sahi 0.4.4

A vision library for performing sliced inference on large images/small objects

SAHI: Slicing Aided Hyper Inference

A vision library for performing sliced inference on large images/small objects.

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.

Installation

• Install sahi using pip:

pip install sahi

• On Windows, Shapely needs to be installed via Conda:

conda install -c conda-forge shapely

• Install your desired version of pytorch and torchvision:

pip install torch torchvision

• Install your desired detection framework (such as mmdet or yolov5):

pip install mmdet mmcv

pip install yolov5

Usage

• Sliced inference:

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 YOLOv5 Inference Notebook or MMDetection Inference Notebook for detailed usage.

• Slice an image:

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:

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 for detailed usage.

Scripts

Find detailed info on script usage (predict, coco2yolov5, coco_error_analysis) at SCRIPTS.md.

COCO Utilities

Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at COCO.md.

Adding new detection framework support

sahi library currently supports all YOLOv5 models and MMDetection models. Moreover, it is easy to add new frameworks.

All you need to do is, creating a new class in model.py that implements DetectionModel class. You can take the MMDetection wrapper or YOLOv5 wrapper as a reference.

Contributers

• Fatih Cagatay Akyon
• Cemil Cengiz
• Sinan Onur Altinuc