Use feature.peak_local_max instead of custom peak detection.

doc/examples/plot_template.py

@@ -13,7 +13,7 @@ a simple peak extraction algorithm.
 """
 
 import numpy as np
-from skimage.feature import match_template
+from skimage.feature import match_template, peak_local_max
 from numpy.random import randn
 import matplotlib.pyplot as plt
 
@@ -30,21 +30,14 @@ image += randn(400, 400)*2
 # Match the template.
 result = match_template(image, target, method='norm-corr')
 
-# 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 = np.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
+found_positions = peak_local_max(result)
+
+if len(found_positions) > 2:
+    # Keep the two maximum peaks.
+    intensities = result[tuple(found_positions.T)]
+    i_maxsort = np.argsort(intensities)[::-1]
+    found_positions = found_positions[i_maxsort][:2]
+
 x_found, y_found = np.transpose(found_positions)
 
 plt.gray()
@@ -67,3 +60,4 @@ plt.autoscale(tight=True)
 plt.axis('off')
 
 plt.show()
+