Skip to content

GitLab

Commit 78249416 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
Browse files

Patched preprocessors tests

parent a950ce94
Hide whitespace changes
Inline Side-by-side
bob/bio/face/test/test_preprocessors.py
View file @ 78249416
......@@ -29,202 +29,279 @@ import bob.bio.face
import bob.db.base
def _compare(data, reference, write_function = bob.bio.base.save, read_function = bob.bio.base.load, atol = 1e-5, rtol = 1e-8):
# write reference?
if regenerate_refs:
write_function(data, reference)
# compare reference
reference = read_function(reference)
assert numpy.allclose(data, reference, atol=atol, rtol=rtol)
return reference
def _compare(
data,
reference,
write_function=bob.bio.base.save,
read_function=bob.bio.base.load,
atol=1e-5,
rtol=1e-8,
):
# write reference?
if regenerate_refs:
write_function(data, reference)
# compare reference
reference = read_function(reference)
assert numpy.allclose(data, reference, atol=atol, rtol=rtol)
return reference
def _image():
return bob.io.base.load(pkg_resources.resource_filename('bob.bio.face.test', 'data/testimage.jpg'))
return bob.io.base.load(
pkg_resources.resource_filename("bob.bio.face.test", "data/testimage.jpg")
)
def _annotation():
return bob.db.base.read_annotation_file(pkg_resources.resource_filename('bob.bio.face.test', 'data/testimage.pos'), 'named')
return bob.db.base.read_annotation_file(
pkg_resources.resource_filename("bob.bio.face.test", "data/testimage.pos"),
"named",
)
def test_base():
base = bob.bio.base.load_resource('base', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(base, bob.bio.face.preprocessor.Base)
assert isinstance(base, bob.bio.base.preprocessor.Preprocessor)
# read input
image = _image()
preprocessed = base(image)
base = bob.bio.base.load_resource(
"base", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(base, bob.bio.face.preprocessor.Base)
assert preprocessed.ndim == 2
assert preprocessed.dtype == numpy.float64
assert numpy.allclose(preprocessed, bob.ip.color.rgb_to_gray(image))
# read input
image = _image()
# color output
base = bob.bio.face.preprocessor.Base(color_channel='rgb', dtype=numpy.uint8)
colored = base(bob.ip.color.rgb_to_gray(image))
assert colored.ndim == 3
assert colored.dtype == numpy.uint8
assert all(numpy.allclose(colored[c], bob.ip.color.rgb_to_gray(image)) for c in range(3))
colored = base(image)
assert colored.ndim == 3
assert colored.dtype == numpy.uint8
assert numpy.all(colored == image)
preprocessed = base.transform(image)
assert preprocessed.ndim == 2
assert preprocessed.dtype == numpy.float64
assert numpy.allclose(preprocessed, bob.ip.color.rgb_to_gray(image))
# color output
base = bob.bio.face.preprocessor.Base(color_channel="rgb", dtype=numpy.uint8)
colored = base.transform(bob.ip.color.rgb_to_gray(image))
assert colored.ndim == 3
assert colored.dtype == numpy.uint8
assert all(
numpy.allclose(colored[c], bob.ip.color.rgb_to_gray(image)) for c in range(3)
)
colored = base.transform(image)
assert colored.ndim == 3
assert colored.dtype == numpy.uint8
assert numpy.all(colored == image)
def test_face_crop():
# read input
image, annotation = _image(), _annotation()
cropper = bob.bio.base.load_resource('face-crop-eyes', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(cropper, bob.bio.face.preprocessor.FaceCrop)
assert isinstance(cropper, bob.bio.face.preprocessor.Base)
assert isinstance(cropper, bob.bio.base.preprocessor.Preprocessor)
# execute face cropper
reference = pkg_resources.resource_filename('bob.bio.face.test', 'data/cropped.hdf5')
ref_image = _compare(cropper(image, annotation), reference, cropper.write_data, cropper.read_data)
# test the preprocessor with fixed eye positions (which correspond to th ones
fixed_cropper = bob.bio.face.preprocessor.FaceCrop(cropper.cropped_image_size, cropper.cropped_positions, fixed_positions = {'reye' : annotation['reye'], 'leye' : annotation['leye']})
# result must be identical to the original face cropper (same eyes are used)
_compare(fixed_cropper(image), reference, cropper.write_data, cropper.read_data)
# check color cropping
cropper.channel = 'rgb'
cropped = cropper(image, annotation)
assert cropped.ndim == 3
assert cropped.shape[0] == 3
assert cropped.shape[1:] == ref_image.shape
assert numpy.allclose(bob.ip.color.rgb_to_gray(cropped), ref_image, atol = 1., rtol = 1.)
