Merge branch 'cleanup' into 'master'

Code clean-up

See merge request !1

bob/ip/qualitymeasure/__init__.py

@@ -4,6 +4,7 @@
 from .galbally_iqm_features import compute_quality_features
 from .msu_iqa_features import compute_msu_iqa_features
 
+
 def get_config():
     """
     Returns a string containing the configuration information.
@@ -13,7 +14,5 @@ def get_config():
     return bob.extension.get_config(__name__)
 
 
-
 # gets sphinx autodoc done right - don't remove it
 __all__ = [_ for _ in dir() if not _.startswith('_')]
-

bob/ip/qualitymeasure/script/__init__.py

@@ -2,7 +2,6 @@
 # vim: set fileencoding=utf-8 :
 
 
-
 def get_config():
     """
     Returns a string containing the configuration information.
@@ -12,7 +11,5 @@ def get_config():
     return bob.extension.get_config(__name__)
 
 
-
 # gets sphinx autodoc done right - don't remove it
 __all__ = [_ for _ in dir() if not _.startswith('_')]
-

bob/ip/qualitymeasure/script/compute_qualitymeasures.py

@@ -7,7 +7,7 @@ Created on 08 Mar 2017
 @author: sbhatta
 '''
 
-import os, sys
+import sys
 import argparse
 
 import bob.io.base
@@ -18,81 +18,97 @@ from bob.ip.qualitymeasure import galbally_iqm_features as iqm
 from bob.ip.qualitymeasure import msu_iqa_features as iqa
 
 
-def computeVideoIQM(video4d): 
+def computeVideoIQM(video4d):
     '''computes image-quality features for a set of frames comprising a video.
-    @param video4d: a '4d' video (N frames, each frame representing an r-g-b image).
-    returns  a set of feature-vectors, 1 vector per frame of video4d
+    @param video4d: a '4d' video (N frames, each frame representing an r-g-b
+        image). returns  a set of feature-vectors, 1 vector per frame of
+        video4d
     '''
     numframes = video4d.shape[0]
-    numframes=3
-    #print(numframes)
-    
-    #process first frame separately, to get the no. of iqm features
-    f=0
+    numframes = 3
+    # print(numframes)
+
+    # process first frame separately, to get the no. of iqm features
+    f = 0
     rgbFrame = video4d[f]
-    print('processing frame #: %d' %f)
-    iqmSet = iqm.compute_quality_features(rgbFrame)     #iqmSet = iqm.compute_quality_features(grayFrame)
+    print('processing frame #: %d' % f)
+    # iqmSet = iqm.compute_quality_features(grayFrame)
+    iqmSet = iqm.compute_quality_features(rgbFrame)
     numIQM = len(iqmSet)
     iqaSet = iqa.compute_msu_iqa_features(rgbFrame)
     numIQA = len(iqaSet)
 
-    #now initialize fset to store iqm features for all frames of input video.
+    # now initialize fset to store iqm features for all frames of input video.
     bobfset = np.zeros([numframes, numIQM])
     bobfset[f] = iqmSet
     msufset = np.zeros([numframes, numIQA])
     msufset[f] = iqaSet
-    
-    for f in range(1,numframes):
-        print('processing frame #: %d' %f)
+
+    for f in range(1, numframes):
+        print('processing frame #: %d' % f)
         rgbFrame = video4d[f]
 #         print(rgbFrame.shape)
-        bobQFeats = iqm.compute_quality_features(rgbFrame) 
-        msuQFeats  = iqa.compute_msu_iqa_features(rgbFrame)
+        bobQFeats = iqm.compute_quality_features(rgbFrame)
+        msuQFeats = iqa.compute_msu_iqa_features(rgbFrame)
         bobfset[f] = bobQFeats
         msufset[f] = msuQFeats
-    
+
     return (bobfset, msufset)
 
 
 def computeIQM_1video(vidPath):
-    """ loads a video, and returns 2 arrays of feature-vectors -- one per feature-family.
-    Each array contains one feature-vector per frame
+    """ loads a video, and returns 2 arrays of feature-vectors -- one per
+    feature-family. Each array contains one feature-vector per frame
     """
-    #1. load video from input path
+    # 1. load video from input path
     inputVideo = bob.io.video.reader(vidPath)
     vin = inputVideo.load()
-    #2. compute and return feature-sets 
+    # 2. compute and return feature-sets
     return computeVideoIQM(vin)
-    
 
