MIN Replace http:// links with https://

CITATION

@@ -2,7 +2,7 @@ If you use mahotas on a scientific publication, please cite:
 
     **Luis Pedro Coelho** Mahotas: Open source software for scriptable computer
     vision in Journal of Open Research Software, vol 1, 2013. [`DOI
-    <http://dx.doi.org/10.5334/jors.ac>`__]
+    <https://dx.doi.org/10.5334/jors.ac>`__]
 
 
 In Bibtex format::
@@ -12,7 +12,7 @@ In Bibtex format::
         title = {Mahotas: Open source software for scriptable computer vision},
         journal = {Journal of Open Research Software},
         year = {2013},
-        doi = {http://dx.doi.org/10.5334/jors.ac},
+        doi = {https://dx.doi.org/10.5334/jors.ac},
         month = {July},
         volume = {1}
     }

CITATION.cff

@@ -4,7 +4,7 @@ title: 'Mahotas: Open source software for scriptable computer vision'
 authors:
 - family-names: Coelho
   given-names: Luis Pedro
-  orcid: http://orcid.org/0000-0002-9280-7885
+  orcid: https://orcid.org/0000-0002-9280-7885
 doi: 10.5334/jors.ac
 date-released: 2013-7-29
 version: 0.0.0

INSTALL

@@ -8,10 +8,10 @@ From binary packages
 There are a few binary packages.
 
 For **Windows**: Christoph Gohlke maintains binary packages at
-http://www.lfd.uci.edu/~gohlke/pythonlibs/
+https://www.lfd.uci.edu/~gohlke/pythonlibs/
 
 For FreeBSD, there are ports packages at
-http://www.freshports.org/graphics/mahotas/ Unfortunately, they may be slightly
+https://www.freshports.org/graphics/mahotas/ Unfortunately, they may be slightly
 out of date.
 
 On NixOS, there are up-to-date packages available as ``pythonPackages.mahotas``
@@ -46,7 +46,7 @@ Only **numpy** is required, except:
 You will need a way to load images into numpy arrays. Try one of the following
 packages
 
-1. `imread <http://github.com/luispedro/imread>`__
+1. `imread <https://github.com/luispedro/imread>`__
 2. matplotlib
 3. Freeimage
 

README.md

@@ -7,7 +7,7 @@ in C++ for speed) operating over numpy arrays.
 
 ![GH Actions Status](https://github.com/luispedro/mahotas/workflows/Python%20Package%20using%20Conda/badge.svg)
 [![Coverage Status](https://coveralls.io/repos/github/luispedro/mahotas/badge.svg?branch=master)](https://coveralls.io/github/luispedro/mahotas?branch=master)
-[![License](https://img.shields.io/badge/License-MIT-blue)](http://opensource.org/licenses/MIT)
+[![License](https://img.shields.io/badge/License-MIT-blue)](https://opensource.org/licenses/MIT)
 [![Downloads](https://pepy.tech/badge/mahotas/month)](https://pepy.tech/project/mahotas/month)
 [![Install with Conda](https://anaconda.org/conda-forge/mahotas/badges/downloads.svg)](https://anaconda.org/conda-forge/mahotas)
 [![Install with Anaconda](https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg)](https://anaconda.org/conda-forge/mahotas)
@@ -16,14 +16,14 @@ Python versions 2.7, 3.4+, are supported.
 
 Notable algorithms:
 
-- [watershed](http://mahotas.readthedocs.io/en/latest/distance.html)
-- [convex points calculations](http://mahotas.readthedocs.io/en/latest/polygon.html).
+- [watershed](https://mahotas.readthedocs.io/en/latest/distance.html)
+- [convex points calculations](https://mahotas.readthedocs.io/en/latest/polygon.html).
 - hit & miss, thinning.
 - Zernike & Haralick, LBP, and TAS features.
 - [Speeded-Up Robust Features
-  (SURF)](http://mahotas.readthedocs.io/en/latest/surf.html), a form of local
+  (SURF)](https://mahotas.readthedocs.io/en/latest/surf.html), a form of local
   features.
-- [thresholding](http://mahotas.readthedocs.io/en/latest/thresholding.html).
+- [thresholding](https://mahotas.readthedocs.io/en/latest/thresholding.html).
 - convolution.
 - Sobel edge detection.
 - spline interpolation
@@ -41,7 +41,7 @@ years, but in the meanwhile, you only get a warning). In a few
 unfortunate cases, there was a bug in the old code and your results will
 change for the better.
 
