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MATLAB代做|FPGA代做|python代做-Clean the slate

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  核心提示:MATLAB代做|FPGA代做|python代做-Clean the slate...
%% Clean the slate
% The demo here in the main.html document can be run directly from main.m
% In this section the memory is cleared, some parameters are set, and the
% original images are loaded and displayed.
close all
clear
clc

% Setting up parameters and filenames
params.VERBOSE          = 1;                    %Get more plots out (True or False)
params.PLANE            = 3;                    %Which plane of the RGB is of interest to us for opperations on a single plane
params.BLACK_BACKGROUND = 1;                    %Images have a black background (True or False)
params.map                      = copper(256);  %Set colormap to something that is similar to wood
params.number_angular_divisions = 2^7;          %Powers of two are faster FFT
params.number_radial_divisions  = 40;           %Arbitrary constant
base_filename = 'dowel01.jpg';                  %Filename of non-moving image  
move_filename = 'dowel02.jpg';                  %Filename of image that will move to the base image

base_image = imread(base_filename);    %reads in the image
move_image = imread(move_filename);    %reads in the image

% Data visualization.  
subplot(1,2,1)                    
subimage(base_image);          
title('Base image') 
subplot(1,2,2)                    
subimage(move_image);          
title('Image to move')
set (gcf, 'color', 'w')

%% Correct the X and Y displacements
% The two images are displaced from each other in the X and Y direcions.
% First the X and Y direction will be corrected by lining up the centroids
% of the two images.
%
% Method: First mask the image from background then find
% centroid of the image. Finally center the image based 
% on its centroid.

base_plane_of_interest              = base_image(:,:,params.PLANE);
%Must use a single layer grayscale for most operations.  Blue plane is best
%contrast for these images
base_bw_plane_of_interest           = im2bw(base_plane_of_interest, graythresh(base_plane_of_interest));
%Turn to binary based on threshold gotten from graythresh
base_plane_of_interest_segmented    = bwmorph(base_bw_plane_of_interest, 'open');
%morphology
base_binary_mask                    = imfill (base_plane_of_interest_segmented, 'holes');
%fill it in

% Get some data about the new region
base_properties                     = regionprops(real(base_binary_mask),'all');
base_centroid_row                   = round(base_properties.Centroid(2));
base_centroid_col                   = round(base_properties.Centroid(1));

% Place a dot at the centroid, one for each layer
base_image(base_centroid_row, base_centroid_col, 1) = 255;
base_image(base_centroid_row, base_centroid_col, 2) = 255;
base_image(base_centroid_row, base_centroid_col, 3) = 255;

% Nice to have image be 0dd number of pixels in each diension
base_image       = make_odd_by_odd(base_image);
base_binary_mask = make_odd_by_odd(base_binary_mask);
% Grab the new size
[base_num_rows, base_num_cols, base_num_layers] = size(base_image);

% Where is the center of the image?
base_goal_row   = (base_num_rows - 1) / 2 + 1;
base_goal_col   = (base_num_cols - 1) / 2 + 1;

% how much do I need to move to center this?
base_delta_rows = base_goal_row - base_centroid_row;
base_delta_cols = base_goal_col - base_centroid_col;

%shift the images to be centered
base_image       = circshift(base_image      , [base_delta_rows, base_delta_cols]);
base_binary_mask = circshift(base_binary_mask, [base_delta_rows, base_delta_cols]);

% Same thing for the second image.  In production code this would be in 
% a function, but this script was made for seminar presentation where all
% the code should be visible.
% Begin repeated code -------------------------------------
move_plane_of_interest              = move_image(:,:,params.PLANE);
move_bw_plane_of_interest           = im2bw(move_plane_of_interest, graythresh(move_plane_of_interest));
move_plane_of_interest_segmented    = bwmorph(move_bw_plane_of_interest, 'open');
move_binary_mask                    = imfill (move_plane_of_interest_segmented, 'holes');
move_properties                     = regionprops(real(move_binary_mask),'all');
move_centroid_row                   = round(move_properties.Centroid(2));
move_centroid_col                   = round(move_properties.Centroid(1));
move_image(move_centroid_row, move_centroid_col, 1) = 255;
move_image(move_centroid_row, move_centroid_col, 2) = 255;
move_image(move_centroid_row, move_centroid_col, 3) = 255;
move_image                          = make_odd_by_odd(move_image);
move_binary_mask                    = make_odd_by_odd(move_binary_mask);
[move_num_rows, move_num_cols, move_num_layers]     = size(move_image);
move_goal_row                       = (move_num_rows - 1) / 2 + 1;
move_goal_col                       = (move_num_cols - 1) / 2 + 1;
move_delta_rows                     = move_goal_row - move_centroid_row;
move_delta_cols                     = move_goal_col - move_centroid_col;
move_image                          = circshift(move_image      , [move_delta_rows, move_delta_cols]);
move_binary_mask                    = circshift(move_binary_mask, [move_delta_rows, move_delta_cols]);
% End of repeated code -------------------------------------

