TamuraTexture

A comprehensive Python implementation of Tamura texture features for image analysis, supporting both NumPy and TensorFlow backends

A new way for AI to search for data

View on GitHub

1GitHub stars

13commits on master

0forks

Python 80.3%, Batchfile 19.7% · created 2025-10-24 · last push 2025-10-24

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit d865fa1073. It is the newest commit on master.

Selected work

Tuhin Chakrabarty
@Tuhin-SnapD

Licence

The repository

NONE

LICENSE file checked at the pinned commit; the author agreed to inclusion.

The XERJ corpus

NONE, with credit

The owner added it on hub.xerj.org after signing in with GitHub, on 2026-10-29. Same terms as the repository. Answers name the repo, the commit and the file:line.

selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution

The prompt

Paste it into your AI agent. It clones Tuhin-SnapD/TamuraTexture at the pinned commit and indexes it on your machine.

Search the GitHub repository Tuhin-SnapD/TamuraTexture with XERJ (https://hub.xerj.org/corpus/tuhin-snapd--tamuratexture).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/Tuhin-SnapD/TamuraTexture TamuraTexture && git -C TamuraTexture checkout d865fa10732c88604122df4e63e13484fb6a7abe
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./TamuraTexture --url http://127.0.0.1:9200 --prefix tamuratexture --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: check the LICENSE file. Keep the copyright notice and credit the authors.

Tuhin Chakrabarty AI, prompts and agents corpora

Stats: GitHub API, fetched 2026-10-29. Selected work and consent: the XERJ registry.