Merge branch 'release/v0.1.4'

.travis.yml

@@ -7,7 +7,6 @@ matrix:
   - python: 2.7
     env:
     - PYTHON_VERSION="2.7"
-    - NUMPY_VERSION="1.13.3"
 
 notifications:
   slack: colour-science:Y6lPPcN7y53Js94geqUpqsAP

BIBLIOGRAPHY.bib

@@ -1,51 +1,76 @@
 @incollection{Losson2010c,
-abstract = {Mono-CCD color cameras acquire only one color component at each pixel by means of their color filter array (CFA) covering the CCD sensor. To obtain a color image, a procedure - called demosaicing - is then necessary to estimate the other two missing color components at each pixel. This chapter deals with the quality of color images generated in such a way. We attempt to determine which demosaicing method provides the best results according to several comparison criteria, particularly for subsequent needs of color image analyses like edge detection.},
-author = {Losson, O. and Macaire, L. and Yang, Y.},
-booktitle = {Advances in Imaging and Electron Physics},
-doi = {10.1016/S1076-5670(10)62005-8},
-file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Losson, Macaire, Yang - 2010 - Comparison of Color Demosaicing Methods.pdf:pdf},
-isbn = {9780123813169},
-issn = {10765670},
-number = {C},
-pages = {173--265},
-title = {{Comparison of Color Demosaicing Methods}},
-url = {http://linkinghub.elsevier.com/retrieve/pii/S1076567010620058},
-volume = {162},
-year = {2010}
+  abstract     = {Mono-CCD color cameras acquire only one color
+    component at each pixel by means of their color filter array (CFA)
+    covering the CCD sensor. To obtain a color image, a procedure -
+    called demosaicing - is then necessary to estimate the other two
+    missing color components at each pixel. This chapter deals with
+    the quality of color images generated in such a way. We attempt to
+    determine which demosaicing method provides the best results
+    according to several comparison criteria, particularly for
+    subsequent needs of color image analyses like edge detection.},
+  author       = {Losson, O. and Macaire, L. and Yang, Y.},
+  booktitle    = {Advances in Imaging and Electron Physics},
+  doi          = {10.1016/S1076-5670(10)62005-8},
+  isbn         = 9780123813169,
+  issn         = 10765670,
+  number       = {C},
+  pages        = {173--265},
+  title        = {{Comparison of Color Demosaicing Methods}},
+  url          = {http://linkinghub.elsevier.com/retrieve/pii/S1076567010620058},
+  volume       = 162,
+  year         = 2010,
 }
 @inproceedings{Malvar2004a,
-abstract = {<p>This paper introduces a new interpolation technique for demosaicing of color
-images produced by single-CCD digital cameras. We show that the proposed simple
-linear filter can lead to an improvement in PSNR of over 5.5 dB when compared to
-bilinear demosaicing, and about 0.7 dB improvement in R and B interpolation when
-compared to a recently introduced linear interpolator. The proposed filter also
-outperforms most nonlinear demosaicing algorithms, without the artifacts due to
-nonlinear processing, and a much reduced computational complexity.</p>},
-author = {Malvar, Henrique S and He, Li-Wei and Cutler, Ross and Way, One Microsoft},
-booktitle = {International Conference of Acoustic, Speech and Signal Processing},
-file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Malvar et al. - 2004 - High-Quality Linear Interpolation for Demosaicing of Bayer-Patterned Color Images.pdf:pdf},
-month = {may},
-pages = {5--8},
-publisher = {Institute of Electrical and Electronics Engineers, Inc.},
-title = {{High-Quality Linear Interpolation for Demosaicing of Bayer-Patterned Color Images}},
-url = {http://research.microsoft.com/apps/pubs/default.aspx?id=102068},
-year = {2004}
+  abstract     = {<p>This paper introduces a new interpolation
+    technique for demosaicing of color images produced by single-CCD
+    digital cameras. We show that the proposed simple linear filter
+    can lead to an improvement in PSNR of over 5.5 dB when compared to
+    bilinear demosaicing, and about 0.7 dB improvement in R and B
+    interpolation when compared to a recently introduced linear
+    interpolator. The proposed filter also outperforms most nonlinear
+    demosaicing algorithms, without the artifacts due to nonlinear
+    processing, and a much reduced computational complexity.</p>},
+  author       = {Malvar, Henrique S and He, Li-Wei and Cutler, Ross
+    and Way, One Microsoft},
+  booktitle    = {International Conference of Acoustic, Speech and
+    Signal Processing},
+  month        = may,
+  pages        = {5--8},
+  publisher    = {Institute of Electrical and Electronics Engineers,
+    Inc.},
+  title        = {{High-Quality Linear Interpolation for Demosaicing
+    of Bayer-Patterned Color Images}},
+  url          = {http://research.microsoft.com/apps/pubs/default.aspx?id=102068},
+  year         = 2004,
 }
 @article{Menon2007c,
-abstract = {Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques.},
-author = {Menon, Daniele and Andriani, Stefano and Calvagno, Giancarlo},
-doi = {10.1109/TIP.2006.884928},
-file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Menon, Andriani, Calvagno - 2007 - Demosaicing With Directional Filtering and a posteriori Decision.pdf:pdf},
-isbn = {2003506969},
-issn = {1057-7149},
-journal = {IEEE Transactions on Image Processing},
-keywords = {Bayer pattern,Color filter array (CFA) interpolation,Demosaicing,Digital cameras,Directional filtering},
-month = {jan},
-number = {1},
-pages = {132--141},
-pmid = {17283772},
-title = {{Demosaicing With Directional Filtering and a posteriori Decision}},
-url = {http://ieeexplore.ieee.org/document/4032820/},
-volume = {16},
-year = {2007}
+  abstract     = {Most digital cameras use a color filter array to
+    capture the colors of the scene. Downsampled versions of the red,
+    green, and blue components are acquired, and an interpolation of
+    the three colors is necessary to reconstruct a full representation
+    of the image. This color interpolation is known as demosaicing.
+    The most effective demosaicing techniques proposed in the
+    literature are based on directional filtering and a posteriori
+    decision. In this paper, we present a novel approach to this
+    reconstruction method. A refining step is included to further
+    improve the resulting reconstructed image. The proposed approach
+    requires a limited computational cost and gives good performance
+    even when compared to more demanding techniques.},
+  author       = {Menon, Daniele and Andriani, Stefano and Calvagno,
+    Giancarlo},
+  doi          = {10.1109/TIP.2006.884928},
+  isbn         = 2003506969,
+  issn         = {1057-7149},
+  journal      = {IEEE Transactions on Image Processing},
+  keywords     = {Bayer pattern,Color filter array (CFA)
+    interpolation,Demosaicing,Digital cameras,Directional filtering},
+  month        = jan,
+  number       = 1,
+  pages        = {132--141},
+  pmid         = 17283772,
+  title        = {{Demosaicing With Directional Filtering and a
+    posteriori Decision}},
+  url          = {http://ieeexplore.ieee.org/document/4032820/},
+  volume       = 16,
+  year         = 2007,
 }

