Albumentations library in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Albumentations library.

Computer Vision

import [1] as A

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

Anumpy

Bcv2

Calbumentations

Dtorch

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'cv2' instead of 'albumentations'.

Using 'torch' which is for deep learning, not augmentations.

The Albumentations library is imported using 'import albumentations as A'.

2fill in blank

medium

Complete the code to create a horizontal flip augmentation with a 50% chance.

Computer Vision

transform = A.[1](p=0.5)

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

AHorizontalFlip

BVerticalFlip

CRandomBrightnessContrast

DRotate

Attempts:

3 left

💡 Hint

Common Mistakes

Using VerticalFlip which flips images top to bottom.

Using Rotate which rotates images instead of flipping.

HorizontalFlip is the correct augmentation to flip images horizontally with probability p=0.5.

3fill in blank

hard

Fix the error in the code to apply a random brightness and contrast adjustment.

Computer Vision

transform = A.[1](brightness_limit=0.2, contrast_limit=0.2, p=0.5)

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

ABrightnessContrast

BRandomContrast

CRandomBrightness

DRandomBrightnessContrast

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomBrightness or RandomContrast alone which only adjust one property.

Using a non-existent class 'BrightnessContrast'.

RandomBrightnessContrast is the correct class to adjust brightness and contrast randomly.

4fill in blank

hard

Fill both blanks to create a Compose pipeline with horizontal flip and random rotate.

Computer Vision

transform = A.Compose([
    A.[1](p=0.5),
    A.[2](limit=40, p=0.7)
])

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

AHorizontalFlip

BVerticalFlip

CRotate

DRandomRotate90

Attempts:

3 left

💡 Hint

Common Mistakes

Using VerticalFlip instead of HorizontalFlip for horizontal flipping.

Using RandomRotate90 which rotates only by 90 degrees multiples.

HorizontalFlip and Rotate are used to flip images horizontally and rotate them randomly within a limit.

5fill in blank

hard

Fill all three blanks to create a Compose pipeline with normalization, horizontal flip, and random brightness/contrast.

Computer Vision

transform = A.Compose([
    A.[1](mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    A.[2](p=0.5),
    A.[3](brightness_limit=0.2, contrast_limit=0.2, p=0.3)
])

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

ANormalize

BHorizontalFlip

CRandomBrightnessContrast

DResize

Attempts:

3 left

💡 Hint

Common Mistakes

Using Resize instead of Normalize for normalization.

Mixing up the order of augmentations.

Normalize standardizes image colors, HorizontalFlip flips images, and RandomBrightnessContrast adjusts brightness and contrast randomly.

Practice

1. What is the main purpose of the Albumentations library in computer vision?

easy

A. To apply image transformations that help AI models learn better

B. To train deep learning models directly

C. To label images for datasets

D. To evaluate model accuracy

Solution

1. Step 1: Understand Albumentations role

   Albumentations is designed to change images by applying transformations like flips, crops, or color changes.

2. Step 2: Connect transformations to model training

   These changes create many versions of images, helping AI models learn better by seeing varied data.

3. Final Answer:

   To apply image transformations that help AI models learn better -> Option A

4. Quick Check:

   Albumentations = Image transformations for training [OK]

Hint: Albumentations = image changes to improve training [OK]

Common Mistakes:

• Thinking Albumentations trains models directly
• Confusing it with labeling tools
• Assuming it measures accuracy

2. Which of the following is the correct way to import the Compose function from Albumentations?

easy

A. from albumentations import Compose

B. import albumentations.Compose

C. from albumentations.Compose import *

D. import Compose from albumentations

Solution

1. Step 1: Recall Python import syntax

   To import a function from a module, use 'from module import function'.

2. Step 2: Match with Albumentations usage

   Albumentations provides Compose as a function inside the albumentations module, so 'from albumentations import Compose' is correct.

