Template matching in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Template matching

Problem:You want to find a small image (template) inside a bigger image using template matching.

Current Metrics:The current method finds the template but often gives false matches or misses the correct location.

Issue:The template matching is not accurate enough, leading to wrong detections or no detection.

Your Task

Improve the template matching accuracy to correctly locate the template in the image with fewer false matches.

You must use OpenCV's template matching functions.

You cannot change the template or the main image.

You can only adjust the matching method and threshold.

Hint 1

Hint 2

Hint 3

Solution

Computer Vision

import cv2
import numpy as np

# Load the main image and template in grayscale
image = cv2.imread('main_image.jpg', cv2.IMREAD_GRAYSCALE)
template = cv2.imread('template.jpg', cv2.IMREAD_GRAYSCALE)

# Get template width and height
w, h = template.shape[::-1]

# Apply template matching using normalized correlation coefficient
result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)

# Set threshold for detecting good matches
threshold = 0.8

# Find locations where matching result is above threshold
locations = np.where(result >= threshold)

# Draw rectangles on matches
for pt in zip(*locations[::-1]):
    cv2.rectangle(image, pt, (pt[0] + w, pt[1] + h), (255, 255, 255), 2)

# Save or show the result image
cv2.imwrite('matched_result.jpg', image)

# Calculate number of matches found
num_matches = len(locations[0])
print(f'Number of matches found: {num_matches}')

Switched to TM_CCOEFF_NORMED matching method for better accuracy.

Added a threshold of 0.8 to filter out weak matches.

Visualized matches by drawing rectangles on the original image.

Results Interpretation

Before: Template matching gave many false matches or missed the template location.

After: Using TM_CCOEFF_NORMED and thresholding, the model found 3 correct matches with fewer false positives.

Choosing the right matching method and applying a threshold helps improve template matching accuracy by reducing false detections.

Bonus Experiment

Try using multi-scale template matching to find the template even if it appears at different sizes in the image.

💡 Hint

Resize the template to different scales and apply template matching at each scale, then pick the best match.

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