CONTRIBUTORS.rst

@@ -12,19 +12,24 @@ Colour - Demosaicing
 
     Technical support.
 
+-   **Maximilian Schambach**
+
+    Memory optimizations.
+
 Issues & Discussions
 --------------------
 
 -   @Zoldike
 -   @jiawen
 -   @laihaotao
 -   @m0ose
+-   @maaleske
 -   @timmeinhardt
 
 About
 -----
 
 | **Colour - Demosaicing** by Colour Developers
-| Copyright © 2015-2018 – Colour Developers – `[email protected] <[email protected]>`_
+| Copyright © 2015-2019 – Colour Developers – `[email protected] <[email protected]>`_
 | This software is released under terms of New BSD License: http://opensource.org/licenses/BSD-3-Clause
 | `http://github.com/colour-science/colour-demosaicing <http://github.com/colour-science/colour-demosaicing>`_

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2015-2018, Colour Developers
+Copyright (c) 2015-2019, Colour Developers
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

MANIFEST.in

@@ -1,10 +1,10 @@
-include COPYING
 include LICENSE
 include *.bib
 include *.rst
+include docs/_build/latex/ColourDemosaicing.pdf
 graft colour_demosaicing/examples
-graft docs/_build
-graft utilities
+graft docs/_build/html
+graft docs/_build/doctrees
 prune colour_demosaicing/examples/.ipynb_checkpoints
 global-exclude *.pyc
 global-exclude *.pyo

README.rst

@@ -55,6 +55,7 @@ Primary Dependencies
 -  `Python 2.7 <https://www.python.org/download/releases/>`_ or
    `Python 3.5 <https://www.python.org/download/releases/>`_
 -  `NumPy <http://www.numpy.org/>`_
+-  `Colour Science <https://www.colour-science.org>`_
 -  `OpenImageIO <https://github.com/OpenImageIO/oiio>`_
 
 Pypi
@@ -74,6 +75,10 @@ The documentation building dependencies are installed as follows::
 
     pip install 'colour-demosaicing[docs]'
 
+The overall development dependencies are installed as follows::
+
+    pip install 'colour-demosaicing[development]'
+
 Usage
 -----
 
@@ -93,7 +98,7 @@ Contributing
 ------------
 
 If you would like to contribute to `Colour - Demosaicing <https://github.com/colour-science/colour-demosaicing>`_,
-please refer to the following `Contributing <http://colour-science.org/contributing/>`_
+please refer to the following `Contributing <https://www.colour-science.org/contributing/>`_
 guide for `Colour <https://github.com/colour-science/colour>`_.
 
