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

Open Business Software Solutions

Last update: Jan 4, 2023

Related tags

Deep Learning python computer-vision conda pytorch pip object-detection instance-segmentation large-image mmdetection small-object-detection

Overview

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 conda:

conda install -c obss sahi

Install sahi using pip:

pip install sahi

Install your desired version of pytorch and torchvision:

pip install torch torchvision

Install your desired detection framework (such as mmdet):

pip install mmdet

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 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,
)

Adding new detection framework support

sahi library currently only supports MMDetection models. However 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 as a reference.

Contributers

Comments

improve json loading

Fix the bug :

Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\Scripts\labelme2coco-script.py", line 33, in <module>
    sys.exit(load_entry_point('labelme2coco==0.2.1', 'console_scripts', 'labelme2coco')())
  File "C:\ProgramData\Anaconda3\lib\site-packages\labelme2coco-0.2.1-py3.9.egg\labelme2coco\cli.py", line 8, in app
  File "C:\ProgramData\Anaconda3\lib\site-packages\fire\core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "C:\ProgramData\Anaconda3\lib\site-packages\fire\core.py", line 466, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "C:\ProgramData\Anaconda3\lib\site-packages\fire\core.py", line 681, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\labelme2coco-0.2.1-py3.9.egg\labelme2coco\__init__.py", line 32, in convert
  File "C:\ProgramData\Anaconda3\lib\site-packages\labelme2coco-0.2.1-py3.9.egg\labelme2coco\labelme2coco.py", line 41, in get_coco_from_labelme_folder
  File "C:\ProgramData\Anaconda3\lib\site-packages\sahi\utils\file.py", line 66, in load_json
    data = json.load(json_file)
  File "C:\ProgramData\Anaconda3\lib\json\__init__.py", line 293, in load
    return loads(fp.read(),
UnicodeDecodeError: 'gbk' codec can't decode byte 0x90 in position 82: illegal multibyte sequence

enhancement

opened by qingfengtommy 20

add support for video prediction
This PR brings full support for sliced/standard inference for video input format for all MMDetection/Detectron2/YOLOv5 models :fire:

Just give video file as source:

sahi predict --model_path yolov5s.pt --model_type yolov5 --source video.mp4 --export_visual

You can also view video render during video inference with --view_video:

sahi predict --model_path yolov5s.pt --model_type yolov5 --source video.mp4 --export_visual --view_video

To forward 100 frames, on opened window press key D

To revert 100 frames, on opened window press key A

To forward 20 frames, on opened window press key G

To revert 20 frames, on opened window press key F

To exit, on opened window press key Esc

Note: If --view_video is slow, you can add --frame_skip_interval=20 argument to skip interval of 20 frames each time.
enhancement
opened by madenburak 15
add YOLOX model support
SAHI kütüphanesine YOLOX modelini en basit haliyle ekledim. #535

Düzeltilecekler:

[X] Resize modulu yazıldı.

[ ] Test kodu yazılacak.

[ ] Notebook dosyası oluşturulacak.

Resize kodunu özellikle yazmadım. SAHI için ortak bir resize modulu oluşturabilir miyiz? Yolox reposundaki örneği incelleyip geri dönüş yapabilir misiniz? YOLOX-RESIZE

Ayrıca nms_thre değerini kullanıcıdan istesek nasıl olur? Şuan ben manuel değer veriyorum. Aynı durum yolov7 modeli içinde geçerli.
enhancement
opened by kadirnar 13
add automatic slice size calculation
If I do not want to give slicing parameters or

I do not know what to give,

it automatically calculates slicing parameters according to the resolution of the image.
enhancement
opened by mcvarer 7

Added support for exporting predictions in COCO format

changelog: -

COCO prediction object of class CocoPrediction can be added into COCO image object of class sahi.utils.cocoCocoImage using method <CocoImage_Instance>.add_prediction
__repr__ method updated to reflect predictions in class sahi.utils..coco.CocoImage
Additional property prediction_array added in class sahi.utils.coco.Coco to export COCO predictions in json-serializable format. Logic for the property is defined in new function sahi.utils.coco.create_coco_prediction_array
added the test cases for the same in test_cocoutils.py
updated file coco.md with necessary documentation

Basic flow is like this

from sahi.utils.coco import Coco, CocoImage, CocoAnnotation, CocoPrediction
from sahi.utils.file import save_json

coco_obj = Coco()

# add n images to coco_obj
for _ in range(n):
    image = CocoImage(**kwargs)
    
    # add n annotations to the image
    for _ in ange(n):
        image.add_annotation(CocoAnnotation(**kwargs))
    
    # add n predictions to the image
    for _ in range(n)
        image.add_prediction(CocoPrediction(**kwargs))
    
    # add image to coco object
    coco_obj.add_image(image)

# export ground truth annotations
coco_gt = coco_obj.json
save_json(coco_gt , "ground_truth.json")

# export preadictions 
coco_predictions = coco_obj.prediction_array
save_json(coco_predictions, "predictions.json"

Why this is useful?

