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)

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

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]

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

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].

Practice

1. What is the main purpose of template matching in computer vision?

easy

A. To reduce the size of an image without losing quality

B. To classify images into different categories

C. To find a small image inside a larger image by comparing pixel patterns

D. To generate new images from existing ones

Solution

1. Step 1: Understand template matching concept

   Template matching searches for a smaller image (template) inside a bigger image by comparing pixel patterns.

2. Step 2: Compare with other options

   Other options describe classification, resizing, or generation, which are different tasks.

3. Final Answer:

   To find a small image inside a larger image by comparing pixel patterns -> Option C

4. Quick Check:

   Template matching = find small image inside big image [OK]

Hint: Template matching = locating small image inside big one [OK]

Common Mistakes:

• Confusing template matching with image classification
• Thinking it changes image size
• Assuming it creates new images

2. Which of the following is the correct OpenCV function call to perform template matching?

easy

A. cv2.matchTemplate(image, template, method)

B. cv2.templateMatch(image, template)

C. cv2.findTemplate(image, template, method)

D. cv2.match(image, template)

Solution

1. Step 1: Recall OpenCV template matching syntax

   The correct function is cv2.matchTemplate with parameters (image, template, method).

2. Step 2: Check other options for correctness

   Other options use incorrect function names or missing parameters.

3. Final Answer:

   cv2.matchTemplate(image, template, method) -> Option A

4. Quick Check:

   OpenCV template matching = cv2.matchTemplate [OK]

Hint: Remember exact OpenCV function name: matchTemplate [OK]

Common Mistakes:

• Using wrong function names like templateMatch or findTemplate
• Omitting the method parameter
• Confusing with other OpenCV functions

3. Given the following code snippet, what will be the shape of the result from cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED) if image is 100x100 pixels and template is 20x20 pixels?

medium

A. (100, 100)

B. (81, 81)

C. (120, 120)

D. (80, 80)

Solution

1. Step 1: Understand output size formula

   The output size is (W - w + 1, H - h + 1) where W,H are image dims and w,h are template dims.

2. Step 2: Calculate output shape

   For image 100x100 and template 20x20, output = (100-20+1, 100-20+1) = (81, 81).

3. Final Answer:

   (81, 81) -> Option B

4. Quick Check:

   Output shape = (image - template + 1) [OK]

Hint: Output shape = image size minus template size plus one [OK]

Common Mistakes:

• Using image size directly as output shape
• Adding template size instead of subtracting
• Off-by-one errors in calculation

4. You run template matching but get an error: cv2.error: (-215:Assertion failed) src.type() == templ.type() in function 'matchTemplate'. What is the most likely cause?

medium

A. The template image and source image have different data types or channels

B. The template image is larger than the source image

C. The method parameter is missing in the function call

D. The images are not converted to grayscale

Solution

1. Step 1: Analyze error message

   The error says src.type() == templ.type() failed, meaning image and template types differ.

2. Step 2: Identify cause

   Different data types or number of channels (e.g., one grayscale, one color) cause this error.

3. Final Answer:

   The template image and source image have different data types or channels -> Option A

4. Quick Check:

   Image and template must have same type [OK]

Hint: Check image and template have same type and channels [OK]

Common Mistakes:

• Assuming template size causes this error
• Forgetting to pass method parameter causes this error
• Thinking grayscale conversion is mandatory for all cases

5. You want to detect a rotated version of a template inside an image using template matching. Which approach is best to improve detection?

hard

A. Resize the image to match the template size

B. Use the original template only without rotation

C. Convert both images to grayscale before matching

D. Rotate the template at multiple angles and run template matching for each

Solution

1. Step 1: Understand template matching limitation

   Template matching works best when template matches image exactly in size and orientation.

2. Step 2: Handle rotation

   To detect rotated templates, rotate the template at different angles and match each rotated version.

3. Final Answer:

   Rotate the template at multiple angles and run template matching for each -> Option D

4. Quick Check:

   Rotate template for rotated detection [OK]

Hint: Try multiple rotated templates to detect rotated objects [OK]

Common Mistakes:

• Using only original template ignores rotation
• Resizing image does not fix rotation mismatch
• Grayscale conversion helps but doesn't solve rotation