diff --git a/bob/pad/face/config/lbp_64.py b/bob/pad/face/config/lbp_64.py
deleted file mode 100644
index e887e4c3c62f9cc17166818e92e444dfed2005b8..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/lbp_64.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import bob.pipelines as mario
-from bob.bio.face.utils import face_crop_solver, get_default_cropped_positions
-from bob.bio.video.transformer import VideoWrapper
-from bob.pad.face.extractor import LBPHistogram
-
-database = globals().get("database")
-if database is not None:
- annotation_type = database.annotation_type
- fixed_positions = database.fixed_positions
-else:
- annotation_type = None
- fixed_positions = None
-
-# Preprocessor #
-cropped_image_size = (64, 64)
-cropped_positions = get_default_cropped_positions(
- "pad", cropped_image_size, annotation_type
-)
-cropper = face_crop_solver(
- cropped_image_size=cropped_image_size,
- cropped_positions=cropped_positions,
- color_channel="gray",
- fixed_positions=fixed_positions,
-)
-preprocessor = VideoWrapper(cropper)
-preprocessor = mario.wrap(
- ["sample"],
- preprocessor,
- transform_extra_arguments=(("annotations", "annotations"),),
-)
-
-# Extractor #
-extractor = VideoWrapper(
- LBPHistogram(
- lbp_type="uniform",
- elbp_type="regular",
- radius=1,
- neighbors=8,
- circular=False,
- dtype=None,
- )
-)
-extractor = mario.wrap(["sample"], extractor)
diff --git a/bob/pad/face/extractor/LBPHistogram.py b/bob/pad/face/extractor/LBPHistogram.py
deleted file mode 100644
index 0ade56de3cb23aa58f939bfc271b9436f67fad54..0000000000000000000000000000000000000000
--- a/bob/pad/face/extractor/LBPHistogram.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.base import TransformerMixin
-
-
-class LBPHistogram(TransformerMixin, BaseEstimator):
- """Calculates a normalized LBP histogram over an image.
- These features are implemented based on [CAM12]_.
-
- Parameters
- ----------
- lbp_type : str
- The type of the LBP operator (regular, uniform or riu2)
- elbp_type : str
- Which type of LBP codes should be computed; possible values: ('regular',
- 'transitional', 'direction-coded'). For the old 'modified' method,
- specify `elbp_type` as 'regular` and `to_average` as True.
- to_average : bool
- Compare the neighbors to the average of the pixels instead of the central pixel?
- radius : float
- The radius of the circle on which the points are taken (for circular LBP)
- neighbors : int
- The number of points around the central point on which LBP is
- computed (4, 8, 16)
- circular : bool
- True if circular LBP is needed, False otherwise
- n_hor : int
- Number of blocks horizontally for spatially-enhanced LBP/MCT
- histograms. Default: 1
- n_vert
- Number of blocks vertically for spatially-enhanced LBP/MCT
- histograms. Default: 1
-
- Attributes
- ----------
- dtype : numpy.dtype
- If a ``dtype`` is specified in the constructor, it is assured that the
- resulting features have that dtype.
- lbp : bob.ip.base.LBP
- The LPB extractor object.
- """
-
- def __init__(
- self,
- lbp_type="uniform",
- elbp_type="regular",
- to_average=False,
- radius=1,
- neighbors=8,
- circular=False,
- dtype=None,
- n_hor=1,
- n_vert=1,
- **kwargs,
- ):
-
- super().__init__(**kwargs)
- self.lbp_type = lbp_type
- self.elbp_type = elbp_type
- self.to_average = to_average
- self.radius = radius
- self.neighbors = neighbors
- self.circular = circular
- self.dtype = dtype
- self.n_hor = n_hor
- self.n_vert = n_vert
-
- self.fit()
-
- def fit(self, X=None, y=None):
-
- self.lbp_ = LBP(
- neighbors=self.neighbors,
- radius=self.radius,
- circular=self.circular,
- to_average=self.to_average,
- uniform=self.lbp_type in ("uniform", "riu2"),
- rotation_invariant=self.lbp_type == "riu2",
- elbp_type=self.elbp_type,
- )
- return self
-
- def __getstate__(self):
- d = self.__dict__.copy()
- d.pop("lbp_")
- return d
-
- def __setstate__(self, state):
- self.__dict__.update(state)
- self.fit()
-
- def comp_block_histogram(self, data):
- """
- Extracts LBP/MCT histograms from a gray-scale image/block.
-
- Takes data of arbitrary dimensions and linearizes it into a 1D vector;
- Then, calculates the histogram.
- enforcing the data type, if desired.