The user can utilize the exported files to calculate standard coco metrics using offical COCO api pycocotool with relative ease. This is example of using that

from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO

# load json files
coco_ground_truth = COCO(annotation_file="ground_truth.json")
coco_predictions = coco_ground_truth.loadRes("predictions.json")

coco_evaluator = COCOeval(coco_ground_truth, coco_predictions, "bbox")
coco_evaluator.evaluate()
coco_evaluator.accumulate()
coco_evaluator.summarize()

This can be further expanded to define class which can be conveniently used to evaluate the prediction, something like this

class DatasetEvalutor:
    # user can inherit and overwrite the given load_model, get_annotation and get_prediction methods
    def __init__(self, **kwargs):
        self.images = []  # list of image meta data
        self.model = None # model place holder
        self.dataset = Coco() # coco object

    def load_model(self,**kwargs):
        # logic for loading model goes here
        # set self.model to model

    def get_annotation(self, **kwargs):
        # logic for annotation goes here
        # result must return a sahi.utils.coco.CocoAnnotation instance

    def get_prediction(self, **kwargs):
        # logic for prediction goes here
        # result must return a sahi.utils.coco.CocoPrediction instance

    def run_inference(self, **kwargs):
        # loop over images create CocoImage and add annotations and prediction to it
        # add those CocoImage instance into self.dataset

    def evaluate(self, **kwargs):
        # pycocotool code to generate metrics

documentation enhancement

opened by PushpakBhoge 7

Downgrade Pillow

Issue

When running the sahi colab notebooks, an error occurs when attempting to run inference using a pretrained model:

ImportError                               Traceback (most recent call last)
[<ipython-input-5-6c3be4aaa524>](https://localhost:8080/#) in <module>
----> 1 detection_model = AutoDetectionModel.from_pretrained(
      2     model_type='detectron2',
      3     model_path=model_path,
      4     config_path=model_path,
      5     confidence_threshold=0.5,

14 frames
[/usr/local/lib/python3.8/dist-packages/PIL/ImageFont.py](https://localhost:8080/#) in <module>
     35 from . import Image
     36 from ._deprecate import deprecate
---> 37 from ._util import is_directory, is_path
     38 
     39 

ImportError: cannot import name 'is_directory' from 'PIL._util' (/usr/local/lib/python3.8/dist-packages/PIL/_util.py)

Solution

A solution to this problem (as per this stack overflow post) is to downgrade Pillow and freeze it at version 6.2.2. Using this version of Pillow allows users to successfully execute the entire notebook demonstrating the functionality of sahi.

Risks

Freezing the version of Pillow to an earlier state may result in unexpected behaviour, and lead to vulnerabilities in the future if more up to date versions of Pillow are not supported.

opened by hlydecker 5

Changed Mask class to store RLE encoded masks to save RAM.

Changed Mask class to store RLE encoded masks to save RAM usage on large images. (Work in progress)

When doing inference on large images (5300, 3600) pixel with around 100 instances, the sahi library uses around 14 GB of RAM. I introduced internal conversion to RLE encoded masks in the Mask class to save RAM memory and got it down to around 2GB RAM usage for our use-case (see plots).

Since pycocotools are a bit tricky to install on windows, the code are designed to utilize RLE encoding if pycocotools are available.

The inference time does not seem to increase by these changes, but only ran a single test so take the results with a grain of salt.

enhancement

opened by ChristofferEdlund 5

Fix bug that Unable to print for prediction time when verbose=2

Error executing below in current code

result = get_sliced_prediction(
    "demo_data/small-vehicles1.jpeg",
    detection_model,
    slice_height = 256,
    slice_width = 256,
    overlap_height_ratio = 0.2,
    overlap_width_ratio = 0.2,
    verbose=2
)

opened by youngjae-avikus 5

add detectron2 support

Sahi algoritmasına detectron2 kütüphanesini eklenmesi amaçlanmıştır.

Yapılan Değişiklikler:

Model.py: class Detectron2Model(DetectionModel) sınıfı eklendi. utils/detectron2.py: Test cfg kodu ve .pkl yolları eklendi. tests/test_detectron2.py: Modelin test edilmesi için test_detectron2.py dosyası oluşturuldu.
enhancement

opened by kadirnar 5
add support to instantiate a `DetectionModel` from layer
This PR adds support to instantiate a DetectionModel out of a YOLOv5 model trained on Layer. It includes:

A new from_layer() method in AutoDetectionModel to load models from Layer.

