formati slike in branje slike pomen in nekaj primerov EM spekter aplikacije v posameznih delih spektra o matriki slike
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- Jade Day
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1 Strojni vid pri tehnoloških meritvah formati slike in branje slike pomen in nekaj primerov EM spekter aplikacije v posameznih delih spektra o matriki slike
2 formati slike in branje slike slika je običajno v enem od uveljavljenih zapisov, ki uporabljajo različne vrste stiskanja podatkov ali pa tudi ne z izgubo: jpg, Kompresija slike brez izgube: gif, tif, png barvna slika velikosti: 512 * 512 * 3byte = byte sivinska slika velikosti 512 * 512 * 1byte = byte slike smo dobili na v spisku spodaj vidimo velikost datoteke lenag.tif => 256kB, kar pomeni = 256kB * 1024 = B en.wikipedia.org/wiki/lena_söderberg
3 branje slike in prikaz slike v Matlabu beremo sliko z ukazom imread, pokažemo pa jo lahko z različnimi ukazi: clear all imef = 'lenag.tif'; sl = imread(imef); figure(1); imshow(sl) figure(2); image(sl) figure(3); imagesc(sl)
4 prikaz slike in razmerje stranic figure(4); image(sl); colormap gray figure(5); imagesc(sl); colormap gray figure(6); imagesc(sl); colormap gray; axis equal; axis tight whos sl Name Size Bytes Class Attributes sl 512x uint8
5 image handle in lastnosti get(ih) AlphaData = [1] AlphaDataMapping = none Annotation = [ (1 by 1) hg.annotation array] CData = [ (512 by 512) uint8 array] CDataMapping = scaled DisplayName = XData = [1 512] YData = [1 512] BeingDeleted = off ButtonDownFcn = Children = [] Clipping = on CreateFcn = DeleteFcn = BusyAction = queue HandleVisibility = on HitTest = on Interruptible = on Parent = [ ] Selected = off SelectionHighlight = on Tag = Type = image UIContextMenu = [] UserData = [] Visible = on set(ih) AlphaData AlphaDataMapping: [ {none} direct scaled ] CData CDataMapping: [ {direct} scaled ] DisplayName XData YData ButtonDownFcn: string -or- function handle -or- cell array Children Clipping: [ {on} off ] CreateFcn: string -or- function handle -or- cell array DeleteFcn: string -or- function handle -or- cell array BusyAction: [ {queue} cancel ] HandleVisibility: [ {on} callback off ] HitTest: [ {on} off ] Interruptible: [ {on} off ] Parent Selected: [ on off ] SelectionHighlight: [ {on} off ] Tag UIContextMenu UserData Visible: [ {on} off ]
6 %% sprememba velikosti slike I = imread('crosses.tif'); figure(1); imshow(i); IR = imrotate(i,7); figure(2); imshow(ir) [V,S] = size(i); n = 4; ni = imresize(i,[n*v,n*s]); nir = imrotate(ni,7); IRcorr = imresize(nir,[v,s]); figure(3); imshow(ircorr) sprememba velikosti matrike slike; primer uporabe pri rotaciji slike, zato da preprečimo prekrivanje in lomljenje črt (aliasing)
7 funkcija imresize pozna različne metode 'nearest 'bilinear' 'bicubic' 'box' 'triangle' 'cubic' 'lanczos2' 'lanczos3' Nearest-neighbor interpolation; the output pixel is assigned the value of the pixel that the point falls within. No other pixels are considered. Bilinear interpolation; the output pixel value is a weighted average of pixels in the nearest 2-by-2 neighborhood Bicubic interpolation (the default); the output pixel value is a weighted average of pixels in the nearest 4-by-4 neighborhood Box-shaped kernel Triangular kernel (equivalent to 'bilinear') Cubic kernel (equivalent to 'bicubic') Lanczos-2 kernel Lanczos-3 kernel
8 %% imresize - natančneje n = 9; A = magic(n); m = 4; B = imresize(a,[m*n,m*n],'nearest'); C = imresize(a,[m*n,m*n],'bilinear'); D = imresize(a,[m*n,m*n],'bicubic'); E = imresize(a,[m*n,m*n],'lanczos3'); figure(1); clf subplot(1,5,1); imagesc(a); title('original') subplot(1,5,2); imagesc(b); title('nearest'); subplot(1,5,3); imagesc(c); title('bilinear'); subplot(1,5,4); imagesc(d); title('bicubic'); subplot(1,5,5); imagesc(e); title('lanczos3');
9 pretvorba v dvobitno (binarno) sliko; pogosto moramo pred analizo pretvoriti sliko sl v črno-belo sliko v Matlabu to naredimo s funkcijo im2bw(sl, meja) prav določanje pravilne meje je ključnega pomena enojna meja T: pixel 1, če je sl(i,j) > T pixel 0, če je sl(i,j) <= T dvojna meja T1 in T2: pixel 1, če je T1 <= sl(i,j) > T2 pixel 0, sicer
10 %% pretvorba v binarno sliko sl = imread('lenag.tif'); meja = graythresh(sl); sl2a = im2bw(sl,meja); sito = find(sl>255*0.6 & sl<255*1); sl2b = zeros(size(sl)); sl2b(sito) = ones(size(sito)); figure(1); clf subplot(1,3,1); imshow(sl); title('original') subplot(1,3,2); imshow(sl2a); title('ena meja') subplot(1,3,3); imshow(sl2b); title('dve meji')
11 filtri: nizko pasovni in visoko pasovni; nad slikami lahko uporabimo podobne metode kot na časovnih signalih, posplošiti pa jih je treba na dve dimenziji. Tako poznamo nizko-pasovne filtre s katerimi zmanjšujemo šum, gladimo robove ali jih uporabljamo za izenačevanje osvetljenosti; visoko-pasovni filtri, s katerimi odkrijemo robove
12 %% filtri sl = imread('mestece.jpg'); sl = rgb2gray(sl); sl_lp = medfilt2(sl,[5,5]); sh = [-1 0 1; ; ]; sobelh = sh/norm(sh,'fro'); sl_hp = imfilter(sl, sobelh); sl_std = stdfilt(sl,ones(7)); figure(1); subplot(1,3,1); imagesc(sl); axis equal; axis tight subplot(1,3,2); imagesc(sl_lp); axis equal; axis tight subplot(1,3,3); imagesc(sl_hp); axis equal; axis tight
13
14 prva = 41; zadnja= 280; / prva=8501; zadnja=8740; 8740 / 9216 prva = 8021; zadnja= 8260; 8260 / 9216
15 Podatki: expr93 (Natalija) prva = 31; zadnja= 282; prva = 8674; zadnja= 8913; prva =11318; zadnja=11561; prva =11318; zadnja=11561; Smer trajektorije je od modre proti rdeči
16 prva =11318; zadnja=11561; Na desni je ponovljeno sledenje s prg: brlc6.ml
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