【图像算法】图像特征：
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一 原理
几何矩是由Hu(Visual pattern recognition by moment invariants)在1962年提出的，具有平移、旋转和尺度不变性。 定义如下：
① (p+q)阶不变矩定义：
② 对于数字图像，离散化，定义为：
③ 归一化中心矩定义：
④Hu矩定义
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二 实现(源码)
①自编函数模块C++
//#################################################################################//
double M[7] = {0}; //HU不变矩
bool HuMoment(IplImage* img)
{
int bmpWidth = img->width;
int bmpHeight = img->height;
int bmpStep = img->widthStep;
int bmpChannels = img->nChannels;
uchar*pBmpBuf = (uchar*)img->imageData; double m00=0,m11=0,m20=0,m02=0,m30=0,m03=0,m12=0,m21=0; //中心矩
double x0=0,y0=0; //计算中心距时所使用的临时变量（x-x'）
double u20=0,u02=0,u11=0,u30=0,u03=0,u12=0,u21=0;//规范化后的中心矩
//double M[7]; //HU不变矩
double t1=0,t2=0,t3=0,t4=0,t5=0;//临时变量，
//double Center_x=0,Center_y=0;//重心
int Center_x=0,Center_y=0;//重心
int i,j; //循环变量 // 获得图像的区域重心(普通矩)
double s10=0,s01=0,s00=0; //0阶矩和1阶矩
for(j=0;j<bmpHeight;j++)//y
{
for(i=0;i<bmpWidth;i++)//x
{
s10+=i*pBmpBuf[j*bmpStep+i];
s01+=j*pBmpBuf[j*bmpStep+i];
s00+=pBmpBuf[j*bmpStep+i];
}
}
Center_x=(int)(s10/s00+0.5);
Center_y=(int)(s01/s00+0.5); // 计算二阶、三阶矩(中心矩)
m00=s00;
for(j=0;j<bmpHeight;j++)
{
for(i=0;i<bmpWidth;i++)//x
{
x0=(i-Center_x);
y0=(j-Center_y);
m11+=x0*y0*pBmpBuf[j*bmpStep+i];
m20+=x0*x0*pBmpBuf[j*bmpStep+i];
m02+=y0*y0*pBmpBuf[j*bmpStep+i];
m03+=y0*y0*y0*pBmpBuf[j*bmpStep+i];
m30+=x0*x0*x0*pBmpBuf[j*bmpStep+i];
m12+=x0*y0*y0*pBmpBuf[j*bmpStep+i];
m21+=x0*x0*y0*pBmpBuf[j*bmpStep+i];
}
} // 计算规范化后的中心矩: mij/pow(m00,((i+j+2)/2)
u20=m20/pow(m00,2);
u02=m02/pow(m00,2);
u11=m11/pow(m00,2);
u30=m30/pow(m00,2.5);
u03=m03/pow(m00,2.5);
u12=m12/pow(m00,2.5);
u21=m21/pow(m00,2.5); // 计算中间变量
t1=(u20-u02);
t2=(u30-3*u12);
t3=(3*u21-u03);
t4=(u30+u12);
t5=(u21+u03); // 计算不变矩
M[0]=u20+u02;
M[1]=t1*t1+4*u11*u11;
M[2]=t2*t2+t3*t3;
M[3]=t4*t4+t5*t5;
M[4]=t2*t4*(t4*t4-3*t5*t5)+t3*t5*(3*t4*t4-t5*t5);
M[5]=t1*(t4*t4-t5*t5)+4*u11*t4*t5;
M[6]=t3*t4*(t4*t4-3*t5*t5)-t2*t5*(3*t4*t4-t5*t5); returntrue;
}
②调用OpenCV方法
1 // 利用OpenCV函数求7个Hu矩
2 CvMoments moments;
3 CvHuMoments hu;
4 cvMoments(bkImgEdge,&moments,0);
5 cvGetHuMoments(&moments, &hu);
6 cout<<hu.hu1<<"/"<<hu.hu2<<"/"<<hu.hu3<<"/"<<hu.hu4<<"/"<<hu.hu5<<"/"<<hu.hu6<<"/"<<hu.hu7<<"/"<<"/"<<endl;
7 cvMoments(testImgEdge,&moments,0);
8 cvGetHuMoments(&moments, &hu);
9 cout<<hu.hu1<<"/"<<hu.hu2<<"/"<<hu.hu3<<"/"<<hu.hu4<<"/"<<hu.hu5<<"/"<<hu.hu6<<"/"<<hu.hu7<<"/"<<"/"<<endl;
Python调用OpenCV：
#-*-coding:utf-8-*-
import cv2
from datetime import datetime
import numpy as np def test(img):
moments = cv2.moments(img)
