ENH naive pure python implementation of quickshift

skimage/segmentation/quickshift.py

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+import numpy as np
+from itertools import product, combinations_with_replacement
+
+from IPython.core.debugger import Tracer
+tracer = Tracer()
+
+
+def quickshift(image, sigma=5, tau=10):
+    # do smoothing beforehand?
+    width, height = image.shape[:2]
+    densities = np.zeros((width, height))
+    w = 10
+
+    # TODO: normalize density by number of considered points.
+    # important  for the border!
+    # compute densities
+    for x, y in product(xrange(width), xrange(height)):
+        current_pixel = np.hstack([image[x, y, :], x, y])
+        for xx, yy in combinations_with_replacement(xrange(-w / 2, w / 2), 2):
+            x_, y_ = x + xx, y + yy
+            if 0 <= x_ < width and 0 <= y_ < height:
+                other_pixel = np.hstack([image[x_, y_, :], x_, y_])
+                dist = np.sum((current_pixel - other_pixel) ** 2)
+                densities[x, y] += np.exp(-dist / sigma)
+
+    # default parent to self:
+    parent = np.arange(width * height).reshape(width, height)
+    # find nearest node with higher density
+    for x, y in product(xrange(width), xrange(height)):
+        current_density = densities[x, y]
+        current_pixel = np.hstack([image[x, y, :], x, y])
+        closest = np.inf
+        for xx, yy in combinations_with_replacement(xrange(-w / 2, w / 2), 2):
+            x_, y_ = x + xx, y + yy
+            if 0 <= x_ < width and 0 <= y_ < height:
+                if densities[x_, y_] > current_density:
+                    other_pixel = np.hstack([image[x_, y_, :], x_, y_])
+                    dist = np.sum((current_pixel - other_pixel) ** 2)
+                    if dist < closest:
+                        closest = dist
+                        parent[x, y] = x_ * width + y_
+    flat = parent.ravel()
+    old = np.zeros_like(flat)
+    while (old != flat).any():
+        old = flat
+        flat = flat[flat]
+    return flat.reshape(parent.shape)