diff --git a/bob/bio/vein/tests/test.py b/bob/bio/vein/tests/test.py
index 908d3fb8d01a1f95a1eece3d8fa2f773c5bd00f6..9703075304a1c1b57b1f7135035bda62b3b11781 100644
--- a/bob/bio/vein/tests/test.py
+++ b/bob/bio/vein/tests/test.py
@@ -14,7 +14,6 @@ the generated sphinx documentation)
import os
import numpy
-import numpy as np
import nose.tools
import pkg_resources
@@ -32,7 +31,71 @@ def F(parts):
return pkg_resources.resource_filename(__name__, os.path.join(*parts))
-def test_finger_crop():
+def test_cropping():
+
+ # tests if the cropping stage at preprocessors works as planned
+
+ from ..preprocessor.crop import FixedCrop, NoCrop
+
+ shape = (20, 17)
+ test_image = numpy.random.randint(0, 1000, size=shape, dtype=int)
+
+ dont_crop = NoCrop()
+ cropped = dont_crop(test_image)
+ nose.tools.eq_(test_image.shape, cropped.shape)
+ nose.tools.eq_((test_image-cropped).sum(), 0)
+
+ top = 5; bottom = 2; left=3; right=7
+ fixed_crop = FixedCrop(top, bottom, left, right)
+ cropped = fixed_crop(test_image)
+ nose.tools.eq_(cropped.shape, (shape[0]-(top+bottom), shape[1]-(left+right)))
+ nose.tools.eq_((test_image[top:-bottom,left:-right]-cropped).sum(), 0)
+
+
+def test_masking():
+
+ # tests if the masking stage at preprocessors work as planned
+
+ from ..preprocessor.mask import FixedMask, NoMask, AnnotatedRoIMask
+ from ..database import AnnotatedArray
+
+ shape = (17, 20)
+ test_image = numpy.random.randint(0, 1000, size=shape, dtype=int)
+
+ masker = NoMask()
+ mask = masker(test_image)
+ nose.tools.eq_(mask.dtype, numpy.dtype('bool'))
+ nose.tools.eq_(mask.shape, test_image.shape)
+ nose.tools.eq_(mask.sum(), numpy.prod(shape))
+
+ top = 4; bottom = 2; left=3; right=1
+ masker = FixedMask(top, bottom, left, right)
+ mask = masker(test_image)
+ nose.tools.eq_(mask.dtype, numpy.dtype('bool'))
+ nose.tools.eq_(mask.sum(), (shape[0]-(top+bottom)) * (shape[1]-(left+right)))
+ nose.tools.eq_(mask[top:-bottom,left:-right].sum(), mask.sum())
+
+ # this matches the previous "fixed" mask - notice we consider the pixels
+ # under the polygon line to be **part** of the RoI (mask position == True)
+ shape = (10, 10)
+ test_image = numpy.random.randint(0, 1000, size=shape, dtype=int)
+ annotations = [
+ (top, left),
+ (top, shape[1]-(right+1)),
+ (shape[0]-(bottom+1), shape[1]-(right+1)),
+ (shape[0]-(bottom+1), left),
+ ]
+ image = AnnotatedArray(test_image, metadata=dict(roi=annotations))
+ masker = AnnotatedRoIMask()
+ mask = masker(image)
+ nose.tools.eq_(mask.dtype, numpy.dtype('bool'))
+ nose.tools.eq_(mask.sum(), (shape[0]-(top+bottom)) * (shape[1]-(left+right)))
+ nose.tools.eq_(mask[top:-bottom,left:-right].sum(), mask.sum())
+
+
+def test_preprocessor():
+
+ # tests the whole preprocessing mechanism, compares to matlab source
input_filename = F(('preprocessors', '0019_3_1_120509-160517.png'))
output_img_filename = F(('preprocessors',
@@ -42,11 +105,11 @@ def test_finger_crop():
img = bob.io.base.load(input_filename)
- from bob.bio.vein.preprocessor import Preprocessor, NoCropper, LeeMask, \
+ from ..preprocessor import Preprocessor, NoCrop, LeeMask, \
HuangNormalization, NoFilter
processor = Preprocessor(
- NoCropper(),
+ NoCrop(),
LeeMask(filter_height=40, filter_width=4),
HuangNormalization(padding_width=0, padding_constant=0),
NoFilter(),
@@ -83,7 +146,7 @@ def test_max_curvature():
bin_ref = bin_ref.T
