Effacer les filtres
Effacer les filtres

Can the software read more precise numbers (such as decimal points)?

3 vues (au cours des 30 derniers jours)
bozheng
bozheng le 22 Avr 2024
Réponse apportée : Deepu le 24 Avr 2024
i have picture but the matlab show data is the number such like 1,2,3 but i want more detail like 1.32 or 2.16 and so on
To put it simply, can it be one or two digits after minor surgery?
above is my coding and my picture which i want read and caculate
data = adjusted_image(:);
fprintf( 'value：\n');
disp(data);
sample image(jpg) i tried to use tiff but the error is more serious
Note: He should display numbers from 0-5, although there are many

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Réponse acceptée

Vaibhav
Vaibhav le 24 Avr 2024
Ran in:
Hi bozheng
It appears you want to increase the precision of the output values when displaying the data. You can use im2double to convert the image data to double precision directly.
Here is how you can incorporate im2double into your code:
% Read the image
image = imread('peppers.png');
% Convert to grayscale
gray_image = rgb2gray(image);
% Convert the grayscale image to double precision
gray_image_double = im2double(gray_image);
% Adjust the image intensity. Note: im2double scales the image to [0,1], so adjust accordingly
adjusted_image = imadjust(gray_image_double, [0,1], [0,10/255]);
% Since adjusted_image is already in double format, you can directly use it
% The data is already scaled to 0-10 due to the imadjust function
data_scaled = adjusted_image(:);
% Display the data with more precision
fprintf('Value:\n');
Value:
disp(data_scaled);
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0.0200 0.0205 0.0205 0.0205 0.0205 0.0206 0.0206 0.0208 0.0211 0.0211 0.0211 0.0211 0.0211 0.0211 0.0211 0.0211 0.0212 0.0214 0.0214 0.0214 0.0215 0.0218 0.0223 0.0226 0.0229 0.0225 0.0220 0.0215 0.0214 0.0211 0.0208 0.0205 0.0201 0.0198 0.0198 0.0197 0.0195 0.0192 0.0191 0.0188 0.0185 0.0183 0.0183 0.0178 0.0175 0.0175 0.0174 0.0169 0.0178 0.0178 0.0171 0.0183 0.0188 0.0194 0.0194 0.0197 0.0203 0.0197 0.0195 0.0198 0.0198 0.0201 0.0201 0.0201 0.0205 0.0205 0.0198 0.0208 0.0211 0.0209 0.0206 0.0203 0.0194 0.0189 0.0189 0.0185 0.0185 0.0181 0.0180 0.0177 0.0175 0.0172 0.0171 0.0166 0.0165 0.0163 0.0160 0.0161 0.0158 0.0154 0.0148 0.0141 0.0137 0.0132 0.0128 0.0121 0.0121 0.0118 0.0118 0.0115 0.0114 0.0112 0.0109 0.0105 0.0103 0.0098 0.0097 0.0095 0.0092 0.0089 0.0088 0.0088 0.0086 0.0083 0.0083 0.0085 0.0082 0.0080 0.0082 0.0082 0.0078 0.0080 0.0078 0.0078 0.0077 0.0078 0.0077 0.0075 0.0072 0.0074 0.0068 0.0065 0.0063 0.0058 0.0055 0.0055 0.0051 0.0048 0.0043 0.0035 0.0038 0.0072 0.0089 0.0094 0.0097 0.0097 0.0098 0.0103 0.0108 0.0106 0.0108 0.0112 0.0117 0.0120 0.0128 0.0135 0.0140 0.0148 0.0154 0.0154 0.0152 0.0157 0.0157 0.0161 0.0160 0.0160 0.0165 0.0157 0.0149 0.0148 0.0072 0.0075 0.0075 0.0077 0.0074 0.0072 0.0072 0.0071 0.0071 0.0069 0.0072 0.0074 0.0077 0.0077 0.0075 0.0075 0.0077 0.0078 0.0077 0.0072 0.0071 0.0069 0.0069 0.0071 0.0075 0.0074 0.0072 0.0071 0.0072 0.0072 0.0071 0.0069 0.0071 0.0071 0.0071 0.0069 0.0069 0.0075 0.0074 0.0074 0.0074 0.0074 0.0072 0.0071 0.0071 0.0072 0.0077 0.0074 0.0075 0.0074 0.0072 0.0075 0.0077 0.0075 0.0075 0.0072 0.0074 0.0077 0.0075 0.0077 0.0075 0.0075 0.0075 0.0077 0.0075 0.0075 0.0074 0.0075 0.0077 0.0077 0.0078 0.0075 0.0075 0.0078 0.0078 0.0075 0.0072 0.0074 0.0074 0.0074 0.0072 0.0075 0.0075 0.0075 0.0075 0.0074 0.0075 0.0074 0.0075 0.0077 0.0075 0.0074 0.0072 0.0069 0.0069 0.0072 0.0071 0.0069 0.0071 0.0071 0.0072 0.0071 0.0072 0.0074 0.0072 0.0072 0.0072 0.0074 0.0078 0.0078 0.0080 0.0078 0.0078 0.0075 0.0074 0.0074 0.0072 0.0072 0.0072 0.0075 0.0075 0.0072 0.0072 0.0075 0.0072 0.0077 0.0077 0.0077 0.0077 0.0080 0.0077 0.0075 0.0074 0.0075 0.0072 0.0074 0.0075 0.0078 0.0080 0.0078 0.0077 0.0077 0.0077 0.0075 0.0074 0.0074 0.0074 0.0075 0.0075 0.0077 0.0075 0.0077 0.0075 0.0072 0.0072 0.0072 0.0074 ...
Hope this helps!