-Please cite [the mahotas paper](http://dx.doi.org/10.5334/jors.ac) (see
+Please cite [the mahotas paper](https://dx.doi.org/10.5334/jors.ac) (see
 details below under [Citation](#Citation)) if you use it in a publication.
 
 ## Examples
@@ -117,7 +117,7 @@ As you can see, we rely on numpy/matplotlib for many operations.
 
 ## Install
 
-If you are using [conda](http://anaconda.org/), you can install mahotas from
+If you are using [conda](https://anaconda.org/), you can install mahotas from
 [conda-forge](https://conda-forge.github.io/) using the following commands:
 
     conda config --add channels conda-forge
@@ -145,7 +145,7 @@ If you use mahotas on a published publication, please cite:
 
 > **Luis Pedro Coelho** Mahotas: Open source software for scriptable
 > computer vision in Journal of Open Research Software, vol 1, 2013.
-> [[DOI](http://dx.doi.org/10.5334/jors.ac)]
+> [[DOI](https://dx.doi.org/10.5334/jors.ac)]
 
 In Bibtex format:
 
@@ -154,7 +154,7 @@ In Bibtex format:
 >       title = {Mahotas: Open source software for scriptable computer vision},
 >       journal = {Journal of Open Research Software},
 >       year = {2013},
->       doi = {http://dx.doi.org/10.5334/jors.ac},
+>       doi = {https://dx.doi.org/10.5334/jors.ac},
 >       month = {July},
 >       volume = {1}
 >   }
@@ -164,7 +164,7 @@ You can access this information using the `mahotas.citation()` function.
 ## Development
 
 Development happens on github
-([http://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).
+([https://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).
 
 You can set the `DEBUG` environment variable before compilation to get a
 debug version:
@@ -194,11 +194,11 @@ run the test suite.
 issues](https://github.com/luispedro/mahotas/issues)
 
 *Mailing List*: Use the [pythonvision mailing
-list](http://groups.google.com/group/pythonvision?pli=1) for questions,
+list](https://groups.google.com/group/pythonvision?pli=1) for questions,
 bug submissions, etc. Or ask on [stackoverflow (tag
-mahotas)](http://stackoverflow.com/questions/tagged/mahotas)
+mahotas)](https://stackoverflow.com/questions/tagged/mahotas)
 
-*Main Author & Maintainer*: [Luis Pedro Coelho](http://luispedro.org)
+*Main Author & Maintainer*: [Luis Pedro Coelho](https://luispedro.org)
 (follow on [twitter](https://twitter.com/luispedrocoelho) or
 [github](https://github.com/luispedro)).
 
@@ -208,11 +208,11 @@ Gohlke, as well as
 [others](https://github.com/luispedro/mahotas/graphs/contributors).
 
 [Presentation about mahotas for bioimage
-informatics](http://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)
+informatics](https://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)
 
 For more general discussion of computer vision in Python, the
 [pythonvision mailing
-list](http://groups.google.com/group/pythonvision?pli=1) is a much
+list](https://groups.google.com/group/pythonvision?pli=1) is a much
 better venue and generates a public discussion log for others in the
 future. You can use it for mahotas or general computer vision in Python
 questions.

mahotas/__init__.py

@@ -25,7 +25,7 @@
 
     Coelho, Luis Pedro, 2013. Mahotas: Open source software for scriptable
     computer vision. Journal of Open Research Software, 1:e3, DOI:
-    http://dx.doi.org/10.5334/jors.ac
+    https://dx.doi.org/10.5334/jors.ac
 '''
 try:
     from .bbox import bbox, croptobbox
@@ -102,7 +102,7 @@
 
     Coelho, Luis Pedro, 2013. Mahotas: Open source software for scriptable
     computer vision. Journal of Open Research Software, 1:e3, DOI:
-    http://dx.doi.org/10.5334/jors.ac
+    https://dx.doi.org/10.5334/jors.ac
 
