# ### Load feature extractor neural network
if not load_features:
vnet = load_verbatimnet('fc7', paramsfile=paramsfile)
vnet.compile(loss='mse', optimizer='sgd')
# ### Image features
#
# Currently taken as averages of all shard features in the image. You can either load them or extract everything manually, depending on if you have the .npy array.
if load_features:
print "Loading features in from " + featurefile
imfeats = np.load(featurefile)
print "Loaded features"
else:
print "Begin extracting features from " + hdf5images
imfeats = extract_imfeats(hdf5images, vnet, steps=(5, 5), varthresh=0.05)
print h5py.File(hdf5images).keys()
np.save(featurefile, imfeats)
# ### Build classifier
imfeats = (imfeats.T / np.linalg.norm(imfeats, axis=1)).T
F = imfeats.dot(imfeats.T)
np.fill_diagonal(F, -1)
# ### Evaluate classifier on HDF5 file (ICDAR 2013)
# Top k (soft criteria)
k = 10
# Max top (hard criteria)
maxtop = 2
vnet = load_verbatimnet('fc7', paramsfile=paramsfile)
vnet.compile(loss='mse', optimizer='sgd')
### Image features
# Currently taken as averages of all shard features in the image. You can either load them or extract everything manually, depending on if you have the .npy array.
if load_features:
print "Loading features in from " + featurefile
imfeats = np.load(featurefile)
print "Loaded features"
else:
print "Begin extracting features from " + hdf5images
noiseparamfile = '/work/code/repo/models/conv2_linet_iam-bin.hdf5'
imfeats = extract_imfeats(
hdf5images,
vnet,
denoiser=load_denoisenet(noiseparams=noiseparamfile),
outdir=outdir,
steps=(5, 5),
compthresh=250)
print h5py.File(hdf5images).keys()
np.save(featurefile, imfeats)
# ### Build classifier
imfeats = (imfeats.T / np.linalg.norm(imfeats, axis=1)).T
F = imfeats.dot(imfeats.T)
np.fill_diagonal(F, -1)
### Evaluate classifier on HDF5 file (ICDAR 2013)
# Top k (soft criteria)
k = 10
# Max top (hard criteria)
# ### Load feature extractor neural network
if not load_features:
vnet = load_verbatimnet( 'fc7', paramsfile=paramsfile )
vnet.compile(loss='mse', optimizer='sgd')
### Image features
# Currently taken as averages of all shard features in the image. You can either load them or extract everything manually, depending on if you have the .npy array.
if load_features:
print "Loading features in from "+featurefile
imfeats = np.load(featurefile)
print "Loaded features"
else:
print "Begin extracting features from "+hdf5images
noiseparamfile = '/work/code/repo/models/conv2_linet_iam-bin.hdf5'
imfeats = extract_imfeats(hdf5images, vnet, denoiser=load_denoisenet(noiseparams=noiseparamfile),
outdir=outdir, steps=(5,5), compthresh=250 )
print h5py.File(hdf5images).keys()
np.save( featurefile, imfeats )
# ### Build classifier
imfeats = ( imfeats.T / np.linalg.norm( imfeats, axis=1 ) ).T
F = imfeats.dot(imfeats.T)
np.fill_diagonal( F , -1 )
### Evaluate classifier on HDF5 file (ICDAR 2013)
# Top k (soft criteria)
k = 10
# Max top (hard criteria)
maxtop = 3
if not load_features:
vnet = load_verbatimnet( 'fc7', paramsfile=paramsfile )
vnet.compile(loss='mse', optimizer='sgd')
# ### Image features
#
# Currently taken as averages of all shard features in the image. You can either load them or extract everything manually, depending on if you have the .npy array.
if load_features:
print "Loading features in from "+featurefile
imfeats = np.load(featurefile)
print "Loaded features"
else:
print "Begin extracting features from "+hdf5images
imfeats = extract_imfeats( hdf5images, vnet, steps=(5,5), varthresh=0.05 )
print h5py.File(hdf5images).keys()
np.save( featurefile, imfeats )
# ### Build classifier
imfeats = ( imfeats.T / np.linalg.norm( imfeats, axis=1 ) ).T
F = imfeats.dot(imfeats.T)
np.fill_diagonal( F , -1 )
# ### Evaluate classifier on HDF5 file (ICDAR 2013)
# Top k (soft criteria)
k = 10
