upload initial code (#1)

Author: fcakyon

.gitattributes

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+# this drop notebooks from GitHub language stats
+*.ipynb linguist-vendored

.gitignore

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+*.pyc
+*.swp
+*.pkl
+*.pth
+result*
+weights*
+.vscode
+
+# outputs
+outputs
+sliced_prediction_data
+
+# mmdetection
+mmdetection/build
+mmdetection/demo
+mmdetection/experiments
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# Elastic Beanstalk Files
+.elasticbeanstalk/*
+!.elasticbeanstalk/*.cfg.yml
+!.elasticbeanstalk/*.global.yml

README.md

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-# sahi
+# SAHI: Slicing Aided Hyper Inference
+
 A vision library for performing sliced inference on large images/small objects
+
+<img width="700" alt="teaser" src="./demo/sliced_inference.gif">
+
+## Overview
+
+Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems.
+
+## Getting started
+
+### Blogpost
+
+Check the [official SAHI blog post](https://medium.com/@fcakyon/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80).
+
+
+### Installation
+
+- Install sahi using conda:
+
+```console
+conda install -c obss sahi
+```
+
+- Install sahi using pip:
+
+```console
+pip install sahi
+```
+
+- Install your desired version of pytorch and torchvision:
+```console
+pip install torch torchvision
+```
+
+- Install your desired detection framework (such as mmdet):
+```console
+pip install mmdet
+```
+
+## Usage
+
+- Sliced inference:
+```python
+result = get_sliced_prediction(
+    image,
+    detection_model,
+    slice_height = 256,
+    slice_width = 256,
+    overlap_height_ratio = 0.2,
+    overlap_width_ratio = 0.2
+)
+
+```
+Refer to [inference notebook](demo/inference.ipynb) for detailed usage.
+
+- Slice an image:
+```python
+from sahi.slicing import slice_image
+
+slice_image_result, num_total_invalid_segmentation = slice_image(
+    image=image_path,
+    output_file_name=output_file_name,
+    output_dir=output_dir,
+    slice_height=256,
+    slice_width=256,
+    overlap_height_ratio=0.2,
+    overlap_width_ratio=0.2,
+)
+```
+
+- Slice a coco formatted dataset:
+```python
+from sahi.slicing import slice_coco
+
+coco_dict, coco_path = slice_coco(
+    coco_annotation_file_path=coco_annotation_file_path,
+    image_dir=image_dir,
+    slice_height=256,
+    slice_width=256,
+    overlap_height_ratio=0.2,
+    overlap_width_ratio=0.2,
+)
+```
+
+## Adding new detection framework support
+
+sahi library currently only supports [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md). However it is easy to add new frameworks.
+
+All you need to do is, creating a new class in [model.py](sahi/model.py) that implements [DetectionModel class](https://github.com/obss/sahi/blob/651f8e6cdb20467815748764bb198dd50241ab2b/sahi/model.py#L10). You can take the [MMDetection wrapper](https://github.com/obss/sahi/blob/651f8e6cdb20467815748764bb198dd50241ab2b/sahi/model.py#L164) as a reference.

demo/image_dir/small-vehicles1.jpeg

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+opencv-python>=4.2.0.32
+shapely>=1.7.0
+tqdm>=4.48.2
+imantics>=0.1.12
+scikit-image>=0.14.2

demo/inference.ipynb

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+__version__ = "0.1.0"