-
- Parameters
- ----------
- data : numpy.ndarray
- The preprocessed data to be transformed into one vector.
-
- Returns
- -------
- 1D :py:class:`numpy.ndarray`
- The extracted feature vector, of the desired ``dtype`` (if
- specified)
- """
- assert isinstance(data, np.ndarray)
-
- # allocating the image with lbp codes
- lbpimage = np.ndarray(self.lbp_.lbp_shape(data), "uint16")
- self.lbp_(data, lbpimage) # calculating the lbp image
- hist = histogram(lbpimage, (0, self.lbp_.max_label - 1), self.lbp_.max_label)
- hist = hist / np.sum(hist) # histogram normalization
- if self.dtype is not None:
- hist = hist.astype(self.dtype)
- return hist
-
- def transform_one_image(self, data):
- """
- Extracts spatially-enhanced LBP/MCT histograms from a gray-scale image.
-
- Parameters
- ----------
- data : numpy.ndarray
- The preprocessed data to be transformed into one vector.
-
- Returns
- -------
- 1D :py:class:`numpy.ndarray`
- The extracted feature vector, of the desired ``dtype`` (if
- specified)
-
- """
-
- # Make sure the data can be split into equal blocks:
- row_max = int(data.shape[0] / self.n_vert) * self.n_vert
- col_max = int(data.shape[1] / self.n_hor) * self.n_hor
- data = data[:row_max, :col_max]
-
- blocks = [
- sub_block
- for block in np.hsplit(data, self.n_hor)
- for sub_block in np.vsplit(block, self.n_vert)
- ]
-
- hists = [self.comp_block_histogram(block) for block in blocks]
-
- hist = np.hstack(hists)
-
- hist = hist / len(blocks) # histogram normalization
-
- return hist
-
- def transform(self, images):
- return [self.transform_one_image(img) for img in images]
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
diff --git a/bob/pad/face/extractor/__init__.py b/bob/pad/face/extractor/__init__.py
index 2878cf784b6bcd3e5996f11a9fa580a452674daa..3f778fb4306e949a32b0131ce3d35a3a3e9aec69 100644
--- a/bob/pad/face/extractor/__init__.py
+++ b/bob/pad/face/extractor/__init__.py
@@ -1,4 +1,3 @@
-from .LBPHistogram import LBPHistogram
from .ImageQualityMeasure import ImageQualityMeasure
@@ -21,7 +20,6 @@ def __appropriate__(*args):
__appropriate__(
- LBPHistogram,
ImageQualityMeasure,
)
__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/pad/face/test/dummy/database.py b/bob/pad/face/test/dummy/database.py
index dc687aed30abf15ab55b7ff3cdbf9be979140218..3928d5e797a8f1f36555794396ef79264248aaac 100644
--- a/bob/pad/face/test/dummy/database.py
+++ b/bob/pad/face/test/dummy/database.py
@@ -2,7 +2,7 @@ import bob.io.base
import os
from bob.pipelines import DelayedSample
from bob.pad.base.pipelines.vanilla_pad.abstract_classes import Database
-from bob.db.base.utils import check_parameters_for_validity, convert_names_to_lowlevel
+from bob.bio.base.database.legacy import check_parameters_for_validity, convert_names_to_lowlevel
from bob.bio.video import VideoLikeContainer
from bob.bio.base.database import AtntBioDatabase
diff --git a/bob/pad/face/test/test_transformers.py b/bob/pad/face/test/test_transformers.py
index 6bdc4cc6dcef531b59972fd0a0ca78888e1437cd..000a64f199d55371f12ad8ed719a45bb80b829ac 100644
--- a/bob/pad/face/test/test_transformers.py
+++ b/bob/pad/face/test/test_transformers.py
@@ -14,79 +14,9 @@ import bob.bio.video
from bob.bio.face.color import rgb_to_gray
-from bob.pad.face.extractor import LBPHistogram, ImageQualityMeasure
+from bob.pad.face.extractor import ImageQualityMeasure
-def test_lbp_histogram():
- lbp = LBPHistogram()
- img = load(datafile("testimage.jpg", "bob.bio.face.test"))
- img = rgb_to_gray(img)
- features = lbp.transform([img])[0]
- reference = load(datafile("lbp.hdf5", "bob.pad.face.test"))
- assert np.allclose(features, reference)
-
-
-def notest_video_lbp_histogram():
- """
- Test LBPHistogram with Wrapper extractor.
