omarkamelte/imtile
Fast image tiling, sliding-window cropping, and seamless reconstruction with overlap blending. Designed for deep learning inference on large images (segmentation, detection, super-resolution). Pure NumPy with optional GPU (CuPy) support.
What's novel
Fast image tiling, sliding-window cropping, and seamless reconstruction with overlap blending. Designed for deep learning inference on large images (segmentation, detection, super-resolution). Pure NumPy with optional GPU (CuPy) support.
Code Analysis
7 files read · 3 roundsSplits large 2D/3D NumPy images into fixed-size overlapping tiles with boundary snapping, and reassembles them via weighted-average blending for lossless round-trip reconstruction.
Strengths
Clean, well-documented implementation with thorough deterministic tests (32+ tests covering exact pixel verification, edge cases, dtype preservation, coverage guarantees) and a sensible minimal API. The boundary-snapping logic and weighted-average reconstruction are correctly implemented and well-tested.
Weaknesses
The GPU acceleration claim is overstated — reconstruct() always uses NumPy on CPU even when CuPy is available, and the backend abstraction is only partially utilized. The core algorithm (sliding-window tiling + weighted blending) is a well-known standard technique with no novel contribution.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
9
Contributors
1
Files
15
Active weeks
2
Repository
Language
Python
Stars
1
Forks
0
License
MIT