Revision history [back]

Okay i just made it with your steps. First i extracted the features and trained a Linear SVM Classifier.

To classify i made a python script using sliding windows and then predict the window. First i load the Classifier i created, and then i load the testimage. I downscale the image and iterate. In this iteration i use the sliding window. For each window i calculate the HOG features and use predict. The detections are stored in a list. The detector is working, but i got two problems.

First problem is, that it's very slow. Is there a alternative to sliding windows, because they are very slow? Some kind of contour detection to find the signs? The second problem is, that i receive the following DepricationWarning message:

    Traceback (most recent call last)
    File "classify.py", line 79, in <module>
    pred = clf.predict(fd)
  File "/home/pi/.virtualenvs/py2cv3/local/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 336, in predict
    scores = self.decision_function(X)
  File "/home/pi/.virtualenvs/py2cv3/local/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 312, in decision_function
    X = check_array(X, accept_sparse='csr')
  File "/home/pi/.virtualenvs/py2cv3/local/lib/python2.7/site-packages/sklearn/utils/validation.py", line 395, in check_array
    DeprecationWarning)
    DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise 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.

I don't know why it does appear, but it's so annoying, because it shows up for each iteration (>100 times per image).