Improved 2D preprocessing script #287
Open
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Thank you for this repo. I ran into issues with the 2D script so i modified it:
Optimize image and mask processing for improved performance and storage efficiency
Multiprocessing: Implemented parallel processing using Python's multiprocessing module, significantly reducing execution time by utilizing all available CPU cores.
Image data type optimization: Changed from implicit float32 (4 bytes/pixel) to explicit uint8 (1 byte/pixel), reducing image data storage by approximately 75%.
Compressed NPZ format: Implemented np.savez_compressed() instead of np.save(), further reducing file sizes through compression.
Single file storage: Combined image and mask data into a single compressed NPZ file, reducing overhead and improving file management.
These changes significantly reduce storage requirements, especially for large datasets. For example, a 1024x1024 RGB image that previously required about 12 MB (4 bytes * 1024 * 1024 * 3) now requires only about 3 MB (1 byte * 1024 * 1024 * 3), before additional compression from the NPZ format.