# Apply Python implementation
- from bob.bio.vein.extractor.MaximumCurvature import MaximumCurvature
+ from ..extractor.MaximumCurvature import MaximumCurvature
MC = MaximumCurvature(3) #value used to create references
kappa = MC.detect_valleys(image, mask)
@@ -113,10 +176,10 @@ 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 bob.bio.vein.preprocessor import Preprocessor, NoCropper, LeeMask, \
+ from ..preprocessor import Preprocessor, NoCrop, LeeMask, \
HuangNormalization, HistogramEqualization
processor = Preprocessor(
- NoCropper(),
+ NoCrop(),
LeeMask(filter_height=40, filter_width=4),
HuangNormalization(padding_width=0, padding_constant=0),
HistogramEqualization(),
@@ -124,7 +187,7 @@ 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 bob.bio.vein.extractor.MaximumCurvature import MaximumCurvature
+ from ..extractor.MaximumCurvature import MaximumCurvature
MC = MaximumCurvature(sigma = 5)
extr_data = MC(preproc_data)
#preprocessor_utils.show_image((255.*extr_data).astype('uint8'))
@@ -143,7 +206,7 @@ def test_repeated_line_tracking():
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from bob.bio.vein.extractor.RepeatedLineTracking import RepeatedLineTracking
+ from ..extractor.RepeatedLineTracking import RepeatedLineTracking
RLT = RepeatedLineTracking(3000, 1, 21, False)
output_img = RLT((input_img, input_fvr))
@@ -163,10 +226,10 @@ 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 bob.bio.vein.preprocessor import Preprocessor, NoCropper, LeeMask, \
+ from ..preprocessor import Preprocessor, NoCrop, LeeMask, \
HuangNormalization, HistogramEqualization
processor = Preprocessor(
- NoCropper(),
+ NoCrop(),
LeeMask(filter_height=40, filter_width=4),
HuangNormalization(padding_width=0, padding_constant=0),
HistogramEqualization(),
@@ -174,7 +237,7 @@ 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 bob.bio.vein.extractor.RepeatedLineTracking import RepeatedLineTracking
+ from ..extractor.RepeatedLineTracking import RepeatedLineTracking
# Maximum number of iterations
NUMBER_ITERATIONS = 3000
# Distance between tracking point and cross section of profile
@@ -198,7 +261,7 @@ def test_wide_line_detector():
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from bob.bio.vein.extractor.WideLineDetector import WideLineDetector
+ from ..extractor.WideLineDetector import WideLineDetector
WL = WideLineDetector(5, 1, 41, False)
output_img = WL((input_img, input_fvr))
@@ -217,10 +280,10 @@ 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 bob.bio.vein.preprocessor import Preprocessor, NoCropper, LeeMask, \
+ from ..preprocessor import Preprocessor, NoCrop, LeeMask, \
HuangNormalization, HistogramEqualization
processor = Preprocessor(
- NoCropper(),
+ NoCrop(),
LeeMask(filter_height=40, filter_width=4),
HuangNormalization(padding_width=0, padding_constant=0),
HistogramEqualization(),
@@ -228,7 +291,7 @@ 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 bob.bio.vein.extractor.WideLineDetector import WideLineDetector
+ from ..extractor.WideLineDetector import WideLineDetector
# Radius of the circular neighbourhood region
RADIUS_NEIGHBOURHOOD_REGION = 5
NEIGHBOURHOOD_THRESHOLD = 1
@@ -251,7 +314,7 @@ 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 bob.bio.vein.algorithm.MiuraMatch import MiuraMatch
+ from ..algorithm.MiuraMatch import MiuraMatch
MM = MiuraMatch(ch=18, cw=28)
score_gen = MM.score(template_vein, probe_gen_vein)