context.m

pkg load image

function [N] = naive_corr(pat,img)
    [n,m] = size(img);
    [np,mp] = size(pat);
    N = zeros(n-np+1,m-mp+1);
    for i = 1:n-np+1
        for j = 1:m-mp+1
            N(i,j) = sum(dot(pat,img(i:i+np-1,j:j+mp-1)));
        end
    end
end


%w_arr the array of coefficients for the boxes
%box_arr of size [k,4] where k is the number boxes, each box represented by
%4 something ...
function [C] = box_corr2(img,box_arr,w_arr,n_p,m_p)

    % construct integral image + zeros pad (for boundary problems)
    I = cumsum(cumsum(img,2),1);
    I = [zeros(1,size(I,2)+2); [zeros(size(I,1),1) I zeros(size(I,1),1)]; zeros(1,size(I,2)+2)];

    % initialize result matrix
    [n,m] = size(img);
    C = zeros(n-n_p+1,m-m_p+1);
    %C = zeros(n,m);

    jump_x = 1;
    jump_y = 1;

    x_start = ceil(n_p/2);
    x_end = n-x_start+mod(n_p,2);
    x_span = x_start:jump_x:x_end;

    y_start = ceil(m_p/2);
    y_end = m-y_start+mod(m_p,2);
    y_span = y_start:jump_y:y_end;

    arr_a = box_arr(:,1) - x_start;
    arr_b = box_arr(:,2) - x_start+1;
    arr_c = box_arr(:,3) - y_start;
    arr_d = box_arr(:,4) - y_start+1;

    % cumulate box responses
    k = size(box_arr,1); % == numel(w_arr)

    for i = 1:k
        a = arr_a(i);
        b = arr_b(i);
        c = arr_c(i);
        d = arr_d(i);

        C = C ...
            + w_arr(i) * ( I(x_span+b,y_span+d) ...
                           - I(x_span+b,y_span+c) ...
                           - I(x_span+a,y_span+d) ...
                           + I(x_span+a,y_span+c) );
    end

end



function [NCC]  = naive_normxcorr2(temp,img)

    [n_p,m_p]=size(temp);

    M = n_p*m_p;

    % compute template mean & std
    temp_mean = mean(temp(:));
    temp = temp - temp_mean;

    temp_std = sqrt(sum(temp(:).^2)/M);

    % compute windows' mean & std
    wins_mean =  box_corr2(img,[1,n_p,1,m_p],1/M,  n_p,m_p);
    wins_mean2 = box_corr2(img.^2,[1,n_p,1,m_p],1/M,n_p,m_p);


    wins_std = real(sqrt(wins_mean2 - wins_mean.^2));
    NCC_naive = naive_corr(temp,img);

    NCC = NCC_naive ./ (M .* temp_std .* wins_std);
end

n = 170;

particle_1=rand(54,54,n);
particle_2=rand(56,56,n);

[n_p1,m_p1,c_p1]=size(particle_1);
[n_p2,m_p2,c_p2]=size(particle_2);

L1 = zeros(n,1);
L2 = zeros (n,1);


tic
for i=1:n
    C1=normxcorr2(particle_1(:,:,i),particle_2(:,:,i));

    C1_unpadded = C1(n_p1:n_p2 , m_p1:m_p2);
    L1(i)=max(C1_unpadded(:));

end
toc


tic
for i=1:n

    C2=naive_normxcorr2(particle_1(:,:,i),particle_2(:,:,i));
    L2(i)=max(C2(:));
end
toc

context.py

from PIL import Image
import numpy as npy





img_1 = npy.asarray(Image.open('image_1.jpg').convert('L'))
print(img_1)

[x, y] = img_1.shape
print(x, 'x')

N = npy.zeros(shape=(5,5))
print(N, 'N')

# N(3, 3) = 40
N[3][3] = 40
print(N[3][3], 'the entry 3,3')







def naive_corr(pat, img):
    [n, m] = img.shape
    [np, mp] = pat.shape
    N = npy.zeros(shape=(n - np + 1, m - mp + 1))
    for i in range(0, n - np + 1):
        for j in range(0, m - mp + 1):
            img_sub = img[i : i + np : 1, j : j + mp : 1 ]
            N[i][j] = npy.sum(npy.dot(pat, img_sub))
    return N
            




def box_corr2(img, box_arr, w_arr, n_p, m_p):
    return 0






def naive_normxcorr2(temp, img):
    return 0

frag.m

N(i,j) = sum(dot(pat,img(i:i+np-1,j:j+mp-1)));

but you say img is same size as pat

pat and img are both matrices of dimensions npXmp.

this cannot be true if we are to eval dot(pat, img(i:i+np-1,j:j+mp-1))); unless the img indexing evals to same size matrix as img itself, which would be odd and only possible if the indexes can wrap around the size limitation.

shouldn't the dot operation produce a scalar ? so why is it summed ? there should only be a single scalar to sum .
because the dot product on matrices is actually matrix multiplication, and the sum operation takes the sum of all the entries of the matrix. right ?

    I = cumsum(cumsum(img,2),1);
    I = [zeros(1,size(I,2)+2); [zeros(size(I,1),1) I zeros(size(I,1),1)]; zeros(1,size(I,2)+2)];

confused about these lines. is I of cumsum just a scalar ?
if so, what is the meaning of size of this scalar ?