sahi 0.4.6

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

From Python:

• 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
)

Check YOLOv5 + SAHI demo:

Check MMDetection + SAHI demo:

• 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.

From CLI:

python scripts/predict.py --source image/file/or/folder --model_path path/to/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:

python scripts/predict.py --slice_width 256 --slice_height 256 --overlap_height_ratio 0.1 --overlap_width_ratio 0.1 --iou_thresh 0.25 --source image/file/or/folder --model_path path/to/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 --match_metric IOS for intersection over smaller area or --match_metric IOU for intersection over union

• Specify postprocess match threshold as --match_thresh 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 --pickle and --crop arguments. If you want to change crop extension type, set it as --visual_export_format JPG.

• If you don't want to export prediction visuals, add --novisual argument.

• If you want to perform standard prediction instead of sliced prediction, add --standard_pred argument.

python scripts/predict.py --coco_file path/to/coco/file --source coco/images/directory --model_path path/to/model --config_path path/to/config

will perform inference using provided coco file, then export results as a coco json file to runs/predict/exp/results.json

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

COCO Utilities

COCO dataset creation:

• import required classes:

from sahi.utils.coco import Coco, CocoCategory, CocoImage, CocoAnnotation

• init Coco object:

coco = Coco()

• add categories starting from id 0:

coco.add_category(CocoCategory(id=0, name='human'))
coco.add_category(CocoCategory(id=1, name='vehicle'))

• create a coco image:

coco_image = CocoImage(file_name="image1.jpg", height=1080, width=1920)

• add annotations to coco image:

coco_image.add_annotation(
  CocoAnnotation(
    bbox=[x_min, y_min, width, height],
    category_id=0,
    category_name='human'
  )
)
coco_image.add_annotation(
  CocoAnnotation(
    bbox=[x_min, y_min, width, height],
    category_id=1,
    category_name='vehicle'
  )
)

• add coco image to Coco object:

coco.add_image(coco_image)

• after adding all images, convert coco object to coco json:

coco_json = coco.json

• you can export it as json file:

from sahi.utils.file import save_json

save_json(coco_json, "coco_dataset.json")

Convert COCO dataset to ultralytics/yolov5 format:

from sahi.utils.coco import Coco

# init Coco object
coco = Coco.from_coco_dict_or_path("coco.json", image_dir="coco_images/")

# export converted YoloV5 formatted dataset into given output_dir with a 85% train/15% val split
coco.export_as_yolov5(
  output_dir="output/folder/dir",
  train_split_rate=0.85
)

Get dataset stats:

from sahi.utils.coco import Coco

# init Coco object
coco = Coco.from_coco_dict_or_path("coco.json")

# get dataset stats
coco.stats
{
  'num_images': 6471,
  'num_annotations': 343204,
  'num_categories': 2,
  'num_negative_images': 0,
  'num_images_per_category': {'human': 5684, 'vehicle': 6323},
  'num_annotations_per_category': {'human': 106396, 'vehicle': 236808},
  'min_num_annotations_in_image': 1,
  'max_num_annotations_in_image': 902,
  'avg_num_annotations_in_image': 53.037243084530985,
  'min_annotation_area': 3,
  'max_annotation_area': 328640,
  'avg_annotation_area': 2448.405738278109,
  'min_annotation_area_per_category': {'human': 3, 'vehicle': 3},
  'max_annotation_area_per_category': {'human': 72670, 'vehicle': 328640},
}

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

MOT Challenge Utilities

MOT Challenge formatted ground truth dataset creation:

• import required classes:

from sahi.utils.mot import MotAnnotation, MotFrame, MotVideo

• init video:

mot_video = MotVideo(name="sequence_name")

• init first frame:

mot_frame = MotFrame()

• add annotations to frame:

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

• add frame to video:

mot_video.add_frame(mot_frame)

• export in MOT challenge format:

mot_video.export(export_dir="mot_gt", type="gt")

• your MOT challenge formatted ground truth files are ready under mot_gt/sequence_name/ folder.

Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at MOT.md.

Contributing

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