# test a ValueError is raised if eye annotations are swapped
try:
annot = dict(reye=annotation['leye'], leye=annotation['reye'])
cropper(image, annot)
assert 0, 'FaceCrop did not raise a ValueError for swapped eye annotations'
except ValueError:
pass
# reset the configuration, so that later tests don't get screwed.
cropper.channel = 'gray'
# read input
image, annotation = _image(), _annotation()
cropper = bob.bio.base.load_resource(
"face-crop-eyes", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(cropper, bob.bio.face.preprocessor.FaceCrop)
assert isinstance(cropper, bob.bio.face.preprocessor.Base)
# execute face cropper
reference = pkg_resources.resource_filename(
"bob.bio.face.test", "data/cropped.hdf5"
)
ref_image = _compare(cropper.transform(image, annotation), reference)
# test the preprocessor with fixed eye positions (which correspond to th ones
fixed_cropper = bob.bio.face.preprocessor.FaceCrop(
cropper.cropped_image_size,
cropper.cropped_positions,
fixed_positions={"reye": annotation["reye"], "leye": annotation["leye"]},
)
# result must be identical to the original face cropper (same eyes are used)
_compare(fixed_cropper.transform(image), reference)
# check color cropping
cropper.channel = "rgb"
cropped = cropper.transform(image, annotation)
assert cropped.ndim == 3
assert cropped.shape[0] == 3
assert cropped.shape[1:] == ref_image.shape
assert numpy.allclose(
bob.ip.color.rgb_to_gray(cropped), ref_image, atol=1.0, rtol=1.0
)
# test a ValueError is raised if eye annotations are swapped
try:
annot = dict(reye=annotation["leye"], leye=annotation["reye"])
cropper.transform(image, annot)
assert 0, "FaceCrop did not raise a ValueError for swapped eye annotations"
except ValueError:
pass
# reset the configuration, so that later tests don't get screwed.
cropper.channel = "gray"
def test_face_detect():
image, annotation = _image(), None
cropper = bob.bio.base.load_resource('face-detect', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(cropper, bob.bio.face.preprocessor.FaceDetect)
assert isinstance(cropper, bob.bio.face.preprocessor.Base)
assert isinstance(cropper, bob.bio.base.preprocessor.Preprocessor)
assert cropper.flandmark is None
# execute face detector
reference = pkg_resources.resource_filename('bob.bio.face.test', 'data/detected.hdf5')
_compare(cropper(image, annotation), reference, cropper.write_data, cropper.read_data)
assert abs(cropper.quality - 39.209601948013685) < 1e-5
# execute face detector with flandmark
cropper = bob.bio.face.preprocessor.FaceDetect(face_cropper='face-crop-eyes', use_flandmark=True)
reference = pkg_resources.resource_filename('bob.bio.face.test', 'data/flandmark.hdf5')
_compare(cropper(image, annotation), reference, cropper.write_data, cropper.read_data)
assert abs(cropper.quality - 39.209601948013685) < 1e-5
# execute face detector with tan-triggs
cropper = bob.bio.face.preprocessor.TanTriggs(face_cropper='landmark-detect')
preprocessed = cropper(image, annotation)
# load reference and perform Tan-Triggs
detected = bob.bio.base.load(pkg_resources.resource_filename('bob.bio.face.test', 'data/flandmark.hdf5'))
tan_triggs = bob.bio.base.load_resource('tan-triggs', 'preprocessor', preferred_package='bob.bio.face')
reference = tan_triggs(detected)
assert numpy.allclose(preprocessed, reference, atol=1e-5)
image, annotation = _image(), None
cropper = bob.bio.base.load_resource(
"face-detect", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(cropper, bob.bio.face.preprocessor.FaceDetect)
assert isinstance(cropper, bob.bio.face.preprocessor.Base)
assert cropper.flandmark is None
# execute face detector
reference = pkg_resources.resource_filename(
"bob.bio.face.test", "data/detected.hdf5"
)
_compare(cropper.transform(image, annotation), reference)
assert abs(cropper.quality - 39.209601948013685) < 1e-5
# execute face detector with flandmark
cropper = bob.bio.face.preprocessor.FaceDetect(
face_cropper="face-crop-eyes", use_flandmark=True
)
reference = pkg_resources.resource_filename(
"bob.bio.face.test", "data/flandmark.hdf5"
)
_compare(cropper.transform(image, annotation), reference)
assert abs(cropper.quality - 39.209601948013685) < 1e-5
# execute face detector with tan-triggs
cropper = bob.bio.face.preprocessor.TanTriggs(face_cropper="landmark-detect")
preprocessed = cropper.transform(image, annotation)
# load reference and perform Tan-Triggs
detected = bob.bio.base.load(
pkg_resources.resource_filename("bob.bio.face.test", "data/flandmark.hdf5")
)
tan_triggs = bob.bio.base.load_resource(
"tan-triggs", "preprocessor", preferred_package="bob.bio.face"
)
reference = tan_triggs.transform(detected)
assert numpy.allclose(preprocessed, reference, atol=1e-5)
def test_tan_triggs():
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource('tan-triggs-crop', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor, bob.bio.face.preprocessor.TanTriggs)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor, bob.bio.base.preprocessor.Preprocessor)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute face cropper
_compare(preprocessor(image, annotation), pkg_resources.resource_filename('bob.bio.face.test', 'data/tan_triggs_cropped.hdf5'), preprocessor.write_data, preprocessor.read_data)
# test the preprocessor without cropping
preprocessor = bob.bio.base.load_resource('tan-triggs', 'preprocessor', preferred_package='bob.bio.face')
assert preprocessor.cropper is None