 
 In BibTex format:
@@ -114,13 +114,13 @@
     year = {2013},
     volume = {1},
     doi = {10.5334/jors.ac},
-    url = {http://dx.doi.org/10.5334/jors.ac}
+    url = {https://dx.doi.org/10.5334/jors.ac}
 }
 '''
 
 short_citation_text = \
 '''If you use mahotas in a scientific publication, please cite
-    Coelho, LP (2013). http://dx.doi.org/10.5334/jors.ac
+    Coelho, LP (2013). https://dx.doi.org/10.5334/jors.ac
 '''
 
 def citation(print_out=True, short=False):

mahotas/_histogram.cpp

@@ -63,7 +63,7 @@ PyObject* py_histogram(PyObject* self, PyObject* args) {
 
 int otsu(const double* hist, const int n) {
 // Otsu calculated according to CVonline:
-// http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MORSE/threshold.pdf
+// https://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MORSE/threshold.pdf
     gil_release nogil;
     std::vector<double> nB, nO;
     if (n <= 1) return 0;

mahotas/_labeled.cpp

@@ -233,8 +233,8 @@ void labeled_foldl(const numpy::aligned_array<T> array, const numpy::aligned_arr
 // In certain versions of g++, in certain environments,
 // multiple versions of std::min & std::max are in scope and the compiler is
 // unable to resolve them. Therefore, based on
-// http://www.cplusplus.com/reference/algorithm/min/ &
-// http://www.cplusplus.com/reference/algorithm/max/ I implemented equivalents
+// https://www.cplusplus.com/reference/algorithm/min/ &
+// https://www.cplusplus.com/reference/algorithm/max/ I implemented equivalents
 // here:
 
 template <class T>

mahotas/colors.py

@@ -58,12 +58,12 @@ def rgb2xyz(rgb, dtype=None):
 
     The input is interpreted as sRGB. See Wikipedia for more details:
 
-    http://en.wikipedia.org/wiki/SRGB
+    https://en.wikipedia.org/wiki/SRGB
 
     Parameters
     ----------
     rgb : ndarray
-    dtype : dtype, optional 
+    dtype : dtype, optional
         What dtype to return
 
     Returns
@@ -95,7 +95,7 @@ def xyz2rgb(xyz, dtype=None):
 
     The output should be interpreted as sRGB. See Wikipedia for more details:
 
-    http://en.wikipedia.org/wiki/SRGB
+    https://en.wikipedia.org/wiki/SRGB
 
     Parameters
     ----------
@@ -132,7 +132,7 @@ def xyz2lab(xyz, dtype=None):
 
     Convert CIE XYZ to L*a*b* coordinates
 
-    http://en.wikipedia.org/wiki/CIELAB
+    https://en.wikipedia.org/wiki/CIELAB
 
     Parameters
     ----------
@@ -168,7 +168,7 @@ def rgb2lab(rgb, dtype=None):
 
     Convert sRGB to L*a*b* coordinates
 
-    http://en.wikipedia.org/wiki/CIELAB
+    https://en.wikipedia.org/wiki/CIELAB
 
     Parameters
     ----------

mahotas/distance.py

@@ -46,7 +46,7 @@ def distance(bw, metric='euclidean2'):
     Cornell Computing and Information.* 2004.
 
     Available at:
-    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.1647&rep=rep1&type=pdf.
+    https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.1647&rep=rep1&type=pdf.
 
     For n-D images (with n > 2), a slower hand-craft method is used.
     '''

mahotas/euler.py

@@ -49,7 +49,7 @@ def euler(f, n=8, mode='constant'):
 
     References
     ----------
-    http://en.wikipedia.org/wiki/Euler_characteristic
+    https://en.wikipedia.org/wiki/Euler_characteristic
 
     References
     ----------

mahotas/features/_surf.cpp

@@ -441,7 +441,7 @@ void build_pyramid(numpy::aligned_array<T> integral,
                     // Survey."by Tuytelaars T and Mikolajczyk K.; Foundations
                     // and Trends® in Computer Graphics and Vision.
                     // 2007;3(3):177-280. Available at:
-                    // http://www.nowpublishers.com/product.aspx?product=CGV&doi=0600000017.
+                    // https://www.nowpublishers.com/product.aspx?product=CGV&doi=0600000017.
                     //
                     // Also uses 0.6
                     double determinant = Dxx*Dyy - 0.36*Dxy*Dxy;

mahotas/features/tas.py

@@ -60,7 +60,7 @@ def tas(img):
     Compute Threshold Adjacency Statistics
 
     TAS were presented by Hamilton et al.  in "Fast automated cell phenotype
-    image classification" (http://www.biomedcentral.com/1471-2105/8/110)
+    image classification" (https://www.biomedcentral.com/1471-2105/8/110)
 
     Also returns a version computed on the negative of the binarisation defined
     by Hamilton et al.
@@ -90,15 +90,15 @@ def pftas(img, T=None):
     Compute parameter free Threshold Adjacency Statistics
 
     TAS were presented by Hamilton et al.  in "Fast automated cell phenotype
-    image classification" (http://www.biomedcentral.com/1471-2105/8/110)
+    image classification" (https://www.biomedcentral.com/1471-2105/8/110)
 
