PyPI 官网下载 | sahi-0.8.3.tar.gz

<div align="center"> <h1> SAHI: Slicing Aided Hyper Inference </h1> <h4> A lightweight vision library for performing large scale object detection & instance segmentation </h4> <h4> <img width="700" alt="teaser" src="./resources/sliced_inference.gif"> </h4> <div> <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a> <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a> <br> <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/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/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> </div> ## <div align="center">Overview</div> 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 with many vision utilities. | Command | Description | |---|---| | [predict](https://github.com/obss/sahi/blob/main/docs/CLI.md#predict-command-usage) | perform sliced/standard prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection) model | | [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/CLI.md#predict-fiftyone-command-usage) | perform sliced/standard prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection) model and explore results in [fiftyone app](https://github.com/voxel51/fiftyone) | | [coco slice](https://github.com/obss/sahi/blob/main/docs/CLI.md#coco-slice-command-usage) | automatically slice COCO annotation and image files | | [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/CLI.md#coco-fiftyone-command-usage) | explore multiple prediction results on your COCO dataset with [fiftyone ui](https://github.com/voxel51/fiftyone) ordered by number of misdetections | | [coco evaluate](https://github.com/obss/sahi/blob/main/docs/CLI.md#coco-evaluate-command-usage) | evaluate classwise COCO AP and AR for given predictions and ground truth | | [coco analyse](https://github.com/obss/sahi/blob/main/docs/CLI.md#coco-analyse-command-usage) | calcualate and export many detection and segmentation error margin plots | | [coco yolov5](https://github.com/obss/sahi/blob/main/docs/CLI.md#coco-yolov5-command-usage) | automatically convert any COCO dataset to [yolov5](https://github.com/ultralytics/yolov5) format | ## <div align="center">Getting Started</div> <details open> <summary> <big><b>Blogpost</b></big> </summary> Check the [official SAHI blog post](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80). </details> <details open> <summary> <big><b>Installation</b></big> </summary> - Install `sahi` using pip: ```console pip install sahi ``` - On Windows, `Shapely` needs to be installed via Conda: ```console conda install -c conda-forge shapely ``` - Install your desired version of pytorch and torchvision: ```console pip install torch torchvision ``` - Install your desired detection framework (such as mmdet or yolov5): ```console pip install mmdet mmcv ``` ```console pip install yolov5 ``` </details> ## <div align="center">Usage</div> <details open> <summary> <big><b>From Python:</b></big> </summary> - 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 ) ``` Check `YOLOv5` + `SAHI` demo: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> Check `MMDetection` + `SAHI` demo: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> - Slice an image: ```python from sahi.slicing import slice_image slice_image_result = 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. </details> <details closed> <summary> <big><b>From CLI:</b></big> </summary> ```bash sahi predict --source image/file/or/folder --model_path path/to/model --model_config_path path/to/config ``` will perform sliced inference on default parameters and export the prediction visuals to runs/predict/exp folder. You can specify sliced inference parameters as: ```bash sahi predict --slice_width 256 --slice_height 256 --overlap_height_ratio 0.1 --overlap_width_ratio 0.1 --model_confidence_threshold 0.25 --source image/file/or/folder --model_path path/to/model --model_config_path path/to/config ``` - Specify postprocess type as `--postprocess_type UNIONMERGE` or `--postprocess_type NMS` to be applied over sliced predictions - Specify postprocess match metric as `--postprocess_match_metric IOS` for intersection over smaller area or `--match_metric IOU` for intersection over union - Specify postprocess match threshold as `--postprocess_match_threshold 0.5` - Add `--class_agnostic` argument to ignore category ids of the predictions during postprocess (merging/nms) - If you want to export prediction pickles and cropped predictions add `--export_pickle` and `--export_crop` arguments. If you want to change crop extension type, set it as `--visual_export_format JPG`. - If you want to export prediction visuals, add `--export_visual` argument. - By default, scripts apply both standard and sliced prediction (multi-stage inference). If you don't want to perform sliced prediction add `--no_sliced_prediction` argument. If you don't want to perform standard prediction add `--no_standard_prediction` argument. - If you want to perform prediction using a COCO annotation file, provide COCO json path as add `--dataset_json_path dataset.json` and coco image folder as `--source path/to/coco/image/folder`, predictions will be exported as a coco json file to runs/predict/exp/results.json. Then you can use `coco_evaluation` command to calculate COCO evaluation results or `coco_error_analysis` command to calculate detailed COCO error plots. <b>Find detailed info on cli command usage (`coco fiftyone`, `coco yolov5`, `coco evaluate`, `coco analyse`) at [CLI.md](docs/CLI.md).</b> </details> ## <div align="center">FiftyOne Utilities</div> <details open> <summary> <big><b>Explore COCO dataset via FiftyOne app:</b></big> </summary> For supported version: `pip install fiftyone>=0.11.1` ```python from sahi.utils.fiftyone import launch_fiftyone_app # launch fiftyone app: session = launch_fiftyone_app(coco_image_dir, coco_json_path) # close fiftyone app: session.close() ``` </details> <details closed> <summary> <big><b>Convert predictions