Geometric transforms (rotate, flip, crop) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of rotating an image by 90 degrees clockwise

Given a 3x3 grayscale image represented as a 2D list, what is the output after rotating it 90 degrees clockwise?

Computer Vision

import numpy as np
image = np.array([[1,2,3],[4,5,6],[7,8,9]])
rotated = np.rot90(image, k=-1)
print(rotated.tolist())

A [[7,4,1],[8,5,2],[9,6,3]]

B [[9,8,7],[6,5,4],[3,2,1]]

C [[3,6,9],[2,5,8],[1,4,7]]

D [[1,2,3],[4,5,6],[7,8,9]]

💡 Hint

Rotating 90 degrees clockwise means columns become rows from bottom to top.

The numpy rot90 function with k=-1 rotates the array 90 degrees clockwise. The first column becomes the first row reversed.

❓ Model Choice

intermediate

Choosing the correct transform for horizontal flip

Which numpy operation correctly flips a 2D image array horizontally?

A np.transpose(image)

B np.flipud(image)

C np.rot90(image, k=2)

D np.fliplr(image)

💡 Hint

Horizontal flip means flipping left to right.

np.fliplr flips the array left to right (horizontally). np.flipud flips up and down (vertical flip).

🔧 Debug

advanced

Identify the error in cropping code

What error will this code raise when cropping a 2D image array?

Computer Vision

import numpy as np
image = np.arange(16).reshape(4,4)
cropped = image[1:3, 3:1]
print(cropped)

A TypeError: slice indices must be integers or None or have an __index__ method

B IndexError: slice indices must be increasing

C Returns an empty array with shape (2,0)

D SyntaxError due to invalid slice

💡 Hint

Check how slicing with start index greater than stop index behaves in numpy.

Slicing with start > stop returns an empty slice, so cropped has shape (2,0) and prints []

❓ Hyperparameter

advanced

Effect of interpolation parameter in rotation

When rotating an image using OpenCV's cv2.warpAffine, which interpolation method preserves the most image quality for small rotations?

A cv2.INTER_CUBIC

B cv2.INTER_LINEAR

C cv2.INTER_AREA

D cv2.INTER_NEAREST

💡 Hint

Higher order interpolation methods produce smoother results.

cv2.INTER_CUBIC uses bicubic interpolation which preserves quality better than linear or nearest neighbor for rotations.

❓ Metrics

expert

Evaluating effect of geometric transforms on classification accuracy

You apply random rotations and flips as data augmentation during training a CNN classifier. After training, which metric change best indicates the augmentation improved model generalization?

A Training accuracy increases but validation accuracy decreases

B Training accuracy decreases but validation accuracy increases

C Both training and validation accuracy decrease

D Both training and validation accuracy increase

💡 Hint

Good augmentation reduces overfitting, so training accuracy may drop but validation improves.

Data augmentation makes training harder, lowering training accuracy, but helps generalization, raising validation accuracy.

Practice

1. Which geometric transform flips an image horizontally, like looking in a mirror?

easy

A. Horizontal flip

B. Rotate 90 degrees

C. Crop center

D. Vertical flip

Solution

1. Step 1: Understand horizontal flip

   Horizontal flip reverses the image left to right, like a mirror reflection.

2. Step 2: Compare with other transforms

   Rotate changes orientation, crop cuts part of image, vertical flip flips top to bottom.

3. Final Answer:

   Horizontal flip -> Option A

4. Quick Check:

   Mirror image = Horizontal flip [OK]

Hint: Mirror effect means horizontal flip [OK]

Common Mistakes:

• Confusing vertical flip with horizontal flip
• Thinking rotate is flipping
• Mixing crop with flip

2. Which Python code correctly rotates an image by 90 degrees clockwise using OpenCV?

easy

A. rotated = cv2.crop(image, (0,0,100,100))

B. rotated = cv2.flip(image, 1)

C. rotated = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

D. rotated = cv2.rotate(image, cv2.ROTATE_180)

Solution

1. Step 1: Identify OpenCV rotate syntax

   cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) rotates image 90 degrees clockwise.

2. Step 2: Check other options

   Flip flips image, crop is not a cv2 function, ROTATE_180 rotates 180 degrees, not 90.

3. Final Answer:

   rotated = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) -> Option C

4. Quick Check:

   Rotate 90 clockwise = cv2.ROTATE_90_CLOCKWISE [OK]

Hint: Use cv2.rotate with ROTATE_90_CLOCKWISE for 90° turn [OK]

Common Mistakes:

• Using flip instead of rotate
• Wrong rotate constant
• Trying crop as a cv2 function

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

import cv2
image = cv2.imread('img.jpg')
cropped = image[50:150, 30:130]

medium

A. (100, 100, 3)

B. (150, 130, 3)

C. (80, 100, 3)

D. (100, 80, 3)

Solution

1. Step 1: Calculate crop height

   Height = 150 - 50 = 100 pixels.

2. Step 2: Calculate crop width

   Width = 130 - 30 = 100 pixels.

3. Step 3: Confirm channels

   Image has 3 color channels (RGB/BGR).

4. Final Answer:

   (100, 100, 3) -> Option A

5. Quick Check:

   Crop size = (height, width, channels) = (100, 100, 3) [OK]

Hint: Subtract start from end index for height and width [OK]

Common Mistakes:

• Mixing width and height order
• Forgetting zero-based indexing
• Ignoring color channels

4. This code tries to flip an image vertically but causes an error. What is wrong?

import cv2
image = cv2.imread('img.jpg')
flipped = cv2.flip(image, 2)

medium

A. image must be grayscale for flip

B. Flip code 2 is invalid; use 0 for vertical flip

C. cv2.flip requires three arguments

D. cv2.flip does not flip images

Solution

1. Step 1: Check cv2.flip flipCode values

   flipCode=0 flips vertically, 1 flips horizontally, -1 flips both axes.

2. Step 2: Identify invalid flipCode

   flipCode=2 is invalid and causes error.

3. Final Answer:

   Flip code 2 is invalid; use 0 for vertical flip -> Option B

4. Quick Check:

   Vertical flip = flipCode 0 [OK]

Hint: Use flipCode 0 for vertical flip in cv2.flip [OK]

Common Mistakes:

• Using wrong flipCode values
• Thinking flip needs grayscale
• Assuming cv2.flip needs extra args

5. You want to rotate an image 90 degrees clockwise and then crop the central 100x100 pixels. Which code correctly does this?

hard

A. crop = img[50:150, 50:150]
rot = cv2.rotate(crop, cv2.ROTATE_90_CLOCKWISE)

B. rot = cv2.flip(img, 1)
crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50]

C. rot = cv2.rotate(img, cv2.ROTATE_180)
crop = rot[0:100, 0:100]

D. rot = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50]

Solution

1. Step 1: Rotate image 90 degrees clockwise

   Use cv2.rotate with ROTATE_90_CLOCKWISE on original image.

2. Step 2: Crop center 100x100 pixels

   Calculate center indices and slice 50 pixels before and after center in both dimensions.

3. Step 3: Verify order and correctness

   Rotation first, then crop center; other options rotate wrong angle or crop before rotate.

4. Final Answer:

   Rotate then crop center 100x100 -> Option D

5. Quick Check:

   Rotate 90° clockwise then center crop = rot = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50] [OK]

Hint: Rotate first, then crop center using shape indices [OK]

Common Mistakes:

• Cropping before rotating changes crop area
• Using wrong rotate angle
• Using flip instead of rotate