Template_Matching.py

"""
Template matching has various applications and is used in such fields as
face recognition (see facial recognition system) and medical image processing.
Systems have been developed and used in the past to count the number of faces
that walk across part of a bridge within a certain amount of time. Other systems include
automated calcified nodule detection within digital chest X-rays.[20] Recently, this
method was implemented in geostatistical simulation which could provide a fast algorithm.

-------------------
This program is an implemntation of Template Matching
"""
import numpy as np
import matplotlib.pyplot as plt

from skimage import data
from skimage.feature import match_template


image = data.coins()
coin = image[170:220, 75:130]

result = match_template(image, coin)
ij = np.unravel_index(np.argmax(result), result.shape)
x, y = ij[::-1]

fig = plt.figure(figsize=(8, 3))
ax1 = plt.subplot(1, 3, 1)
ax2 = plt.subplot(1, 3, 2)
ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2)

ax1.imshow(coin, cmap=plt.cm.gray)
ax1.set_axis_off()
ax1.set_title('template')

ax2.imshow(image, cmap=plt.cm.gray)
ax2.set_axis_off()
ax2.set_title('image')
# highlight matched region
hcoin, wcoin = coin.shape
rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
ax2.add_patch(rect)

ax3.imshow(result)
ax3.set_axis_off()
ax3.set_title('`match_template`\nresult')
# highlight matched region
ax3.autoscale(False)
ax3.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)

plt.show()