diff --git a/bob/bio/vein/algorithm/Correlate.py b/bob/bio/vein/algorithm/Correlate.py
index 4e82f02140b2be0c18fd40e714dc5e376f2a8597..e35310617c82d465e96a244b07494f229f1125e1 100644
--- a/bob/bio/vein/algorithm/Correlate.py
+++ b/bob/bio/vein/algorithm/Correlate.py
@@ -47,7 +47,7 @@ class Correlate(Algorithm):
"""
- I = probe.astype(numpy.float64)
+ image_ = probe.astype(numpy.float64)
if len(model.shape) == 2:
model = numpy.array([model])
@@ -58,7 +58,7 @@ class Correlate(Algorithm):
for md in model:
R = md.astype(numpy.float64)
- Nm = skimage.feature.match_template(I, R)
+ Nm = skimage.feature.match_template(image_, R)
# figures out where the maximum is on the resulting matrix
t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
diff --git a/bob/bio/vein/algorithm/MiuraMatch.py b/bob/bio/vein/algorithm/MiuraMatch.py
index a70a93febdb1bf3599f2a2c209f4991994028ef0..463342593629d3b112333cc31e8ce9d6d819beef 100644
--- a/bob/bio/vein/algorithm/MiuraMatch.py
+++ b/bob/bio/vein/algorithm/MiuraMatch.py
@@ -83,7 +83,7 @@ class MiuraMatch(BioAlgorithm):
"""
- I = probe.astype(numpy.float64)
+ image_ = probe.astype(numpy.float64)
if len(model.shape) == 2:
model = numpy.array([model])
@@ -103,7 +103,7 @@ class MiuraMatch(BioAlgorithm):
# yields best results. Otherwise, you may try the other options bellow
# -> check our test_correlation() method on the test units for more
# details and benchmarks.
- Nm = scipy.signal.fftconvolve(I, numpy.rot90(crop_R, k=2), "valid")
+ Nm = scipy.signal.fftconvolve(image_, numpy.rot90(crop_R, k=2), "valid")
# 2nd best: use convolve2d or correlate2d directly;
# Nm = scipy.signal.convolve2d(I, numpy.rot90(crop_R, k=2), 'valid')
# 3rd best: use correlate2d
@@ -122,7 +122,7 @@ class MiuraMatch(BioAlgorithm):
Nmm
/ (
crop_R.sum()
- + I[
+ + image_[
t0 : t0 + h - 2 * self.ch, s0 : s0 + w - 2 * self.cw
].sum()
)
diff --git a/bob/bio/vein/extractor/MaximumCurvature.py b/bob/bio/vein/extractor/MaximumCurvature.py
index 698ff1b770e97e174d683d01bc9f9dbda9c00ab3..5248d972cfd18730ec85c41a838de2cf7089459c 100644
--- a/bob/bio/vein/extractor/MaximumCurvature.py
+++ b/bob/bio/vein/extractor/MaximumCurvature.py
@@ -33,7 +33,7 @@ class MaximumCurvature(Extractor):
self.sigma = sigma
def detect_valleys(self, image, mask):
- """Detects valleys on the image respecting the mask
+ r"""Detects valleys on the image respecting the mask
This step corresponds to Step 1-1 in the original paper. The objective is,
for all 4 cross-sections (z) of the image (horizontal, vertical, 45 and -45
@@ -178,7 +178,7 @@ class MaximumCurvature(Extractor):
)
def eval_vein_probabilities(self, k):
- """Evaluates joint vein centre probabilities from cross-sections
+ r"""Evaluates joint vein centre probabilities from cross-sections
This function will take $\kappa$ and will calculate the vein centre
probabilities taking into consideration valley widths and depths. It
@@ -318,7 +318,7 @@ class MaximumCurvature(Extractor):
return V
def connect_centres(self, V):
- """Connects vein centres by filtering vein probabilities ``V``
+ r"""Connects vein centres by filtering vein probabilities ``V``
This function does the equivalent of Step 2 / Equation 4 at Miura's paper.