# result must be identical to the original face cropper (same eyes are used)
_compare(preprocessor(image, annotation), pkg_resources.resource_filename('bob.bio.face.test', 'data/tan_triggs_none.hdf5'), preprocessor.write_data, preprocessor.read_data)
preprocessor = bob.bio.base.load_resource('tan-triggs-landmark', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
assert preprocessor.cropper.flandmark is not None
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource(
"tan-triggs-crop", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.TanTriggs)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute face cropper
_compare(
preprocessor.transform(image, annotation),
pkg_resources.resource_filename(
"bob.bio.face.test", "data/tan_triggs_cropped.hdf5"
)
)
# test the preprocessor without cropping
preprocessor = bob.bio.base.load_resource(
"tan-triggs", "preprocessor", preferred_package="bob.bio.face"
)
assert preprocessor.cropper is None
# result must be identical to the original face cropper (same eyes are used)
_compare(
preprocessor.transform(image, annotation),
pkg_resources.resource_filename(
"bob.bio.face.test", "data/tan_triggs_none.hdf5"
)
)
preprocessor = bob.bio.base.load_resource(
"tan-triggs-landmark", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
assert preprocessor.cropper.flandmark is not None
def test_inorm_lbp():
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource('inorm-lbp-crop', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor, bob.bio.face.preprocessor.INormLBP)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor, bob.bio.base.preprocessor.Preprocessor)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(preprocessor(image, annotation), pkg_resources.resource_filename('bob.bio.face.test', 'data/inorm_lbp_cropped.hdf5'), preprocessor.write_data, preprocessor.read_data)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource('inorm-lbp', 'preprocessor', preferred_package='bob.bio.face')
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource('inorm-lbp-landmark', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource(
"inorm-lbp-crop", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.INormLBP)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(
preprocessor.transform(image, annotation),
pkg_resources.resource_filename(
"bob.bio.face.test", "data/inorm_lbp_cropped.hdf5"
)
)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource(
"inorm-lbp", "preprocessor", preferred_package="bob.bio.face"
)
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource(
"inorm-lbp-landmark", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
def test_heq():
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource('histogram-crop', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor, bob.bio.face.preprocessor.HistogramEqualization)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor, bob.bio.base.preprocessor.Preprocessor)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(preprocessor(image, annotation), pkg_resources.resource_filename('bob.bio.face.test', 'data/histogram_cropped.hdf5'), preprocessor.write_data, preprocessor.read_data)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource('histogram', 'preprocessor', preferred_package='bob.bio.face')
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource('histogram-landmark', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource(
"histogram-crop", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.HistogramEqualization)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(
preprocessor.transform(image, annotation),
pkg_resources.resource_filename(
"bob.bio.face.test", "data/histogram_cropped.hdf5"
),
)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource(
"histogram", "preprocessor", preferred_package="bob.bio.face"
)
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource(
"histogram-landmark", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
def test_sqi():
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource('self-quotient-crop', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor, bob.bio.face.preprocessor.SelfQuotientImage)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor, bob.bio.base.preprocessor.Preprocessor)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(preprocessor(image, annotation), pkg_resources.resource_filename('bob.bio.face.test', 'data/self_quotient_cropped.hdf5'), preprocessor.write_data, preprocessor.read_data)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource('self-quotient', 'preprocessor', preferred_package='bob.bio.face')
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource('self-quotient-landmark', 'preprocessor', preferred_package='bob.bio.face')
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
# read input
image, annotation = _image(), _annotation()
preprocessor = bob.bio.base.load_resource(
"self-quotient-crop", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.SelfQuotientImage)
assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(
preprocessor.transform(image, annotation),
pkg_resources.resource_filename(
"bob.bio.face.test", "data/self_quotient_cropped.hdf5"
)
)
# load the preprocessor without cropping
preprocessor = bob.bio.base.load_resource(
"self-quotient", "preprocessor", preferred_package="bob.bio.face"
)
assert preprocessor.cropper is None
# load the preprocessor landmark detection
preprocessor = bob.bio.base.load_resource(
"self-quotient-landmark", "preprocessor", preferred_package="bob.bio.face"
)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment