, 0.951057, 0.945519, 0.939693, 0.933580, 0.927184, 0.920505, 0.913545, 0.906308, 0.898794, 0.891007, 0.882948, 0.874620, 0.866025, 0.857167, 
0.848048, 0.838671, 0.829038, 0.819152, 0.809017, 0.798636, 0.788011, 0.777146, 0.766044, 0.754710, 0.743145, 0.731354, 0.719340, 0.707107, 0.694658, 0.681998, 
0.669131, 0.656059, 0.642788, 0.629320, 0.615662, 0.601815, 0.587785, 0.573576, 0.559193, 0.544639, 0.529919, 0.515038, 0.500000, 0.484810, 0.469472, 0.453991, 
0.438371, 0.422618, 0.406737, 0.390731, 0.374607, 0.358368, 0.342020, 0.325568, 0.309017, 0.292372, 0.275637, 0.258819, 0.241922, 0.224951, 0.207912, 0.190809, 
0.173648, 0.156434, 0.139173, 0.121869, 0.104528, 0.087156, 0.069757, 0.052336, 0.034900, 0.017452, 0.000000, -0.017452, -0.034899, -0.052336, -0.069756, -0.087156, 
-0.104528, -0.121869, -0.139173, -0.156434, -0.173648, -0.190809, -0.207912, -0.224951, -0.241922, -0.258819, -0.275637, -0.292372, -0.309017, -0.325568, -0.342020, -0.358368, 
-0.374607, -0.390731, -0.406737, -0.422618, -0.438371, -0.453990, -0.469472, -0.484810, -0.500000, -0.515038, -0.529919, -0.544639, -0.559193, -0.573576, -0.587785, -0.601815, 
-0.615661, -0.629320, -0.642788, -0.656059, -0.669131, -0.681998, -0.694658, -0.707107, -0.719340, -0.731354, -0.743145, -0.754710, -0.766044, -0.777146, -0.788011, -0.798635, 
-0.809017, -0.819152, -0.829038, -0.838671, -0.848048, -0.857167, -0.866025, -0.874620, -0.882948, -0.891007, -0.898794, -0.906308, -0.913545, -0.920505, -0.927184, -0.933580, 
-0.939693, -0.945519, -0.951056, -0.956305, -0.961262, -0.965926, -0.970296, -0.974370, -0.978148, -0.981627, -0.984808, -0.987688, -0.990268, -0.992546, -0.994522, -0.996195, 
-0.997564, -0.998630, -0.999391, -0.999848, -1.000000
};

float carSin[] = {
0.000000, 0.017452, 0.034899, 0.052336, 0.069756, 0.087156, 0.104528, 0.121869, 0.139173, 0.156434, 0.173648, 0.190809, 0.207912, 0.224951, 0.241922, 0.258819, 
0.275637, 0.292372, 0.309017, 0.325568, 0.342020, 0.358368, 0.374607, 0.390731, 0.406737, 0.422618, 0.438371, 0.453990, 0.469472, 0.484810, 0.500000, 0.515038, 
0.529919, 0.544639, 0.559193, 0.573576, 0.587785, 0.601815, 0.615661, 0.629320, 0.642788, 0.656059, 0.669131, 0.681998, 0.694658, 0.707107, 0.719340, 0.731354, 
0.743145, 0.754710, 0.766044, 0.777146, 0.788011, 0.798635, 0.809017, 0.819152, 0.829038, 0.838671, 0.848048, 0.857167, 0.866025, 0.874620, 0.882948, 0.891007, 
0.898794, 0.906308, 0.913545, 0.920505, 0.927184, 0.933580, 0.939693, 0.945519, 0.951056, 0.956305, 0.961262, 0.965926, 0.970296, 0.974370, 0.978148, 0.981627, 
0.984808, 0.987688, 0.990268, 0.992546, 0.994522, 0.996195, 0.997564, 0.998630, 0.999391, 0.999848, 1.000000, 0.999848, 0.999391, 0.998630, 0.997564, 0.996195, 
0.994522, 0.992546, 0.990268, 0.987688, 0.984808, 0.981627, 0.978148, 0.974370, 0.970296, 0.965926, 0.961262, 0.956305, 0.951057, 0.945519, 0.939693, 0.933580, 
0.927184, 0.920505, 0.913545, 0.906308, 0.898794, 0.891007, 0.882948, 0.874620, 0.866025, 0.857167, 0.848048, 0.838671, 0.829038, 0.819152, 0.809017, 0.798636, 
0.788011, 0.777146, 0.766044, 0.754710, 0.743145, 0.731354, 0.719340, 0.707107, 0.694658, 0.681998, 0.669131, 0.656059, 0.642788, 0.629320, 0.615662, 0.601815, 
0.587785, 0.573576, 0.559193, 0.544639, 0.529919, 0.515038, 0.500000, 0.484810, 0.469472, 0.453991, 0.438371, 0.422618, 0.406737, 0.390731, 0.374607, 0.358368, 
0.342020, 0.325568, 0.309017, 0.292372, 0.275637, 0.258819, 0.241922, 0.224951, 0.207912, 0.190809, 0.173648, 0.156435, 0.139173, 0.121869, 0.104529, 0.087156, 
0.069757, 0.052336, 0.034900, 0.017452, 0.000000
};