diff --git a/bob/bio/vein/extractor/PrincipalCurvature.py b/bob/bio/vein/extractor/PrincipalCurvature.py
index 751be994d8d2df7bea5b164721520bfbc925438c..84c1bbea95819d6b1baa22a2c469084c8b69d912 100644
--- a/bob/bio/vein/extractor/PrincipalCurvature.py
+++ b/bob/bio/vein/extractor/PrincipalCurvature.py
@@ -46,14 +46,14 @@ class PrincipalCurvature(Extractor):
fingervein image"""
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
+ finger_mask[mask == True] = 1 # noqa: E712
sigma = numpy.sqrt(self.sigma**2 / 2)
gx = self.ut_gauss(image, self.sigma, 1, 0)
gy = self.ut_gauss(image, self.sigma, 0, 1)
- Gmag = numpy.sqrt(gx**2 + gy**2) # Gradient magnitude
+ Gmag = numpy.sqrt(gx**2 + gy**2) # Gradient magnitude
# Apply threshold
gamma = (self.threshold / 100) * numpy.max(Gmag)
diff --git a/bob/bio/vein/extractor/RepeatedLineTracking.py b/bob/bio/vein/extractor/RepeatedLineTracking.py
index 65d627c6060b3f630fe6dbb72fa2330643657563..6a9ec79752c833ba2586528ece614e4c9a841da5 100644
--- a/bob/bio/vein/extractor/RepeatedLineTracking.py
+++ b/bob/bio/vein/extractor/RepeatedLineTracking.py
@@ -54,10 +54,10 @@ class RepeatedLineTracking(Extractor):
numpy.random.seed(self.seed)
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
+ finger_mask[mask == True] = 1 # noqa: E712
# Rescale image if required
- if self.rescale == True:
+ if self.rescale:
scaling_factor = 0.6
# finger_image = bob.ip.base.scale(finger_image, scaling_factor)
# finger_mask = bob.ip.base.scale(finger_mask, scaling_factor)
@@ -121,14 +121,14 @@ class RepeatedLineTracking(Extractor):
finger_mask[:, 0:border] = 0
finger_mask[:, finger_mask.shape[1] - border :] = 0
- ## Uniformly distributed starting points
- aux = numpy.argwhere((finger_mask > 0) == True)
+ # Uniformly distributed starting points
+ aux = numpy.argwhere(finger_mask > 0)
indices = numpy.random.permutation(aux)
indices = indices[
0 : self.iterations, :
] # Limit to number of iterations
- ## Iterate through all starting points
+ # Iterate through all starting points
for it in range(0, self.iterations):
yc = indices[it, 0] # Current tracking point, y
xc = indices[it, 1] # Current tracking point, x
@@ -175,7 +175,6 @@ class RepeatedLineTracking(Extractor):
bool
)
).T.reshape(-1)
- == True
)
Nc = numpy.concatenate(
(xc + filtermask[tmp, 0], yc + filtermask[tmp, 1]), axis=1
@@ -184,10 +183,10 @@ class RepeatedLineTracking(Extractor):
Vl = -1
continue
- ## Detect dark line direction near current tracking point
+ # Detect dark line direction near current tracking point
Vdepths = numpy.zeros((Nc.shape[0], 1)) # Valley depths
for i in range(0, Nc.shape[0]):
- ## Horizontal or vertical
+ # Horizontal or vertical
if Nc[i, 1] == yc:
# Horizontal plane
yp = Nc[i, 1]
@@ -217,7 +216,7 @@ class RepeatedLineTracking(Extractor):
+ finger_image[int(yp), int(xp - hW)]
)
- ## Oblique directions
+ # Oblique directions
if ((Nc[i, 0] > xc) and (Nc[i, 1] < yc)) or (
(Nc[i, 0] < xc) and (Nc[i, 1] > yc)
):
diff --git a/bob/bio/vein/extractor/WideLineDetector.py b/bob/bio/vein/extractor/WideLineDetector.py
index 9350e728991622c295d97944bc56613dbcfdee3f..d64899ffcc42a25a9cdc5695d173658a59beeefe 100644
--- a/bob/bio/vein/extractor/WideLineDetector.py
+++ b/bob/bio/vein/extractor/WideLineDetector.py
@@ -45,10 +45,10 @@ class WideLineDetector(Extractor):
finger_image = finger_image.astype(numpy.float64)
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
+ finger_mask[mask == True] = 1 # noqa: E712
