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Commit 1ca402d9 authored by Olegs NIKISINS's avatar Olegs NIKISINS
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Merge branch 'svmalgo' into 'master' 

SVM and GMM can be used with normal features, not only FrameContainers

See merge request !25
parents dd003319 d3d21f8a
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1 merge request!25SVM and GMM can be used with normal features, not only FrameContainers
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bob/pad/face/algorithm/VideoGmmPadAlgorithm.py
+ 68
− 60
View file @ 1ca402d9
...@@ -6,7 +6,7 @@ Created on Mon Aug 28 16:47:47 2017 ...@@ -6,7 +6,7 @@ Created on Mon Aug 28 16:47:47 2017
@author: Olegs Nikisins @author: Olegs Nikisins
""" """
#============================================================================== # ==============================================================================
# Import what is needed here: # Import what is needed here:
from bob.pad.base.algorithm import Algorithm from bob.pad.base.algorithm import Algorithm
...@@ -20,7 +20,7 @@ import bob.io.base ...@@ -20,7 +20,7 @@ import bob.io.base
from sklearn import mixture from sklearn import mixture
#============================================================================== # ==============================================================================
# Main body : # Main body :
class VideoGmmPadAlgorithm(Algorithm): class VideoGmmPadAlgorithm(Algorithm):
...@@ -51,15 +51,14 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -51,15 +51,14 @@ class VideoGmmPadAlgorithm(Algorithm):
""" """
def __init__(self, def __init__(self,
n_components = 1, n_components=1,
random_state = 3, random_state=3,
frame_level_scores_flag = False): frame_level_scores_flag=False):
Algorithm.__init__(self, Algorithm.__init__(self,
n_components = n_components, n_components=n_components,
random_state = random_state, random_state=random_state,
frame_level_scores_flag = frame_level_scores_flag, frame_level_scores_flag=frame_level_scores_flag,
performs_projection=True, performs_projection=True,
requires_projector_training=True) requires_projector_training=True)
...@@ -69,17 +68,17 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -69,17 +68,17 @@ class VideoGmmPadAlgorithm(Algorithm):
self.frame_level_scores_flag = frame_level_scores_flag self.frame_level_scores_flag = frame_level_scores_flag
self.machine = None # this argument will be updated with pretrained GMM machine self.machine = None # this argument will be updated with pretrained GMM machine
self.features_mean = None # this argument will be updated with features mean self.features_mean = None # this argument will be updated with features mean
self.features_std = None # this argument will be updated with features std self.features_std = None # this argument will be updated with features std
# names of the arguments of the pretrained GMM machine to be saved/loaded to/from HDF5 file: # names of the arguments of the pretrained GMM machine to be saved/loaded to/from HDF5 file:
self.gmm_param_keys = ["covariance_type", "covariances_", "lower_bound_", "means_", "n_components", "weights_", "converged_", "precisions_", "precisions_cholesky_" ] self.gmm_param_keys = ["covariance_type", "covariances_", "lower_bound_", "means_", "n_components", "weights_",
"converged_", "precisions_", "precisions_cholesky_"]
#========================================================================== # ==========================================================================
def convert_frame_cont_to_array(self, frame_container): def convert_frame_cont_to_array(self, frame_container):
""" """
This function converts a single Frame Container into an array of features. This function converts a single Frame Container into an array of features.
...@@ -103,11 +102,9 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -103,11 +102,9 @@ class VideoGmmPadAlgorithm(Algorithm):
frame_dictionary = {} frame_dictionary = {}
for frame in frame_container: for frame in frame_container:
frame_dictionary[frame[0]] = frame[1] frame_dictionary[frame[0]] = frame[1]
for idx, _ in enumerate(frame_container): for idx, _ in enumerate(frame_container):
# Frames are stored in a mixed order, therefore we get them using incrementing frame index: # Frames are stored in a mixed order, therefore we get them using incrementing frame index:
feature_vectors.append(frame_dictionary[str(idx)]) feature_vectors.append(frame_dictionary[str(idx)])
...@@ -115,8 +112,32 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -115,8 +112,32 @@ class VideoGmmPadAlgorithm(Algorithm):
return features_array return features_array
# ==========================================================================
def convert_and_prepare_features(self, features):
"""
This function converts a list or a frame container of features into a 2D array of features.
If the input is a list of frame containers, features from different frame containers (individuals)
are concatenated into the same list. This list is then converted to an array. The rows are samples,
the columns are features.
**Parameters:**
``features`` : [2D :py:class:`numpy.ndarray`] or [FrameContainer]
A list or 2D feature arrays or a list of Frame Containers, see ``bob.bio.video.utils.FrameContainer``.
