CNN – Image Resizing VS Padding (keeping aspect ratio or not?)

Image Preprocessing for CNNs: Resizing vs Padding

Introduction

Convolutional Neural Networks (CNNs) excel at image analysis, but require consistent input sizes. This often necessitates image preprocessing: resizing and padding. This article explores the trade-offs between these methods.

Image Resizing

Keeping Aspect Ratio

Resizing with aspect ratio preservation maintains the image’s proportions. This is crucial for tasks like object recognition, where distortion can negatively impact results.

Example:

from PIL import Image
img = Image.open('image.jpg')
width, height = img.size
new_size = (224, int(height * 224 / width))  # Preserve aspect ratio
img = img.resize(new_size)

Non-Aspect Ratio Preservation

For some tasks (like image classification), aspect ratio may not be critical. Resizing to a fixed size can simplify processing.

Example:

from PIL import Image
img = Image.open('image.jpg')
img = img.resize((224, 224)) 

Padding

Concept

Padding adds pixels to an image’s border, usually with a constant value (e.g., 0). This increases the size without distortion. Two main types:

• Zero Padding: Adds 0 values to the borders.
• Reflection Padding: Mirrors pixels from the edge to create the padding.

Keeping Aspect Ratio

Padding can be used to ensure images maintain their aspect ratio after resizing. This is valuable when dealing with datasets of varying dimensions.

Example:

from PIL import Image
img = Image.open('image.jpg')
width, height = img.size
target_size = 224
padding = (target_size - width) // 2, (target_size - height) // 2
img = Image.new('RGB', (target_size, target_size), (0, 0, 0)) # Black padding
img.paste(img, padding) 

Non-Aspect Ratio Preservation

Padding can also be used to create square images for tasks where aspect ratio is not crucial.

Example:

from PIL import Image
img = Image.open('image.jpg')
width, height = img.size
max_size = max(width, height)
padding = (max_size - width) // 2, (max_size - height) // 2
img = Image.new('RGB', (max_size, max_size), (0, 0, 0)) # Black padding
img.paste(img, padding)

Trade-offs

Conclusion

The choice between resizing and padding for image preprocessing depends on the specific task and dataset. Aspect ratio preservation is crucial for tasks like object detection, while padding can be useful for tasks like image classification. Experimentation is often necessary to determine the optimal approach for a given problem.