光流法_光流算法MATLAB_光流法_光流法下载_

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Name: Musawir Ali Shah %%% Assignment #3 %%% Project Title: Lucas Kanade Motion Estimation Using Pyramids (Level 4) %%% Note: The project specification says use density of 10 in plotting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Usage: Lucas_Kanade('1.bmp','2.bmp',10) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Lucas_Kanade(file1,file2,density); %% Read Images %% img1 = im2double (imread (file1)); %% Take alternating rows and columns %% [odd1, even1] = split (img1); img2 = im2double (imread (file2)); [odd2, even2] = split (img2); %% Run Lucas Kanade %% [Dx, Dy] = Estimate (odd1, odd2); %% Plot %% figure; [maxI,maxJ]=size(Dx); Dx=Dx(1:density:maxI,1:density:maxJ); Dy=Dy(1:density:maxI,1:density:maxJ); quiver(1:density:maxJ,(maxI):(-density):1,Dx,-Dy,1); axis square; %% Run Lucas Kanade on all levels and interpolate %% function [Dx, Dy] = Estimate (img1, img2) level = 4; half_window_size=2; [m, n] = size (img1); G00 = img1; G10 = img2; if (level>0) G01 = reduce (G00); G11 = reduce (G10); end if (level>1) G02 = reduce (G01); G12 = reduce (G11); end if (level>2) G03 = reduce (G02); G13 = reduce (G12); end if (level>3) G04 = reduce (G03); G14 = reduce (G13); end l = level; for i=level:-1:0, if (l == level) switch (l) case 4, Dx = zeros (size (G04)); Dy = zeros (size (G04)); case 3, Dx = zeros (size (G03)); Dy = zeros (size (G03)); case 2, Dx = zeros (size (G02)); Dy = zeros (size (G02)); case 1, Dx = zeros (size (G01)); Dy = zeros (size (G01)); case 0, Dx = zeros (size (G00)); Dy = zeros (size (G00)); end else Dx = expand (Dx); Dy = expand (Dy); Dx = Dx .* 2; Dy = Dy .* 2; end switch (l) case 4, W = warp (G04, Dx, Dy); [Vx, Vy] = EstimateMotion (W, G14, half_window_size); case 3, W = warp (G03, Dx, Dy); [Vx, Vy] = EstimateMotion (W, G13, half_window_size); case 2, W = warp (G02, Dx, Dy); [Vx, Vy] = EstimateMotion (W, G12, half_window_size); case 1, W = warp (G01, Dx, Dy); [Vx, Vy] = EstimateMotion (W, G11, half_window_size); case 0, W = warp (G00, Dx, Dy); [Vx, Vy] = EstimateMotion (W, G10, half_window_size); end [m, n] = size (W); Dx(1:m, 1:n) = Dx(1:m,1:n) + Vx; Dy(1:m, 1:n) = Dy(1:m, 1:n) + Vy; smooth (Dx); smooth (Dy); l = l - 1; end %% Lucas Kanade on the image sequence at pyramid step %% function [Vx, Vy] = EstimateMotion (W, G1, half_window_size) [m, n] = size (W); Vx = zeros (size (W)); Vy = zeros (size (W)); N = zeros (2*half_window_size+1, 5); for i = 1:m, l = 0; for j = 1-half_window_size:1+half_window_size, l = l + 1; N (l,:) = getSlice (W, G1, i, j, half_window_size); end replace = 1; for j = 1:n, t = sum (N); [v, d] = eig ([t(1) t(2);t(2) t(3)]); namda1 = d(1,1); namda2 = d(2,2); if (namda1 > namda2) tmp = namda1; namda1 = namda2; namda2 = tmp; tmp1 = v (:,1); v(:,1) = v(:,2); v(:,2) = tmp1; end if (namda2 < 0.001) Vx (i, j) = 0; Vy (i, j) = 