Each frame Container contains feature vectors for the particular individual/person.
**Returns:**
``features_array`` : 2D :py:class:`numpy.ndarray`
An array containing features for all samples and frames.
"""
if isinstance(features[0], FrameContainer): # if FrameContainer convert to 2D numpy array
return self.convert_list_of_frame_cont_to_array(features)
else:
return np.vstack(features)
#========================================================================== # ==========================================================================
def convert_list_of_frame_cont_to_array(self, frame_containers): def convert_list_of_frame_cont_to_array(self, frame_containers):
""" """
This function converts a list of Frame containers into an array of features. This function converts a list of Frame containers into an array of features.
...@@ -139,18 +160,16 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -139,18 +160,16 @@ class VideoGmmPadAlgorithm(Algorithm):
feature_vectors = [] feature_vectors = []
for frame_container in frame_containers: for frame_container in frame_containers:
video_features_array = self.convert_frame_cont_to_array(frame_container) video_features_array = self.convert_frame_cont_to_array(frame_container)
feature_vectors.append( video_features_array ) feature_vectors.append(video_features_array)
features_array = np.vstack(feature_vectors) features_array = np.vstack(feature_vectors)
return features_array return features_array
# ==========================================================================
#========================================================================== def mean_std_normalize(self, features, features_mean=None, features_std=None):
def mean_std_normalize(self, features, features_mean= None, features_std = None):
""" """
The features in the input 2D array are mean-std normalized. The features in the input 2D array are mean-std normalized.
The rows are samples, the columns are features. If ``features_mean`` The rows are samples, the columns are features. If ``features_mean``
...@@ -185,14 +204,13 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -185,14 +204,13 @@ class VideoGmmPadAlgorithm(Algorithm):
# Compute mean and std if not given: # Compute mean and std if not given:
if features_mean is None: if features_mean is None:
features_mean = np.mean(features, axis=0) features_mean = np.mean(features, axis=0)
features_std = np.std(features, axis=0) features_std = np.std(features, axis=0)
row_norm_list = [] row_norm_list = []
for row in features: # row is a sample for row in features: # row is a sample
row_norm = (row - features_mean) / features_std row_norm = (row - features_mean) / features_std
...@@ -202,8 +220,7 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -202,8 +220,7 @@ class VideoGmmPadAlgorithm(Algorithm):
return features_norm, features_mean, features_std return features_norm, features_mean, features_std
# ==========================================================================
#==========================================================================
def train_gmm(self, real, n_components, random_state): def train_gmm(self, real, n_components, random_state):
""" """
Train GMM classifier given real class. Prior to the training the data is Train GMM classifier given real class. Prior to the training the data is
...@@ -236,16 +253,15 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -236,16 +253,15 @@ class VideoGmmPadAlgorithm(Algorithm):
features_norm, features_mean, features_std = self.mean_std_normalize(real) features_norm, features_mean, features_std = self.mean_std_normalize(real)
# real is now mean-std normalized # real is now mean-std normalized
machine = mixture.GaussianMixture(n_components = n_components, machine = mixture.GaussianMixture(n_components=n_components,
random_state = random_state, random_state=random_state,
covariance_type = 'full') covariance_type='full')
machine.fit( features_norm ) machine.fit(features_norm)
return machine, features_mean, features_std return machine, features_mean, features_std
# ==========================================================================
#==========================================================================
def save_gmm_machine_and_mean_std(self, projector_file, machine, features_mean, features_std): def save_gmm_machine_and_mean_std(self, projector_file, machine, features_mean, features_std):
""" """
Saves the GMM machine, features mean and std to the hdf5 file. Saves the GMM machine, features mean and std to the hdf5 file.
...@@ -268,22 +284,20 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -268,22 +284,20 @@ class VideoGmmPadAlgorithm(Algorithm):
Standart deviation of the features. Standart deviation of the features.
""" """
f = bob.io.base.HDF5File(projector_file, 'w') # open hdf5 file to save to f = bob.io.base.HDF5File(projector_file, 'w') # open hdf5 file to save to
for key in self.gmm_param_keys: for key in self.gmm_param_keys:
data = getattr(machine, key)
data = getattr( machine, key ) f.set(key, data)
f.set( key, data )
f.set( "features_mean", features_mean ) f.set("features_mean", features_mean)
f.set( "features_std", features_std ) f.set("features_std", features_std)
del f del f
# ==========================================================================
#==========================================================================
def train_projector(self, training_features, projector_file): def train_projector(self, training_features, projector_file):
""" """
Train GMM for feature projection and save it to file. Train GMM for feature projection and save it to file.
