s=np.std(specklePixels)#std. of specularity (of specular-pixels)
returnnp.asarray((r,m/150.0,s/150.0),dtype=np.float32)#scaling by factor of 150 is as done by Wen et al. in their matlab code.
:param image: 2D gray-level (face) image
:param regionMask: (optional) 2D matrix (binary image), where 1s mark the pixels belonging to a region of interest, and 0s indicate pixels outside ROI.
"""
defmarzilianoBlur(image):
"""Method proposed by Marziliano et al. for determining the average width of vertical edges, as a measure of blurredness in an image.
(Reimplemented from the Matlab code provided by MSU.)
:param image: 2D gray-level (face) image
:param regionMask: (optional) 2D matrix (binary image), where 1s mark the pixels belonging to a region of interest, and 0s indicate pixels outside ROI.
"""
assertlen(image.shape)==2,'marzilianoBlur():: input image should be a 2D array (gray level image)'
edgeMap=sobelEdgeMap(image,'vertical')# compute vertical edge-map of image using sobel
#There will be some difference between the result of this function and the Matlab version, because the
#edgeMap produced by sobelEdgeMap() is not exactly the same as that produced by Matlab's edge() function.
# Test edge-map generated in Matlab produces the same result as the matlab version of MarzilianoBlur().
@@ -40,13 +40,16 @@ The examples below show how to use the functions in the two modules.
Note that both feature-sets are extracted from still-images. However, in face-PAD experiments, we typically process videos.
Therefore, the examples below use a video as input, but show how to extract image-quality features for a single frame.
Note also, that in the examples below, the input to the feature-extraction functions are full-frames. If you wish to extract features only for the face-region, you will have to first construct an image containing only the region of interest, and pass that as the parameter to the feature-extraction functions.
Computing Galbally's image-quality measures
-------------------------------------------
The function ``compute_quality_features()`` (in the module galbally_iqm_features) can be used to compute 18 image-quality measures
proposed by Galbally et al. Note that Galbally et al. proposed 25 features in their paper. This package implements the following
Therefore, the function ``galbally_iqm_features::compute_quality_features()`` returns a tuple of 18 scalars, in the order listed above.
The function ``galbally_iqm_features::compute_quality_features()`` returns a 18-D numpy array, containing the feature-values in the order listed above.
.. doctest::
...
...
@@ -70,13 +73,14 @@ is considered to represent a color RGB image, and is first converted to a gray-l
If the input is 2-dimensional (say, a numpy array of shape [480, 720]), then it is considered to represent a gray-level
image, and the RGB-to-gray conversion step is skipped.
Computing Wen's image-quality measures
--------------------------------------
Computing Wen's (MSU) image-quality measures
--------------------------------------------
The code below shows how to compute the image-quality features proposed by Wen et al. (Here, we refer to these features as
'MSU features'.)
These features are computed from a RGB color-image. The 2 feature-types (image-blur, color-diversity) all together form
a 118-D feature-vector.
The function ``compute_msu_iqa_features()`` (from the module ``msu_iqa_features``) returns a 1D numpy array of length 118.
These features are computed from a RGB color-image. The 3 feature-types (specularity, image-blur, color-diversity) all together form
a 121-D feature-vector.
The function ``compute_msu_iqa_features()`` (from the module ``msu_iqa_features``) returns a 1D numpy array of length 121.
.. doctest::
...
...
@@ -85,7 +89,7 @@ The function ``compute_msu_iqa_features()`` (from the module ``msu_iqa_features`