0; elseif (namda2 > 100 * namda1) n2 = v(1,2) * t(4) + v(2,2) * t(5); Vx (i,j) = n2 * v(1,2) / namda2; Vy (i,j) = n2 * v(2,2) / namda2; else n1 = v(1,1) * t(4) + v(2,1) * t(5); n2 = v(1,2) * t(4) + v(2,2) * t(5); Vx (i,j) = n1 * v(1,1) / namda1 + n2 * v(1,2) / namda2; Vy (i,j) = n1 * v(2,1) / namda1 + n2 * v(2,2) / namda2; end N (replace, :) = getSlice (W, G1, i, j+half_window_size+1, half_window_size); replace = replace + 1; if (replace == 2 * half_window_size + 2) replace = 1; end end end %% The Reduce Function for pyramid %% function result = reduce (ori) [m,n] = size (ori); mid = zeros (m, n); m1 = round (m/2); n1 = round (n/2); result = zeros (m1, n1); w = generateFilter (0.4); for j=1:m, tmp = conv([ori(j,n-1:n) ori(j,1:n) ori(j,1:2)], w); mid(j,1:n1) = tmp(5:2:n+4); end for i=1:n1, tmp = conv([mid(m-1:m,i); mid(1:m,i); mid(1:2,i)]', w); result(1:m1,i) = tmp(5:2:m+4)'; end %% The Expansion Function for pyramid %% function result = expand (ori) [m,n] = size (ori); mid = zeros (m, n); m1 = m * 2; n1 = n * 2; result = zeros (m1, n1); w = generateFilter (0.4); for j=1:m, t = zeros (1, n1); t(1:2:n1-1) = ori (j,1:n); tmp = conv ([ori(j,n) 0 t ori(j,1) 0], w); mid(j,1:n1) = 2 .* tmp (5:n1+4); end for i=1:n1, t = zeros (1, m1); t(1:2:m1-1) = mid (1:m,i)'; tmp = conv([mid(m,i) 0 t mid(1,i) 0], w); result(1:m1,i) = 2 .* tmp (5:m1+4)'; end function filter = generateFilter (alpha) filter = [0.25-alpha/2 0.25 alpha 0.25 0.25-alpha/2]; function [N] = getSlice (W, G1, i, j, half_window_size) N = zeros (1, 5); [m, n] = size (W); for y=-half_window_size:half_window_size, Y1 = y +i; if (Y1 < 1) Y1 = Y1 + m; elseif (Y1 > m) Y1 = Y1 - m; end X1 = j; if (X1 < 1) X1 = X1 + n; elseif (X1 > n) X1 = X1 - n; end DeriX = Derivative (G1, X1, Y1, 'x'); DeriY = Derivative (G1, X1, Y1, 'y'); N = N + [ DeriX * DeriX, ... DeriX * DeriY, ... DeriY * DeriY, ... DeriX * (G1 (Y1, X1) - W (Y1, X1)), ... DeriY * (G1 (Y1, X1) - W (Y1, X1))]; end function result = smooth (img) result = expand (reduce (img)); function [odd, even] = split (img); [m, n] = size (img); odd = img (1:2:m, :); even = img (2:2:m, :); %% Interpolation %% function result = warp (img, Dx, Dy) [m, n] = size (img); [x,y] = meshgrid (1:n, 1:m); x = x + Dx (1:m, 1:n); y = y + Dy (1:m,1:n); for i=1:m, for j=1:n, if x(i,j)>n x(i,j) = n; end if x(i,j)<1 x(i,j) = 1; end if y(i,j)>m y(i,j) = m; end if y(i,j)<1 y(i,j) = 1; end end end result = interp2 (img, x, y, 'linear'); %% Calculates the Fx Fy %% function result = Derivative (img, x, y, direction) [m, n] = size (img); switch (direction) case 'x', if (x == 1) result = img (y, x+1) - img (y, x); elseif (x == n) result = img (y, x) - img (y, x-1); else result = 0.5 * (img (y, x+1) - img (y, x-1)); end case 'y', if (y == 1) result = img (y+1, x) - img (y, x); elseif (y == m) result = img (y, x) - img (y-1, x); else result = 0.5 * (img (y+1, x) - img (y-1, x)); end end