From 1a51ef4dfe12f810ddb33f3e470e2958cb1fa0a9 Mon Sep 17 00:00:00 2001
From: Rakesh Mehta <rakesh.mehta@idiap.ch>
Date: Tue, 27 Aug 2013 14:25:29 +0200
Subject: [PATCH] Comments added to features
---
xbob/boosting/core/gaussiantrainer.py | 61 -------------------------
xbob/boosting/features/local_feature.py | 29 +++++++-----
2 files changed, 17 insertions(+), 73 deletions(-)
delete mode 100644 xbob/boosting/core/gaussiantrainer.py
diff --git a/xbob/boosting/core/gaussiantrainer.py b/xbob/boosting/core/gaussiantrainer.py
deleted file mode 100644
index 851fef0..0000000
--- a/xbob/boosting/core/gaussiantrainer.py
+++ /dev/null
@@ -1,61 +0,0 @@
-
-class GaussianMachine():
-
- def __init__(self, num_classes):
- self.means = numpy.zeros(num_classes)
- self.variance = numpy.zeros(num_classes)
- self.selected_index = 0
-
-
- def get_weak_scores(self, features):
- num_classes = self.means.shape[0]
-
-
- for i in range(num_classes):
- mean_i = means[i]
- variance_i = variance[i]
- feature_i = features[:,i]
- demon = numpy.sqrt(2*numpy.pi*variance_i)
- scores[i] = numpy.exp(-(((feature_i - mean_i)**2)/2*variance_i))
-
- return scores
-
-class GaussianTrainer():
-
- def __init__(self, num_classes):
- self.num_classes = num_classes
-
-
- def compute_weak_trainer(self, features, loss_grad):
-
- num_features = features.shape[1]
- means = numpy.zeros([num_features,self.num_classes])
- variances = numpy.zeros([num_features,self.num_classes])
- summed_loss = numpy.zeros(num_features)
- gauss_machine = GaussianMachine()
-
- for feature_index in range(num_features)
- single_feature = features[:,feature_index]
- means[i,;], variances[i,:], loss[i] = compute_current_loss(single_feature, classes, loss_grad)
-
- gauss_machine.selected_index = numpy.argmin(loss)
- gauss_machine.means = means[optimum_index,:]
- gauss_machine.variance = variances[optimum_index,:]
-
-
-
- def compute_current_loss(feature, classes, loss_grad):
- num_samples = feature.shape[0]
- scores = numpy.zeros([num_samples, self.num_classes])
-
- for class_index in range(self.num_classes):
- samples_i = feature[loss_grad[:,class_index] < 0]
- mean[i] = numpy.mean(samples_i)
- variance[i] = numpy.std(samples_i)**2
- scores[:,class_index] = numpy.exp(-(((feature_i - mean_i)**2)/2*variance_i))
-
-
- scores_sum = numpy.sum(scores)
-
- return mean, variance, scores_sum
-
diff --git a/xbob/boosting/features/local_feature.py b/xbob/boosting/features/local_feature.py
index 913f8fe..755971d 100644
--- a/xbob/boosting/features/local_feature.py
+++ b/xbob/boosting/features/local_feature.py
@@ -37,7 +37,7 @@ class lbp_feature():
img: Input images
return:
- int_img: The integral image of the input image."""
+ integral_xy: The integral image of the input image."""
integral_y = numpy.cumsum(img,0)
integral_xy = numpy.cumsum(integral_y,1)
@@ -100,14 +100,12 @@ class lbp_feature():
def lbp(self, block_sum):
- """Function to compute the LBP for a image at single scale.
+ """Function to compute the LBP(3x3) for a image at single scale.
The LBP features of the given image is computed and the feature map is returned
Inputs:
- coord: The coordinates specify the neighbour to be considered.
- feature_map_dimx: feature map's dimension along the columns.
- feature_map_dimy: Feature maps dimension along the rows.
+ block_sum: The image with each pixel representing the sum of a block.
Return:
feature_map: The lbp feature map
@@ -129,9 +127,7 @@ class lbp_feature():
The tLBP features of the given image is computed and the feature map is returned
Inputs:
- coord: The coordinates specify the neighbour to be considered.
- feature_map_dimx: feature map's dimension along the columns.
- feature_map_dimy: Feature maps dimension along the rows.
+ block_sum: The image with each pixel representing the sum of a block.
Return:
feature_map: The lbp feature map
@@ -158,9 +154,7 @@ class lbp_feature():
The dLBP features of the given image is computed and the feature map is returned
Inputs:
- coord: The coordinates specify the neighbour to be considered.
- feature_map_dimx: feature map's dimension along the columns.
- feature_map_dimy: Feature maps dimension along the rows.
+ block_sum: The image with each pixel representing the sum of a block.
Return:
feature_map: The lbp feature map
@@ -189,7 +183,7 @@ class lbp_feature():
The mLBP features of the given image is computed and the feature map is returned.
Inputs:
- block_sum: The image that to apply mlbp operator on.
+ block_sum: The image with each pixel representing the sum of a block.
Return:
feature_map: The lbp feature map
@@ -234,6 +228,17 @@ class lbp_feature():
return feature_vector.shape[0]
def get_map_dimension(self, block_sum):
+ """ The function computes the dimension of the LBP (R = 1, P = 8) type feature image
+
+ The feature image returned by LBP with radius R is less than the image size by 2*R -1, for
+ R = 1, the feature map is smaller than the image by 2 pixel in both direction.
+
+ Input:
+
+ block_sum: The image with each pixel representing the sum of a block.
+
+ Return:
+ feature_map_dimx, feature_map_dimy : The dimensions of the feature map."""
# Initialize the size of the final feature map that will be obtained
feature_map_dimy = block_sum.shape[0] -2
--
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