Template matching in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2

image = cv2.[1]('image.jpg', cv2.IMREAD_COLOR)

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Aimread

Bimshow

Cimwrite

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread to load the image.

Using cv2.imwrite which saves an image, not loads it.

The cv2.imread function reads an image from a file.

2fill in blank

medium

Complete the code to perform template matching using OpenCV.

Computer Vision

result = cv2.matchTemplate(image, template, cv2.[1])

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ATM_CCOEFF_NORMED

BTM_SQDIFF

CTM_CCORR

DTM_SQDIFF_NORMED

Attempts:

3 left

💡 Hint

Common Mistakes

Using TM_SQDIFF which measures squared differences and requires different interpretation.

Using TM_CCORR which is less robust to lighting changes.

TM_CCOEFF_NORMED is a common method for template matching that normalizes the correlation coefficient.

3fill in blank

hard

Fix the error in the code to find the best match location after template matching.

Computer Vision

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
best_match = [1]

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Amin_val

Bmax_val

Cmax_loc

Dmin_loc

Attempts:

3 left

💡 Hint

Common Mistakes

Using min_loc which is the location of the minimum value, incorrect for this method.

Using min_val or max_val which are values, not locations.

For TM_CCOEFF_NORMED, the best match is at the location with the maximum value, so max_loc is correct.

4fill in blank

hard

Fill both blanks to draw a rectangle around the matched region.

Computer Vision

top_left = best_match
bottom_right = (top_left[0] + [1], top_left[1] + [2])
cv2.rectangle(image, top_left, bottom_right, (0, 255, 0), 2)

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Atemplate.shape[1]

Btemplate.shape[0]

Cimage.shape[1]

Dimage.shape[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using image dimensions instead of template dimensions.

Swapping width and height values.

The width of the rectangle is the template's width (template.shape[1]) and the height is the template's height (template.shape[0]).

5fill in blank

hard

Fill all three blanks to complete the template matching and display the result.

Computer Vision

import cv2

image = cv2.imread('scene.jpg')
template = cv2.imread('template.jpg')
result = cv2.matchTemplate(image, template, cv2.[1])
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
best_match = [2]
top_left = best_match
bottom_right = (top_left[0] + [3], top_left[1] + template.shape[0])
cv2.rectangle(image, top_left, bottom_right, (255, 0, 0), 3)
cv2.imshow('Matched Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Drag options to blanks, or click blank then click option'

ATM_CCOEFF_NORMED

Bmax_loc

Ctemplate.shape[1]

Dmin_loc

Attempts:

3 left

💡 Hint

Common Mistakes

Using min_loc instead of max_loc for best match.

Using image width instead of template width for rectangle.

Using a different matching method that requires different interpretation.

The method TM_CCOEFF_NORMED is used for matching. The best match location is max_loc. The rectangle width is the template's width template.shape[1].