def gen_coxlab_features(chip_resized, feature_x, feature_y):
image_vector = []
im_array = np.asarray(chip_resized).astype('f')
# -- get L3 prime SLM description (from sthor)
desc = sthor.model.parameters.fg11.fg11_ht_l3_1_description
# -- generate random PLOS09 3-layer model
#desc = sthor.model.parameters.plos09.get_random_plos09_l3_description()
# -- instantiate the SLM model
model = SequentialLayeredModel((feature_x, feature_y), desc)
# -- compute feature map, shape [height, width, depth]
f_map = model.transform(im_array, pad_apron=True, interleave_stride=False)
f_map_dims = f_map.shape
print "shape", f_map.shape
for j in range(f_map_dims[0]):
for k in range(f_map_dims[1]):
for l in range(f_map_dims[2]):
image_vector.append(f_map[j][k][l])
return image_vector
def test_outout_with_interleave_and_stride_and_no_interleave():
in_shape = 200, 200
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.random.randn(200, 200).astype('f')
full_features = slm.process(img, pad_apron=True, interleave_stride=True)
features = slm.process(img, pad_apron=True, interleave_stride=False)
assert_allclose(features, full_features[::8, ::8], rtol=RTOL, atol=ATOL)
def test_null_image_same_size_as_receptive_field():
in_shape = 121, 121
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.zeros(in_shape).astype('f')
features = slm.process(img)
assert features.shape == (1, 1, 256)
assert features.sum() == 0.
def test_zero_input_image_with_pad_with_interleave():
in_shape = 200, 200
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.zeros(in_shape).astype('f')
features = slm.process(img, pad_apron=True, interleave_stride=True)
assert features.shape == (200, 200, 256)
assert features.sum() == 0.
def test_null_image_same_size_as_receptive_field():
in_shape = 121, 121
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.zeros(in_shape).astype('f')
features = slm.process(img)
assert features.shape == (1, 1, 256)
assert features.sum() == 0.
def test_outout_with_interleave_and_stride_and_no_interleave():
in_shape = 200, 200
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.random.randn(200, 200).astype('f')
full_features = slm.process(img, pad_apron=True, interleave_stride=True)
features = slm.process(img, pad_apron=True, interleave_stride=False)
assert_allclose(features, full_features[::8, ::8], rtol=RTOL, atol=ATOL)
def test_zero_input_image_with_pad_with_interleave():
in_shape = 200, 200
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
img = np.zeros(in_shape).astype('f')
features = slm.process(img, pad_apron=True, interleave_stride=True)
assert features.shape == (200, 200, 256)
assert features.sum() == 0.
def eDN_features(img, desc, outSize=None):
""" Computes eDN features for given image or image sequence 'img' based
on the given descriptor(s) 'desc'"""
# iterates through individual DN models (Deep Networks) in the blend
for i in xrange(len(desc)):
imgC = img.copy()
if desc[i]['colorSp'] == 'yuv':
# either a single image or an image sequence
for j in xrange(imgC.shape[2] / 3):
imgC[:, :,
j * 3:j * 3 + 3] = convertRGB2YUV(imgC[:, :,
j * 3:j * 3 + 3])
imgC = imgC.astype('f')
model = SequentialLayeredModel((imgC.shape[0], imgC.shape[1]),
desc[i]['desc'])
fm = model.transform(imgC, pad_apron=True, interleave_stride=False)
if outSize:
# zoom seems to round down when non-integer shapes are requested
# and zoom does not accept an `output_shape` parameter
fMap = sp.ndimage.interpolation.zoom(
fm, (outSize[0] * (1 + 1e-5) / fm.shape[0], outSize[1] *
(1 + 1e-5) / fm.shape[1], 1.0))
else:
if i == 0:
fmShape = fm.shape[:2]
fMap = fm
else:
if fm.shape[:2] == fmShape:
fMap = fm
else:
# models with different number of layers have different
# output sizes, so resizing is necessary
fMap = sp.ndimage.interpolation.zoom(
fm,
(fmShape[0] * (1 + 1e-5) / fm.shape[0], fmShape[1] *
(1 + 1e-5) / fm.shape[1], 1.0))
fMap = fMap.reshape(fMap.shape[0] * fMap.shape[1], -1, order='F')
if i == 0:
fMaps = fMap
else:
fMaps = np.hstack((fMaps, fMap))
return fMaps, fmShape
def eDN_features(img, desc, outSize=None):
""" Computes eDN features for given image or image sequence 'img' based
on the given descriptor(s) 'desc'"""
# iterates through individual DN models (Deep Networks) in the blend
for i in xrange(len(desc)):
imgC = img.copy()
if desc[i]['colorSp'] == 'yuv':
# either a single image or an image sequence
for j in xrange(imgC.shape[2]/3):
imgC[:,:,j*3:j*3+3] = convertRGB2YUV(imgC[:,:,j*3:j*3+3])
imgC = imgC.astype('f')
model = SequentialLayeredModel((imgC.shape[0],
imgC.shape[1]), desc[i]['desc'])
fm = model.transform(imgC, pad_apron=True,
interleave_stride=False)
if outSize:
# zoom seems to round down when non-integer shapes are requested
# and zoom does not accept an `output_shape` parameter
fMap = sp.ndimage.interpolation.zoom(fm,
(outSize[0]*(1+1e-5)/fm.shape[0],
outSize[1]*(1+1e-5)/fm.shape[1],
1.0))
else:
if i == 0:
fmShape = fm.shape[:2]
fMap = fm
else:
if fm.shape[:2] == fmShape:
fMap = fm
else:
# models with different number of layers have different
# output sizes, so resizing is necessary
fMap = sp.ndimage.interpolation.zoom(fm,
(fmShape[0]*(1+1e-5)/fm.shape[0],
fmShape[1]*(1+1e-5)/fm.shape[1],
1.0))
fMap = fMap.reshape(fMap.shape[0]*fMap.shape[1], -1, order='F')
if i == 0:
fMaps = fMap
else:
fMaps = np.hstack((fMaps, fMap))
return fMaps, fmShape
def test_no_description():
in_shape = 256, 256
desc = []
slm = SequentialLayeredModel(in_shape, desc)
assert slm.n_layers == 0
assert slm.ops_nbh_nbw_stride == []
assert slm.receptive_field_shape == (1, 1)
def test_L3_first_desc():
in_shape = 256, 256
desc = L3_first_desc
slm = SequentialLayeredModel(in_shape, desc)
assert slm.n_layers == 4
assert slm.ops_nbh_nbw_stride == [('lnorm', 9, 9, 1), ('fbcorr', 3, 3, 1),
('lpool', 7, 7, 2), ('lnorm', 5, 5, 1),
('fbcorr', 5, 5, 1), ('lpool', 5, 5, 2),
('lnorm', 7, 7, 1), ('fbcorr', 5, 5, 1),
('lpool', 7, 7, 2), ('lnorm', 3, 3, 1)]
assert slm.receptive_field_shape == (121, 121)