humoments = cv2.HuMoments(moments)
# humoments = no.log(np.abs(humoments)) # 同样建议取对数
print(humoments) if __name__ == '__main__':
t1 = datetime.now()
fp = '/home/mamq/images/3.jpg'
img = cv2.imread(fp)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
test(img_gray)
print datetime.now() - t1
Python方法：
#-*-coding:utf-8-*-
import cv2
from datetime import datetime
import numpy as np
np.set_printoptions(suppress=True) def humoments(img_gray):
'''
由于7个不变矩的变化范围很大,为了便于比较,可利用取对数的方法进行数据压缩;同时考虑到不变矩有可能出现负值的情况,因此,在取对数之前先取绝对值
经修正后的不变矩特征具有平移 、旋转和比例不变性
'''
# 标准矩定义为m_pq = sumsum(x^p * y^q * f(x, y))
row, col = img_gray.shape
#计算图像的0阶几何矩
m00 = img_gray.sum()
m10 = m01 = 0
# 计算图像的二阶、三阶几何矩
m11 = m20 = m02 = m12 = m21 = m30 = m03 = 0
for i in range(row):
m10 += (i * img_gray[i]).sum()
m20 += (i ** 2 * img_gray[i]).sum()
m30 += (i ** 3 * img_gray[i]).sum()
for j in range(col):
m11 += i * j * img_gray[i][j]
m12 += i * j ** 2 * img_gray[i][j]
m21 += i ** 2 * j * img_gray[i][j]
for j in range(col):
m01 += (j * img_gray[:, j]).sum()
m02 += (j ** 2 * img_gray[:, j]).sum()
m30 += (j ** 3 * img_gray[:, j]).sum()
# 由标准矩我们可以得到图像的"重心"
u10 = m10 / m00
u01 = m01 / m00
# 计算图像的二阶中心矩、三阶中心矩
y00 = m00
y10 = y01 = 0
y11 = m11 - u01 * m10
y20 = m20 - u10 * m10
y02 = m02 - u01 * m01
y30 = m30 - 3 * u10 * m20 + 2 * u10 ** 2 * m10
y12 = m12 - 2 * u01 * m11 - u10 * m02 + 2 * u01 ** 2 * m10
y21 = m21 - 2 * u10 * m11 - u01 * m20 + 2 * u10 ** 2 * m01
y03 = m03 - 3 * u01 * m02 + 2 * u01 ** 2 * m01
# 计算图像的归格化中心矩
n20 = y20 / m00 ** 2
n02 = y02 / m00 ** 2
n11 = y11 / m00 ** 2
n30 = y30 / m00 ** 2.5
n03 = y03 / m00 ** 2.5
n12 = y12 / m00 ** 2.5
n21 = y21 / m00 ** 2.5
# 计算图像的七个不变矩
h1 = n20 + n02
h2 = (n20 - n02) ** 2 + 4 * n11 ** 2
h3 = (n30 - 3 * n12) ** 2 + (3 * n21 - n03) ** 2
h4 = (n30 + n12) ** 2 + (n21 + n03) ** 2
h5 = (n30 - 3 * n12) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n21 - n03) * (n21 + n03) \
* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)
h6 = (n20 - n02) * ((n30 + n12) ** 2 - (n21 + n03) ** 2) + 4 * n11 * (n30 + n12) * (n21 + n03)
h7 = (3 * n21 - n03) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n12 - n30) * (n21 + n03) \
* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)
inv_m7 = [h1, h2, h3, h4, h5, h6, h7]
inv_m7 = np.log(np.abs(inv_m7))
return inv_m7 if __name__ == '__main__':
t1 = datetime.now()
fp = '/home/mamq/images/3.jpg'
img = cv2.imread(fp)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print humoments(img_gray)
print datetime.now() - t1
MATLAB方法：
invariable_moment(imread('lena.jpg'));
function inv_m7 = invariable_moment(in_image)
