good evening,
i'm new to coding CNN
i'v got ShanghaiTech crowd counting dataset that has (beside the images) .mat files for what i believe the ground truth for (counting) for images.
i try to print the content of one .mat file in python, here is what i get:
{'image_info': array([[array([[(array([[ 855.32345978, 590.49587357],
[ 965.5908524 , 472.79472415],
[ 937.09478464, 400.93507502],
...,
[ 42.5852337 , 359.87860699],
[1017.48233659, 8.99748811],
[1017.48233659, 23.31916643]]), array([[920]], dtype=uint16))]],
dtype=[('location', 'O'), ('number', 'O')])]], dtype=object), '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Fri Nov 18 20:06:05 2016', '__globals__': []}
each .mat file corresponds to one image,
i know at some point in CNN we need to calculate the error between the network result and the ground truth we have, but i don't seem to understand the structure and the content of these .mat files.
can someone explain whats in these files and how or for what that content is used in crowd estimation.
so i got the answer,
the data in the .mat presented in the question containes (or at least what we are interested in) two arrays,
the first one:
array([[ 855.32345978, 590.49587357],
[ 965.5908524 , 472.79472415],
[ 937.09478464, 400.93507502],
...,
[ 42.5852337 , 359.87860699],
[1017.48233659, 8.99748811],
[1017.48233659, 23.31916643]])
is an N by 2 array, the 2 corresponds tho the targeted object X and Y coordinates, and the N is the number of targeted objects (ground-truth)
also, the second array contains the ground-truth
the data of the .mat file was extracted through scipy.io.loadmat,
and the structure of the data is dictionary, now getting to the ground-trouth in that was quite tricy, but it went like that:
matContent=spy.io.loadmat(os.path.join(gtPath,gtList[1])) #var type is dictionary
gt=matContent['image_info'][0][0][0][0][1] #getting the ground-thruth number
Related
Unfortunately I am not too tech proficient and only have a basic MATLAB/programming background...
I have several csv data files in a folder, and would like to make a histogram plot of all of them simultaneously in order to compare them. I am not sure how to go about doing this. Some digging online gave a script:
d=dir('*.csv'); % return the list of csv files
for i=1:length(d)
m{i}=csvread(d(i).name); % put into cell array
end
The problem is I cannot now simply write histogram(m(i)) command, because m(i) is a cell type not a csv file type (I'm not sure I'm using this terminology correctly, but MATLAB definitely isn't accepting the former).
I am not quite sure how to proceed. In fact, I am not sure what exactly is the nature of the elements m(i) and what I can/cannot do with them. The histogram command wants a matrix input, so presumably I would need a 'vector of matrices' and a command which plots each of the vector elements (i.e. matrices) on a separate plot. I would have about 14 altogether, which is quite a lot and would take a long time to load, but I am not sure how to proceed more efficiently.
Generalizing the question:
I will later be writing a script to reduce the noise and smooth out the data in the csv file, and binarise it (the csv files are for noisy images with vague shapes, and I want to distinguish these shapes by setting a cut off for the pixel intensity/value in the csv matrix, such as to create a binary image showing these shapes). Ideally, I would like to apply this to all of the images in my folder at once so I can shift out which images are best for analysis. So my question is, how can I run a script with all of the csv files in my folder so that I can compare them all at once? I presume whatever technique I use for the histogram plots can apply to this too, but I am not sure.
It should probably be better to write a script which:
-makes a histogram plot and/or runs the binarising script for each csv file in the folder
-and puts all of the images into a new, designated folder, so I can sift through these.
I would greatly appreciate pointers on how to do this. As I mentioned, I am quite new to programming and am getting overwhelmed when looking at suggestions, seeing various different commands used to apparently achieve the same thing- reading several files at once.
The function csvread returns natively a matrix. I am not sure but it is possible that if some elements inside the csv file are not numbers, Matlab automatically makes a cell array out of the output. Since I don't know the structure of your csv-files I will recommend you trying out some similar functions(readtable, xlsread):
M = readtable(d(i).name) % Reads table like data, most recommended
M = xlsread(d(i).name) % Excel like structures, but works also on similar data
Try them out and let me know if it worked. If not please upload a file sample.
The function csvread(filename)
always return the matrix M that is numerical matrix and will never give the cell as return.
If you have textual data inside the .csv file, it will give you an error for not having the numerical data only. The only reason I can see for using the cell array when reading the files is if the dimensions of individual matrices read from each file are different, for example first .csv file contains data organised as 3xA, and second .csv file contains data organised as 2xB, so you can place them all into a single structure.
However, it is still possible to use histogram on cell array, by extracting the element as an array instead of extracting it as cell element.
If M is a cell matrix, there are two options for extracting the data:
M(i) and M{i}. M(i) will give you the cell element, and cannot be used for histogram, however M{i} returns element in its initial form which is numerical matrix.
TL;DR use histogram(M{i}) instead of histogram(M(i)).
I have large .bin files (10GB 60GB) that I want to import to MATLAB; each binary file represents the output of two sensors, thus there are too columns of data. Here is a more manageable sized example of my data.
You will notice that there is a .txt version of the data; I need to upload the .bin files directly to MATLAB, I can't use the .txt version because it takes hours to convert with larger files.
The problem I have is that the .bin file has header information that I can't seem to interpret properly, and thus I cannot extract the data in MATLAB every time I try I seem to get gibberish values.
