Template matching is a technique that is used to locate a specific image (a template) within a larger image. In simple terms, the goal is to find a place where the smaller image matches the larger image.

Template matching involves comparing the template image with different regions of the bigger image. The different properties of the template image such as size, shape, color, and intensity value are matched against the bigger image during comparison.

The comparison occurs until a region with the best match is found between the template image and the bigger image.

Template Matching in Mahotas

In Mahotas, we can use the mahotas.template_match() function to perform template matching. The function compares the template image to every region of the bigger image having the same size as the template image.

The function uses the sum of squared differences (SSD) method to perform template matching. The SSD method works in the following way −

• The first step is to calculate the difference between the pixel values of the template image and the larger image.

• In the next step, the differences are squared.

• Finally, the squared differences are summed for all pixels in the larger image.

The final SSD values determine the similarity between the template image and the larger image. The smaller the value, the greater is the match between the template image and the larger image.

The mahotas.template_match() function

The mahotas.template_match() function takes an image and a template image as input.

It returns a region from the larger image that best matches the input template image.

The best match is the region which has the lowest SSD value.

Syntax

Following is the basic syntax of the template_match() function in mahotas −

mahotas.template_match(f, template, mode=''reflect'', cval=0.0, out=None)

Where,

• f − It is the input image.

• template − It is the pattern that will be matched against the input image.

• mode (optional) − It determines how input image is extended when the template is applied near its boundaries (default is ”reflect”).

• cval (optional) − It is the constant value used in padding when mode is ”constant” (default is 0.0).

• out (optional) − It defines the array in which the output image is stored (default is None).

Example

In the following example, we are performing template matching using the mh.template_match() function.

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread(''tree.tiff'', as_grey=True)
template = mh.imread(''cropped tree.tiff'', as_grey=True)
# Applying template matching algorithm
template_matching = mh.template_match(image, template)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 3)
# Displaying the original image
axes[0].imshow(image, cmap=''gray'')
axes[0].set_title(''Original Image'')
axes[0].set_axis_off()
# Displaying the template image
axes[1].imshow(template, cmap=''gray'')
axes[1].set_title(''Template Image'')
axes[1].set_axis_off()
# Displaying the matched image
axes[2].imshow(template_matching, cmap=''gray'')
axes[2].set_title(''Matched Image'')
axes[2].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

Output

Following is the output of the above code −

Matching by Wrapping Boundaries

We can wrap the boundaries of an image when performing template matching in Mahotas. Wrapping boundaries refers to folding the image boundaries to the opposite side of the image.

Thus, the pixels that are outside the boundary are repeated on the other side of the image.

This helps us in handling the pixels that are outside of the image boundaries during template matching.

In mahotas, we can wrap the boundaries of an image when performing template matching by specifying the value ”wrap” to the mode parameter of the template_match() function.

Example

In the example mentioned below, we are performing template matching by wrapping the boundaries of an image.

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread(''sun.png'', as_grey=True)
template = mh.imread(''cropped sun.png'', as_grey=True)
# Applying template matching algorithm
template_matching = mh.template_match(image, template, mode=''wrap'')
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 3)
# Displaying the original image
axes[0].imshow(image, cmap=''gray'')
axes[0].set_title(''Original Image'')
axes[0].set_axis_off()
# Displaying the template image
axes[1].imshow(template, cmap=''gray'')
axes[1].set_title(''Template Image'')
axes[1].set_axis_off()
# Displaying the matched image
axes[2].imshow(template_matching, cmap=''gray'')
axes[2].set_title(''Matched Image'')
axes[2].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

Output

Output of the above code is as follows −

Matching by Ignoring Boundaries

We can also perform template matching by ignoring the boundaries of an image. The pixels that are beyond the boundaries of an image are excluded when performing template matching by ignoring boundaries.

In mahotas, we specify the value ”ignore” to the mode parameter of template_match() function to ignore the boundaries of an image, when performing template matching.

Example

In here, we are ignoring the boundaries of an image when performing template matching.

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread(''nature.jpeg'', as_grey=True)
template = mh.imread(''cropped nature.jpeg'', as_grey=True)
# Applying template matching algorithm
template_matching = mh.template_match(image, template, mode=''ignore'')
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 3)
# Displaying the original image
axes[0].imshow(image, cmap=''gray'')
axes[0].set_title(''Original Image'')
axes[0].set_axis_off()
# Displaying the template image
axes[1].imshow(template, cmap=''gray'')
axes[1].set_title(''Template Image'')
axes[1].set_axis_off()
# Displaying the matched image
axes[2].imshow(template_matching, cmap=''gray'')
axes[2].set_title(''Matched Image'')
axes[2].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

Output

After executing the above code, we get the following output −