% 功能：计算图像的Hu的七个不变矩
% 输入：in_image-RGB图像
% 输出：inv_m7-七个不变矩
% 将输入的RGB图像转换为灰度图像
image=rgb2gray(in_image);
%将图像矩阵的数据类型转换成双精度型
image=double(image);
%%%=================计算 、 、 =========================
%计算灰度图像的零阶几何矩
m00=sum(sum(image));
m10=0;
m01=0;
[row,col]=size(image);
for i=1:row
for j=1:col
m10=m10+i*image(i,j);
m01=m01+j*image(i,j);
end
end
%%%=================计算 、 ================================
u10=m10/m00;
u01=m01/m00;
%%%=================计算图像的二阶几何矩、三阶几何矩============
m20 = 0;m02 = 0;m11 = 0;m30 = 0;m12 = 0;m21 = 0;m03 = 0;
for i=1:row
for j=1:col
m20=m20+i^2*image(i,j);
m02=m02+j^2*image(i,j);
m11=m11+i*j*image(i,j);
m30=m30+i^3*image(i,j);
m03=m03+j^3*image(i,j);
m12=m12+i*j^2*image(i,j);
m21=m21+i^2*j*image(i,j);
end
end
%%%=================计算图像的二阶中心矩、三阶中心矩============
y00=m00;
y10=0;
y01=0;
y11=m11-u01*m10;
y20=m20-u10*m10;
y02=m02-u01*m01;
y30=m30-3*u10*m20+2*u10^2*m10;
y12=m12-2*u01*m11-u10*m02+2*u01^2*m10;
y21=m21-2*u10*m11-u01*m20+2*u10^2*m01;
y03=m03-3*u01*m02+2*u01^2*m01;
%%%=================计算图像的归格化中心矩====================
n20=y20/m00^2;
n02=y02/m00^2;
n11=y11/m00^2;
n30=y30/m00^2.5;
n03=y03/m00^2.5;
n12=y12/m00^2.5;
n21=y21/m00^2.5;
%%%=================计算图像的七个不变矩======================
h1 = n20 + n02;
h2 = (n20-n02)^2 + 4*(n11)^2;
h3 = (n30-3*n12)^2 + (3*n21-n03)^2;
h4 = (n30+n12)^2 + (n21+n03)^2;
h5 = (n30-3*n12)*(n30+n12)*((n30+n12)^2-3*(n21+n03)^2)+(3*n21-n03)*(n21+n03)*(3*(n30+n12)^2-(n21+n03)^2);
h6 = (n20-n02)*((n30+n12)^2-(n21+n03)^2)+4*n11*(n30+n12)*(n21+n03);
h7 = (3*n21-n03)*(n30+n12)*((n30+n12)^2-3*(n21+n03)^2)+(3*n12-n30)*(n21+n03)*(3*(n30+n12)^2-(n21+n03)^2);
inv_m7= [h1 h2 h3 h4 h5 h6 h7];
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三 相似性准则
①法一
// 计算相似度1
double dbR =0; //相似度
double dSigmaST =0;
double dSigmaS =0;
double dSigmaT =0;
double temp =0;
{for(int i=0;i<7;i++)
{
temp = fabs(Sa[i]*Ta[i]);
dSigmaST+=temp;
dSigmaS+=pow(Sa[i],2);
dSigmaT+=pow(Ta[i],2);
}}
dbR = dSigmaST/(sqrt(dSigmaS)*sqrt(dSigmaT));
②法二
1 // 计算相似度2
2 double dbR2 =0; //相似度
3 double temp2 =0;
4 double temp3 =0;
5 {for(int i=0;i<7;i++)
6 {
7 temp2 += fabs(Sa[i]-Ta[i]);
8 temp3 += fabs(Sa[i]+Ta[i]);
9 }}
10 dbR2 =1- (temp2*1.0)/(temp3);
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Author: SKySeraph
Email/GTalk: [email protected] QQ：452728574
From： http://www.cnblogs.com/skyseraph/
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作者：skyseraph
出处：http://www.cnblogs.com/skyseraph/
更多精彩请直接访问SkySeraph个人站点：http://skyseraph.com//
Email/GTalk: [email protected]
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