def learn_dictionary (self, images, npatches=50000, niter=1000, njobs=-1):
""" Learn a Sparse Code dictionary for this ScSPM.
This method trains a sparse codes dictionary for the ScSPM descriptor
object. This only needs to be run once before multiple calls to the
extract() method can be made.
Arguments:
images: list, a list of paths to images to use for training.
npatches: int (default 50000) number of SIFT patches to extract from
the images to use for training the dictionary.
niter: int (default 1000), the number of iterations of dictionary
learning (Lasso) to perform.
njobs: int (default -1), the number of threads to use. -1 means the
number of threads will be equal to the number of cores.
"""
# Get SIFT training patches
print('Getting training patches...')
patches = sw.training_patches(images, npatches, self.psize, self.maxdim,
verbose=True)
patches = pch.norm_patches(patches)
print('{0} patches requested, {1} patches found.'.format(npatches,
patches.shape[0]))
time.sleep(3) # Give people a chance to see this message
# Learn dictionary
print('Learning dictionary...')
self.dic = trainDL(np.asfortranarray(patches.T, np.float64), mode=0,
K=self.dsize, lambda1=0.15, iter=niter, numThreads=njobs)
print('done.')
def learn_dictionary(self, images, npatches=50000, niter=1000, njobs=-1):
""" Learn a Sparse Code dictionary for this ScSPM.
This method trains a sparse codes dictionary for the ScSPM descriptor
object. This only needs to be run once before multiple calls to the
extract() method can be made.
Arguments:
images: list, a list of paths to images to use for training.
npatches: int (default 50000) number of SIFT patches to extract from
the images to use for training the dictionary.
niter: int (default 1000), the number of iterations of dictionary
learning (Lasso) to perform.
njobs: int (default -1), the number of threads to use. -1 means the
number of threads will be equal to the number of cores.
"""
# Get SIFT training patches
print('Getting training patches...')
patches = sw.training_patches(images,
npatches,
self.psize,
self.maxdim,
verbose=True)
patches = pch.norm_patches(patches)
print('{0} patches requested, {1} patches found.'.format(
npatches, patches.shape[0]))
time.sleep(3) # Give people a chance to see this message
# Learn dictionary
print('Learning dictionary...')
self.dic = trainDL(np.asfortranarray(patches.T, np.float64),
mode=0,
K=self.dsize,
lambda1=0.15,
iter=niter,
numThreads=njobs)
print('done.')
def test_norm_patches (self):
""" Test patch contrast normalisation/unitisation. """
diff = np.abs(np.ones((self.tpatch.shape[0],1))
- np.sqrt((patch.norm_patches(self.tpatch)**2).sum(axis=1)))
self.assertTrue((diff < 1e-15).all())