3. Final Answer:

   from albumentations import Compose -> Option A

4. Quick Check:

   Correct import syntax = from albumentations import Compose [OK]

Hint: Use 'from module import function' for imports [OK]

Common Mistakes:

• Using dot notation in import incorrectly
• Trying to import with 'import Compose from ...'
• Using wildcard import from Compose

3. What will be the output shape of the image after applying this Albumentations transform?

import albumentations as A
transform = A.Compose([A.Resize(128, 128)])
augmented = transform(image=image)
result = augmented['image'].shape

medium

A. (256, 256, 3)

B. (128, 128)

C. (3, 128, 128)

D. (128, 128, 3)

Solution

1. Step 1: Understand Resize transform

   The Resize transform changes the image size to the given height and width, here 128x128 pixels.

2. Step 2: Check image shape format

   Albumentations uses height, width, and channels format (H, W, C). Assuming input image has 3 color channels, output shape is (128, 128, 3).

3. Final Answer:

   (128, 128, 3) -> Option D

4. Quick Check:

   Resize to 128x128 with 3 channels = (128,128,3) [OK]

Hint: Resize sets height and width; channels stay same [OK]

Common Mistakes:

• Confusing channel order with PyTorch (C,H,W)
• Ignoring color channels in output shape
• Assuming Resize doubles size

4. Identify the error in this Albumentations code snippet:

import albumentations as A
transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=1.2)
])
augmented = transform(image=image)

medium

A. Compose cannot take a list of transforms

B. HorizontalFlip does not accept probability parameter

C. Probability p=1.2 is invalid; must be between 0 and 1

D. Missing import for RandomBrightnessContrast

Solution

1. Step 1: Check probability parameters

   Albumentations expects probability values (p) between 0 and 1 for transforms.

2. Step 2: Identify invalid p value

   RandomBrightnessContrast has p=1.2, which is invalid and will cause an error.

3. Final Answer:

   Probability p=1.2 is invalid; must be between 0 and 1 -> Option C

4. Quick Check:

   Probability p must be ≤ 1 [OK]

Hint: Probabilities must be between 0 and 1 [OK]

Common Mistakes:

• Setting p > 1 by mistake
• Thinking HorizontalFlip lacks p parameter
• Assuming Compose needs special import

5. You want to create a training pipeline that randomly flips images horizontally 50% of the time and then applies either a random brightness or contrast change (but not both) with equal chance. Which Albumentations Compose setup achieves this?

hard

A. A.Compose([A.OneOf([A.HorizontalFlip(p=0.5), A.RandomBrightness(p=1), A.RandomContrast(p=1)], p=1)])

B. A.Compose([A.HorizontalFlip(p=0.5), A.OneOf([A.RandomBrightness(p=1), A.RandomContrast(p=1)], p=1)])

C. A.Compose([A.HorizontalFlip(p=0.5), A.RandomBrightness(p=0.5), A.RandomContrast(p=0.5)])

D. A.Compose([A.HorizontalFlip(p=1), A.RandomBrightness(p=0.5), A.RandomContrast(p=0.5)])

Solution

1. Step 1: Understand HorizontalFlip probability

   We want horizontal flip with 50% chance, so use A.HorizontalFlip(p=0.5).

2. Step 2: Apply either brightness or contrast randomly

   Use A.OneOf with two transforms inside, each with p=1, and OneOf's p=1 to ensure exactly one is applied.

3. Step 3: Check options

   A.Compose([A.HorizontalFlip(p=0.5), A.OneOf([A.RandomBrightness(p=1), A.RandomContrast(p=1)], p=1)]) matches this logic exactly. Others either apply both brightness and contrast or misuse probabilities.

4. Final Answer:

   A.Compose([A.HorizontalFlip(p=0.5), A.OneOf([A.RandomBrightness(p=1), A.RandomContrast(p=1)], p=1)]) -> Option B

5. Quick Check:

   Use OneOf for exclusive choice [OK]

Hint: Use OneOf to pick one transform randomly [OK]

Common Mistakes:

• Applying brightness and contrast together
• Setting wrong probabilities in OneOf
• Using OneOf to include HorizontalFlip