...@@ -303,21 +317,20 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -303,21 +317,20 @@ class VideoGmmPadAlgorithm(Algorithm):
""" """
# training_features[0] - training features for the REAL class. # training_features[0] - training features for the REAL class.
real = self.convert_list_of_frame_cont_to_array(training_features[0]) # output is array real = self.convert_and_prepare_features(training_features[0]) # output is array
# training_features[1] - training features for the ATTACK class. # training_features[1] - training features for the ATTACK class.
# attack = self.convert_list_of_frame_cont_to_array(training_features[1]) # output is array # attack = self.convert_and_prepare_features(training_features[1]) # output is array
# Train the GMM machine and get normalizers: # Train the GMM machine and get normalizers:
machine, features_mean, features_std = self.train_gmm(real = real, machine, features_mean, features_std = self.train_gmm(real=real,
n_components = self.n_components, n_components=self.n_components,
random_state = self.random_state) random_state=self.random_state)
# Save the GNN machine and normalizers: # Save the GNN machine and normalizers:
self.save_gmm_machine_and_mean_std(projector_file, machine, features_mean, features_std) self.save_gmm_machine_and_mean_std(projector_file, machine, features_mean, features_std)
# ==========================================================================
#==========================================================================
def load_gmm_machine_and_mean_std(self, projector_file): def load_gmm_machine_and_mean_std(self, projector_file):
""" """
Loads the machine, features mean and std from the hdf5 file. Loads the machine, features mean and std from the hdf5 file.
...@@ -341,14 +354,13 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -341,14 +354,13 @@ class VideoGmmPadAlgorithm(Algorithm):
Standart deviation of the features. Standart deviation of the features.
""" """
f = bob.io.base.HDF5File(projector_file, 'r') # file to read the machine from f = bob.io.base.HDF5File(projector_file, 'r') # file to read the machine from
# initialize the machine: # initialize the machine:
machine = mixture.GaussianMixture() machine = mixture.GaussianMixture()
# set the params of the machine: # set the params of the machine:
for key in self.gmm_param_keys: for key in self.gmm_param_keys:
data = f.read(key) data = f.read(key)
setattr(machine, key, data) setattr(machine, key, data)
...@@ -361,8 +373,7 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -361,8 +373,7 @@ class VideoGmmPadAlgorithm(Algorithm):
return machine, features_mean, features_std return machine, features_mean, features_std
# ==========================================================================
#==========================================================================
def load_projector(self, projector_file): def load_projector(self, projector_file):
""" """
Loads the machine, features mean and std from the hdf5 file. Loads the machine, features mean and std from the hdf5 file.
...@@ -394,8 +405,7 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -394,8 +405,7 @@ class VideoGmmPadAlgorithm(Algorithm):
self.features_std = features_std self.features_std = features_std
# ==========================================================================
#==========================================================================
def project(self, feature): def project(self, feature):
""" """
This function computes a vector of scores for each sample in the input This function computes a vector of scores for each sample in the input
...@@ -427,7 +437,7 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -427,7 +437,7 @@ class VideoGmmPadAlgorithm(Algorithm):
""" """
# 1. Convert input array to numpy array if necessary. # 1. Convert input array to numpy array if necessary.
if isinstance(feature, FrameContainer): # if FrameContainer convert to 2D numpy array if isinstance(feature, FrameContainer): # if FrameContainer convert to 2D numpy array
features_array = self.convert_frame_cont_to_array(feature) features_array = self.convert_frame_cont_to_array(feature)
...@@ -437,12 +447,11 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -437,12 +447,11 @@ class VideoGmmPadAlgorithm(Algorithm):
features_array_norm, _, _ = self.mean_std_normalize(features_array, self.features_mean, self.features_std) features_array_norm, _, _ = self.mean_std_normalize(features_array, self.features_mean, self.features_std)
scores = self.machine.score_samples( features_array_norm ) scores = self.machine.score_samples(features_array_norm)
return scores return scores
# ==========================================================================
#==========================================================================
def score(self, toscore): def score(self, toscore):
""" """
Returns a probability of a sample being a real class. Returns a probability of a sample being a real class.
...@@ -470,7 +479,6 @@ class VideoGmmPadAlgorithm(Algorithm): ...@@ -470,7 +479,6 @@ class VideoGmmPadAlgorithm(Algorithm):
else: else:
score = [np.mean( toscore )] # compute a single score per video score = [np.mean(toscore)] # compute a single score per video
return score return score
bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
+ 161
− 143
View file @ 1ca402d9
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