A new set_model() method in DetectionModel to create a DetectionModel out of an already loaded model.

Tests for from_layer() and set_model().

Example

You can pass a path of a YOLOv5 model trained on Layer to instantiate a DetectionModel. Here we fetch the yolo5vs pretrained model from this Layer project

from sahi import AutoDetectionModel detection_model = AutoDetectionModel.from_layer("layer/yolov5/models/yolov5s")

Tests

To run the tests related to from_layer() functionality you can:

pytest tests/test_layer.py

Screenshot
enhancement
opened by mecevit 3
add `get_coco_with_clipped_bboxes` utility to coco class
Limit overflowing coco bounding boxes to image dimensions

Usage:

coco = Coco.from_coco_dict_or_path( coco_path, clip_bboxes_to_img_dims=True)

or,

coco = coco.get_coco_with_clipped_bboxes()
opened by ssahinnkadir 3
add Tensorflow Hub detector support
Testlerden geçebilmesi için tensorflow ve tensorflow_hub kütüphanelerin kurulması gerekiyor.

package_testing.yml

ci.yml

- name: Install tensorflow and tensorflow_hub run: > pip install tensorflow pip install tensorflow_hub

Bu format uygun mudur? @fcakyon
enhancement workflows
opened by kadirnar 19

Releases(0.11.9)

0.11.9(Dec 24, 2022)
What's Changed

update yolov5 version in ci by @fcakyon in https://github.com/obss/sahi/pull/797

fix torch dependency in HuggingfaceDetectionModel by @fcakyon in https://github.com/obss/sahi/pull/798

update installation, support latest transformers version by @fcakyon in https://github.com/obss/sahi/pull/799

Full Changelog: https://github.com/obss/sahi/compare/0.11.8...0.11.9
Source code(tar.gz)
Source code(zip)
0.11.8(Dec 23, 2022)
What's Changed

update coco to yolov5 export by @fcakyon in https://github.com/obss/sahi/pull/794

Full Changelog: https://github.com/obss/sahi/compare/0.11.7...0.11.8
Source code(tar.gz)
Source code(zip)
0.11.7(Dec 20, 2022)
What's Changed

make default verbose false for min version check by @fcakyon in https://github.com/obss/sahi/pull/765

download mmdet yolox model from hfhub in tests by @fcakyon in https://github.com/obss/sahi/pull/766

update torchvision demo by @fcakyon in https://github.com/obss/sahi/pull/776

The coco.md file has been updated. by @kadirnar in https://github.com/obss/sahi/pull/777

Added indent parameter to save_json parameter. by @kadirnar in https://github.com/obss/sahi/pull/783

fix slicing tests by @fcakyon in https://github.com/obss/sahi/pull/787

fix json.decoder.JSONDecodeError in coco to yolov5 conversion by @kadirnar in https://github.com/obss/sahi/pull/786

Full Changelog: https://github.com/obss/sahi/compare/0.11.6...0.11.7
Source code(tar.gz)
Source code(zip)
0.11.6(Dec 1, 2022)
What's Changed

fix detectron2 device by @fcakyon in https://github.com/obss/sahi/pull/763

update dependency versions in ci by @fcakyon in https://github.com/obss/sahi/pull/760

remove python3.6 in ci by @fcakyon in https://github.com/obss/sahi/pull/762

Full Changelog: https://github.com/obss/sahi/compare/0.11.5...0.11.6
Source code(tar.gz)
Source code(zip)
0.11.5(Nov 27, 2022)
What's Changed

fixes a bug that prevents cuda device selection by @fcakyon in https://github.com/obss/sahi/pull/756

Full Changelog: https://github.com/obss/sahi/compare/0.11.4...0.11.5
Source code(tar.gz)
Source code(zip)
0.11.4(Nov 15, 2022)
What's Changed

improve bbox data structure by @fcakyon in https://github.com/obss/sahi/pull/730

make some classes importable from highest level by @fcakyon in https://github.com/obss/sahi/pull/731

add min dependency version check by @fcakyon in https://github.com/obss/sahi/pull/734

handle numpy bbox in object annotation by @fcakyon in https://github.com/obss/sahi/pull/735

add 2 slicing utils by @fcakyon in https://github.com/obss/sahi/pull/736

fix predict script by @fcakyon in https://github.com/obss/sahi/pull/737

update torch torchvision versions in ci by @fcakyon in https://github.com/obss/sahi/pull/729

