Median cut creation of palettes added.

Author: hbldh

hitherdither/__init__.py

@@ -17,7 +17,7 @@
 
 
 # Version information.
-__version__ = '0.1.1'
+__version__ = '0.1.2.dev1'
 version = __version__  # backwards compatibility name
 try:
     version_info = [int(x) if x.isdigit() else x for x in

hitherdither/palette.py

@@ -94,11 +94,50 @@ def pixel_closest_colour(self, pixel, order=2):
 
     @classmethod
     def create_by_kmeans(cls, image):
-        pass
+        raise NotImplementedError()
 
     @classmethod
-    def create_by_median_cut(cls, image):
-        pass
+    def create_by_median_cut(cls, image, n=16, dim=None):
+        img = np.array(image)
+        # Create pixel buckets to simplify sorting and splitting.
+        if img.ndim == 3:
+            pixels = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))
+        elif img.ndim == 2:
+            pixels = img.reshape((img.shape[0] * img.shape[1], 1))
+
+        def median_cut(p, dim=None):
+            """Median cut method.
+
+            Reference:
+            https://en.wikipedia.org/wiki/Median_cut
+
+            :param p: The pixel array to split in two.
+            :return: Two numpy arrays, split by median cut method.
+            """
+            if dim is not None:
+                sort_dim = dim
+            else:
+                mins = p.min(axis=0)
+                maxs = p.max(axis=0)
+                sort_dim = np.argmax(maxs - mins)
+
+            argument = np.argsort(p[:, sort_dim])
+            p = p[argument, :]
+            m = np.median(p[:, sort_dim])
+            split_mask = p[:, sort_dim] >= m
+            return [p[-split_mask, :].copy(), p[split_mask, :].copy()]
+
+        # Do actual splitting loop.
+        bins = [pixels, ]
+        while len(bins) < n:
+            new_bins = []
+            for bin in bins:
+                new_bins += median_cut(bin, dim)
+            bins = new_bins
+
+        # Average over pixels in each bin to create
+        colours = np.array([np.array(bin.mean(axis=0).round(), 'uint8') for bin in bins], 'uint8')
+        return cls(colours)
 
     def create_PIL_png_from_closest_colour(self, cc):
         """Create a ``P`` PIL image with this palette.
@@ -108,15 +147,15 @@ def create_PIL_png_from_closest_colour(self, cc):
         Reference: http://stackoverflow.com/a/29438149
 
         :param :class:`numpy.ndarray` cc: A ``[M x N]`` array with integer
-            values representing palette colour indices to build image from. 
+            values representing palette colour indices to build image from.
         :return: A :class:`PIL.Image.Image` image of mode ``P``.
 
         """
         pa_image = Image.new("P", cc.shape[::-1])
         pa_image.putpalette(self.colours.flatten().tolist())
         im = Image.fromarray(np.array(cc, 'uint8')).im.convert("P", 0, pa_image.im)
         return pa_image._makeself(im)
-    
+
     def create_PIL_png_from_rgb_array(self, img_array):
         """Create a ``P`` PIL image from a RGB image with this palette.
 

run.py

@@ -26,14 +26,16 @@
 s = data.scene()
 p = Palette(hitherdither.data.palette())
 
+p2 = Palette.create_by_median_cut(s)
+
 # Map raw image to the palette
 closest_colour = p.image_closest_colour(s, order=2)
 # Render the undithered image with only colours in
 # the palette as a RGB numpy array.
 undithered_image = p.render(closest_colour)
 # Create a PIL Image of mode "P" from the palette colour index matrix.
-#s_png = p.create_PIL_png_from_closest_colour(closest_colour)
-#s_png.show()
+s_png = p.create_PIL_png_from_closest_colour(closest_colour)
+s_png.show()
 
 #print(np.linalg.norm(undithered_image - np.array(s_png.convert("RGB"))))
 

setup.py

@@ -22,7 +22,6 @@
 basedir = os.path.dirname(os.path.abspath(__file__))
 
 if sys.argv[-1] == 'publish':
-    os.system('python setup.py register')
     os.system('python setup.py sdist upload')
     os.system('python setup.py bdist_wheel upload')
     sys.exit()
@@ -42,7 +41,7 @@ def read(f):
     author='Henrik Blidh',
     author_email='[email protected]',
     url='https://github.com/hbldh/hitherdither',
-    description='PIL extension with dithering algorithms that is applicable for arbitrary palettes',
+    description='Dithering algorithms for arbitrary palettes in PIL',
     long_description=read('README.rst'),
     license='MIT',
     keywords=['PIL', 'Pillow', 'dithering', 'dither'],