In this example, we'll use cross-correlation with the eye template image (using a kernel with the image for cross-correlation) and the location of the eye in the raccoon-face image can be found as follows:
face_image = misc.face(gray=True) - misc.face(gray=True).mean()
template_image = np.copy(face_image[300:365, 670:750]) # right eye
template_image -= template_image.mean()
face_image = face_image + np.random.randn(*face_image.shape) * 50 # add random noise
correlation = signal.correlate2d(face_image, template_image, boundary='symm', mode='same')
y, x = np.unravel_index(np.argmax(correlation), correlation.shape) # find the match
fig, (ax_original, ax_template, ax_correlation) = pylab.subplots(3, 1, figsize=(6, 15))
ax_original.imshow(face_image, cmap='gray')
ax_original.set_title('Original', size=20)
ax_original.set_axis_off()
ax_template.imshow(template_image, cmap='gray')
ax_template.set_title('Template', size=20)
ax_template.set_axis_off()
ax_correlation.imshow(correlation, cmap='afmhot')
ax_correlation.set_title('Cross-correlation', size=20)
ax_correlation.set_axis_off()
ax_original.plot(x, y, 'ro')
fig.show()
You have marked the location with the largest cross-correlation value (the best match with the template) with a red dot:
Here's the template:
Applying cross-correlation gives the following output:
As can be seen from the previous image, one of the eyes of the raccoon in the input image has got the highest cross-correlation with the eye-template image.