Svenska ElektronikForumet

Kod: Markera allt

Technical specs
• Processor: NXP  LPC4330, 204 MHz, dual core 
• Image sensor: Omnivision OV9715, 1/4", 1280x800 
•Lens field-of-view: 75 degrees horizontal, 47 degrees vertical
•Lens type: standard M12 (several different types available)
•Power consumption: 140 mA typical
•Power input: USB input (5V) or unregulated input (6V to 10V)
•RAM: 264K bytes
•Flash: 1M bytes
•Available data outputs: UART serial, SPI, I2C, USB, digital, analog
•Dimensions: 2.1" x 1.75" x 1.4"

Kod: Markera allt

close all;
clc;
clear all;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%  Phase correlation of Images based motion estimation example
%            (uses a webcam to demo the algorithm)
%
%  Note: Only translation, not rotation nor scaling is identified.
%
%  By Emil Fresk, Luleå University of Technology
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


% Create video input object.
% Follow the steps here to install neccessary software:
% http://www.mathworks.se/help/imaq/basic-image-acquisition-procedure.html

% Work computer webcam:
% vid = videoinput('winvideo',1,'I420_176x144');
% Laptop webcam:
vid = videoinput('winvideo',1,'MJPG_352x288');

vid_size = 256;

% Set video input object properties for this application.
% Note that example uses both SET method and dot notation method.
set(vid,'TriggerRepeat',100);
vid.FrameGrabInterval = 4;

% Set value of a video source object property.
vid_src = getselectedsource(vid);
%set(vid_src,'Tag','motion detection setup');
%set(vid_src,'FrameRate', '20.0000');
%set(vid_src,'ExposureMode', 'manual');

% Create a figure window.
f = figure(1); 

% Start acquiring frames.
start(vid)

% Create local variables
old_fft = zeros(vid_size, vid_size);
x = 0;
y = 0;
first = true;

while (1)
    
    % Wait until a frame is available
    while(vid.FramesAvailable == 0)
    end

    % Get the new image from the webcam
    new_image = getdata(vid,1); 
    
    % Convert the data uint8 -> double
    new_image = double(new_image(1:vid_size, 1:vid_size, 1));
    
    % Calculate the optic flow based on the old fft and the new image
    [of, old_fft, dx, dy] = get_optic_flow(new_image, old_fft, vid_size);
    
    % Minor thing to cancel start up defects
    if first == true
        dx = 0;
        dy = 0;
        first = false;
    end
    
    % Integrate the deltas to get total translation
    x = x + dx;
    y = y - dy;
    
    % Plot the image and correlation in a subplot with results as text
    subplot(1,2,1), imshow(imresize(new_image ./ max(max(new_image)), 3));
    title(['x: ', int2str(x), ', y: ', int2str(y)])
   
    subplot(1,2,2), imshow(imresize(of, 3));
    title(['(dx: ', int2str(dx), ', dy: ', int2str(-dy), ')']);
    
    set(findall(f,'type','text'),'FontSize',30,'fontWeight','bold')    
    drawnow     % update figure window
    
    % Check if 'e' has been pressed, if so exit the loop.
    key = get(f,'CurrentCharacter');
    if (key == 'e')
        fprintf('End of flow.\n')
        break;
    end
end

stop(vid) 

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function [A, new_fft, dx, dy] = get_optic_flow(new_img, old_fft, vid_size)
  D1 = old_fft;
  D2 = fft2(apply_window(new_img));
  
  M = D1.*conj(D2);
  A = abs(ifft2(M ./ abs(M)));
  A = fftshift(A ./ max(max(A)));
  
  [~, I] = max(A(:));
  
  dx = round(I / vid_size) - (vid_size / 2) - 1;
  dy = mod(I, vid_size) - (vid_size / 2) - 1;
  
  new_fft = D2;
end

Kod: Markera allt

function X = apply_window(img)
  % w - Window function, N x 1
  % d - Image N x N
  
  N = length(img);
  m = zeros(N,N); % Initialize space for mask
  
  w = hamming(N);

  % Initialize windows along column direction
  for n=1:N
    m(:,n) = w;
  end

  % Apply windows along rows
  m = m .* m';
  
  X = img .* m;
end