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I need to detect more and fewer landmarks on the face according to functions. İs it possible? Can I use clmtools and clmtracker for this purpose? Thanks.

c:\Miniconda2\lib\site-packages\sklearn\utils\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample. DeprecationWarning)

I am using scikit-learn 0.17 on Python 2.7.11 on Windows.

Hello.
I generated the model but her accuracy much worse the original model (https://github.com/auduno/clmtools/blob/master/annotater/js/model_pca_20_svm.js). I used numpy 1.9.3, scikit-image 0.11.3, pillow 3.0.0 and scikit-learn 0.15.2.
How can I increase accuracy learning model?

In the archive are results works learning model and original model for compare.

report 13.12.2015.zip
log.txt

I added some new points to the face training images and configured the tool to take them into account.
I get the following Wargning:

/Library/Python/2.7/site-packages/sklearn/preprocessing/data.py:153: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.
  warnings.warn("Numerical issues were encountered "
/Library/Python/2.7/site-packages/sklearn/preprocessing/data.py:169: UserWarning: Numerical issues were encountered when scaling the data and might not be solved. The standard deviation of the data is probably very close to 0.

And the precision of the tracker is horrible now.

Any tips? I started with the provided anotations.csv and adapted the model_viewer and pdm_builder to account for the new points.

Sorry again, I fixed all issues by myself, thank you for clmtools! :D

I want to use clmtools to train the model with existing data , but The clmtool not working.
its giving this error.
Can you please help regarding this issue.

getting negative examples from landscape images
C:\Users\anwaar\Anaconda2\lib\site-packages\sklearn\preprocessing\data.py:160: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.
warnings.warn("Numerical issues were encountered "
C:\Users\anwaar\Anaconda2\lib\site-packages\sklearn\preprocessing\data.py:177: UserWarning: Numerical issues were encountered when scaling the data and might not be solved. The standard deviation of the data is probably very close to 0.
warnings.warn("Numerical issues were encountered "
training patch:0
0
C:\Users\anwaar\Anaconda2\lib\site-packages\sklearn\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
C:\Users\anwaar\Anaconda2\lib\site-packages\sklearn\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
DeprecationWarning)
Traceback (most recent call last):
File "pdm_builder.py", line 54, in
patchModel = build_patches(data_patches, gradient=True, lbp=True, weights=weights, optimize_params=True)
File "c:\clm\clmtools\pdm_builder\buildlib\buildpatch.py", line 169, in build_patches
clfg = GridSearchCV(SVR(kernel="linear"), {'C':[0.1], 'epsilon' : [0.4, 0.3, 0.2, 0.1]}, loss_func=mean_squared_error, verbose=100)
TypeError: init() got an unexpected keyword argument 'loss_func'

When I download this image, it gives me an image with an error "hot link protection activated", so I am not sure this is the right image

http://blog.54ka.org/wp-content/uploads/2008/11/accidental_street_portrait_01_by_54ka.jpg

Can model training only be performed on version 2.7? 3.7 No?

Hi I am trying to run through your steps to build out a new model. There are a couple of errors but I am now stuck on the step where it cannot find anything in the "negatives" image folder. What should I put in there? Anything that is not a face or is there a dataset I have to download?

Thanks!

me config.py
data_folder = "./data/"
images ="./data/images/"
annotations = "./data/annotations.csv"
message:
No images were found for training. Please make sure that folders in config.py are correct, and that images for training are downloaded.

I want to do batch execution.
Do you have a working document?
Help me

I'm trying to match faces at high angle (> 30°),
so I load the image and try to annotate it using the annotater, but it doesn't manage to converge so it gives its best estimate that is quite wrong. Nothing unexpected here, but moving every 70pts one by one is such a chore, is there a way to select multiple points at once?
Also, is there a way to modify the model PCA parameters on the given best estimate? As the annotater does not converge on it's own I'd like to do it myself.

File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\decomposition\pca.py", line 425, in _fit_full
% (n_components, min(n_samples, n_features)))
ValueError: n_components=20 must be between 0 and min(n_samples, n_features)=2 with svd_solver='full'

I want to use clmtracker for my project. the problem is that how i can fix models on face. You have suggested clmtools for it but the problem is that i have no experience in python nor i can understand it's document. Is there any other way to make image appear on face.

If you can help me it will be of great help.

In https://www.auduno.com/2014/01/05/fitting-faces/ it states the following

in our case we implemented a logistic regression classifier with an SVM kernel (which is what the original paper suggests)

My knowledge on classification is limited. I know what SVMs are on a high level, I know what logistic regression is. But I didn't know what a kernel was. So I looked it up and learned what it more or less is (if I remember correctly it is a dot product that somehow equates to intersecting transposed data points in an arbitrary higher dimensional space). But in all those explanations the words "SVM kernel" do not exist, since support vector machines use of kernel themselves such as a linear or gaussian kernel, or you name it. I searched on via Google for quite a while.

So I took a look in the source code and the word "SVM kernel" does not seem to exist. I just queried on the word "kernel".

https://github.com/auduno/clmtools/search?utf8=%E2%9C%93&q=kernel

To me it seems that the implementation in actuality is a logistic regression classifier with a linear kernel.

My question is: is this the case? Or is there something inherent SVM-like about this particular kernel? When I look at Wikipedia, it does not mention SVM kernels: https://en.wikipedia.org/wiki/Support_vector_machine (it mentions linear kernels, etc.). But when I look at http://scikit-learn.org/stable/modules/svm.html it does mention SVM kernels but not in conjunction with logistic regression.