Principle:Kornia Kornia Image Loading
| Knowledge Sources |
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| Domains | Vision, IO |
| Last Updated | 2026-02-09 15:00 GMT |
Overview
Technique for reading image data from files and converting to GPU-ready tensor format for differentiable computer vision pipelines.
Description
Image loading converts stored pixel data into multi-dimensional tensors suitable for GPU computation. This includes decoding compressed formats (JPEG, PNG), converting color spaces (RGB, grayscale), normalizing values to [0,1] float range, and placing tensors on the target compute device.
Proper loading ensures consistent data format throughout downstream processing. The loading step must handle diverse file formats, bit depths, and color space conventions while producing a uniform tensor representation that all subsequent pipeline stages can consume without format-specific branching.
Usage
Use when ingesting image data from disk into a PyTorch-based vision pipeline. Required as the first step before any augmentation, feature detection, or model inference. Any Kornia-based workflow that processes real image files will begin with an image loading operation.
Theoretical Basis
Images are represented as tensors of shape (C, H, W) where:
- C is the number of channels (e.g., 3 for RGB, 1 for grayscale)
- H is the height in pixels
- W is the width in pixels
Float normalization maps uint8 values in the range [0, 255] to float32 values in the range [0.0, 1.0] via division by 255:
# Normalization formula
x_float = x_uint8.float() / 255.0
This normalization is important for numerical stability in gradient-based optimization. Neural networks and differentiable operations expect inputs in a bounded floating-point range to prevent gradient explosion and ensure consistent learning rates across different input scales.