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Find your data in the assignment '00data: Your data' in the submission system; the image `daliborka_01.jpg`

and the set of seven 2D points `U2`

.

Make Matlab script `hw01.m`

to do the following.

- Load
`daliborka_01.jpg`

image into your Matlab workspace and display it. Use`subfig.m`

utility in Tools repository.img = imread( 'daliborka_01.jpg' ); % load the image subfig(2,2,1); % create a new figure image( img ); % display the image, keep axes visible axis image % display it with square pixels

- Manually acquire the set of seven points in the image corresponding to the tops of the five chimneys and the two towers. The points must be ordered
**from left to right**. Use[x,y] = ginput(7)

- Store the point coordinates in a 2×7 matrix
`u`

. Every point is represented by a**column**vector of coordinates, i.e.`u(:,1)`

are coordinates of the first point. - In the bitmap image, colorize the pixels that are nearest to the acquired points (use the
`round`

function to convert fractional coordinates into integer coordinates). Use the following colors in the following order:**red = [255 0 0], green = [0 255 0], blue = [0 0 255], magenta = [255 0 255], cyan = [0 255 255], yellow = [255 255 0], white = [255 255 255]**to colorize the respective seven points. The colors are defined by their R, G, B values:**color = [R G B]**. The order of colors must correspond to the order of points in the matrix`u`

. Store the modified bitmap image as a file`01_daliborka_points.png`

.imwrite(img, '01_daliborka_points.png');

- Implement estimation of the affine transformation
`A`

(2×3 matrix) from n given points`u2`

to points`u`

as a functionA = estimate_A( u2, u ); % u2 and u are 2xn matrices

- Perform all possible selections of 3 point correspondences from all n correspondences. Use e.g.
nchoosek(1:n, 3 )

- For each triple compute the affine transformation
`Ai`

(exactly). - Compute transfer errors of the particular transformation for every correspondence, i.e., the euclidean distances between points
`U`

and points`U2`

transferred by`A`

. Find the maximum error over the correspondences. - From the all computed transformations
`Ai`

select the one`A`

that has the maximum transfer error minimal. - Assume general number of points, though we have only 7 points and 35 possibilities
n = size(u,2); % number of columns

- Display the modified image, the set of points
`u`

using the same colors as above, and 100× magnified transfer errors for the best`A`

as red lines.% assume we have points in ''u'' and points ''u2'' transferred by ''A'' in ''ux'' e = 100 * ( ux - u ); % magnified error displacements ... hold on % to plot over the image ... plot( u(1,4), u(2,4), 'o', 'linewidth', 2, 'color', 'magenta' ) % the 4-th point plot( [ u(1,4) u(1,4)+e(1,4) ], [ u(2,4) u(2,4)+e(2,4) ], 'r-', 'linewidth', 2 ); % the 4-th error ... hold off

(If the errors are too large, it is optional to plot them magnified only 10×, as blue lines). - Export (print) the plot as a pdf file
`01_daliborka_errs.pdf`

. Use`fig2pdf.m`

utility in Tools repository.fig2pdf( gcf, '01_daliborka_errs.pdf' )

- Store the points and the matrix in a file
`01_points.mat`

.save( '01_points.mat', 'u', 'A' )

Upload an archive containing the following files:

`01_points.mat`

`01_daliborka_points.png`

`01_daliborka_errs.pdf`

`estimate_A.m`

`hw01.m`

- your Matlab implementation. It makes all required figures, output files and prints.- any other your files required by hw01.m.

Note: All files must be in the same directory. Do not use any subdirectories.

courses/gvg/labs/gvg-2017-hw-01.txt · Last modified: 2019/01/19 18:22 (external edit)