Improve sample viewing script

bob/bio/vein/script/view_mask.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Mon 07 Nov 2016 15:20:26 CET
-
-
-"""Visualizes masks applied to vein imagery
-
-Usage: %(prog)s [-v...] [options] <file> [<file>...]
-       %(prog)s --help
-       %(prog)s --version
-
-
-Arguments:
-  <file>  The HDF5 file to load image and mask from
-
-
-Options:
-  -h, --help             Shows this help message and exits
-  -V, --version          Prints the version and exits
-  -v, --verbose          Increases the output verbosity level
-  -s path, --save=path   If set, saves image into a file instead of displaying
-                         it
-
-
-Examples:
-
-  Visualize the mask on a single image:
-
-     $ %(prog)s data.hdf5
-
-  Visualize multiple masks (like in a proof-sheet):
-
-     $ %(prog)s *.hdf5
-
-"""
-
-
-import os
-import sys
-
-import bob.core
-logger = bob.core.log.setup("bob.bio.vein")
-
-from ..preprocessor import utils
-
-
-def main(user_input=None):
-
-  if user_input is not None:
-    argv = user_input
-  else:
-    argv = sys.argv[1:]
-
-  import docopt
-  import pkg_resources
-
-  completions = dict(
-      prog=os.path.basename(sys.argv[0]),
-      version=pkg_resources.require('bob.bio.vein')[0].version
-      )
-
-  args = docopt.docopt(
-      __doc__ % completions,
-      argv=argv,
-      version=completions['version'],
-      )
-
-  # Sets-up logging
-  verbosity = int(args['--verbose'])
-  bob.core.log.set_verbosity_level(logger, verbosity)
-
-  # Loads the image, the mask and save it to a PNG file
-  from ..preprocessor import utils
-  for filename in args['<file>']:
-    f = bob.io.base.HDF5File(filename)
-    image = f.read('image')
-    mask  = f.read('mask')
-    img = utils.draw_mask_over_image(image, mask)
-    if args['--save']:
-      img.save(args['--save'])
-    else:
-      img.show()

