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Commit 0e3d6af0 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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2 merge requests!185Wrappers and aggregators,!180[dask] Preparing bob.bio.base for dask pipelines
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bob/bio/base/config/examples/gabor_mobio-male.py deleted 100644 → 0
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− 76
View file @ ebc2cb4c
from bob.bio.base.pipelines.vanilla_biometrics.implemented import CheckpointDistance
from bob.bio.base.pipelines.vanilla_biometrics.legacy import (
DatabaseConnector,
Preprocessor,
Extractor,
AlgorithmAsBioAlg,
)
from bob.bio.face.database.mobio import MobioBioDatabase
from bob.bio.face.preprocessor import FaceCrop
from bob.extension import rc
from bob.pipelines.transformers import CheckpointSampleLinearize, CheckpointSamplePCA
from sklearn.pipeline import make_pipeline
import functools
import os
import bob.bio.face
import math
base_dir = "example"
database = DatabaseConnector(
MobioBioDatabase(
original_directory=rc["bob.db.mobio.directory"],
annotation_directory=rc["bob.db.mobio.annotation_directory"],
original_extension=".png",
protocol="mobile0-male",
)
)
database.allow_score_multiple_references = True
# Using face crop
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
# eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# FaceCrop
preprocessor = bob.bio.face.preprocessor.INormLBP(
face_cropper=bob.bio.face.preprocessor.FaceCrop(
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
color_channel="gray",
),
)
extractor = bob.bio.face.extractor.GridGraph(
# Gabor parameters
gabor_sigma=math.sqrt(2.0) * math.pi,
# what kind of information to extract
normalize_gabor_jets=True,
# setup of the fixed grid
node_distance=(8, 8),
)
transformer = make_pipeline(
Preprocessor(preprocessor, features_dir=os.path.join(base_dir, "face_cropper")),
Extractor(extractor, features_dir=os.path.join(base_dir, "gabor_graph")),
)
## algorithm
gabor_jet = bob.bio.face.algorithm.GaborJet(
gabor_jet_similarity_type="PhaseDiffPlusCanberra",
multiple_feature_scoring="max_jet",
gabor_sigma=math.sqrt(2.0) * math.pi,
)
algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir, allow_score_multiple_references=True)
#algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir)
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
#pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm), npartitions=48)
pipeline = VanillaBiometrics(transformer, algorithm)
bob/bio/base/config/examples/isv_atnt_legacy_all_legacy.py deleted 100644 → 0
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− 75
View file @ ebc2cb4c
from bob.bio.face.database import AtntBioDatabase
from bob.bio.gmm.algorithm import ISV
from bob.bio.face.preprocessor import FaceCrop
from sklearn.pipeline import make_pipeline
from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector, Preprocessor, AlgorithmAsTransformer, AlgorithmAsBioAlg, Extractor
import functools
from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
Distance,
CheckpointDistance,
)
import os
# DATABASE
database = DatabaseConnector(
AtntBioDatabase(original_directory="./atnt", protocol="Default"),
)
database.allow_scoring_with_all_biometric_references = True
base_dir = "example/isv"
# PREPROCESSOR LEGACY
# Cropping
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
# eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# RANDOM EYES POSITIONS
# I JUST MADE UP THESE NUMBERS
FIXED_RIGHT_EYE_POS = (30, 30)
FIXED_LEFT_EYE_POS = (20, 50)
face_cropper = functools.partial(
FaceCrop,
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
fixed_positions={"leye": FIXED_LEFT_EYE_POS, "reye": FIXED_RIGHT_EYE_POS},
)
import bob.bio.face
extractor = functools.partial(
bob.bio.face.extractor.DCTBlocks,
block_size=12,
block_overlap=11,
number_of_dct_coefficients=45,
)
# ALGORITHM LEGACY
isv = functools.partial(ISV, subspace_dimension_of_u=10, number_of_gaussians=2)
model_path=os.path.join(base_dir, "ubm_u.hdf5")
transformer = make_pipeline(
Preprocessor(callable=face_cropper, features_dir=os.path.join(base_dir,"face_crop")),
Extractor(extractor, features_dir=os.path.join(base_dir, "dcts")),
AlgorithmAsTransformer(
callable=isv, features_dir=os.path.join(base_dir,"isv"), model_path=model_path
),
)
algorithm = AlgorithmAsBioAlg(callable=isv, features_dir=base_dir, model_path=model_path)
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm))
bob/bio/base/config/examples/lda_atnt_legacy.py deleted 100644 → 0
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− 65
View file @ ebc2cb4c
from bob.bio.face.database import AtntBioDatabase
from bob.bio.base.algorithm import LDA
from bob.bio.face.preprocessor import FaceCrop
from sklearn.pipeline import make_pipeline
from bob.pipelines.transformers import CheckpointSampleLinearize
from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector, Preprocessor, AlgorithmAsTransformer
import functools
from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
Distance,
CheckpointDistance,
)
# DATABASE
database = DatabaseConnector(
AtntBioDatabase(original_directory="./atnt", protocol="Default"),
)
# PREPROCESSOR LEGACY
# Cropping
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
# eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# RANDOM EYES POSITIONS
# I JUST MADE UP THESE NUMBERS
FIXED_RIGHT_EYE_POS = (30, 30)
FIXED_LEFT_EYE_POS = (20, 50)
face_cropper = functools.partial(
FaceCrop,
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
fixed_positions={"leye": FIXED_LEFT_EYE_POS, "reye": FIXED_RIGHT_EYE_POS},
)
# ALGORITHM LEGACY
lda = functools.partial(LDA, use_pinv=True, pca_subspace_dimension=0.90)
transformer = make_pipeline(
Preprocessor(callable=face_cropper, features_dir="./example/transformer0"),
CheckpointSampleLinearize(features_dir="./example/transformer1"),
AlgorithmAsTransformer(
callable=lda, features_dir="./example/transformer2", model_path="./example/lda_projector.hdf5"
),
)
algorithm = CheckpointDistance(features_dir="./example/")
# algorithm = Distance()
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm))
bob/bio/base/config/examples/lda_atnt_legacy_all_legacy.py deleted 100644 → 0
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− 64
View file @ ebc2cb4c
from bob.bio.face.database import AtntBioDatabase
from bob.bio.base.algorithm import LDA
from bob.bio.face.preprocessor import FaceCrop
from sklearn.pipeline import make_pipeline
from bob.pipelines.transformers import CheckpointSampleLinearize
from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector, Preprocessor, AlgorithmAsTransformer, AlgorithmAsBioAlg
import functools
from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
Distance,
CheckpointDistance,
)
import os
# DATABASE
database = DatabaseConnector(
AtntBioDatabase(original_directory="./atnt", protocol="Default"),
)
base_dir = "example"
# PREPROCESSOR LEGACY
# Cropping
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
# eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# RANDOM EYES POSITIONS
# I JUST MADE UP THESE NUMBERS
FIXED_RIGHT_EYE_POS = (30, 30)
FIXED_LEFT_EYE_POS = (20, 50)
face_cropper = functools.partial(
FaceCrop,
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
fixed_positions={"leye": FIXED_LEFT_EYE_POS, "reye": FIXED_RIGHT_EYE_POS},
)
# ALGORITHM LEGACY
lda = functools.partial(LDA, use_pinv=True, pca_subspace_dimension=0.90)
transformer = make_pipeline(
Preprocessor(callable=face_cropper, features_dir=os.path.join(base_dir,"transformer0")),
CheckpointSampleLinearize(features_dir=os.path.join(base_dir,"transformer1")),
AlgorithmAsTransformer(
callable=lda, features_dir=os.path.join(base_dir,"transformer2"), model_path=os.path.join(base_dir, "lda.hdf5")
),
)
algorithm = AlgorithmAsBioAlg(callable=lda, features_dir="./example/")
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm))
bob/bio/base/config/examples/pca_atnt.py deleted 100644 → 0
+ 0
− 33
View file @ ebc2cb4c
from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector
from sklearn.pipeline import make_pipeline
from bob.pipelines.transformers import CheckpointSampleLinearize, CheckpointSamplePCA
from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
CheckpointDistance,
)
from bob.bio.face.database import AtntBioDatabase
import os
base_dir = "example"
database = DatabaseConnector(AtntBioDatabase(original_directory="./atnt", protocol="Default"))
database.allow_scoring_with_all_biometric_references = True
transformer = make_pipeline(
CheckpointSampleLinearize(features_dir=os.path.join(base_dir, "linearize")),
CheckpointSamplePCA(
features_dir=os.path.join(base_dir, "pca_features"), model_path=os.path.join(base_dir, "pca.pkl")
),
)
algorithm = CheckpointDistance(features_dir=base_dir, allow_score_multiple_references=True)
# # comment out the code below to disable dask
from bob.pipelines.mixins import estimator_dask_it, mix_me_up
from bob.bio.base.pipelines.vanilla_biometrics.mixins import (
BioAlgDaskMixin,
)
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
pipeline = VanillaBiometrics(transformer, algorithm)
#pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm))
bob/bio/base/config/examples/pca_mobio-male.py deleted 100644 → 0
+ 0
− 50
View file @ ebc2cb4c
from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
CheckpointDistance,
)
from bob.bio.base.pipelines.vanilla_biometrics.legacy import (
DatabaseConnector,
Preprocessor,
)
from bob.bio.face.database.mobio import MobioBioDatabase
from bob.bio.face.preprocessor import FaceCrop
from bob.extension import rc
from bob.pipelines.transformers import CheckpointSampleLinearize, CheckpointSamplePCA
from sklearn.pipeline import make_pipeline
import functools
database = DatabaseConnector(
MobioBioDatabase(
original_directory=rc["bob.db.mobio.directory"],
annotation_directory=rc["bob.db.mobio.annotation_directory"],
original_extension=".png",
protocol="mobile0-male",
)
)
# Using face crop
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
# eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# FaceCrop
preprocessor = functools.partial(
FaceCrop,
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
)
transformer = make_pipeline(
Preprocessor(preprocessor, features_dir="./example/extractor0"),
CheckpointSampleLinearize(features_dir="./example/extractor1"),
CheckpointSamplePCA(
features_dir="./example/extractor2", model_path="./example/pca.pkl"
),
)
algorithm = CheckpointDistance(features_dir="./example/")
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm), npartitions=48)
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