--- a
+++ b/4x/reference/Untitled0.py
@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+# <nbformat>3.0</nbformat>
+
+# <codecell>
+
+import os
+
+import numpy as np
+
+from scipy.ndimage import maximum_filter, minimum_filter, binary_fill_holes
+
+import skimage as ski
+from skimage import io, filter, color, exposure, morphology, feature, draw, measure, transform
+
+figsize(16, 10)
+
+# <codecell>
+
+l = os.listdir("../data")
+print l[6]
+
+qstain = np.array([[.26451728, .5205347, .81183386], [.9199094, .29797825, .25489032], [.28947765, .80015373, .5253158]])
+
+# <codecell>
+
+# Original image
+A = io.imread("../data/" + l[2])
+io.imshow(A)
+
+# <codecell>
+
+# Colour Deconvolution
+deconv = ski.img_as_float(color.separate_stains(A, np.linalg.inv(qstain)))
+
+io.imshow(exposure.adjust_sigmoid(
+            filter.gaussian_filter(
+                exposure.rescale_intensity(
+                    deconv[:, :, 0] + deconv[:, :, 2], 
+                    out_range=(0, 1)), 
+                17), 
+            gain=7, cutoff=0.6))
+
+# <codecell>
+
+# Blood
+
+blood = \
+morphology.remove_small_objects(
+    filter.threshold_adaptive(
+        exposure.adjust_sigmoid(
+            filter.gaussian_filter(
+                exposure.rescale_intensity(
+                    deconv[:, :, 0] + deconv[:, :, 2], 
+                    out_range=(0, 1)), 
+                15), 
+            gain=10, cutoff=0.7),
+        301, offset=-0.15),
+    500)
+
+io.imshow(blood)
+
+# <codecell>
+
+# Veins
+
+veins = \
+binary_fill_holes(
+    morphology.remove_small_objects(
+        filter.threshold_adaptive(
+            filter.gaussian_filter(
+                exposure.adjust_sigmoid(A[:,:,1]), 
+                31), 
+            501, offset=-0.07), 
+        3000) #### SRSLY ?
+    )
+
+io.imshow(veins)
+
+# <codecell>
+
+# Inflammation
+
+inflammation = \
+morphology.remove_small_objects(
+    filter.threshold_adaptive(
+        exposure.adjust_sigmoid(
+            filter.gaussian_filter(
+                exposure.rescale_intensity(
+                    deconv[:, :, 1],
+                    out_range = (0, 1)),
+                25),
+            gain = 12, cutoff = 0.32),
+        501, offset = -0.1),
+    4000)
+
+
+io.imshow(inflammation)
+#io.imshow(filter.threshold_adaptive(exposure.adjust_sigmoid(filter.gaussian_filter(exposure.rescale_intensity(deconv[:, :, 1], out_range=(0, 1)), 19), gain=15, cutoff=0.4), 401))
+
+# <codecell>
+
+# Labelled
+io.imshow(veins + 2 * blood + 3 * inflammation)
+
+# <codecell>
+
+# All regions of interest
+
+#%timeit
+
+"""
+all = \
+minimum_filter(
+    maximum_filter(
+        np.logical_or(np.logical_or(blood, veins), inflammation).astype(float),
+        size = 101),
+    size = 51)
+"""
+
+
+
+"""
+morphology.binary_erosion(
+    morphology.binary_dilation(
+        np.logical_or(np.logical_or(blood, veins), inflammation),
+        morphology.disk(31)),
+    morphology.disk(21))
+"""
+
+full = (np.logical_or(np.logical_or(blood, veins), inflammation))
+
+#io.imshow(binary_fill_holes(minimum_filter(maximum_filter(full, size=51, footprint=morphology.diamond(31)), size=51)))
+
+# <codecell>
+
+io.imshow(ski.img_as_float(A)*0.5 + full[:, :, newaxis]*0.5)
+
+# <codecell>
+
+io.imsave("__1.gif", A)
+io.imsave("__2.gif", ski.img_as_float(full))
+
+# <codecell>
+
+os.system("gifsicle --delay=80 --loop __*.gif > test.gif")
+
+# <codecell>
+
+