Plus de réponses (2)

DGM
DGM le 24 Avr 2024
Modifié(e) : DGM le 24 Avr 2024
If you're worried about accuracy, then the answer is to stop, go back to the original data and work with it, not what appears to be a low-quality pseudocolor JPG using some unknown colormap and unknown scale. In other words, it doesn't matter that the image has suffered merely by being quantized, because there are other, more significant sources of error which cannot be fixed.
If the colormapping parameters (the colortable and the map limits) are unknown, then back-calculating the original values is mostly guesswork. The pseudocolor result of this unknown mapping isn't just quantized to integer values on the interval [0 255], the chroma data is also downsampled. For a 4:2:0 JPG, 75% of the chroma data is discarded, making it all that much more difficult to estimate the original data from the pseudocolor result. If the specific pixel values once meant something that needed to be known precisely, that information has largely already been lost.
If the result isn't pseudocolor, then it's unclear what the color means, and why specifically BT601 luma should yield values which map linearly to the data in a specific nonstandard interval.
If there is no original data, then you're left with a quantized, downsampled color image where the color has some unknown correlation to the represented data. Unless you have more information, it's guesswork. As @Stephen23 says, convert to unit-scale float before doing anything, and inheritance should keep the data in float. Do whatever you have to do, and then save the result in an appropriate format.
Almost all image file formats store data in integer form constrained to a closed interval. JPG has the additional limitation that without special effort, the result will also be degraded by downsampling and lossy compression. Don't use JPG. TIFF is the only common image format to which MATLAB can write float data. While float TIFF files are possible, they're cumbersome to read and write, and almost no other applications will properly handle them.
Given that float TIFF sacrifices compatibility, and you appear to want the data in an arbitrary scale, I don't see any point in even trying to use TIFF here. If you have floating point raster data on an arbitrary scale, just save it as a .mat file using save().
Deepu
Deepu le 24 Avr 2024
To display the data with more precision, you can use the fprintf function with a format specifier that specifies the desired number of decimal places. Here's how you can modify your code to achieve this:
image = imread('image.jpg');
gray_image = rgb2gray(image);
adjusted_image = imadjust(gray_image, [0, 1], [0, 10/255]);
data = adjusted_image(:);
fprintf('Values:\n');
fprintf('%.2f\n', data); % Display values with two decimal places
In the fprintf function, the format specifier '%.2f' specifies that the values should be displayed as floating-point numbers with two decimal places. Adjust the number 2 in the format specifier to change the precision as desired.

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