 Bibliography
@@ -107,6 +112,6 @@ About
 -----
 
 | **Colour - Demosaicing** by Colour Developers
-| Copyright © 2015-2018 – Colour Developers – `[email protected] <[email protected]>`_
+| Copyright © 2015-2019 – Colour Developers – `[email protected] <[email protected]>`_
 | This software is released under terms of New BSD License: http://opensource.org/licenses/BSD-3-Clause
 | `http://github.com/colour-science/colour-demosaicing <http://github.com/colour-science/colour-demosaicing>`_

TODO.rst

@@ -4,12 +4,14 @@ Colour - Demosaicing - TODO
 TODO
 ----
 
+-   colour_demosaicing/__init__.py
 
+    -   Line 51 : # TODO: Remove legacy printing support when deemed appropriate.
 
 About
 -----
 
 | **Colour - Demosaicing** by Colour Developers
-| Copyright © 2015-2018 – Colour Developers – `[email protected] <[email protected]>`_
+| Copyright © 2015-2019 – Colour Developers – `[email protected] <[email protected]>`_
 | This software is released under terms of New BSD License: http://opensource.org/licenses/BSD-3-Clause
 | `http://github.com/colour-science/colour-demosaicing <http://github.com/colour-science/colour-demosaicing>`_

colour_demosaicing/__init__.py

@@ -12,15 +12,19 @@
 
 from __future__ import absolute_import
 
+import numpy as np
 import os
+import subprocess  # nosec
+
+import colour
 
 from .bayer import (
     demosaicing_CFA_Bayer_bilinear, demosaicing_CFA_Bayer_DDFAPD,
     demosaicing_CFA_Bayer_Malvar2004, demosaicing_CFA_Bayer_Menon2007,
     masks_CFA_Bayer, mosaicing_CFA_Bayer)
 
 __author__ = 'Colour Developers'
-__copyright__ = 'Copyright (C) 2015-2018 - Colour Developers'
+__copyright__ = 'Copyright (C) 2015-2019 - Colour Developers'
 __license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
 __maintainer__ = 'Colour Developers'
 __email__ = '[email protected]'
@@ -42,8 +46,24 @@
 
 __major_version__ = '0'
 __minor_version__ = '1'
-__change_version__ = '3'
+__change_version__ = '4'
 __version__ = '.'.join(
     (__major_version__,
      __minor_version__,
      __change_version__))  # yapf: disable
+
+try:
+    version = subprocess.check_output(  # nosec
+        ['git', 'describe'], cwd=os.path.dirname(__file__)).strip()
+    version = version.decode('utf-8')
+except Exception:
+    version = __version__
+
+colour.utilities.ANCILLARY_COLOUR_SCIENCE_PACKAGES['colour-demosaicing'] = (
+    version)
+
+# TODO: Remove legacy printing support when deemed appropriate.
+try:
+    np.set_printoptions(legacy='1.13')
+except TypeError:
+    pass

colour_demosaicing/bayer/demosaicing/bilinear.py

@@ -14,15 +14,14 @@
 
 from __future__ import division, unicode_literals
 
-import numpy as np
 from scipy.ndimage.filters import convolve
 
-from colour.utilities import tstack
+from colour.utilities import as_float_array, tstack
 
 from colour_demosaicing.bayer import masks_CFA_Bayer
 
 __author__ = 'Colour Developers'
-__copyright__ = 'Copyright (C) 2015-2018 - Colour Developers'
+__copyright__ = 'Copyright (C) 2015-2019 - Colour Developers'
 __license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
 __maintainer__ = 'Colour Developers'
 __email__ = '[email protected]'
@@ -60,10 +59,11 @@ def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
 
     References
     ----------
-    -   :cite:`Losson2010c`
+    :cite:`Losson2010c`
 
     Examples
     --------
+    >>> import numpy as np
     >>> CFA = np.array(
     ...     [[0.30980393, 0.36078432, 0.30588236, 0.3764706],
     ...      [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
@@ -92,15 +92,15 @@ def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
             [ 0.67058827,  0.18431373,  0.10196078]]])
     """
 
-    CFA = np.asarray(CFA)
+    CFA = as_float_array(CFA)
     R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
 
-    H_G = np.asarray(
+    H_G = as_float_array(
         [[0, 1, 0],
          [1, 4, 1],
          [0, 1, 0]]) / 4  # yapf: disable
 
-    H_RB = np.asarray(
+    H_RB = as_float_array(
         [[1, 2, 1],
          [2, 4, 2],
          [1, 2, 1]]) / 4  # yapf: disable
@@ -109,4 +109,6 @@ def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
     G = convolve(CFA * G_m, H_G)
     B = convolve(CFA * B_m, H_RB)
 
-    return tstack((R, G, B))
+    del R_m, G_m, B_m, H_RB, H_G
+
+    return tstack([R, G, B])