def test_regression():
np.random.seed(220)
img = np.random.random_sample((1024, 1024))
img *= 255
img = img.astype(np.uint8)
features = pftas(img)
assert not np.any(features == 0.)
def test_regression():
np.random.seed(220)
img = np.random.random_sample((1024,1024))
img *= 255
img = img.astype(np.uint8)
features = pftas(img)
assert not np.any(features == 0.)
def calculate(self, resource):
""" Append descriptors to SURF h5 table """
#initalizing
except_image_only(resource)
image_uri = resource.image
#image_uri = BQServer().prepare_url(image_uri, remap='display')
im = image2numpy(image_uri, remap='display')
im = np.uint8(im)
descriptor = pftas(im)
#initalizing rows for the table
return descriptor
def get_mahotas_features(image, bin_image):
"""
"""
# pdb.set_trace()
zer = zernike.zernike_moments(image, len(image)/2) #adding 25
mm = []
for i in range(7):
for j in range(7):
mm.append(np.log10(moments(image, i,j))) #adding 49
rou = features.roundness(bin_image) #adding 1
el1 = features.ellipse_axes(bin_image)[0]
el2 = features.ellipse_axes(bin_image)[0]#adding 2
exe = features.eccentricity(bin_image) #adding 1
pft = features.pftas(image) #adding 54
hara = mahotas.features.haralick(image).reshape(-1) #adding 52
# return np.concatenate((zer, np.array([rou]), np.array(ellip), hara.reshape(-1), lbps.reshape(-1)))
#adding in total 184 features
return np.concatenate((zer, np.array(mm), np.array([rou, el1, el2, exe]), pft, hara))
def get_TAS(img_array, grid_size=4):
"""
Parameterless TAS for one image.
:param img_array:
:param grid_size:
:return: 27* grid_size^2 (check)
"""
img = np.asarray(img_array)
window_size = (np.asarray([img.shape])[0:2] / grid_size).astype(int)[0]
im_grid = np.asarray(skimage.util.view_as_blocks(img, tuple([window_size[0], window_size[1], 3])))
windows = []
for i in range(grid_size):
for j in range(grid_size):
windows.append(im_grid[i, j, 0])
tas_features = []
for i in range(len(windows)):
t = pftas(windows[i])
t = np.ravel(np.asarray(t))
tas_features.append(t)
out = np.ravel(np.asarray(tas_features))
return out
def get_TAS(im, grid_size=4):
"""
Parameterless TAS para una imagen.
:param im_path:
:param grid_size:
:return: 27* grid_size^2 (check)
"""
if type(im) == str:
img = mahotas.imread(im)
else:
img = im
window_size = (np.asarray([img.shape])[0:2] / grid_size).astype(int)[0]
if len(img.shape) == 3:
im_grid = np.asarray(
skimage.util.view_as_blocks(
img, tuple([window_size[0], window_size[1], 3])))
windows = []
for i in range(grid_size):
for j in range(grid_size):
windows.append(im_grid[i, j, 0])
else:
im_grid = np.asarray(
skimage.util.view_as_blocks(img, tuple(window_size)))
windows = []
for i in range(grid_size):
for j in range(grid_size):
windows.append(im_grid[i, j])
tas_features = []
for i in range(len(windows)):
# if (i % 10 == 0):
# print("|", end="", flush=True)
t = pftas(windows[i])
t = np.ravel(np.asarray(t))
tas_features.append(t)
# print(" ")
out = np.ravel(np.asarray(tas_features))
return out
def test_tas():
np.random.seed(22)
f = np.random.rand(1024, 1024)
f = (f * 255).astype(np.uint8)
assert np.abs(tas(f).sum()-6) < 0.0001
assert np.abs(pftas(f).sum()-6) < 0.0001
def test_zero_image():
features = pftas(np.zeros((64,64), np.uint8))
assert not np.any(np.isnan(features))
def test_tas():
np.random.seed(22)
f = np.random.rand(1024, 1024)
f = (f * 255).astype(np.uint8)
assert np.abs(tas(f).sum() - 6) < 0.0001
assert np.abs(pftas(f).sum() - 6) < 0.0001
def test_zero_image():
features = pftas(np.zeros((64, 64), np.uint8))
assert not np.any(np.isnan(features))
def masked_pftas(intensity_image):
T = otsu(intensity_image, ignore_zeros=True)
return pftas(intensity_image, T=T)