This is all the information I have about the binary header:
Loading Labview Binary Data into Matlab
LabVIEW Data Logger: Binary Header File Format
Any help/advice would be much appreciated I have been trying to solve this problem for days now.
P.S. Someone has already written a function to solve this problem but it does not seem to work with my binary data (could be something to do with the dimensions/size of my data): http://www.mathworks.co.uk/matlabcentral/fileexchange/27195-load-labview-binary-data
Below is the code that I am using to import my data, I believe that that d1 and d2 are the dimensions of my binary data. D2 is probably incorrect for the example file in the dropbox because it has been truncated.
The problem I have is that the code extracts my data and I know it is correct because I can check it with the .txt file (also in the drop box) however there are seaming random bad values between the good data points. These bad values result from the following strings following strings: "NI_ChannelName", "Sensor A", "Sensor B", "NI_UnitDescription", and "Volts" scatted throughout the binary file.
clear all
clc
fname = 'RTL5_57.bin';
fid = fopen(fname,'r','ieee-be');
d1 = fread(fid,4);
trash=fread(fid,2,'double');
d2 = fread(fid,4);
trash=fread(fid,1,'double');
data=fread(fid,'double');
I suppose you will need to change the data-format. See Matlab help.
https://decibel.ni.com/content/docs/DOC-39038
Scope:
1) Write a binary file in matlab and read into labview. 2) Write a binary file in labview and read into matlab.
Background:
IMPORTANT:
You must know (3) things about the binary data in the file before you can read the data:
1) what binary format (precision) was used to store the data
2) the exact number of values in the file to read.
3) Endianness
There is no row or column in binary files. Think of a long row/or a long column that needs to be mapped to a 2D array.
Resources on data in binary format.
http://cse.unl.edu/~sincovec/Matlab/Lesson%2024/Binary/CS211%20Lesson%2024%20-%20Binary%20File%20Input-Output.htm
I have done PCA for 21 images of the same person in different conditions. LAst step of the PCA is projection of original data : signals=PC'*data. Size of signals is 21*21, now I want to write this to a CSV file with a label as +1. Please guide me how to do this in matlab. I tried csvwrite but it does not write the labels, only the data.
[signals,V]=pca(im2double(inputdata));
for i=1:length(signals)
for j=1:1
label(i,j)=+1;
end(both for)
csvwrite('f1.csv',[label signals]);
Sorry I am new to matlab.
What I have: A folder containing about 80 subfolders, labeled Day01, Day02, Day03, etc. Each subfolder has a file called "sample_ids.txt" It is a n x m matrix in a tab delimited format.
What I need: 1 data structure that is an array of matrices, where each matrix is the data from "sample_ids.txt" and it should be in the alphabetical order of Day01, Day02, Day03, etc.
I have no idea how to get from point A to point B. Any guidance would be greatly appreciated.
You can decompose this problem into two parts: finding the files, and reading them into memory.
Finding the files is pretty easy, and has already been covered on StackOverflow.
For loading them into memory, you want a multidimensional array, which is as simple as creating a regular array and start using more index dimensions: A = ones(2); A(:,:,2) = ones(2); will, for example, give you a 3-dimensional array of size 2-by-2-by-2, with ones all over.
What you want, is probably want something like this:
A = [] % No prealocation. Fix for speed-up.
files = dir('./Day*/sample_ids.txt');
for file = files
temp = load(file.name);
A(:,:,size(A,3)+1) = temp;
end
disp(A) % display the contents of A afterards...
I haven't tested this code extensively, but it should work OK.
A few important points:
All files must contain matrices of the exact same dimensions - MATLAB can't handle arrays that have different dimensions in different layers (at least not with regular arrays - you could use cell arrays, but that quickly becomes more complicated...). Think of it as trying to build a matrix from vectors of different lengths.
If you have a lot of data, and you know how much, you can save a lot of time by pre-allocating A. This is as easy as A = zeros(k,l,m) for m datafiles with k rows and l columns in each. If you do this, you'll also have to figure out the index of the current file, so you can use that as the third index in the assignment (on the second line in the loop block). I leave this as an internet research excersize :)
I am trying to load feature vectors into classifiers such as a k-nearest neighbors classifier.
I have my code for GLCM, so I get contrast, correlation, energy, homogeneity in numbers (feature vectors).
My question is, how can I save every set of feature vectors from all the training images? I have seen somewhere that people had a .set file to load into classifiers (may be it is a special case for the particular classifier toolbox).
load 'mydata.set';
for example.
I suppose it does not have to be a .set file.
I'd just need a way to store all the feature vectors from all the training images in a separate file that can be loaded.
I've google,
and I found this that may be useful
but I am not entirely sure.
Thanks for your time and help in advance.
Regards.
If you arrange your feature vectors as the columns of an array called X, then just issue the command
save('some_description.mat','X');
Alternatively, if you want the save file to be readable, say in ASCII, then just use this instead:
save('some_description.txt', 'X', '-ASCII');
Later, when you want to re-use the data, just say
var = {'X'}; % <-- You can modify this if you want to load multiple variables.
load('some_description.mat', var{:});
load('some_description.txt', var{:}); % <-- Use this if you saved to .txt file.
Then the variable named 'X' will be loaded into the workspace and its columns will be the same feature vectors you computed before.
You will want to replace the some_description part of each file name above and instead use something that allows you to easily identify which data set's feature vectors are saved in the file (if you have multiple data sets). Your array of feature vectors may also be called something besides X, so you can change the name accordingly.