"""
=================
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. We mark the locations of matches (red dots), which are detected using
a simple peak extraction algorithm. Note that the peaks in the output of
``match_template`` correspond to the origin (i.e. top-left corner) of the
template.
"""

import numpy as np
from skimage.feature import match_template, peak_local_max
from numpy.random import randn
import matplotlib.pyplot as plt

# We first construct a simple image target:
size = 100
target = np.tri(size) + np.tri(size)[::-1]
# place target in an image at two positions, and add noise.
image = np.zeros((400, 400))
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

# Match the template.
result = match_template(image, target, method='norm-corr')

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)


fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 3))
plt.gray()

ax0.imshow(target)
ax0.set_title("Target image")

ax1.imshow(image)
ax1.plot(x_found, y_found, 'ro', alpha=0.5)
ax1.set_title("Test image")
ax1.autoscale(tight=True)

ax2.imshow(result)
ax2.plot(x_found, y_found, 'ro', alpha=0.5)
ax2.set_title("Result from\n``match_template``")
ax2.autoscale(tight=True)

for ax in (ax0, ax1, ax2):
    ax.axis('off')

plt.show()