Merge branch 'issue-5-remove-io-dependency' into 'master'

[conda] make bob.io. packages test requirements

Closes #5

See merge request !14

bob/ip/qualitymeasure/script/compute_qualitymeasures.py

@@ -2,23 +2,19 @@
 # vim: set fileencoding=utf-8 :
 
 
-import sys
 import argparse
 
-import bob.io.base
-import bob.io.video
-import bob.ip.color
-import numpy as np
-from bob.ip.qualitymeasure import galbally_iqm_features as iqm
-from bob.ip.qualitymeasure import msu_iqa_features as iqa
-
 
 def computeVideoIQM(video4d):
-    '''computes image-quality features for a set of frames comprising a video.
+    """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
-    '''
+    """
+    from bob.ip.qualitymeasure import galbally_iqm_features as iqm
+    from bob.ip.qualitymeasure import msu_iqa_features as iqa
+    import numpy as np
+
     numframes = video4d.shape[0]
     numframes = 3
     # print(numframes)
@@ -26,7 +22,7 @@ def computeVideoIQM(video4d):
     # process first frame separately, to get the no. of iqm features
     f = 0
     rgbFrame = video4d[f]
-    print('processing frame #: %d' % f)
+    print("processing frame #: %d" % f)
     # iqmSet = iqm.compute_quality_features(grayFrame)
     iqmSet = iqm.compute_quality_features(rgbFrame)
     numIQM = len(iqmSet)
@@ -40,9 +36,9 @@ def computeVideoIQM(video4d):
     msufset[f] = iqaSet
 
     for f in range(1, numframes):
-        print('processing frame #: %d' % f)
+        print("processing frame #: %d" % f)
         rgbFrame = video4d[f]
-#         print(rgbFrame.shape)
+        #         print(rgbFrame.shape)
         bobQFeats = iqm.compute_quality_features(rgbFrame)
         msuQFeats = iqa.compute_msu_iqa_features(rgbFrame)
         bobfset[f] = bobQFeats
@@ -56,6 +52,8 @@ def computeIQM_1video(vidPath):
     feature-family. Each array contains one feature-vector per frame
     """
     # 1. load video from input path
+    import bob.io.video
+
     inputVideo = bob.io.video.reader(vidPath)
     vin = inputVideo.load()
     # 2. compute and return feature-sets
@@ -67,29 +65,31 @@ def main(command_line_parameters=None):
     feature-arrays in specified output hdf5 file"""
 
     argParser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
     argParser.add_argument(
-        '-i',
-        '--input_videofile',
-        dest='inpVidFile',
+        "-i",
+        "--input_videofile",
+        dest="inpVidFile",
         default=None,
-        help='filename of video to be processed (including complete path). ')
+        help="filename of video to be processed (including complete path). ",
+    )
 
     argParser.add_argument(
-        '-o',
-        '--output_featurefile',
-        dest='outFile',
+        "-o",
+        "--output_featurefile",
+        dest="outFile",
         default=None,
-        help='filename where computed features will be stored. Output file '
-             'will be in hdf5 format.')
+        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')
+        argParser.error("Specify parameter --input_videofile")
     if not args.outFile:
-        argParser.error('Specify parameter --output_featurefile')
+        argParser.error("Specify parameter --output_featurefile")
 
     # 1. compute features, 1 vector per frame of input video, per
     # feature-family (galbally,msu).
@@ -98,12 +98,16 @@ def main(command_line_parameters=None):
     # 2. save features in file
     outfile = args.outFile
     print("Saving features in output file: %s" % outfile)
-    ohf = bob.io.base.HDF5File(outfile, 'w')
-    ohf.set('bobiqm', bobIqmFeats)
-    ohf.set('msuiqa', msuIqaFeats)
+    import bob.io.base
+
+    ohf = bob.io.base.HDF5File(outfile, "w")
+    ohf.set("bobiqm", bobIqmFeats)
+    ohf.set("msuiqa", msuIqaFeats)
     del ohf
-    print('Done')
+    print("Done")
+
 
+if __name__ == "__main__":
+    import sys
 
-if __name__ == '__main__':
     main(sys.argv[1:])

bob/ip/qualitymeasure/script/remove_highlights.py

@@ -2,15 +2,8 @@
 # vim: set fileencoding=utf-8 :
 