update ci workflow name by @fcakyon in https://github.com/obss/sahi/pull/732

Full Changelog: https://github.com/obss/sahi/compare/0.11.3...0.11.4
Source code(tar.gz)
Source code(zip)
0.11.3(Nov 14, 2022)
What's Changed

update model dependency and device management by @fcakyon in https://github.com/obss/sahi/pull/725

implement indexing support for slice image result by @fcakyon in https://github.com/obss/sahi/pull/726

update version by @fcakyon in https://github.com/obss/sahi/pull/727

Full Changelog: https://github.com/obss/sahi/compare/0.11.2...0.11.3
Source code(tar.gz)
Source code(zip)
0.11.2(Nov 14, 2022)
What's Changed

support uppercase letter image/video extensions by @fcakyon in https://github.com/obss/sahi/pull/713

add cited paper list and competition winners by @fcakyon in https://github.com/obss/sahi/pull/705

fix a typo in readme by @fcakyon in https://github.com/obss/sahi/pull/706

update version by @fcakyon in https://github.com/obss/sahi/pull/707

make ci trigger more efficient by @fcakyon in https://github.com/obss/sahi/pull/709

update mmcv mmdet in ci by @fcakyon in https://github.com/obss/sahi/pull/714

update contributing and contributors sections in readme by @fcakyon in https://github.com/obss/sahi/pull/715

update predict docs by @fcakyon in https://github.com/obss/sahi/pull/723

Full Changelog: https://github.com/obss/sahi/compare/0.11.1...0.11.2
Source code(tar.gz)
Source code(zip)
0.11.1(Nov 1, 2022)
What's Changed

fix coco segm eval by @fcakyon in https://github.com/obss/sahi/pull/701

add float bbox and segm point support, remove mot utils by @fcakyon in https://github.com/obss/sahi/pull/702

Full Changelog: https://github.com/obss/sahi/compare/0.11.0...0.11.1
Source code(tar.gz)
Source code(zip)
0.11.0(Oct 29, 2022)
What's Changed

refactor models structure by @fcakyon in https://github.com/obss/sahi/pull/694

Full Changelog: https://github.com/obss/sahi/compare/0.10.8...0.11.0
Source code(tar.gz)
Source code(zip)
0.10.8(Oct 26, 2022)
What's Changed

remove layer support by @fcakyon in https://github.com/obss/sahi/pull/692

Fixed a bug in greedynmm func by @youngjae-avikus in https://github.com/obss/sahi/pull/691

flake8 dependency fix for python3.7. by @devrimcavusoglu in https://github.com/obss/sahi/pull/676

fix links in readme by @fcakyon in https://github.com/obss/sahi/pull/689

Full Changelog: https://github.com/obss/sahi/compare/0.10.7...0.10.8
Source code(tar.gz)
Source code(zip)
0.10.7(Sep 27, 2022)
What's Changed

update pybboxes version by @fcakyon in https://github.com/obss/sahi/pull/598

add support for yolov5==6.1.9 by @fcakyon in https://github.com/obss/sahi/pull/601

refactor demo notebooks by utilizing newly introduced AutoDetectionMo… by @ishworii in https://github.com/obss/sahi/pull/516

add norfair>=2.0.0 support by @fcakyon in https://github.com/obss/sahi/pull/595

Changed Mask class to store RLE encoded masks to save RAM. by @ChristofferEdlund in https://github.com/obss/sahi/pull/599

update workflow versions by @fcakyon in https://github.com/obss/sahi/pull/603

New Contributors

@ishworii made their first contribution in https://github.com/obss/sahi/pull/516

@ChristofferEdlund made their first contribution in https://github.com/obss/sahi/pull/599

Full Changelog: https://github.com/obss/sahi/compare/0.10.6...0.10.7
Source code(tar.gz)
Source code(zip)
0.10.6(Sep 24, 2022)
What's Changed

support yolov5>=6.1.9 by @fcakyon in https://github.com/obss/sahi/pull/592

update installation in readme by @fcakyon in https://github.com/obss/sahi/pull/584

Full Changelog: https://github.com/obss/sahi/compare/0.10.5...0.10.6
Source code(tar.gz)
Source code(zip)
0.10.5(Sep 4, 2022)
What's Changed

bugfix

fix auto slice warning by @fcakyon in https://github.com/obss/sahi/pull/574

fix a slice_image error by @fcakyon in https://github.com/obss/sahi/pull/575

other

fix layer tests by @fcakyon in https://github.com/obss/sahi/pull/577

update ci package versions by @fcakyon in https://github.com/obss/sahi/pull/576

Full Changelog: https://github.com/obss/sahi/compare/0.10.4...0.10.5
Source code(tar.gz)
Source code(zip)
0.10.4(Aug 12, 2022)
What's Changed

hotfix for occasional segmentation fault by @fcakyon in https://github.com/obss/sahi/pull/546

refactor requirement checking by @fcakyon in https://github.com/obss/sahi/pull/549

