Convert template matching Pull Request to skimage

This commit takes the template matching implementation from holtzhau/template (Pull Request #13) and converts the code to use the new package name (scikits.image --> skimage).

doc/examples/plot_template.py

@@ -0,0 +1,70 @@
+"""
+=================
+Template Matching
+=================
+
+In this example, we use template matching to identify the occurrence of an
+object in an image. The ``match_template`` function uses normalised correlation
+techniques to find instances of the "target image" in the "test image".
+
+The output of ``match_template`` is an image where we can easily identify peaks
+by eye. Nevertheless, this example concludes with a simple peak extraction
+algorithm to quantify the locations of matches.
+"""
+
+import numpy as np
+from skimage.detection import match_template
+from numpy.random import randn
+import matplotlib.pyplot as plt
+import math
+
+# We first construct a simple image target:
+size = 100
+target = np.tri(size) + np.tri(size)[::-1]
+target = target.astype(np.float32)
+
+plt.gray()
+plt.imshow(target)
+plt.title("Target image")
+plt.axis('off')
+
+# place target in an image at two positions, and add noise.
+image = np.zeros((400, 400), dtype=np.float32)
+target_positions = [(50, 50), (200, 200)]
+for x, y in target_positions:
+    image[x:x+size, y:y+size] = target
+image += randn(400, 400)*2
+
+plt.figure()
+plt.imshow(image)
+plt.title("Test image")
+plt.axis('off')
+
+# Match the template.
+result = match_template(image, target, method='norm-corr')
+
+plt.figure()
+plt.imshow(result)
+plt.title("Result from ``match_template``")
+plt.axis('off')
+
+plt.show()
+
+# peak extraction algorithm.
+delta = 5
+found_positions = []
+for i in range(50):
+    index = np.argmax(result)
+    y, x = np.unravel_index(index, result.shape)
+    if not found_positions:
+        found_positions.append((x, y))
+    for position in found_positions:
+        distance = math.sqrt((x - position[0]) ** 2 + (y - position[1]) ** 2)
+        if distance > delta:
+            found_positions.append((x, y))
+    result[y, x] = 0
+    if len(found_positions) == len(target_positions):
+        break
+
+assert np.all(found_positions == target_positions)
+