matlab实现hog特征

时间：2021-04-08 15:50:47
%%matlab实现hog特征

%修改自http://www.cnblogs.com/tiandsp/archive/2013/05/24/3097503.html

%input: img

%output: final_descriptor

clear all; close all; clc;

%img=double(imread('lena.jpg'));

%img=imread('man.png');

img=imread('e:/work/matlab/data/252.jpg');

img=rgb2gray(img); %简单起见，彩图转灰度图。后续可以改进。

img=imresize(img, [128 64]);

img=double(img);

[h, w, ~] = size(img);

%下面是求cell

cell_size=8;     %step*step个像素作为一个cell. cell_size=pixels_per_cell

orient=9;   %方向直方图包含的方向数

angle_range=180/orient; %每个方向包含的角度数

h=round(h/cell_size)*cell_size;

w=round(w/cell_size)*cell_size;

img=img(1:h,1:w,:);

%img = sqrt(img); %伽马校正。J=AI^r 此处取A=1,r=0.5

% 下面是求边缘

fy=[-1 0 1]; %定义竖直模版

fx=fy';      %定义水平模版

Gy=imfilter(img, fy, 'replicate'); %竖直梯度

Gx=imfilter(img, fx, 'replicate'); %水平梯度

Gmag=sqrt(Gx.^2+Gy.^2);            %梯度幅值

%为每个cell计算其decriptor(梯度方向直方图，即一个1*orient规格的向量）

cell_descriptors=zeros(orient, h/cell_size, w/cell_size);

idx_y=1;

for y=1:cell_size:h

    idx_x=1;

    for x=1:cell_size:w

        tmpx=Gx(y:y+cell_size-1, x:x+cell_size-1);

        tmpy=Gy(y:y+cell_size-1, x:x+cell_size-1);

        tmped=Gmag(y:y+cell_size-1,x:x+cell_size-1);

        tmped=tmped/sum(sum(tmped)); %局部边缘强度归一化

        cell_hist=zeros(1, orient); %当前cell_size*cell_size像素统计角度直方图，就是cell

        for p=1:cell_size

            for q=1:cell_size

                ang=atan2(tmpy(p,q), tmpx(p,q));   %atan2返回的是[-pi,pi]之间的弧度值

                ang=mod(ang*180/pi, 180);   %先转角度，再划归到[0,180)之间。因为mod的参数现在不是整数，因此会大于179.

                ang=ang+0.0000001; %防止ang为0

                bin_id = ceil(ang/angle_range);%得到的bin_id \in [1,9]

                cell_hist(bin_id)=cell_hist(bin_id)+tmped(p,q); %ceil向上取整，使用边缘强度加权。此处根据梯度方向进行vote，权值为梯度幅值

            end

        end

        cell_descriptors(:,idx_y,idx_x) = cell_hist;

        idx_x = idx_x + 1;

    end

    idx_y = idx_y + 1;

end    

%下面是计算feature,block_size*block_size个cell合成一个block

%比如block_size取2

[~, h, w]=size(cell_descriptors);

block_size=2; %cells_per_block=2，即每个block_size=2*8=16像素

stride=1;

h_max=floor((h-block_size)/stride)+1;

w_max=floor((w-block_size)/stride)+1;

block_descriptors=zeros(block_size*block_size*orient, h_max, w_max);

for i=1:h_max

    for j=1:w_max

        blk_mat=cell_descriptors(:,i:i+block_size-1, j:j+block_size-1);

        normed_blk_mat=zz_normalize(blk_mat);

        reshaped_blk_mat=reshape(normed_blk_mat, [1 block_size*block_size*orient]);

        block_descriptors(:,i,j)=reshaped_blk_mat;

    end

end

%将block_descriptors进行拼接，得到final_descriptor

[d1,d2,d3]=size(block_descriptors);

dimensions=d1*d2*d3;

final_descriptor=zeros(1, dimensions);

k=1;

for i=1:d2

    for j=1:d3

        final_descriptor(k:k+d1-1)=block_descriptors(:,i,j);

        k=k+d1;

    end

end

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