Code formatting and checks are moved to a single module. by @devrimcavusoglu in https://github.com/obss/sahi/pull/548

fix a typo in type hinting by @ymerkli in https://github.com/obss/sahi/pull/553

fix an incorrect url in comments by @aynursusuz in https://github.com/obss/sahi/pull/554

update version by @fcakyon in https://github.com/obss/sahi/pull/555

fix linting script by @devrimcavusoglu in https://github.com/obss/sahi/pull/558

fix an incorrect url in comments by @aynursusuz in https://github.com/obss/sahi/pull/559

New Contributors

@ymerkli made their first contribution in https://github.com/obss/sahi/pull/553

@aynursusuz made their first contribution in https://github.com/obss/sahi/pull/554

Full Changelog: https://github.com/obss/sahi/compare/0.10.3...0.10.4
Source code(tar.gz)
Source code(zip)
0.10.3(Aug 2, 2022)
What's Changed

fix coco2fiftyone script by @fcakyon in https://github.com/obss/sahi/pull/540

fix segmentation fault in same cases by @fcakyon in https://github.com/obss/sahi/pull/541

Full Changelog: https://github.com/obss/sahi/compare/0.10.2...0.10.3
Source code(tar.gz)
Source code(zip)
0.10.2(Jul 28, 2022)
What's Changed

add automatic slice size calculation by @mcvarer in https://github.com/obss/sahi/pull/512

Fix bug that Unable to print for prediction time when verbose=2 by @youngjae-avikus in https://github.com/obss/sahi/pull/521

pybboxes allow oob, strict=False fix. by @devrimcavusoglu in https://github.com/obss/sahi/pull/528

update predict verbose by @fcakyon in https://github.com/obss/sahi/pull/514

update pybboxes version by @fcakyon in https://github.com/obss/sahi/pull/531

New Contributors

@youngjae-avikus made their first contribution in https://github.com/obss/sahi/pull/521

Full Changelog: https://github.com/obss/sahi/compare/0.10.1...0.10.2
Source code(tar.gz)
Source code(zip)
0.10.1(Jun 25, 2022)
What's Changed

add python 3.10 support by @fcakyon in https://github.com/obss/sahi/pull/503

add file_name to export_visuals by @mcvarer in https://github.com/obss/sahi/pull/507

refactor automodel to lazyload models by @fcakyon in https://github.com/obss/sahi/pull/509

update automodel loading method to from_pretrained by @fcakyon in https://github.com/obss/sahi/pull/510

New Contributors

@mcvarer made their first contribution in https://github.com/obss/sahi/pull/507

Full Changelog: https://github.com/obss/sahi/compare/0.10.0...0.10.1
Source code(tar.gz)
Source code(zip)
0.10.0(Jun 21, 2022)
New Features

- Layer.ai integration

from sahi import AutoDetectionModel detection_model = AutoDetectionModel.from_layer("layer/yolov5/models/yolov5s") result = get_sliced_prediction( "image.jpeg", detection_model, slice_height = 512, slice_width = 512, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2 )

- HuggingfFace Transformers object detectors

from sahi.model import HuggingfaceDetectionModel detection_model = HuggingfaceDetectionModel( model_path="facebook/detr-resnet-50", image_size=640, confidence_threshold=0.5 ) result = get_sliced_prediction( "image.jpeg", detection_model, slice_height = 512, slice_width = 512, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2 )

- TorchVision object detectors

import torchvision from sahi.model import TorchVisionDetectionModel model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) detection_model = TorchVisionDetectionModel( model=model, image_size=640, confidence_threshold=0.5 ) result = get_sliced_prediction( "image.jpeg", detection_model, slice_height = 512, slice_width = 512, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2 )

- Support for exporting predictions in COCO format

from sahi.utils.coco import Coco, CocoImage, CocoAnnotation, CocoPrediction from sahi.utils.file import save_json from pycocotools.cocoeval import COCOeval from pycocotools.coco import COCO coco_obj = Coco() # add n images to coco_obj for _ in range(n): image = CocoImage(**kwargs) # add n annotations to the image for _ in ange(n): image.add_annotation(CocoAnnotation(**kwargs)) # add n predictions to the image for _ in range(n) image.add_prediction(CocoPrediction(**kwargs)) # add image to coco object coco_obj.add_image(image) # export ground truth annotations coco_gt = coco_obj.json save_json(coco_gt , "ground_truth.json") # export predictions coco_predictions = coco_obj.prediction_array save_json(coco_predictions, "predictions.json") coco_ground_truth = COCO(annotation_file="coco_dataset.json") coco_predictions = coco_ground_truth.loadRes("coco_predictions.json") coco_evaluator = COCOeval(coco_ground_truth, coco_predictions, "bbox") coco_evaluator.evaluate() coco_evaluator.accumulate() coco_evaluator.summarize()