% Data visualization
subplot(1,2,1)                    
subimage(base_image);
title('Centered base image') 
subplot(1,2,2)                    
subimage(move_image);          
title('Centered moveable image')
set (gcf, 'color', 'w')

%%  

% Data visualization
subplot(1,2,1)                    
subimage(base_binary_mask);
title('Centered base binary mask') 
subplot(1,2,2)                    
subimage(move_binary_mask);          
title('Centered movable binary mask')
set (gcf, 'color', 'w')

%% Convert the data from X-Y coordinates to Theta-Radius coordinates
% This section transforms the image such that each set of pixels that radiate 
% out at a given angle from the centroid become a vertical line in a new image.
% These images can then be 'slid' left and right across each other to 
% find the highest correlation between the images.  

[base_radii, base_theta, base_intensity] = image2angularintesity(base_binary_mask, rgb2gray(base_image), params);
set (gcf, 'color', 'w')

%%
[move_radii, move_theta, move_intensity] = image2angularintesity(move_binary_mask, rgb2gray(move_image), params);
set (gcf, 'color', 'w')

%% Find the angular displacement by using frequency domain mathematics
% The Fast Fourier Tranform has some nice properties that allow the
% angle of highest correlation be found quickly and cleanly.
% The three dimensional plot of correlation as a function of
% angle and radius can be used to figure out which angular shift
% yields the highest correlation between the two images.  
%
% Looking at the graph, it can be seen that most of the radius
% have reached a consenus as to which angular shift should be used, however
% some of the angles of smaller radius tend to disagree.  Because the
% smaller radii have less pixels, their votes are less informed, so must be
% given less weight in the final decision as to the proper angular shift.

BI = fft(base_intensity);  %Base Intensity FFT
MI = fft(move_intensity);  %Move Intensity FFT
CI = conj(BI) .* (MI);     %Correlation Intensity FFT
ci = real(ifft(CI));       %Correlation out of FFT space
nci = ci;                  %normalized correlation being built
nci = nci - repmat(mean(nci), size(nci,1), 1); % normalized correlation removes the mean value

%%
h = surf(move_radii(:,2:end), move_theta(:,2:end), nci(:,2:end));
set (h, 'linestyle', 'none')
title('Correlation of the two images')
xlabel('radius')
ylabel('angle')
zlabel('correlation')
colormap(jet)
shading interp
set (gcf, 'color', 'w')

%%
% Find the peak correlation
[value_of_correlation, indice_of_peak_correlation_at_given_radius] = max(ci);
% Do voting to find out how much to shift by
index_of_peak_correlation = best_correlation_index(indice_of_peak_correlation_at_given_radius, params);

%% Correct the theta displacement
% The angle decided upon in the previous section is now used as the input
% argument to an IMROTATE command which will rotate the image as needed.

% mathmatical housekeeping
degrees_per_step = 360 / params.number_angular_divisions;
angle_to_shift = index_of_peak_correlation * degrees_per_step;

% Finally rotate the images (mask and the image itself)
rotated_image       = imrotate(move_image      , angle_to_shift,'bicubic','crop');
rotated_binary_mask = imrotate(move_binary_mask, angle_to_shift,'bicubic','crop');

% Data visualization
subplot(1,3,1)
subimage(base_image)
title('Base Image')

subplot(1,3,2)
subimage(rotated_image)
title(['Image rotated by ' num2str(angle_to_shift) ' degrees'])

subplot(1,3,3)
subimage(move_image)
title('Image to rotate')
set (gcf, 'color', 'w')

%%
%    Doug Hull <hull@mathworks.com>     5/20/2002
%    Copyright 1984-2001 The MathWorks, Inc.
%    This function is not supported by The MathWorks, Inc.
%    It is provided 'as is' without any guarantee of
%    accuracy or functionality.
% Copyright 2002 - 2009 The MathWorks, Inc.

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