- """
-
- from ..preprocessor import FaceCropAlign
- from bob.bio.video.preprocessor import Wrapper
-
- FACE_SIZE = 64 # The size of the resulting face
- RGB_OUTPUT_FLAG = False # Gray-scale output
- USE_FACE_ALIGNMENT = False # use annotations
- MAX_IMAGE_SIZE = None # no limiting here
- FACE_DETECTION_METHOD = None # use annotations
- MIN_FACE_SIZE = 50 # skip small faces
-
- image_preprocessor = FaceCropAlign(
- face_size=FACE_SIZE,
- rgb_output_flag=RGB_OUTPUT_FLAG,
- use_face_alignment=USE_FACE_ALIGNMENT,
- max_image_size=MAX_IMAGE_SIZE,
- face_detection_method=FACE_DETECTION_METHOD,
- min_face_size=MIN_FACE_SIZE,
- )
-
- preprocessor = Wrapper(image_preprocessor)
-
- image = load(datafile("test_image.png", "bob.pad.face.test"))
- annotations = {"topleft": (95, 155), "bottomright": (215, 265)}
-
- video, annotations = convert_image_to_video_data(image, annotations, 20)
-
- faces = preprocessor(frames=video, annotations=annotations)
-
- LBPTYPE = "uniform"
- ELBPTYPE = "regular"
- RAD = 1
- NEIGHBORS = 8
- CIRC = False
- DTYPE = None
-
- extractor = bob.bio.video.extractor.Wrapper(
- LBPHistogram(
- lbptype=LBPTYPE,
- elbptype=ELBPTYPE,
- rad=RAD,
- neighbors=NEIGHBORS,
- circ=CIRC,
- dtype=DTYPE,
- )
- )
-
- lbp_histograms = extractor(faces)
-
- assert len(lbp_histograms) == 20
- assert len(lbp_histograms[0][1]) == 59
- assert (lbp_histograms[0][1] == lbp_histograms[-1][1]).all()
- assert (lbp_histograms[0][1][0] - 0.12695109261186263) < 0.000001
- assert (lbp_histograms[0][1][-1] - 0.031737773152965658) < 0.000001
-
-
-# ==============================================================================
def notest_video_quality_measure():
"""
Test ImageQualityMeasure with Wrapper extractor.
diff --git a/conda/meta.yaml b/conda/meta.yaml
index dc509333cc14b524665839c1e926ced104cb79e1..f59d7342d7ab795d5569e21033dcb5eeb61b26c3 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -24,7 +24,6 @@ requirements:
- pip {{ pip }}
- bob.extension
- bob.io.base
- - bob.ip.base
- bob.ip.color
- bob.ip.qualitymeasure
- bob.bio.base
diff --git a/doc/baselines.rst b/doc/baselines.rst
index 84cfe4e7fe8b7d28cbbdc5da276b13ced008a92c..55c66bbe9a1c5c2e6a1cbe51fbb30a93c327d630 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -32,7 +32,7 @@ Usually, it is a good idea to have at least verbose level 2 (i.e., calling
To run the experiments in parallel, you can use an existing or (define a new)
SGE grid or local host multiprocessing configuration. To run the experiment
in the Idiap SGE grid, you can simply add the ``--dask-client sge`` command
- line option. To run experiments in parallel on the local machine, add the
+ line option. To run experiments in parallel on the local machine, add the
``--dask-client local-parallel`` option.
See :any:`this <pipeline_simple_features>` for more
@@ -89,79 +89,6 @@ more detail you can check the corresponding configuration file:
``bob/pad/face/config/replay_attack.py``.
-LBP features of facial region + SVM classifier
-===================================================
-
-Detailed description of this PAD pipe-line is given at
-:ref:`bob.pad.face.resources.face_pad.lbp_svm_replayattack`.
-
-To run this baseline on the `replayattack`_ database, using the ``grandtest``
-protocol, execute the following:
-
-.. code-block:: sh
-
- $ bob pad vanilla-pad replay-attack lbp svm-frames \
- --output <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- If you are at `idiap`_ you can use the SGE grid to speed-up the calculations.