What's Changed

refactor torch utils by @fcakyon in https://github.com/obss/sahi/pull/468

add automodel structure for unified model loading by @fcakyon in https://github.com/obss/sahi/pull/469

add support to instantiate a DetectionModel from layer by @mecevit in https://github.com/obss/sahi/pull/462

refactor automodel by @fcakyon in https://github.com/obss/sahi/pull/470

update layer versions in workflows by @fcakyon in https://github.com/obss/sahi/pull/471

update version to 0.10.0 by @fcakyon in https://github.com/obss/sahi/pull/474

Added support for exporting predictions in COCO format by @PushpakBhoge in https://github.com/obss/sahi/pull/465

update contributors in readme by @fcakyon in https://github.com/obss/sahi/pull/477

update device priority for Detectron2DetectionModel by @fcakyon in https://github.com/obss/sahi/pull/479

fix pickle export for video by @fcakyon in https://github.com/obss/sahi/pull/481

update continuous integration by @fcakyon in https://github.com/obss/sahi/pull/483

refactor import and torch utils by @fcakyon in https://github.com/obss/sahi/pull/484

make detectionmodel classes more explicit in automodel by @fcakyon in https://github.com/obss/sahi/pull/485

utilize check_requirements in several modules by @fcakyon in https://github.com/obss/sahi/pull/487

update package versions in workflows by @fcakyon in https://github.com/obss/sahi/pull/488

add support for huggingface transformers object detectors by @devrimcavusoglu in https://github.com/obss/sahi/pull/475

add torchvision detector support by @fcakyon in https://github.com/obss/sahi/pull/486

remove legacy image_size parameter by @kadirnar in https://github.com/obss/sahi/pull/494

AutoDetectionModel can be imported from sahi by @fcakyon in https://github.com/obss/sahi/pull/498

add python3.6 support by @fcakyon in https://github.com/obss/sahi/pull/489

refactor exception handling by @kadirnar in https://github.com/obss/sahi/pull/499

improve requirements and import handling by @fcakyon in https://github.com/obss/sahi/pull/502

New Contributors

@mecevit made their first contribution in https://github.com/obss/sahi/pull/462

@PushpakBhoge made their first contribution in https://github.com/obss/sahi/pull/465

Full Changelog: https://github.com/obss/sahi/compare/0.9.4...0.10.0
Source code(tar.gz)
Source code(zip)
0.9.4(May 28, 2022)
What's Changed

reduce ram usage by adding buffer based merging by @weypro in https://github.com/obss/sahi/pull/445

improve json loading by @qingfengtommy in https://github.com/obss/sahi/pull/453

New Contributors

@weypro made their first contribution in https://github.com/obss/sahi/pull/445

@qingfengtommy made their first contribution in https://github.com/obss/sahi/pull/453

Full Changelog: https://github.com/obss/sahi/compare/0.9.3...0.9.4
Source code(tar.gz)
Source code(zip)
0.9.3(May 8, 2022)
What's Changed

add support for video prediction by @madenburak in https://github.com/obss/sahi/pull/442

export prediction visuals by default @fcakyon in ##

add detection_model input to predict function by @ssahinnkadir in https://github.com/obss/sahi/pull/443

refactor postprocess call by @fcakyon in https://github.com/obss/sahi/pull/458

update yolov5, mmdet, norfair versions in ci by @fcakyon in https://github.com/obss/sahi/pull/459

New Contributors

@madenburak made their first contribution in https://github.com/obss/sahi/pull/442

Full Changelog: https://github.com/obss/sahi/compare/0.9.2...0.9.3
Source code(tar.gz)
Source code(zip)
0.9.2(Apr 9, 2022)
What's Changed

fix fiftyone utils by @fcakyon in https://github.com/obss/sahi/pull/423

update paper doi badge by @fcakyon in https://github.com/obss/sahi/pull/424

update env setup in readme by @kadirnar in https://github.com/obss/sahi/pull/408

update contributing section in readme by @fcakyon in https://github.com/obss/sahi/pull/434

update cli docs by @fcakyon in https://github.com/obss/sahi/pull/437

update package versions in ci by @fcakyon in https://github.com/obss/sahi/pull/439

