Using MeshLab I get quality histogram of distances after applying Hausdorff distance between two mashes. I want to export the histogram to external file so I can analysis the histogram in external tool like python or MATLAB.
Can I do it? How?
Thanks
Niv
You can do it easy if you use the filter "Distance from reference mesh" instead of "Hausdorff Distance". That filter will leave the distances stored as quality value on each vertex of the Measured mesh.
After that, you can save the mesh to plot the distances outside of meshlab. The recommended file format is PLY, and ensure "Quality" checkbox is checked and "Binary encoding" is not checked.
The output file has a 11 lines header and then a line per each vertex containing 4 numbers. First 3 numbers are the XYZ coordinates, and the last value is the quality (which is the distance you are looking for your plot)
0 -2 0 1.902114
0 2 0 1.902113
1 -2 0 1.701302
0.9848077 -2 0.1736482 1.714225
0.9396926 -2 0.3420202 1.722303
This method works not only with distances, but also with any value that meshlab can store as quality : curvature, distance to boundary, etc..
Related
I have a 2048 point FFT IP. How may I use it to calculate 512 point FFT ?
There are different ways to accomplish this, but the simplest is to replicate the input data 4 times, to obtain a signal of 2048 samples. Note that the DFT (which is what the FFT computes) can be seen as assuming the input signal being replicated infinitely. Thus, we are just providing a larger "view" of this infinitely long periodic signal.
The resulting FFT will have 512 non-zero values, with zeros in between. Each of the non-zero values will also be four times as large as the 512-point FFT would have produced, because there are four times as many input samples (that is, if the normalization is as commonly applied, with no normalization in the forward transform and 1/N normalization in the inverse transform).
Here is a proof of principle in MATLAB:
data = randn(1,512);
ft = fft(data); % 512-point FFT
data = repmat(data,1,4);
ft2 = fft(data); % 2048-point FFT
ft2 = ft2(1:4:end) / 4; % 512-point FFT
assert(all(ft2==ft))
(Very surprising that the values were exactly equal, no differences due to numerical precision appeared in this case!)
An alternate solution from the correct solution provided by Cris Luengo which does not require any rescaling is to pad the data with zeros to the required length of 2048 samples. You then get your result by reading every 2048/512 = 4 outputs (i.e. output[0], output[3], ... in a 0-based indexing system).
Since you mention making use of a hardware module, this could be implemented in hardware by connecting the first 512 input pins and grounding all other inputs, and reading every 4th output pin (ignoring all other output pins).
Note that this works because the FFT of the zero-padded signal is an interpolation in the frequency-domain of the original signal's FFT. In this case you do not need the interpolated values, so you can just ignore them. Here's an example computing a 4-point FFT using a 16-point module (I've reduced the size of the FFT for brievety, but kept the same ratio of 4 between the two):
x = [1,2,3,4]
fft(x)
ans> 10.+0.j,
-2.+2.j,
-2.+0.j,
-2.-2.j
x = [1,2,3,4,0,0,0,0,0,0,0,0,0,0,0,0]
fft(x)
ans> 10.+0.j, 6.499-6.582j, -0.414-7.242j, -4.051-2.438j,
-2.+2.j, 1.808+1.804j, 2.414-1.242j, -0.257-2.3395j,
-2.+0.j, -0.257+2.339j, 2.414+1.2426j, 1.808-1.8042j,
-2.-2.j, -4.051+2.438j, -0.414+7.2426j, 6.499+6.5822j
As you can see in the second output, the first column (which correspond to output 0, 3, 7 and 11) is identical to the desired output from the first, smaller-sized FFT.
I have to binarize the output o of a torch model (lua script), the value range is [-1,+1], i want to threshold those values in such a way that:
0 if o[i]<0
1 if o[i]>=0
The output is composed by 32 layers with size 1x1 float tensors, so 32 floats, i want to get 32 bits from those 32 floats but i cannot find a layer that allows to do that.
At the moment I have a for cycle that checks the value of each level but it is very slow.
Maybe I can use the threshold layer or implement one by my own, do you have any advice?
You can use the 'greater or equal than' operator https://github.com/torch/torch7/blob/master/doc/maths.md#torchgea-b
local threshold_tensor = o:ge(0)
I have the following problem:
I am given a set of images and I need to devide them to photos and pictures(graphics) with means of OpenCV library.
I've already tried
to analyze RGB histogram (in average picture has empty bins of histogram),
to analyze HSV histogram (in average picture has not much colors),
to search for contours (in average the number of contours on picture is less than on photo).