 
 def main(command_line_parameters=None):
-    """Computes image-quality features for specified video-file, and stores the feature-arrays in specified output hdf5 file"""
-    
-    argParser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    argParser.add_argument('-i', '--input_videofile', dest='inpVidFile', default = None,
-       help='filename of video to be processed (including complete path). ')
-
-    argParser.add_argument('-o', '--output_featurefile', dest='outFile', default = None,
-       help='filename where computed features will be stored. Output file will be in hdf5 format.')
-    
+    """Computes image-quality features for specified video-file, and stores the
+    feature-arrays in specified output hdf5 file"""
+
+    argParser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter)
+    argParser.add_argument(
+        '-i',
+        '--input_videofile',
+        dest='inpVidFile',
+        default=None,
+        help='filename of video to be processed (including complete path). ')
+
+    argParser.add_argument(
+        '-o',
+        '--output_featurefile',
+        dest='outFile',
+        default=None,
+        help='filename where computed features will be stored. Output file '
+             'will be in hdf5 format.')
+
     args = argParser.parse_args(command_line_parameters)
-    #make sure the user specifies a folder where feature-files exist
-    if not args.inpVidFile: argParser.error('Specify parameter --input_videofile')
-    if not args.outFile: argParser.error('Specify parameter --output_featurefile')
+    # make sure the user specifies a folder where feature-files exist
+    if not args.inpVidFile:
+        argParser.error('Specify parameter --input_videofile')
+    if not args.outFile:
+        argParser.error('Specify parameter --output_featurefile')
 
-    #1. compute features, 1 vector per frame of input video, per feature-family (galbally,msu).
+    # 1. compute features, 1 vector per frame of input video, per
+    # feature-family (galbally,msu).
     infile = args.inpVidFile
     (bobIqmFeats, msuIqaFeats) = computeIQM_1video(infile)
-    #2. save features in file 
+    # 2. save features in file
     outfile = args.outFile
-    print("Saving features in output file: %s" %outfile)
+    print("Saving features in output file: %s" % outfile)
     ohf = bob.io.base.HDF5File(outfile, 'w')
     ohf.set('bobiqm', bobIqmFeats)
     ohf.set('msuiqa', msuIqaFeats)
     del ohf
     print('Done')
-    
+
 
 if __name__ == '__main__':
     main(sys.argv[1:])

bob/ip/qualitymeasure/test.py

@@ -15,9 +15,6 @@ import bob.io.base.test_utils
 import bob.io.video
 import bob.ip.color
 
-#from .utils import detect_landmarks, draw_landmarks, save_landmarks, Result
-#from .utils import detect_landmarks_on_boundingbox
-#from .script.detect_landmarks import main as app
 from . import galbally_iqm_features as iqm
 from . import msu_iqa_features as iqa
 
@@ -25,81 +22,87 @@ from . import msu_iqa_features as iqa
 REF_VIDEO_FILE = 'real_client001_android_SD_scene01.mp4'
 REF_FEATURE_FILE = 'real_client001_android_SD_scene01_ref_feats.h5'
 
-F = lambda n: pkg_resources.resource_filename(__name__, os.path.join('data', n))
+
+def F(n):
+  return pkg_resources.resource_filename(
+      __name__, os.path.join('data', n))
+
 
 input_video_file = F(REF_VIDEO_FILE)
-assert os.path.isfile(input_video_file), "File: not found: %s" % input_video_file
+assert os.path.isfile(
+    input_video_file), "File: not found: %s" % input_video_file
 inputVideo = bob.io.video.reader(input_video_file)
 video_data = inputVideo.load()
-numframes=3
+numframes = 3
+
 
 def load_reference_features():
-    ref_feat_file = F(REF_FEATURE_FILE)
-    assert os.path.isfile(ref_feat_file), "File: not found: %s" % ref_feat_file
-    rf = bob.io.base.HDF5File(ref_feat_file)
-    assert rf.has_key('/bobiqm'), "Key: /bobiqm not found in file %s" % ref_feat_file
-    assert rf.has_key('/msuiqa'), "Key: /msuiqa not found in file %s" % ref_feat_file
-    galbally_ref_features = rf.read('/bobiqm')
-    msu_ref_features = rf.read('/msuiqa')
-    del rf
-    return (galbally_ref_features, msu_ref_features)
-
-#load reference-features into global vars.
+  ref_feat_file = F(REF_FEATURE_FILE)
+  assert os.path.isfile(ref_feat_file), "File: not found: %s" % ref_feat_file
+  rf = bob.io.base.HDF5File(ref_feat_file)
+  assert rf.has_key('/bobiqm'), "Key: /bobiqm not found in file %s" % ref_feat_file
+  assert rf.has_key('/msuiqa'), "Key: /msuiqa not found in file %s" % ref_feat_file
+  galbally_ref_features = rf.read('/bobiqm')
+  msu_ref_features = rf.read('/msuiqa')
+  del rf
+  return (galbally_ref_features, msu_ref_features)
+
+
+# load reference-features into global vars.
 galbally_ref_features, msu_ref_features = load_reference_features()
 