update version by @fcakyon in https://github.com/obss/sahi/pull/440

Full Changelog: https://github.com/obss/sahi/compare/0.9.1...0.9.2
Source code(tar.gz)
Source code(zip)
test.mp4(686.92 KB)
0.9.1(Mar 17, 2022)
What's Changed

add the list of competitions sahi made us win by @fcakyon in https://github.com/obss/sahi/pull/385

add citation to paper by @fcakyon in https://github.com/obss/sahi/pull/387

add arxiv url for the SAHI paper by @fcakyon in https://github.com/obss/sahi/pull/388

handle invalid mask prediction by @fcakyon in https://github.com/obss/sahi/pull/390

improve code quality by @fcakyon in https://github.com/obss/sahi/pull/398

improve nms postprocess by @tureckova in https://github.com/obss/sahi/pull/405

Full Changelog: https://github.com/obss/sahi/compare/0.9.0...0.9.1
Source code(tar.gz)
Source code(zip)
0.9.0(Feb 12, 2022)
What's Changed

add detectron2 support by @kadirnar in https://github.com/obss/sahi/pull/322

update detectron notebook by @fcakyon in https://github.com/obss/sahi/pull/355

refactor readme by @fcakyon in https://github.com/obss/sahi/pull/316

refactor slice_coco script and cli by @fcakyon in https://github.com/obss/sahi/pull/359

update analysis gif in readme by @fcakyon in https://github.com/obss/sahi/pull/362

update slice_coco export naming by @fcakyon in https://github.com/obss/sahi/pull/361

fix broken links in readme by @fcakyon in https://github.com/obss/sahi/pull/365

add kaggle notebook into readme by @rkinas in https://github.com/obss/sahi/pull/366

handle when class name contains invalid char by @fcakyon in https://github.com/obss/sahi/pull/369

handle out of image bbox predictions by @fcakyon in https://github.com/obss/sahi/pull/373

handle annotation dict without segmentation by @tureckova in https://github.com/obss/sahi/pull/374

fix unused coco util by @fcakyon @oulcan in https://github.com/obss/sahi/pull/375

fix coco util tests by @fcakyon in https://github.com/obss/sahi/pull/376

update torch, torchvision, mmdet, mmcv in tests by @fcakyon in https://github.com/obss/sahi/pull/379

handle nms-postprocess in edge cases by @fcakyon in https://github.com/obss/sahi/pull/370

New Contributors

@kadirnar made their first contribution in https://github.com/obss/sahi/pull/322

@tureckova made their first contribution in https://github.com/obss/sahi/pull/374

Full Changelog: https://github.com/obss/sahi/compare/0.8.22...0.9.0
Source code(tar.gz)
Source code(zip)
0.8.22(Jan 13, 2022)
What's Changed

fix LSNMS potprocess handle numpy array index in ObjectPredictionList by @fcakyon in https://github.com/obss/sahi/pull/350

fix unused coco merge util by @fcakyon in https://github.com/obss/sahi/pull/353

Full Changelog: https://github.com/obss/sahi/compare/0.8.20...0.8.22
Source code(tar.gz)
Source code(zip)
0.8.20(Jan 11, 2022)
What's Changed

fix typo in print by @fcakyon in https://github.com/obss/sahi/pull/339

Update error analysis by @fcakyon in https://github.com/obss/sahi/pull/340

fix warning messages in coco analysis by @fcakyon in https://github.com/obss/sahi/pull/341

add auto postprocess type to predict by @fcakyon in https://github.com/obss/sahi/pull/342

refactor fiftyone utils by @fcakyon in https://github.com/obss/sahi/pull/345

Full Changelog: https://github.com/obss/sahi/compare/0.8.19...0.8.20
Source code(tar.gz)
Source code(zip)
0.8.19(Jan 6, 2022)
What's Changed

fix visual export for windows by @fcakyon in https://github.com/obss/sahi/pull/335

refactor scripts by @fcakyon in https://github.com/obss/sahi/pull/333

Full Changelog: https://github.com/obss/sahi/compare/0.8.18...0.8.19
Source code(tar.gz)
Source code(zip)
sample_dataset.zip(1.98 MB)
0.8.18(Jan 2, 2022)
What's Changed

refactor postprocessing and coco eval for 100x speed up by @fcakyon in https://github.com/obss/sahi/pull/320

refactor image_size and model_confidence for faster inference by @fcakyon in https://github.com/obss/sahi/pull/329

remove deprecated coco util by @fcakyon in https://github.com/obss/sahi/pull/323

fix LSNMSPostprocess by @fcakyon in https://github.com/obss/sahi/pull/330

fix rmtree in tests by @fcakyon in https://github.com/obss/sahi/pull/326

Full Changelog: https://github.com/obss/sahi/compare/0.8.16...0.8.18
Source code(tar.gz)
Source code(zip)
0.8.16(Dec 26, 2021)
What's Changed

refactorize model classes, handle invalid polygons, minor improvements by @fcakyon in https://github.com/obss/sahi/pull/311

refactor tests and coco utils by @fcakyon in https://github.com/obss/sahi/pull/313

fix nms postprocess by @fcakyon in https://github.com/obss/sahi/pull/314

update predict verbose by @fcakyon in https://github.com/obss/sahi/pull/317

print exports dirs after process finishes by @fcakyon in https://github.com/obss/sahi/pull/318