# Rescale image if required
- if self.rescale == True:
+ if self.rescale:
scaling_factor = 0.24
new_size = tuple(
diff --git a/bob/bio/vein/preprocessor/crop.py b/bob/bio/vein/preprocessor/crop.py
index 7064ff0d150a8437b00c573be5b94accbf82a769..4ca5b2d56d80d090d79f5d81f9f45e123f1225d8 100644
--- a/bob/bio/vein/preprocessor/crop.py
+++ b/bob/bio/vein/preprocessor/crop.py
@@ -35,7 +35,7 @@ class Cropper(object):
"""
- raise NotImplemented("You must implement the __call__ slot")
+ raise NotImplementedError("You must implement the __call__ slot")
class FixedCrop(Cropper):
diff --git a/bob/bio/vein/preprocessor/filters.py b/bob/bio/vein/preprocessor/filters.py
index 31f3ef1f1b424a561c3964189889c55afcdaca74..9ef0a9c013a117a8fd24972d4e433f02d3dfb13f 100644
--- a/bob/bio/vein/preprocessor/filters.py
+++ b/bob/bio/vein/preprocessor/filters.py
@@ -31,7 +31,7 @@ class Filter(object):
"""
- raise NotImplemented("You must implement the __call__ slot")
+ raise NotImplementedError("You must implement the __call__ slot")
class NoFilter(Filter):
diff --git a/bob/bio/vein/preprocessor/mask.py b/bob/bio/vein/preprocessor/mask.py
index 7e1551db14de856368b3a03dde9011b0a47ffc0c..01dd15d79b28b16cd96a95fe49459a381b478029 100644
--- a/bob/bio/vein/preprocessor/mask.py
+++ b/bob/bio/vein/preprocessor/mask.py
@@ -91,7 +91,7 @@ class Masker(object):
"""
- raise NotImplemented("You must implement the __call__ slot")
+ raise NotImplementedError("You must implement the __call__ slot")
class FixedMask(Masker):
diff --git a/bob/bio/vein/preprocessor/normalize.py b/bob/bio/vein/preprocessor/normalize.py
index f047c1f3934e95a7661a3d1e20d8628b8d311dd2..7c4f38e4a9231475e90af1376ecac64b94bd81ba 100644
--- a/bob/bio/vein/preprocessor/normalize.py
+++ b/bob/bio/vein/preprocessor/normalize.py
@@ -38,7 +38,7 @@ class Normalizer(object):
"""
- raise NotImplemented("You must implement the __call__ slot")
+ raise NotImplementedError("You must implement the __call__ slot")
class NoNormalization(Normalizer):
diff --git a/bob/bio/vein/preprocessor/utils.py b/bob/bio/vein/preprocessor/utils.py
index b4de1617c5df03c66b3afb45440d1601ee4cc5b7..81931d2dd80725327255a3ddbb036ec2eb24f712 100644
--- a/bob/bio/vein/preprocessor/utils.py
+++ b/bob/bio/vein/preprocessor/utils.py
@@ -235,7 +235,7 @@ def show_mask_over_image(image, mask, color="red"):
def jaccard_index(a, b):
- """Calculates the intersection over union for two masks
+ r"""Calculates the intersection over union for two masks
This function calculates the Jaccard index:
diff --git a/bob/bio/vein/script/view_sample.py b/bob/bio/vein/script/view_sample.py
index 4dccc88b87eecc49e6dacb8770df250271a5557b..4c7dd42a97c2b6ba582b63ce6a5a7adaa92d4e60 100644
--- a/bob/bio/vein/script/view_sample.py
+++ b/bob/bio/vein/script/view_sample.py
@@ -256,7 +256,7 @@ def main(user_input=None):
try:
binary = f.load(os.path.join(args["<processed>"], "extracted"))
binary = numpy.rot90(binary, k=1)
- except:
+ except Exception:
binary = None
fig = proof_figure(stem, image, mask, image_pp, binary)
if args["--save"]:
diff --git a/bob/bio/vein/tests/test_databases.py b/bob/bio/vein/tests/test_databases.py
index d06a2fbbbc73635241617419311ea925a2e58189..2e4cf9fe5753ecc09f77e90de35042da1c7402d6 100644
--- a/bob/bio/vein/tests/test_databases.py
+++ b/bob/bio/vein/tests/test_databases.py
@@ -96,7 +96,7 @@ def test_utfvp():
except Exception:
pass
- N_SUBJECTS, N_FINGERS, N_SESSIONS = 60, 6, 4
+ N_SUBJECTS, N_SESSIONS = 60, 4
# nom
nom_parameters = {
"N_train": 10,