bob/bio/vein/script/view_sample.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Mon 07 Nov 2016 15:20:26 CET
+
+
+"""Visualizes a particular sample throughout many processing stages
+
+Usage: %(prog)s [-v...] [-s <path>] <database> <processed> <stem> [<stem>...]
+       %(prog)s --help
+       %(prog)s --version
+
+
+Arguments:
+
+  <database>  Path to the database with the image to be inspected
+  <processed>  Path with the directory holding the preprocessed and extracted
+               sub-directories containing the processing results of a
+               bob.bio.vein toolchain
+  <stem>       Name of the object on the database to display, with the root or
+               the extension
+
+
+Options:
+
+  -h, --help                Shows this help message and exits
+  -V, --version             Prints the version and exits
+  -v, --verbose             Increases the output verbosity level
+  -s <path>, --save=<path>  If set, saves image into a file instead of
+                            displaying it
+
+
+Examples:
+
+  Visualize to processing toolchain over a single image
+
+     $ %(prog)s /database /mc client/sample
+
+  Visualize multiple masks (like in a proof-sheet):
+
+     $ %(prog)s /database /mc client/sample1 client/sample2
+
+"""
+
+
+import os
+import sys
+
+import numpy
+import bob.core
+logger = bob.core.log.setup("bob.bio.vein")
+
+import matplotlib.pyplot as mpl
+from ..preprocessor import utils
+
+import bob.io.base
+import bob.io.image
+
+
+def save_figures(title, image, mask, image_pp, binary):
+  '''Saves individual images on a directory
+
+
+  Parameters:
+
+    title (str): A title for this plot
+
+    image (numpy.ndarray): The original image representing the finger vein (2D
+      array with dtype = ``uint8``)
+
+    mask (numpy.ndarray): A 2D boolean array with the same size of the original
+      image containing the pixels in which the image is valid (``True``) or
+      invalid (``False``).
+
+    image_pp (numpy.ndarray): A version of the original image, pre-processed by
+      one of the available algorithms
+
+    binary (numpy.ndarray): A binarized version of the original image in which
+      all pixels (should) represent vein (``True``) or not-vein (``False``)
+
+  '''
+
+  os.makedirs(title)
+  bob.io.base.save(image, os.path.join(title, 'original.png'))
+
+  # add preprocessed image
+  from ..preprocessor import utils
+  img = utils.draw_mask_over_image(image_pp, mask)
+  img = numpy.array(img).transpose(2,0,1)
+  bob.io.base.save(img[:3], os.path.join(title, 'preprocessed.png'))
+
+  # add binary image
+  bob.io.base.save(binary.astype('uint8')*255, os.path.join(title,
+    'binarized.png'))
+
+
+def proof_figure(title, image, mask, image_pp, binary):
+  '''Builds a proof canvas out of individual images
+
+
+  Parameters:
+
+    title (str): A title for this plot
+
+    image (numpy.ndarray): The original image representing the finger vein (2D
+      array with dtype = ``uint8``)
+
+    mask (numpy.ndarray): A 2D boolean array with the same size of the original
+      image containing the pixels in which the image is valid (``True``) or
+      invalid (``False``).
+
+    image_pp (numpy.ndarray): A version of the original image, pre-processed by
+      one of the available algorithms
+
+    binary (numpy.ndarray): A binarized version of the original image in which
+      all pixels (should) represent vein (``True``) or not-vein (``False``)
+
+
+  Returns:
+
+    matplotlib.pyplot.Figure: A figure canvas containing the proof for the
+    particular sample on the database
+
+  '''
+
+  fig = mpl.figure(figsize=(6,9), dpi=50) #900 x 300 pixels
+
+  # add original image
+  mpl.subplot(3, 1, 1)
+  mpl.title('%s - original' % title)
+  mpl.imshow(image, cmap="gray")
+
+  # add preprocessed image
+  from ..preprocessor import utils
+  img = utils.draw_mask_over_image(image_pp, mask)
+  mpl.subplot(3, 1, 2)
+  mpl.title('Preprocessed')
+  mpl.imshow(img)
+
+  # add binary image
+  mpl.subplot(3, 1, 3)
+  mpl.title('Binarized')
+  mpl.imshow(binary.astype('uint8')*255, cmap="gray")
+
+  return fig
+
+
+def main(user_input=None):
+
+  if user_input is not None:
+    argv = user_input
+  else:
+    argv = sys.argv[1:]
+
+  import docopt
+  import pkg_resources
+
+  completions = dict(
+      prog=os.path.basename(sys.argv[0]),
+      version=pkg_resources.require('bob.bio.vein')[0].version
+      )
+
+  args = docopt.docopt(
+      __doc__ % completions,
+      argv=argv,
+      version=completions['version'],
+      )
+
+  # Sets-up logging
+  verbosity = int(args['--verbose'])
+  bob.core.log.set_verbosity_level(logger, verbosity)
+
+  # Loads the image, the mask and save it to a PNG file
+  for stem in args['<stem>']:
+    image = bob.bio.base.load(os.path.join(args['<database>'], stem + '.png'))
+    pp = bob.io.base.HDF5File(os.path.join(args['<processed>'],
+      'preprocessed', stem + '.hdf5'))
+    mask  = pp.read('mask')
+    image_pp = pp.read('image')
+    binary = bob.io.base.load(os.path.join(args['<processed>'],
+      'extracted', stem + '.hdf5'))
+    fig = proof_figure(stem, image, mask, image_pp, binary)
+    #fig.title(stem)
+    if args['--save']:
+      #fig.savefig(args['--save'])
+      save_figures(args['--save'], image, mask, image_pp, binary)
+    else:
+      mpl.show()
+      print('Close window to continue...')

doc/baselines.rst

@@ -346,23 +346,30 @@ mask
 can use the option ``-n 5`` to print the 5 worst cases according to each of the
 metrics.
 
-You can use the program ``view_mask.py`` to display the images after the
-preprocessing step using:
+You can use the program ``view_sample.py`` to display the images after
+full processing using:
 
 .. code-block:: sh
 
-   $ view_mask.py /path/to/verafinger/mc/preprocessed/098-F/098_R_1.hdf5 --save=example.png
-   $ # open example.png
+   $ ./bin/view_sample.py /path/to/verafinger/database /path/to/bob-bio-vein/output/for/toolchain 030-M/030_L_1 -s output-directory
+   $ # open output-directory
 
-And you should be able to view an image like this (example taken from the Vera
-fingervein database, using the automatic annotator):
+And you should be able to view images like these (example taken from the Vera
+fingervein database, using the automatic annotator and Maximum Curvature
+feature extractor):
 
-.. figure:: img/vein-mask.*
+.. figure:: img/preprocessed.*
    :scale: 50%
 
    Example RoI overlayed on finger vein image of the Vera fingervein database,
-   as produced by the script ``view_mask.py``.
+   as produced by the script ``view_sample.py``.
 
 
+.. figure:: img/binarized.*
+   :scale: 50%
+
+   Example of fingervein image from the Vera fingervein database, binarized by
+   using Maximum Curvature, after pre-processing.
+
 
 .. include:: links.rst

requirements.txt

@@ -14,3 +14,4 @@ bob.db.verafinger
 bob.db.putvein
 bob.db.fv3d
 scikit-image
+matplotlib

setup.py

@@ -48,7 +48,7 @@ setup(
 
       'console_scripts': [
         'compare_rois.py = bob.bio.vein.script.compare_rois:main',
-        'view_mask.py = bob.bio.vein.script.view_mask:main',
+        'view_sample.py = bob.bio.vein.script.view_sample:main',
         'blame.py = bob.bio.vein.script.blame:main',
         ]
       },