 
 def test_galbally_feat_extr():
-  iqm_len=18  # change this, if you add more features to galbally_iqm_features module.
-  bobfset = np.zeros([numframes, iqm_len])  # feature-array to hold features for several frames
-  f=0
+  # change this, if you add more features to galbally_iqm_features module.
+  iqm_len = 18
+  # feature-array to hold features for several frames
+  bobfset = np.zeros([numframes, iqm_len])
+  f = 0
 
-  #process first frame separately, to get the no. of iqm features
+  # process first frame separately, to get the no. of iqm features
   rgbFrame = video_data[f]
   iqmSet = iqm.compute_quality_features(rgbFrame)
   numIQM = len(iqmSet)
 
-  #test: check that numIQM is the same as expected iqm_len (18)
-  nose.tools.eq_(numIQM,  iqm_len)
-  
-  #store features for first frame in feature-array
+  # test: check that numIQM is the same as expected iqm_len (18)
+  nose.tools.eq_(numIQM, iqm_len)
+
+  # store features for first frame in feature-array
   bobfset[f] = iqmSet
 
-  #now store iqm features for remaining test-frames of input video.
-  for f in range(1,numframes): 
+  # now store iqm features for remaining test-frames of input video.
+  for f in range(1, numframes):
     rgbFrame = video_data[f]
     bobfset[f] = iqm.compute_quality_features(rgbFrame)
-    #bobQFeats = iqm.compute_quality_features(rgbFrame)
-    #bobfset[f] = bobQFeats
-
-  #test: compare feature-values in bobfset[] with those loaded from hdf5 file
-  nose.tools.assert_true((bobfset==galbally_ref_features).all())
-  #np.allclose(A,B)
 
+  # test: compare feature-values in bobfset[] with those loaded from hdf5 file
+  nose.tools.assert_true((bobfset == galbally_ref_features).all())
+  # np.allclose(A,B)
 
 
 def test_msu_feat_extr():
-  iqa_len = 121  # change this, if you change the no. of features in msu_iqa_features module.
-  msufset = np.zeros([numframes, iqa_len]) # feature-array to hold features for several frames
-  f=0
+  # change this, if you change the no. of features in msu_iqa_features module.
+  iqa_len = 121
+  # feature-array to hold features for several frames
+  msufset = np.zeros([numframes, iqa_len])
+  f = 0
 
-  #process first frame separately, to get the no. of iqa features
+  # process first frame separately, to get the no. of iqa features
   rgbFrame = video_data[f]
   iqaSet = iqa.compute_msu_iqa_features(rgbFrame)
   numIQA = len(iqaSet)
 
-  #test: check that numIQA matches the expected iqa_len(121)
-  nose.tools.eq_(numIQA,  iqa_len)
-  
-  #store features for first frame in feature-array
+  # test: check that numIQA matches the expected iqa_len(121)
+  nose.tools.eq_(numIQA, iqa_len)
+
+  # store features for first frame in feature-array
   msufset[f] = iqaSet
 
-  #now store iqm features for remaining test-frames of input video.
-  for f in range(1,numframes): 
+  # now store iqm features for remaining test-frames of input video.
+  for f in range(1, numframes):
     rgbFrame = video_data[f]
-    msuQFeats  = iqa.compute_msu_iqa_features(rgbFrame)
+    msuQFeats = iqa.compute_msu_iqa_features(rgbFrame)
     msufset[f] = msuQFeats
 
-  #test: compare feature-values in bobfset[] with those loaded from hdf5 file
-  nose.tools.assert_true((msufset==msu_ref_features).all())
-  #np.allclose(A,B)
-
+  # test: compare feature-values in bobfset[] with those loaded from hdf5 file
+  nose.tools.assert_true((msufset == msu_ref_features).all())

doc/index.rst

@@ -2,6 +2,8 @@
 .. Sushil Bhattacharjee <sushil.bhattacharjee@idiap.ch>
 .. Tue 08 Mar 2017 15:42:29 CET
 
+.. _bob.ip.qualitymeasure:
+
 ===============================================================
  Bob's Routines for Image-Quality Measures for PAD Applications
 ===============================================================