code update; returns numpy array instead of tuple

README.rst

@@ -21,8 +21,9 @@
 
 This package is part of the signal-processing and machine learning toolbox
 Bob_. It provides functions for extracting image-quality features proposed
-for PAD experiments by different research group.
-
+for PAD experiments by different research groups. Image quality measures
+proposed by Galbally et al (TIFS 2014) and by Wen et al. (TIFS 2015)
+are implemented in this package.
 
 Installation
 ------------

bob/ip/qualitymeasure/galbally_iqm_features.py

@@ -188,7 +188,7 @@ def image_quality_measures(refImage, testImage):
     #26 HLFI: high-low frequency index (implemented as done by Galbally in Matlab).
     hlfi25=high_low_freq_index(fftRef, refImage.shape[1])
     
-    return (mse00, psnr01, ad02, sc03, nk04, md05, lmse06, nae07, snrv08, ramdv09, mas10, mams11, sme12, gme16, gpe17, ssim18, vif19, hlfi25)
+    return np.asarray((mse00, psnr01, ad02, sc03, nk04, md05, lmse06, nae07, snrv08, ramdv09, mas10, mams11, sme12, gme16, gpe17, ssim18, vif19, hlfi25), dtype=np.float32)
 
 
 """

bob/ip/qualitymeasure/script/compute_qualitymeasures.py

@@ -34,24 +34,24 @@ def computeVideoIQM(video4d):
     print(rgbFrame.shape)
     iqmSet = iqm.compute_quality_features(rgbFrame)     #iqmSet = iqm.compute_quality_features(grayFrame)
     numIQM = len(iqmSet)
+    print(numIQM)
     iqaSet = iqa.compute_msu_iqa_features(rgbFrame)
     numIQA = len(iqaSet)
+    print(numIQA)
+    print(iqaSet.shape)
+    print(iqmSet.shape)
 
     #now initialize fset to store iqm features for all frames of input video.
     bobfset = np.zeros([numframes, numIQM])
-    bobQFeats = np.asarray(iqmSet) # compute_quality_features() returns a tuple
-    bobfset[f] = bobQFeats
+    bobfset[f] = iqmSet
     msufset = np.zeros([numframes, numIQA])
-    msuQFeats = np.asarray(iqaSet) 
-    msufset[f] = msuQFeats
+    msufset[f] = iqaSet
     
     for f in range(1,numframes):
         print('frame #: %d' %f)
         rgbFrame = video4d[f]
         print(rgbFrame.shape)
-#        grayFrame = matlab_rgb2gray(rgbFrame) #compute gray-level image for input color-frame
-#        bobQFeats = np.asarray(iqm.compute_quality_features(grayFrame)) # computeQualityFeatures() returns a tuple
-        bobQFeats = np.asarray(iqm.compute_quality_features(rgbFrame)) # computeQualityFeatures() returns a tuple
+        bobQFeats = iqm.compute_quality_features(rgbFrame) 
         msuQFeats  = iqa.compute_msu_iqa_features(rgbFrame)
         bobfset[f] = bobQFeats
         msufset[f] = msuQFeats