旋转RGB图像的主要函数：

image： 图像数据

iRotateAngle: 要旋转的角度

width， height：原始图像的宽度，高度

lwidth，lheight：旋转后图像的宽度，高度

unsigned char *RotateRGB(unsigned char *image, float iRotateAngle,int width,int height,int &lwidth,int &lheight)
{

    int i,j,k,m,n; 
	long	lNewWidth;
	long	lNewHeight;
  	float gray;
	long	i0;
	long	j0;
	float	fRotateAngle;
	float	fSina, fCosa;
	float	fSrcX1,fSrcY1,fSrcX2,fSrcY2,fSrcX3,fSrcY3,fSrcX4,fSrcY4;
	float	fDstX1,fDstY1,fDstX2,fDstY2,fDstX3,fDstY3,fDstX4,fDstY4;

	float	f1,f2;


	if(iRotateAngle >= 0)
	{
		fSina = (float)carSin[(int)iRotateAngle];
		fCosa = (float)carCos[(int)iRotateAngle];
	}
	else
	{
		fSina = 0 - (float)carSin[0 -(int)iRotateAngle];
		fCosa = (float)carCos[0 - (int)iRotateAngle];
	}

	fSrcX1 = (float) (- (width  - 1) / 2);
	fSrcY1 = (float) (  (height - 1) / 2);
	fSrcX2 = (float) (  (width  - 1) / 2);
	fSrcY2 = (float) (  (height - 1) / 2);
	fSrcX3 = (float) (- (width  - 1) / 2);
	fSrcY3 = (float) (- (height - 1) / 2);
	fSrcX4 = (float) (  (width  - 1) / 2);
	fSrcY4 = (float) (- (height - 1) / 2);
	

	fDstX1 =  fCosa * fSrcX1 + fSina * fSrcY1;
	fDstY1 = -fSina * fSrcX1 + fCosa * fSrcY1;
	fDstX2 =  fCosa * fSrcX2 + fSina * fSrcY2;
	fDstY2 = -fSina * fSrcX2 + fCosa * fSrcY2;
	fDstX3 =  fCosa * fSrcX3 + fSina * fSrcY3;
	fDstY3 = -fSina * fSrcX3 + fCosa * fSrcY3;
	fDstX4 =  fCosa * fSrcX4 + fSina * fSrcY4;
	fDstY4 = -fSina * fSrcX4 + fCosa * fSrcY4;

	lNewWidth  = (long) ( max( fabs(fDstX4 - fDstX1), fabs(fDstX3 - fDstX2) ) + 0.5);

	lNewHeight = (long) ( max( fabs(fDstY4 - fDstY1), fabs(fDstY3 - fDstY2) )  + 0.5);
    unsigned char *temp=myMalloc(lNewHeight*lNewWidth*3,0,0);

	f1 = (float) (-0.5 * (lNewWidth - 1) * fCosa - 0.5 * (lNewHeight - 1) * fSina
		+ 0.5 * (width  - 1));
	f2 = (float) ( 0.5 * (lNewWidth - 1) * fSina - 0.5 * (lNewHeight - 1) * fCosa
		+ 0.5 * (height - 1));

	for(i = 0; i < lNewHeight; i++)
	{
		for(m=0,j = 0;j < lNewWidth,m<lNewWidth*3;m+=3,j++)
		{
			i0 = (long) (-((float) j) * fSina + ((float) i) * fCosa + f2 + 0.5);
			j0 = (long) ( ((float) j) * fCosa + ((float) i) * fSina + f1 + 0.5);
			
			if( (j0 >= 0) && (j0 < width) && (i0 >= 0) && (i0 < height))
			{
				 n=i0 * width * 3 + j0 * 3;
			     *(temp + lNewWidth * i * 3 + m + 1) = *(image + n + 1);
				 *(temp + lNewWidth * i * 3 + m + 2) = *(image + n + 2);
				 *(temp + lNewWidth * i * 3 + m) = *(image + n);
			}
			else
			{
				 *(temp + lNewWidth * i*3+ m+1)=0;
				 *(temp + lNewWidth * i*3+ m+2)=0;
				 *(temp + lNewWidth * i*3+ m)=0;
			}
		}
	}

  	lwidth = lNewWidth;
	lheight = lNewHeight;

	return temp;
	
}

Hough变化的主要函数

返回值Kmax，就是检测到最长直线的角度，就是车牌的倾斜角度。

int hough(unsigned char *srcBmp,int width,int height)
{
    int kmax=0;
    int pmax=0;
	int yuzhi=0;
	int i,j,k,m,n,p;
	int mp = (int) (sqrt(width*width + height*height)+1);
	int ma = 180;//180
	int ap;
	int npp[180][1000];
	for(i=0;i<180;i++)
		for(j=0;j<1000;j++)
		npp[i][j]=0;
		
	for(i = 0;i < height;i++)
		for(j = 0; j < width;j++)
		{
			if(srcBmp[i * width + j]==255)
			{ 
				for(k = 0; k < ma; k++)
				{
					p=(int)(i * carCos[k] + j * carSin[k]);
					p=(int)(p/2 + mp/2);
					npp[k][p]=npp[k][p]++;
				} 
			}
		}
		kmax=0;
		pmax=0;
		n=0;
		for(i = 0; i < ma; i++)
			for(j = 0; j < mp; j++)
			{
				if(npp[i][j] > yuzhi)
				{
					yuzhi=npp[i][j];
					kmax=i;
					pmax=j;
				}
			}
			for(i = 0; i < height;i++)
				for(j = 0; j < width;j++)
				{
					if(srcBmp[i*width+j]==255)
					{ 
				    	p=(int)(i*carCos[kmax] + j *carSin[kmax]);
						p=(int)(p/2+mp/2);
#if defined(DISPLAYDEBUG)
						if(p==pmax)
					  	putpixel(j,i,RGB(255,0,0));
#endif
					}
				}
				return kmax;
}

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