- Simply add the ``--dask-client sge`` (or ``-l sge``) argument to the above
- command. For example:
-
- .. code-block:: sh
-
- $ bob pad vanilla-pad replay-attack lbp svm-frames \
- --output <PATH_TO_STORE_THE_RESULTS> \
- --dask-client sge
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/lbp_svm.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- $ bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/scores-dev.csv \
- <PATH_TO_STORE_THE_RESULTS>/scores-eval.csv \
- --legends "LBP features of facial region + SVM classifier + REPLAY-ATTACK database" \
- --eval \
- --criterion eer \
- --output <PATH_TO_STORE_THE_RESULTS>/evaluation_report.pdf
-
-
-The error rates for `replayattack`_ database are summarized in the table below:
-
-============== ================= =================
-.. Development Evaluation
-============== ================= =================
-APCER (attack) 17.4% 14.2%
-APCER_AP 17.4% 14.2%
-BPCER 17.4% 16.4%
-ACER 17.4% 15.3%
-FTA 0.0% 0.0%
-FPR 17.4% (1045/6000) 14.2% (1134/7999)
-FNR 17.4% (209/1200) 16.4% (262/1600)
-HTER 17.4% 15.3%
-FAR 17.4% 14.2%
-FRR 17.4% 16.4%
-PRECISION 0.5 0.5
-RECALL 0.8 0.8
-F1_SCORE 0.6 0.7
-AUC 0.9 0.9
-AUC-LOG-SCALE 2.0 2.1
-============== ================= =================
-
-
-The ROC curves for this particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_lbp_svm_replay_attack.pdf>`
-
-------------
-
-
Image Quality Measures as features of facial region + SVM classifier
========================================================================
@@ -235,70 +162,6 @@ This section summarizes the results of baseline face PAD experiments on the `Rep
The description of the database-related settings, which are used to run face PAD baselines on the Replay-Mobile is given here :ref:`bob.pad.face.resources.databases.replay_mobile`. To understand the settings in more detail you can check the corresponding configuration file : ``bob/pad/face/config/replay_mobile.py``.
-LBP features of facial region + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.lbp_svm_replayattack`.
-Note, that the same PAD pipe-line was used to run experiments on the Replay-Attack database.
-
-To run this baseline on the `Replay-Mobile`_ database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ bob pad vanilla-pad replay-mobile lbp svm_frame \
- --output <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel with the
- ``--dask-client`` option.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/lbp_svm.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- $ bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/scores-dev.csv \
- <PATH_TO_STORE_THE_RESULTS>/scores-eval.csv \
- --legends "LBP features of facial region + SVM classifier + Replay-Mobile database" \
- --eval \
- --criterion eer \
- --output <PATH_TO_STORE_THE_RESULTS>/evaluation_report.pdf
-
-The EER/HTER errors for the `Replay-Mobile`_ database are summarized in the table below:
-
-=================== ================= =================
-.. Development Evaluation
-=================== ================= =================
-APCER (mattescreen) 15.60% 25.77%
-APCER (print) 12.97% 8.44%
-APCER_AP 15.60% 25.77%
-BPCER 14.29% 20.03%
-ACER 14.94% 22.90%
-FTA 0.00% 0.00%
-FPR 14.28% (728/5098) 17.02% (647/3802)
-FNR 14.29% (457/3199) 20.03% (439/2192)
-HTER 14.28% 18.52%
-FAR 14.28% 17.02%
-FRR 14.29% 20.03%
-PRECISION 0.79 0.73
-RECALL 0.86 0.80
-F1_SCORE 0.82 0.76
-AUC 0.93 0.88
-AUC-LOG-SCALE 1.83 1.76
-=================== ================= =================
-
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_lbp_svm_replay_mobile.pdf>`
-
-------------
-
Image Quality Measures as features of facial region + SVM classifier
========================================================================
diff --git a/doc/resources.rst b/doc/resources.rst
index 5f905694408c2c9ecec117bc9ea3d8865a6b63de..8448bbf0967c84d63563956f5eaf5ee85ecbd9f8 100644
--- a/doc/resources.rst
+++ b/doc/resources.rst
@@ -59,11 +59,6 @@ The configuration files contain at least the following arguments of the
* ``pipeline`` containing zero, one, or more Transformers and one Classifier
-.. _bob.pad.face.resources.face_pad.lbp_svm_replayattack:
-
-LBP features of facial region + SVM for REPLAY-ATTACK
-================================================================================
-
.. _bob.pad.face.resources.face_pad.qm_svm_replayattack:
diff --git a/requirements.txt b/requirements.txt
index cca96a183570028ec2aa59287a585e0c98e45c1d..e1f008c01974150f45599511889d8cd0ac327864 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,6 @@ numpy
bob.extension
bob.bio.base
bob.io.base
-bob.ip.base
bob.pad.base
bob.bio.face
bob.bio.video
diff --git a/setup.py b/setup.py
index f14cecf4057cc447451a8941a3699920d5311012..4e5cd0e622cdb72f9dc38af73d82268532322ea6 100644
--- a/setup.py
+++ b/setup.py
@@ -76,8 +76,6 @@ setup(
"casiasurf = bob.pad.face.config.casiasurf",
"swan = bob.pad.face.config.swan",
"oulunpu = bob.pad.face.config.oulunpu",
- # LBPs
- "lbp = bob.pad.face.config.lbp_64",
# quality measure
"qm = bob.pad.face.config.qm_64",
# classifiers