Full Changelog: https://github.com/obss/sahi/compare/0.8.15...0.8.16
Source code(tar.gz)
Source code(zip)
0.8.15(Dec 15, 2021)
What's Changed

update default params to match coco eval and error analysis by @fcakyon in https://github.com/obss/sahi/pull/306

utilize max_detections in coco_error_analysis by @fcakyon in https://github.com/obss/sahi/pull/307

reformat coco_error_analysis with black by @fcakyon in https://github.com/obss/sahi/pull/308

Full Changelog: https://github.com/obss/sahi/compare/0.8.14...0.8.15
Source code(tar.gz)
Source code(zip)

Owner

Open Business Software Solutions

Open Source for Open Business

GitHub

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

157 Dec 11, 2022

performing moving objects segmentation using image processing techniques with opencv and numpy

Moving Objects Segmentation On this project I tried to perform moving objects segmentation using background subtraction technique. the introduced meth

15 Dec 12, 2022

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

145 Jan 1, 2023

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

3 Nov 30, 2021

Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX

ONNX msg_chn_wacv20 depth completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20 model in

19 Oct 22, 2022

Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in Tensorflow Lite.

TFLite-msg_chn_wacv20-depth-completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model

2 Oct 4, 2021

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi

78 Jan 7, 2023

Context Axial Reverse Attention Network for Small Medical Objects Segmentation

CaraNet: Context Axial Reverse Attention Network for Small Medical Objects Segmentation This repository contains the implementation of a novel attenti

401 Dec 23, 2022

Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Yolov5 running on TorchServe (GPU compatible) ! This is a dockerfile to run TorchServe for Yolo v5 object detection model. (TorchServe (PyTorch librar

82 Nov 29, 2022

Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

human-pose-estimation-3d-python-cpp RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used) 1. Run 1-1. RealSenseD435 (RGB) 480x640 + CPU

8 Oct 3, 2022

Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

3 Feb 8, 2022

An open source machine learning library for performing regression tasks using RVM technique.

Introduction neonrvm is an open source machine learning library for performing regression tasks using RVM technique. It is written in C programming la

33 May 31, 2022

docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

1.5k Jan 1, 2023

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

Related tags

Overview

SAHI: Slicing Aided Hyper Inference

Overview

Getting started

Blogpost

Installation

Usage

Adding new detection framework support

Contributers

Comments

Added support for exporting predictions in COCO format

changelog: -

Basic flow is like this

Why this is useful?

Example

Tests

Screenshot

Releases(0.11.9)

0.11.9(Dec 24, 2022)

What's Changed

0.11.8(Dec 23, 2022)

What's Changed

0.11.7(Dec 20, 2022)

What's Changed

0.11.6(Dec 1, 2022)

What's Changed

0.11.5(Nov 27, 2022)

What's Changed

0.11.4(Nov 15, 2022)

What's Changed

0.11.3(Nov 14, 2022)

What's Changed

0.11.2(Nov 14, 2022)

What's Changed

0.11.1(Nov 1, 2022)

What's Changed

0.11.0(Oct 29, 2022)

What's Changed

0.10.8(Oct 26, 2022)

What's Changed

0.10.7(Sep 27, 2022)

What's Changed

New Contributors

0.10.6(Sep 24, 2022)

What's Changed

0.10.5(Sep 4, 2022)

What's Changed

bugfix

other

0.10.4(Aug 12, 2022)

What's Changed

New Contributors

0.10.3(Aug 2, 2022)

What's Changed

0.10.2(Jul 28, 2022)

What's Changed

New Contributors

0.10.1(Jun 25, 2022)

What's Changed

New Contributors

0.10.0(Jun 21, 2022)

New Features

- Layer.ai integration

- HuggingfFace Transformers object detectors

- TorchVision object detectors

- Support for exporting predictions in COCO format

What's Changed

New Contributors

0.9.4(May 28, 2022)

What's Changed

New Contributors

0.9.3(May 8, 2022)

What's Changed

New Contributors

0.9.2(Apr 9, 2022)

What's Changed

0.9.1(Mar 17, 2022)

What's Changed