     The current version is an adapted version which is free of parameters. The
     thresholding is done by using Otsu's algorithm (or can be pre-computed and
     passed in by setting `T`), the margin around the mean of pixels to be
     included is the standard deviation. This was first published by Coelho et
     al. in "Structured Literature Image Finder: Extracting Information from
     Text and Images in Biomedical Literature"
-    (http://www.springerlink.com/content/60634778710577t0/)
+    (https://www.springerlink.com/content/60634778710577t0/)
 
     Also returns a version computed on the negative of the binarisation defined
     by Hamilton et al.

mahotas/features/texture.py

@@ -78,7 +78,7 @@ def haralick(f,
             pages = {610--621},
             publisher = {IEEE},
             title = {Textural features for image classification},
-            url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309314},
+            url = {https://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309314},
             volume = {3},
             year = {1973}
         }
@@ -190,7 +190,7 @@ def haralick_features(cmats,
             pages = {610--621},
             publisher = {IEEE},
             title = {Textural features for image classification},
-            url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309314},
+            url = {https://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309314},
             volume = {3},
             year = {1973}
         }

mahotas/io/__init__.py

@@ -25,7 +25,7 @@
 
 On **Windows**, use Christoph Gohlke's packages. See:
 
-http://www.lfd.uci.edu/~gohlke/pythonlibs/#imread
+https://www.lfd.uci.edu/~gohlke/pythonlibs/#imread
 
 
 
@@ -41,7 +41,7 @@
 actually called `libfreeimage3` in debian/ubuntu, for example).
 
 Under Windows, consider using the third-party mahotas packages at
-http://www.lfd.uci.edu/~gohlke/pythonlibs/ (kindly maintained by Christoph
+https://www.lfd.uci.edu/~gohlke/pythonlibs/ (kindly maintained by Christoph
 Gohlke), which already package freeimage.
 
 Full error was: %s'''

mahotas/labeled.py

@@ -38,7 +38,7 @@ def label(array, Bc=None, out=None, output=None):
     This is also called *connected component labeled*, where the connectivity
     is defined by the structuring element ``Bc``.
 
-    See: http://en.wikipedia.org/wiki/Connected-component_labeling
+    See: https://en.wikipedia.org/wiki/Connected-component_labeling
 
     Parameters
     ----------
@@ -524,7 +524,7 @@ def perimeter(bwimage, n=4, mode="constant"):
     ----------
     .. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of
            a Perimeter Estimator. The Queen's University of Belfast.
-           http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
+           https://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
     """
     global _perimeter_values
     perim = bwperim(bwimage, n, mode)

mahotas/morph.py

@@ -747,7 +747,7 @@ def tophat_close(f, Bc=None, out=None):
     This returns objects that are smaller than ``Bc`` and contain lower values
     than their surroundings.
 
-    See: http://en.wikipedia.org/wiki/Top-hat_transform
+    See: https://en.wikipedia.org/wiki/Top-hat_transform
 
     Parameters
     ----------
@@ -781,7 +781,7 @@ def tophat_open(f, Bc=None, out=None):
     This returns objects that are smaller than ``Bc`` and contain higher values
     than their surroundings.
 
-    See: http://en.wikipedia.org/wiki/Top-hat_transform
+    See: https://en.wikipedia.org/wiki/Top-hat_transform
 
     Parameters
     ----------

mahotas/polygon.py

@@ -34,7 +34,7 @@ def line(p0, p1, canvas, color=1):
 
     notes
     -----
-    `Implementation Reference <http://en.wikipedia.org/wiki/Bresenham's_line_algorithm>`__
+    `Implementation Reference <https://en.wikipedia.org/wiki/Bresenham's_line_algorithm>`__
     '''
     y0,x0 = p0
     y1,x1 = p1
@@ -76,7 +76,7 @@ def fill_polygon(polygon, canvas, color=1):
     color : integer, optional
         which colour to use (default: 1)
     '''
-# algorithm adapted from: http://www.alienryderflex.com/polygon_fill/
+# algorithm adapted from: https://www.alienryderflex.com/polygon_fill/
     if not len(polygon):
         return
     min_y = min(y for y,x in polygon)
@@ -108,7 +108,7 @@ def convexhull(bwimg):
     Compute the convex hull as a polygon
 
     This is an implementation of the Graham Scan:
-    http://en.wikipedia.org/wiki/Graham_scan
+    https://en.wikipedia.org/wiki/Graham_scan
 
     Parameters
     ----------