So I have 7% error (tested on 2000 images). I'm confused a little, because I have no a lot of experience in numerous computer vision means.
For example,this photo below. Its histograms (RGB and HSV) are very poor and number of contours is rather small. In addition there is a lot of background color, so I need to find an object to calculate only it histogram (I use findContours() for this). But in any case my algorithm detects this image as picture.
And one more example:
The problem with pictures is noise. I have images of small size (200*150) and in some cases noise is so perceptible, that my algorithm detects this image as photo. I've tried to blur images, but in this case the number of colors increases because of mixing pixels and also it decreases the number of contours (some dim boundaries become indistinguishable).
Example of pictures:
I've also tried color segmentation and MSER, but my best result is still 7%.
Could you advice me what methods can I also try?
I've used your dataset to create really simple models. To do this, I've used Rattle library in R.
Input data
rgbh1 - number of bins in RGB histogram, which value > #param#, in my case #param# = 30 (340 is maximum value)
rgbh2 - number of bins in RGB histogram, which value > 0 (not empty)
hsvh1 - number of bins in HSV histogram, which value > #param#, in my case #param# = 30 (340 is maximum value)
hsvh2 - number of bins in HSV histogram, which value > 0 (not empty)
countours - number of contours on image
PicFlag - flag indicating picture/photo (picture = 1, photo = 0)
Data exploration
To better understand your data, here is a plot of distribution of individual variables by picture/photo group (there is percentage on y axis):
It clearly shows that there are variables with preditive power. Most of them can be used in our model. Next I've created simple scatter plot matrix to see whether some combination of variables can be useful:
You can see the for example combination of number of countours and rgbh1 looks promising.
On the following chart you can notice that there is also quite strong correlation among variables. (Generally, we like to have a lot of variables with low correlation, while you have only a limited number of correlated variables). Pie chart shows how big is the correlation - full circle means 1, empty circle means 0, my opinion is that if correlation exceeds .4 it might not be good idea to have both variables in the model)
Model
Then I created simple models (keeping Rattle's default) using decision tree, random forest, logistic regression and neural network. As input I've used your data with 60/20/20 split (training, validiation, testing dataset). This is my result (please refer to google if you don't understand error matrix):
Error matrix for the Decision Tree model on pics.csv [validate] (counts):
Predicted
Actual 0 1
0 167 22
1 6 204
Error matrix for the Decision Tree model on pics.csv [validate] (%):
Predicted
Actual 0 1
0 42 6
1 2 51
Overall error: 0.07017544
Rattle timestamp: 2013-01-02 11:35:40
======================================================================
Error matrix for the Random Forest model on pics.csv [validate] (counts):
Predicted
Actual 0 1
0 170 19
1 8 202
Error matrix for the Random Forest model on pics.csv [validate] (%):
Predicted
Actual 0 1
0 43 5
1 2 51
Overall error: 0.06766917
Rattle timestamp: 2013-01-02 11:35:40
======================================================================
Error matrix for the Linear model on pics.csv [validate] (counts):
Predicted
Actual 0 1
0 171 18
1 13 197
Error matrix for the Linear model on pics.csv [validate] (%):
Predicted
Actual 0 1
0 43 5
1 3 49
Overall error: 0.07769424
Rattle timestamp: 2013-01-02 11:35:40
======================================================================
Error matrix for the Neural Net model on pics.csv [validate] (counts):
Predicted
Actual 0 1
0 169 20
1 15 195
Error matrix for the Neural Net model on pics.csv [validate] (%):
Predicted
Actual 0 1
0 42 5
1 4 49
Overall error: 0.0877193
Rattle timestamp: 2013-01-02 11:35:40
======================================================================
Results
As you can see the overall error rate oscilates between 6.5% and 8%. I do not think that this result can be significantly improved by tunning parameters of used methods. There are two ways how to decrease overall error rate:
add more uncorrelated variables (we do usually have 100+ input variables in the modeling dataset and +/- 5-10 are in the final model)
add more data (we can then tune the model without being scared by overfitting)
Used sofware:
R http://www.r-project.org/
Rattle http://rattle.togaware.com/
Code used to create corrgram and scatterplot (other outputs were generated using Rattle GUI):
# install.packages("lattice",dependencies=TRUE)
# install.packages("car")
library(lattice)
library(car)
setwd("C:/")
indata <- read.csv2("pics.csv")
str(indata)
# Corrgram
corrgram(indata, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Picture/Photo correlation matrix")
# Scatterplot Matrices
attach(indata)
scatterplotMatrix(~rgbh1+rgbh2+hsvh1+hsvh2+countours|PicFlag,main="Picture/Photo scatterplot matrix",
diagonal=c("histogram"),legend.plot=TRUE,pch=c(1,1))
Well a generic suggestion will be to increase the number of features ( or get better features) and to build a classifier using this features, trained with an appropriate machine learning algorithm. OpenCV already has couple of good machine learning algorithms, which you can make use of.
