Metadata-Version: 2.1
Name: sahi
Version: 0.3.7
Summary: A vision library for performing sliced inference on large images/small objects
Home-page: https://github.com/obss/sahi
Author: OBSS
License: MIT
Description: <h1 align="center">
          SAHI: Slicing Aided Hyper Inference
        </h1>
        
        <h4 align="center">
          A vision library for performing sliced inference on large images/small objects.
        </h4>
        
        <h4 align="center">
            <img width="700" alt="teaser" src="./resources/sliced_inference.gif">
        </h4>
        
        <div align="center">
            <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a>
            <a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a>
            <a href="https://anaconda.org/obss/sahi"><img src="https://anaconda.org/obss/sahi/badges/version.svg" alt="conda version"></a>
            <a href="https://github.com/obss/sahi/actions?query=event%3Apush+branch%3Amain+is%3Acompleted+workflow%3ACI"><img src="https://github.com/obss/sahi/workflows/CI/badge.svg" alt="ci"></a>
        </div>
        
        ## 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/codable/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,
        )
        ```
        
        Refer to [slicing notebook](demo/slicing.ipynb) for detailed usage.
        
        ## Scripts
        
        Find detailed info on script usage (predict, coco2yolov5, coco_error_analysis) at [SCRIPTS.md](docs/SCRIPTS.md).
        
        ## COCO Utilities
        
        Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, merging, splitting) at [COCO.md](docs/COCO.md).
        
        ## 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.
        
        ## Contributers
        
        - [Fatih Cagatay Akyon](https://github.com/fcakyon)
        - [Cemil Cengiz](https://github.com/cemilcengiz)
        - [Sinan Onur Altinuc](https://github.com/sinanonur)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: tests