 
-import sys
 import argparse
-import time
 
-import bob.io.base
-import bob.io.image
-import numpy as np
-
-from bob.ip.qualitymeasure import remove_highlights
 
 def main(command_line_parameters=None):
     """Remove the specular component of the input image and write result to
@@ -18,42 +11,50 @@ def main(command_line_parameters=None):
     """
 
     argParser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
     argParser.add_argument(
-        '-i',
-        '--input',
-        dest='inpImg',
+        "-i",
+        "--input",
+        dest="inpImg",
         default=None,
-        help='filename of image to be processed (including complete path). ')
+        help="filename of image to be processed (including complete path). ",
+    )
 
     argParser.add_argument(
-        '-o',
-        '--output',
-        dest='outImg',
+        "-o",
+        "--output",
+        dest="outImg",
         default=None,
-        help='filename of specular free image.')
+        help="filename of specular free image.",
+    )
 
     argParser.add_argument(
-        '-r',
-        '--residue',
-        dest='resImg',
+        "-r",
+        "--residue",
+        dest="resImg",
         default=None,
-        help='filename of specular residue image.')
+        help="filename of specular residue image.",
+    )
 
     argParser.add_argument(
-        '-e',
-        '--epsilon',
-        dest='epsilon',
+        "-e",
+        "--epsilon",
+        dest="epsilon",
         default=0.5,
-        help='value of epsilon parameter.')
+        help="value of epsilon parameter.",
+    )
 
     args = argParser.parse_args(command_line_parameters)
 
     if not args.inpImg:
-        argParser.error('Specify parameter --input')
+        argParser.error("Specify parameter --input")
     if not args.outImg:
-        argParser.error('Specify parameter --output')
+        argParser.error("Specify parameter --output")
+
+    import bob.io.image
+    import numpy as np
+    from bob.ip.qualitymeasure import remove_highlights
 
     # 1. open input image
     print("Opening file: %s" % args.inpImg)
@@ -66,14 +67,16 @@ def main(command_line_parameters=None):
 
     # 1. save output image
     print("Saving output file: %s" % args.outImg)
-    bob.io.base.save(diff.astype('uint8'), args.outImg)
+    bob.io.base.save(diff.astype("uint8"), args.outImg)
 
     if args.resImg:
         print("Saving residue file: %s" % args.resImg)
-        bob.io.base.save(residue.astype('uint8'), args.resImg)
+        bob.io.base.save(residue.astype("uint8"), args.resImg)
+
+    print("Done")
 
-    print('Done')
 
+if __name__ == "__main__":
+    import sys
 
-if __name__ == '__main__':
     main(sys.argv[1:])

conda/meta.yaml

@@ -30,9 +30,6 @@ requirements:
     - setuptools {{ setuptools }}
     - bob.extension
     - bob.blitz
-    - bob.io.base
-    - bob.io.image
-    - bob.io.video
     - bob.ip.base
     - bob.ip.draw
     - bob.ip.color
@@ -60,6 +57,9 @@ test:
     - coverage
     - sphinx
     - sphinx_rtd_theme
+    - bob.io.base
+    - bob.io.image
+    - bob.io.video
 
 about:
   home: https://www.idiap.ch/software/bob/

doc/guide.rst

@@ -7,7 +7,6 @@
    import os, sys
    import argparse
 
-   import bob.io.image #remove this if possible
    import bob.io.base
    import bob.io.video
    import bob.ip.color
@@ -33,7 +32,7 @@ Two sets of quality-features are implemented in this package:
 2. The image-quality features proposed by Wen et al. (IEEE TIFS 2015).
 
 The package includes separate modules for implementing the two feature-sets.
-The module ``galbally_iqm_features`` implements the features proposed by Gabally et al., and the module ``msu_iqa_features`` implements the features proposed by Wen et al. 
+The module ``galbally_iqm_features`` implements the features proposed by Gabally et al., and the module ``msu_iqa_features`` implements the features proposed by Wen et al.
 In each module, a single function needs to be called, to retrieve all the features implemented in the module.
 The examples below show how to use the functions in the two modules.
 
@@ -45,9 +44,9 @@ Note also, that in the examples below, the input to the feature-extraction funct
 
 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 
+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
-18 features from their paper, namely: 
+18 features from their paper, namely:
 [mse, psnr, ad, sc, nk, md, lmse, nae, snrv, ramdv, mas, mams, sme, gme, gpe, ssim, vif, hlfi].
 The function ``galbally_iqm_features::compute_quality_features()`` returns a 1D numpy array of length 18, containing the feature-values in the order listed above.
 
@@ -71,7 +70,7 @@ or a gray-level image.
 When the input image is 3-dimensional, the first dimension being '3' (as is the case in the example above), the input
 is assumed to represent a color RGB image, and is first converted to a gray-level image.
 If the input is 2-dimensional (say, a numpy array of shape [480, 720]), then it is assumed to represent a gray-level
-image, and the RGB-to-gray conversion step is skipped. 
+image, and the RGB-to-gray conversion step is skipped.
 
 
 Computing Wen's (MSU) image-quality measures
@@ -92,5 +91,5 @@ The function ``compute_msu_iqa_features()`` (from the module ``msu_iqa_features`
    121
 
 
-.. _Bob: https://www.idiap.ch/software/bob/ 
+.. _Bob: https://www.idiap.ch/software/bob/
 .. _documentation: https://menpofit.readthedocs.io/en/stable/

requirements.txt

 setuptools
 bob.extension
 bob.blitz
-bob.io.base
-bob.io.image
-bob.io.video
 bob.ip.base
 bob.ip.draw
 bob.ip.color

test-requirements.txt

+bob.io.base
+bob.io.image
+bob.io.video