MATLAB: Zooming narrow image should fill axes

axesimshowzoom

Hello,

When using imshow to display an narrow image in symmetric axes, zooming in maintains the same aspect ratio, resulting in a very narrow view of the image. The desired behavior is for the image to fill the entire axes at high zoom so more details are visible. Is there any way to alter the zoom behavior to achieve this effect?

Here is some example code that reproduces the problem:

figure; axes('Position',[0.1 0.1 0.8 0.8]);
RGB = imread('peppers.png');
strip = RGB(150:180,:,:);
imshow(strip);

Thank you for your help

Best Answer

Instead of using imshow(), use image() with "axis equal".

Some of prominent volunteers think highly of imshow(), but in my experience it has too many side effects for anything other than casual interactive viewing.

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MATLAB: How to make Narrow Bandpass filter

hello

seems I have already seen similar post somewhere... a buddy of yours ?

you did not mentionned the sampling frequency , but 24 kHz seemed correct

here my little contribution :


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%





% options 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% spectrogram dB scale
spectrogram_dB_scale = 60;  % dB range scale (means , the lowest displayed level is XX dB below the max level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% test data
Fs = 24000;
% t = 0:1/Fs:10-1/Fs;
fid = fopen('G4_12.dat','r')
[signal,samples] = fscanf(fid, '%12f');
fclose(fid);
% decimate
decim = 12;
signal = decimate(signal,decim);
Fs = Fs/decim;
samples = length(signal);
time =(1:samples)/Fs;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
n_max = fix(log2(samples));
NFFT = 2^(n_max-1);    % 2^n with n chosen so that NFFT is below length of signal (even after decimation) by factor > 2 so some averaging is doable
% NFFT = 2048/4;    % 2^n with n chosen so that NFFT is below length of signal (even after decimation) by factor > 2 so some averaging is doable
NOVERLAP = round(0.95*NFFT);
w = hanning(NFFT);     % Hanning window / Use the HANN function to get a Hanning window which has the first and last zero-weighted samples.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



% display : averaged FFT spectrum before filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sensor_spectrum, freq] = pwelch(signal,w,NOVERLAP,NFFT,Fs,'power');
sensor_spectrum_dB = 10*log10(sensor_spectrum);% convert to dB scale (ref = 1)

% sensor_spectrum_dB = sensor_spectrum_dB-min(sensor_spectrum_dB); % y axis shift so dB values are positive (min value = 0)
%% findpeaks
% df = Fs/NFFT;
% MAXW = 10*df;
% MPD = 20;
% MPP = 20;
% [peaks,loc,W,P] = findpeaks(sensor_spectrum_dB,'MinPeakDistance',MPD,'MaxPeakWidth',MAXW,'MinPeakProminence',MPP);
% freq_peaks = freq(loc);
freq_peaks = [159.0574, 359.3519, 459.4992, 709.8674];
peaks = interp1(freq,sensor_spectrum_dB,freq_peaks,'linear');
figure(1),plot(freq,sensor_spectrum_dB,'b',freq_peaks,peaks,'*r');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
xlabel('Frequency (Hz)');ylabel(' dB')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 2 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sg,fsg,tsg] = specgram(signal,NFFT,Fs,hanning(NFFT),NOVERLAP);  
% FFT normalisation and conversion amplitude from linear to dB (peak)
sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg));     % NB : X=fft(x.*hanning(N))*4/N; % hanning only
% scalingf the dB range : 
min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;
sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;
% plots spectrogram
figure(2);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid
title(['Spectrogram / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');
% keep only signal content extracted by bandpass filters 
fc = freq_peaks;      % Hz

bandwith = 8;      % Hz
f_low = fc-0.5*bandwith;
f_high = fc+0.5*bandwith;
N = 4;
signal_filtered = zeros(size(signal));
for ci = 1:length(fc)
    [b,a] = butter(N,2/Fs*[f_low(ci) f_high(ci)]);
    % add filtered signal to total final signal
    tmp = filtfilt(b,a,signal);
    signal_filtered = signal_filtered + tmp;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : averaged FFT spectrum before / after notch filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sensor_spectrum_filtered, freq] = pwelch(signal_filtered,w,NOVERLAP,NFFT,Fs,'power');
sensor_spectrum_filtered_dB = 10*log10(sensor_spectrum_filtered);% convert to dB scale (ref = 1)
figure(4),plot(freq,sensor_spectrum_dB,'b',freq,sensor_spectrum_filtered_dB,'r');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
legend('before filter','after filter');
xlabel('Frequency (Hz)');ylabel(' dB')
figure(5),plot(time,signal_filtered,'b');grid
title('Filtered signal');
xlabel('Time (s)');ylabel(' amplitude')

Best Answer

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MATLAB: Edges of narrow objects in image

MATLAB: How to make Narrow Bandpass filter

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