Suite à quelques difficultés pour faire ce TP ce matin, j'ai constaté que la configuration de l'installation des logiciels chez moi permettait d'aller plus loin.
___________________________________________ scilab-5.1
Consortium Scilab (DIGITEO) Copyright (c) 1989-2009 (INRIA) Copyright (c) 1989-2007 (ENPC) ___________________________________________ Initialisation: Chargement de l'environnement de travail SIVP - Scilab Image and Video Processing Toolbox 0.5.0 Bibliothèque partagée chargée. Link done. --> /home/suitable/Documents -->im2 = imread('bois.jpg'); -->imshow(im2);
!–error 10000
Data type string is not supported. at line 22 of function im2uint8 called by : line 23 of function imshow called by : imshow counts -->imshow(counts); !--error 4 Variable non définie: counts -->boisbruit = imnoise(im2, 'speckle', .001); !--error 4 Variable non définie: d at line 111 of function imnoise called by : boisbruit = imnoise(im2, 'speckle', .001);-->imshow counts !--error 10000 Data type string is not supported. at line 22 of function im2uint8 called by : line 23 of function imshow called by : imshow counts -->imshow(counts); !--error 4 Variable non définie: counts -->boisbruit = imnoise(im2, 'speckle', .001); !--error 4 Variable non définie: d at line 111 of function imnoise called by : boisbruit = imnoise(im2, 'speckle', .001);-->boisbruit = imnoise(im2, 'speckle', ,001); -->imshow(boisbruit); -->ret = imwrite(boisbruit, 'boisbruit.jpg');
-->ret ret = 1. -->h1 = h1/9 h1 = 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 -->res = imfilter(im2, h1); -->imshow( res) -->imwrite(res, 'bois3x3.jpg');
h2 =
column 1 to 5 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 column 6 to 9 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 0.0123457 -->res2 = imfilter(im2, h2); -->imshow(res2); -->imwrite(res2, 'bois9x9.jpg') ans = 1.
—-
help file extract : Name imnoise — Add noise (gaussian, etc.) to an image Calling Sequence imn = imnoise(im, type [,parameters]) Parameters im Input image. type String having one of these values: 'salt & pepper' drop-out/On-off noise 'speckle' multiplicative noise 'gaussian' Gaussian white/additive noise 'localvar' Pixel-specific variance (Zero-mean Gaussian) 'poisson' Not yet implemented parameters A sequence of parameters to control the noise distribution, depending on the chosen type. If omitted, default values are used (see below). imn Noisy image, which has the same size and type as input image im. Description imnoise(im, type [, parameters]) adds a type of noise to the intensity image im. Optionally, you can control the noise parameters starting at the 3rd. Argument to imnoise. Here are example of noise types and their parameterss:
imn = imnoise(im,'salt & pepper',d) adds drop-out noise, where d is the noise density (probability of swapping a pixel). (default: d=0.05). imn = imnoise(im,'gaussian',m,v) adds Gaussian additive noise of mean m and variance v. (default: m=0 and v=0.01) im = imnoise(im,'localvar',V) additive zero-mean Gaussian noise where the variance at im(i,j) is V(i,j). imn = imnoise(im,'localvar', intensity, V) additive zero-mean Gaussian noise, and the local variance of the noise, var, is a function of the image intensity values in im. The variance is matrix( interp1(intensity(:),V(:),im(:)), size(im) ) imn = imnoise(im,'speckle',v) adds multiplicative noise, using imn = im + noise*im, where noise is uniformly distributed with mean 0 and variance v. (default: v=0.04) By default, we consider that “1” corresponds to the maximum intensity value of the image, and “0” to minimum. If the input image im is an integer image, it will be converted to double using im2double function first. Before return the result, the image will be converted to the same type as the input image. The elements in the output matrix imn that exceed the range of the integer or double type will be truncated. Supported classes: INT8, UINT8, INT16, UINT16, INT32, DOUBLE.
Examples
im = imread('lena.png');
imn = imnoise(im, 'gaussian'); imshow(imn);
imn = imnoise(im, 'salt & pepper'); imshow(imn);
imn = imnoise(im(:,:,1), 'salt & pepper', 0.2); imshow(imn);
lowtri = tril(ones(im(:,:,1))); imn = imnoise( im(:,:,1), 'localvar', lowtri/5); imshow(imn);
imn = imnoise( im(:,:,1), 'localvar', [0:0.1:1], [0:0.1:1].^3); imshow(imn);
imn = imnoise(im, 'speckle' ); imshow(imn);
Bugs and Shortcomings 'poisson' noise is not yet implemented.