Merge pull request #16 from ksunden/subsample

Dynamic subsampling example

Author: tacaswell

examples/subsample.py

@@ -0,0 +1,77 @@
+"""
+====================
+Dynamic Downsampling
+====================
+
+Generates a large image with three levels of detail.
+
+When zoomed out, appears as a difference of 2D Gaussians.
+At medium zoom, a diagonal sinusoidal pattern is apparent.
+When zoomed in close, noise is visible.
+
+The image is dynamically subsampled using a local mean which hides the finer details.
+"""
+
+from typing import Tuple, Dict, Any, Union
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+
+import numpy as np
+
+from data_prototype.wrappers import ImageWrapper
+from data_prototype.containers import _Transform
+
+from skimage.transform import downscale_local_mean
+
+
+x = y = np.linspace(-3, 3, 3000)
+X, Y = np.meshgrid(x, y)
+Z1 = np.exp(-(X**2) - Y**2) + 0.08 * np.sin(50 * (X + Y))
+Z2 = np.exp(-((X - 1) ** 2) - (Y - 1) ** 2)
+Z = (Z1 - Z2) * 2
+
+Z += np.random.random(Z.shape) - 0.5
+
+
+class Subsample:
+    def describe(self):
+        return {
+            "xextent": Desc([2], float),
+            "yextent": Desc([2], float),
+            "image": Desc([], float),
+        }
+
+    def query(
+        self,
+        transform: _Transform,
+        size: Tuple[int, int],
+    ) -> Tuple[Dict[str, Any], Union[str, int]]:
+        (x1, y1), (x2, y2) = transform.transform([[0, 0], [1, 1]])
+
+        xi1 = np.argmin(np.abs(x - x1))
+        yi1 = np.argmin(np.abs(y - y1))
+        xi2 = np.argmin(np.abs(x - x2))
+        yi2 = np.argmin(np.abs(y - y2))
+
+        xscale = int(np.ceil((xi2 - xi1) / 50))
+        yscale = int(np.ceil((yi2 - yi1) / 50))
+
+        return {
+            "xextent": [x1, x2],
+            "yextent": [y1, y2],
+            "image": downscale_local_mean(Z[xi1:xi2, yi1:yi2], (xscale, yscale)),
+        }, hash((x1, x2, y1, y2))
+
+
+sub = Subsample()
+cmap = mpl.colormaps["coolwarm"]
+norm = Normalize(-2.2, 2.2)
+im = ImageWrapper(sub, {"image": lambda image: cmap(norm(image))})
+
+fig, ax = plt.subplots()
+ax.add_artist(im)
+ax.set_xlim(-3, 3)
+ax.set_ylim(-3, 3)
+plt.show()

requirements-doc.txt

@@ -6,3 +6,4 @@ sphinx
 sphinx-copybutton
 sphinx-gallery
 ipython
+scikit-image