Resizing images in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the resized image?

Given the following code that resizes an image using OpenCV, what will be the shape of the output image?

Computer Vision

import cv2
import numpy as np

image = np.zeros((100, 200, 3), dtype=np.uint8)
resized_image = cv2.resize(image, (50, 50))
print(resized_image.shape)

A (50, 50)

B (50, 50, 3)

C (100, 50, 3)

D (50, 100, 3)

💡 Hint

Remember that OpenCV uses (width, height) for resizing and color images keep 3 channels.

The original image has shape (100, 200, 3). The resize function uses (width, height) = (50, 50), so the output shape is (height, width, channels) = (50, 50, 3).

🧠 Conceptual

intermediate

Why resize images before training a model?

Why do we usually resize images to a fixed size before feeding them into a machine learning model?

A To ensure all images have the same size so the model input shape is consistent.

B To reduce the number of images in the dataset.

C To increase the number of colors in the image for better accuracy.

D To convert images from color to grayscale automatically.

💡 Hint

Think about how models expect input data shapes.

Models require inputs of the same shape to process batches efficiently. Resizing ensures all images match the expected input size.

❓ Hyperparameter

advanced

Choosing interpolation method for resizing

Which interpolation method is best suited for enlarging images to preserve quality?

A cv2.INTER_CUBIC

B cv2.INTER_LINEAR

C cv2.INTER_AREA

D cv2.INTER_NEAREST

💡 Hint

Some methods are better for shrinking, others for enlarging.

INTER_CUBIC uses bicubic interpolation which produces smoother results when enlarging images compared to INTER_LINEAR or INTER_NEAREST.

🔧 Debug

advanced

Why does this resizing code raise an error?

What error will this code raise and why? import cv2 import numpy as np image = np.zeros((100, 100, 3), dtype=np.uint8) resized = cv2.resize(image, (0, 0), fx=2, fy=2) print(resized.shape)

A TypeError because size cannot be (0, 0)

B AttributeError because resize does not accept fx and fy

C ValueError because fx and fy cannot be used with size

D No error, output shape will be (200, 200, 3)

💡 Hint

Check OpenCV documentation for resize parameters.

When size is (0, 0), OpenCV uses fx and fy to scale the image. So no error occurs and output shape doubles.

❓ Metrics

expert

Effect of resizing on model accuracy

You train two image classifiers: one with images resized to 64x64, another with 256x256. Which is the most likely outcome?

A The 256x256 model will always have higher accuracy due to more detail.

B The 64x64 model will always have higher accuracy due to faster training.

C The 256x256 model may have higher accuracy but risks overfitting and longer training time.

D Both models will have identical accuracy regardless of image size.

💡 Hint

Consider trade-offs between image detail and model complexity.

Larger images provide more detail but increase model complexity and training time, which can cause overfitting if data is limited.

Practice

1. What is the main purpose of resizing images in computer vision tasks?

easy

A. To change the image size to fit model input requirements

B. To add colors to a black and white image

C. To increase the number of image channels

D. To convert images into text format

Solution

1. Step 1: Understand resizing purpose

   Resizing changes the dimensions of an image to match what a model expects.

2. Step 2: Compare options

   Only To change the image size to fit model input requirements correctly describes resizing as changing image size to fit model input.

3. Final Answer:

   To change the image size to fit model input requirements -> Option A

4. Quick Check:

   Resizing = Change size for model input [OK]

Hint: Resizing adjusts image size to fit model needs [OK]

Common Mistakes:

• Thinking resizing adds colors
• Confusing resizing with changing image format
• Believing resizing changes image content

2. Which of the following is the correct syntax to resize an image using OpenCV in Python?

easy

A. cv2.resize(image, width, height)

B. cv2.resize(image, (width, height))

C. cv2.resize((width, height), image)

D. cv2.resize(image, width-height)

Solution

1. Step 1: Recall OpenCV resize syntax

   The correct syntax requires the image and a tuple for new size: (width, height).

2. Step 2: Check options

   Only cv2.resize(image, (width, height)) uses the correct tuple format for size as second argument.

3. Final Answer:

   cv2.resize(image, (width, height)) -> Option B

4. Quick Check:

   Resize syntax = cv2.resize(image, (width, height)) [OK]

Hint: Use tuple (width, height) as second argument in cv2.resize [OK]

Common Mistakes:

• Passing width and height as separate arguments
• Swapping image and size arguments
• Using subtraction instead of tuple for size

3. What will be the shape of the image after running this code?

import cv2
image = cv2.imread('photo.jpg')
resized = cv2.resize(image, (100, 50))
print(resized.shape)

medium

A. (3, 50, 100)

B. (100, 50, 3)

C. (50, 3, 100)

D. (50, 100, 3)

Solution

1. Step 1: Understand cv2.resize size order

   The size tuple is (width, height), but image shape is (height, width, channels).

2. Step 2: Convert size to shape

   Given size (100, 50), shape becomes (50, 100, 3) because height=50, width=100, and 3 color channels.

3. Final Answer:

   (50, 100, 3) -> Option D

4. Quick Check:

   Shape = (height, width, channels) = (50, 100, 3) [OK]

Hint: Shape is (height, width, channels), not (width, height) [OK]

Common Mistakes:

• Confusing width and height order
• Forgetting image channels in shape
• Assuming shape matches size tuple order

4. Identify the error in this code snippet for resizing an image:

import cv2
img = cv2.imread('img.png')
resized_img = cv2.resize(img, 200, 100)
print(resized_img.shape)

medium

A. cv2.resize requires size as a tuple, not separate arguments

B. cv2.imread should be cv2.readimage

C. print statement is missing parentheses

D. Image path must be absolute

Solution

1. Step 1: Check cv2.resize argument format

   cv2.resize expects the size as a single tuple (width, height), not two separate numbers.

2. Step 2: Verify other code parts

   cv2.imread is correct, print has parentheses, and relative path is allowed.

3. Final Answer:

   cv2.resize requires size as a tuple, not separate arguments -> Option A

4. Quick Check:

   Resize size must be tuple (width, height) [OK]

Hint: Pass size as tuple (width, height) to cv2.resize [OK]

Common Mistakes:

• Passing width and height as separate arguments
• Misnaming cv2.imread function
• Assuming print needs no parentheses in Python 3

5. You want to resize a batch of images to 64x64 pixels before feeding them to a neural network. Which approach is best to ensure consistent input size and fast processing?

hard

A. Resize images to different sizes based on their original aspect ratio

B. Resize images manually by cropping without changing size

C. Use cv2.resize on each image to (64, 64) and convert to numpy arrays

D. Feed original images without resizing to keep quality

Solution

1. Step 1: Understand neural network input needs

   Neural networks require fixed-size inputs for batch processing and consistent training.

2. Step 2: Evaluate resizing methods

   Using cv2.resize to (64, 64) ensures all images have the same size and can be efficiently processed.

3. Step 3: Reject other options

   Cropping without resizing changes size inconsistently, feeding original images breaks input size rules, and varying sizes cause errors.

4. Final Answer:

   Use cv2.resize on each image to (64, 64) and convert to numpy arrays -> Option C

5. Quick Check:

   Consistent size = cv2.resize to fixed (64, 64) [OK]

Hint: Resize all images to same size before model input [OK]

Common Mistakes:

• Skipping resizing and feeding varied sizes
• Cropping without resizing causing inconsistent sizes
• Assuming model can handle different image sizes