I have never worked on this problem, but a quick google search led me to this paper by Cutzu et. al. Distinguishing paintings from photographs
One feature that should be useful is the gradient histogram. Natural images have a particular distribution of gradient strengths.
I try to implement a people detecting system based on SVM and HOG using OpenCV2.3. But I got stucked.
I came this far:
I can compute HOG values from an image database and then I calculate with LIBSVM the SVM vectors, so I get e.g. 1419 SVM vectors with 3780 values each.
OpenCV just wants one feature vector in the method hog.setSVMDetector(). Therefore I have to calculate one feature vector from my 1419 SVM vectors, that LIBSVM has calculated.
I found one hint, how to calculate this single feature vector: link
“The detecting feature vector at component i (where i is in the range e.g. 0-3779) is built out of the sum of the support vectors at i * the alpha value of that support vector, e.g.
det[i] = sum_j (sv_j[i] * alpha[j]) , where j is the number of the support vector, i
is the number of the components of the support vector.”
According to this, my routine works this way:
I take the first element of my first SVM vector, multiply it with the alpha value and add it with the first element of the second SVM vector that has been multiplied with alpha value, …
But after summing up all 1419 elements I get quite high values:
16.0657, -0.351117, 2.73681, 17.5677, -8.10134,
11.0206, -13.4837, -2.84614, 16.796, 15.0564,
8.19778, -0.7101, 5.25691, -9.53694, 23.9357,
If you compare them, to the default vector in the OpenCV sample peopledetect.cpp (and hog.cpp in the OpenCV source)
0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
-0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
-0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
you see, that the default vector values are in the boundaries between –1 and +1, but my values exceed them far.
I think, my single feature vector routine needs some adjustment, any ideas?
Regards,
Christoph
The aggregated vector's values do look high.
I used the loadSVMfromModelFile() located in http://lnx.mangaitalia.net/trainer/main.cpp
I had to remove svinstr.sync(); from the code since it caused losing parts of the lines and getting wrong results.
I don't know much about the rest of the file, I only used this function.
According to "Introduction to Neural Networks with Java By Jeff Heaton", the input to the Kohonen neural network must be the values between -1 and 1.
It is possible to normalize inputs where the range is known beforehand:
For instance RGB (125, 125, 125) where the range is know as values between 0 and 255:
1. Divide by 255: (125/255) = 0.5 >> (0.5,0.5,0.5)
2. Multiply by two and subtract one: ((0.5*2)-1)=0 >> (0,0,0)
The question is how can we normalize the input where the range is unknown like our height or weight.
Also, some other papers mention that the input must be normalized to the values between 0 and 1. Which is the proper way, "-1 and 1" or "0 and 1"?
You can always use a squashing function to map an infinite interval to a finite interval. E.g. you can use tanh.
You might want to use tanh(x * l) with a manually chosen l though, in order not to put too many objects in the same region. So if you have a good guess that the maximal values of your data are +/- 500, you might want to use tanh(x / 1000) as a mapping where x is the value of your object It might even make sense to subtract your guess of the mean from x, yielding tanh((x - mean) / max).
From what I know about Kohonen SOM, they specific normalization does not really matter.
Well, it might through specific choices for the value of parameters of the learning algorithm, but the most important thing is that the different dimensions of your input points have to be of the same magnitude.
Imagine that each data point is not a pixel with the three RGB components but a vector with statistical data for a country, e.g. area, population, ....
It is important for the convergence of the learning part that all these numbers are of the same magnitude.
Therefore, it does not really matter if you don't know the exact range, you just have to know approximately the characteristic amplitude of your data.
For weight and size, I'm sure that if you divide them respectively by 200kg and 3 meters all your data points will fall in the ]0 1] interval. You could even use 50kg and 1 meter the important thing is that all coordinates would be of order 1.
Finally, you could a consider running some linear analysis tools like POD on the data that would give you automatically a way to normalize your data and a subspace for the initialization of your map.
Hope this helps.