diff --git a/tests/test_tools.py b/tests/test_tools.py
index 4d6a6d49c78201a6566a79174876efc15717025e..9d5be0447ea63599df47ae47cc4c059dd025bc17 100644
--- a/tests/test_tools.py
+++ b/tests/test_tools.py
@@ -20,7 +20,7 @@ import pkg_resources
import bob.io.base
-from ..preprocessor import utils as preprocessor_utils
+from bob.bio.vein.preprocessor import utils as preprocessor_utils
def F(parts):
@@ -32,7 +32,7 @@ def F(parts):
def test_cropping():
# tests if the cropping stage at preprocessors works as planned
- from ..preprocessor.crop import FixedCrop, NoCrop
+ from bob.bio.vein.preprocessor.crop import FixedCrop, NoCrop
shape = (20, 17)
test_image = numpy.random.randint(0, 1000, size=shape, dtype=int)
@@ -55,7 +55,7 @@ def test_cropping():
assert (test_image[top:-bottom, left:-right] - cropped).sum() == 0
# tests metadata survives after cropping (and it is corrected)
- from ..database import AnnotatedArray
+ from bob.bio.vein.database import AnnotatedArray
annotations = [
(top - 2, left + 2), # slightly above and to the right
@@ -81,8 +81,12 @@ def test_cropping():
def test_masking():
# tests if the masking stage at preprocessors work as planned
- from ..database import AnnotatedArray
- from ..preprocessor.mask import AnnotatedRoIMask, FixedMask, NoMask
+ from bob.bio.vein.database import AnnotatedArray
+ from bob.bio.vein.preprocessor.mask import (
+ AnnotatedRoIMask,
+ FixedMask,
+ NoMask,
+ )
shape = (17, 20)
test_image = numpy.random.randint(0, 1000, size=shape, dtype=int)
@@ -138,7 +142,7 @@ def test_preprocessor():
img = bob.io.base.load(input_filename)
- from ..preprocessor import (
+ from bob.bio.vein.preprocessor import (
HuangNormalization,
LeeMask,
NoCrop,
@@ -190,7 +194,7 @@ def test_max_curvature():
bin_ref = bin_ref.T
# Apply Python implementation
- from ..extractor.MaximumCurvature import MaximumCurvature
+ from bob.bio.vein.extractor.MaximumCurvature import MaximumCurvature
MC = MaximumCurvature(3) # value used to create references
@@ -224,7 +228,7 @@ def test_max_curvature_HE():
input_img = bob.io.base.load(input_img_filename)
# Preprocess the data and apply Histogram Equalization postprocessing (same parameters as in maximum_curvature.py configuration file + postprocessing)
- from ..preprocessor import (
+ from bob.bio.vein.preprocessor import (
HistogramEqualization,
HuangNormalization,
LeeMask,
@@ -241,7 +245,7 @@ def test_max_curvature_HE():
preproc_data = processor(input_img)
# Extract features from preprocessed and histogram equalized data using MC extractor (same parameters as in maximum_curvature.py configuration file)
- from ..extractor.MaximumCurvature import MaximumCurvature
+ from bob.bio.vein.extractor.MaximumCurvature import MaximumCurvature
MC = MaximumCurvature(sigma=5)
MC(preproc_data)
@@ -260,7 +264,7 @@ def test_repeated_line_tracking():
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from ..extractor.RepeatedLineTracking import RepeatedLineTracking
+ from bob.bio.vein.extractor.RepeatedLineTracking import RepeatedLineTracking
RLT = RepeatedLineTracking(3000, 1, 21, False)
output_img = RLT((input_img, input_fvr))
@@ -281,7 +285,7 @@ def test_repeated_line_tracking_HE():
input_img = bob.io.base.load(input_img_filename)
# Preprocess the data and apply Histogram Equalization postprocessing (same parameters as in repeated_line_tracking.py configuration file + postprocessing)
- from ..preprocessor import (
+ from bob.bio.vein.preprocessor import (
HistogramEqualization,
HuangNormalization,
LeeMask,
@@ -298,7 +302,7 @@ def test_repeated_line_tracking_HE():
preproc_data = processor(input_img)
# Extract features from preprocessed and histogram equalized data using RLT extractor (same parameters as in repeated_line_tracking.py configuration file)
- from ..extractor.RepeatedLineTracking import RepeatedLineTracking
+ from bob.bio.vein.extractor.RepeatedLineTracking import RepeatedLineTracking
# Maximum number of iterations
NUMBER_ITERATIONS = 3000
@@ -327,7 +331,7 @@ def test_wide_line_detector():
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from ..extractor.WideLineDetector import WideLineDetector
+ from bob.bio.vein.extractor.WideLineDetector import WideLineDetector
WL = WideLineDetector(5, 1, 41, False)
output_img = WL((input_img, input_fvr))
@@ -347,7 +351,7 @@ def test_wide_line_detector_HE():
input_img = bob.io.base.load(input_img_filename)
# Preprocess the data and apply Histogram Equalization postprocessing (same parameters as in wide_line_detector.py configuration file + postprocessing)
- from ..preprocessor import (
+ from bob.bio.vein.preprocessor import (
HistogramEqualization,
HuangNormalization,
LeeMask,
@@ -364,7 +368,7 @@ def test_wide_line_detector_HE():
preproc_data = processor(input_img)
# Extract features from preprocessed and histogram equalized data using WLD extractor (same parameters as in wide_line_detector.py configuration file)
- from ..extractor.WideLineDetector import WideLineDetector
+ from bob.bio.vein.extractor.WideLineDetector import WideLineDetector
# Radius of the circular neighbourhood region
RADIUS_NEIGHBOURHOOD_REGION = 5
@@ -392,7 +396,7 @@ def test_miura_match():
probe_gen_vein = bob.io.base.load(probe_gen_filename)
probe_imp_vein = bob.io.base.load(probe_imp_filename)
- from ..algorithm.MiuraMatch import MiuraMatch
+ from bob.bio.vein.algorithm.MiuraMatch import MiuraMatch
MM = MiuraMatch(ch=18, cw=28)
score_gen = MM.score(template_vein, probe_gen_vein)
@@ -414,7 +418,7 @@ def test_correlate():
probe_gen_vein = bob.io.base.load(probe_gen_filename)
# probe_imp_vein = bob.io.base.load(probe_imp_filename)
- from ..algorithm.Correlate import Correlate
+ from bob.bio.vein.algorithm.Correlate import Correlate
C = Correlate()
C.score(template_vein, probe_gen_vein)
@@ -650,7 +654,7 @@ def test_intersection_ratio():
def test_hamming_distance():
- from ..algorithm.HammingDistance import HammingDistance
+ from bob.bio.vein.algorithm.HammingDistance import HammingDistance
HD = HammingDistance()