asone 0.1.2.dev13

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AS-One : A Modular Libary for YOLO Object Detection and Object Tracking BETA

Table of Contents

1. Introduction
2. Prerequisites
3. Clone the Repo
4. Installation
5. Running AS-One
6. Usage
7. Benchmarks

1. Introduction

AS-One is a python wrapper for multiple detection and tracking algorithms all at one place. Different trackers such as ByteTrack, DeepSort or NorFair can be integrated with different versions of YOLO with minimum lines of code. This python wrapper provides YOLO models in both ONNX and PyTorch versions. We plan to offer support for future versions of YOLO when they get released.

This is One Library for most of your computer vision needs.

If you would like to dive deeper into YOLO Object Detection and Tracking, then check out our courses and projects

Watch the step-by-step tutorial

2. Prerequisites

• Make sure to install GPU drivers in your system if you want to use GPU . Follow driver installation for further instructions.
• Make sure you have MS Build tools installed in system if using windows.
• Download git for windows if not installed.

3. Clone the Repo

Navigate to an empty folder of your choice.

git clone https://github.com/augmentedstartups/AS-One.git

Change Directory to AS-One

cd AS-One

4. Installation

For Linux

python3 -m venv .env
source .env/bin/activate

pip install numpy Cython
pip install cython-bbox

pip install asone


# for CPU
pip install torch torchvision

# for GPU
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113

For Windows 10/11

python -m venv .env
.env\Scripts\activate
pip install numpy Cython
pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox

pip install asone

# for CPU
pip install torch torchvision

# for GPU
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
or
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio===0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

Run in Google Colab

5. Running AS-One

Run main.py to test tracker on data/sample_videos/test.mp4 video

import asone
from asone import utils
from asone.detectors import Detector
import cv2

img = cv2.imread('data/sample_imgs/test2.jpg')
detector = Detector(asone.YOLOV7_E6_ONNX, use_cuda=True) # Set use_cuda to False for cpu

filter_classes = ['person'] # Set to None to detect all classes

dets, img_info = detector.detect(img, , filter_classes=filter_classes)

bbox_xyxy = dets[:, :4]
scores = dets[:, 4]
class_ids = dets[:, 5]

img = utils.draw_boxes(img, bbox_xyxy, class_ids=class_ids)
cv2.imwrite('result.png', img)

Use Custom Trained Weights

Use your custom weights of a detector model trained on custom data by simply providing path of the weights file.

import asone
from asone import utils
from asone.detectors import Detector
import cv2

img = cv2.imread('data/sample_imgs/test2.jpg')
detector = Detector(asone.YOLOV7_PYTORCH, weights="data/custom_weights/yolov7_custom.pt", use_cuda=True) # Set use_cuda to False for cpu

filter_classes = ['person'] # Set to None to detect all classes

dets, img_info = detector.detect(img, , filter_classes=filter_classes)

bbox_xyxy = dets[:, :4]
scores = dets[:, 4]
class_ids = dets[:, 5]

img = utils.draw_boxes(img, bbox_xyxy, class_ids=class_ids, class_names=['License Plate']) # class_names are names of classes in your dataset
cv2.imwrite('result.png', img)

Changing Detector Models

Change detector by simply changing detector flag. The flags are provided in benchmark tables.

# Change detector
detector = Detector(asone.YOLOX_S_PYTORCH, use_cuda=True)

Run the asone/demo_detector.py to test detector.

# run on gpu
python -m asone.demo_detector data/sample_imgs/test2.jpg

# run on cpu
python -m asone.demo_detector data/sample_imgs/test2.jpg --cpu

Object Tracking

Video

Use tracker on sample video using gpu.

import asone
from asone import ASOne

# Instantiate Asone object
dt_obj = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOX_DARKNET_PYTORCH, use_cuda=True)

filter_classes = ['person'] # set to None to track all classes

# Get tracking function
track_fn = dt_obj.track_video('data/sample_videos/test.mp4', output_dir='data/results', save_result=True, display=True, filter_classes=filter_classes)

# Loop over track_fn to retrieve outputs of each frame 
for bbox_details, frame_details in track_fn:
    bbox_xyxy, ids, scores, class_ids = bbox_details
    frame, frame_num, fps = frame_details
    # Do anything with bboxes here

# To track using webcam
# Get tracking function
track_fn = dt_obj.track_webcam(cam_id=0, output_dir='data/results', save_result=True, display=True, filter_classes=filter_classes)

# Loop over track_fn to retrieve outputs of each frame 
for bbox_details, frame_details in track_fn:
    bbox_xyxy, ids, scores, class_ids = bbox_details
    frame, frame_num, fps = frame_details
    # Do anything with bboxes here

Use Custom Trained Weights for Detector

Use your custom weights of a detector model trained on custom data by simply providing path of the weights file.

import asone
from asone import ASOne

# Instantiate Asone object
dt_obj = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOX_DARKNET_PYTORCH, weights='data/custom_weights/yolov7_custom.pt', use_cuda=True)

filter_classes = ['person'] # set to None to track all classes

# Get tracking function
track_fn = dt_obj.track_video('data/sample_videos/test.mp4', output_dir='data/results', save_result=True, display=True, filter_classes=filter_classes, class_names=['License Plate']) #class_names are class names in your custom data

# Loop over track_fn to retrieve outputs of each frame 
for bbox_details, frame_details in track_fn:
    bbox_xyxy, ids, scores, class_ids = bbox_details
    frame, frame_num, fps = frame_details
    # Do anything with bboxes here

Changing Detector and Tracking Models

Change Tracker by simply changing the tracker flag.

The flags are provided in benchmark tables.

dt_obj = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOX_DARKNET_PYTORCH, use_cuda=True)
// Change tracker
dt_obj = ASOne(tracker=asone.DEEPSORT, detector=asone.YOLOX_DARKNET_PYTORCH, use_cuda=True)

dt_obj = ASOne(tracker=asone.DEEPSORT, detector=asone.YOLOX_S_PYTORCH, use_cuda=True)

To setup ASOne using Docker follow instructions given in docker setup

ToDo

• First Release
• Import trained models
• Simplify code even further
• Add support for other Trackers and Detectors
• M1/2 Apple Silicon Compatibility

Offered By:	Maintained By: