Narknet

Object Detection for Computer Vision using YOLOv3.

This repository is a computer vision library , using YOLOv3 machine learning model. The program is implemented in python3 and will be converted to cython in due time.

Dependencies:

1. Python --3.7.6
2. Opencv --4.2.0
3. Axel --2.17.5
4. Conda --4.8.3
5. Numpy --1.18.1
6. Requests --2.23.0

Setup:

1. conda install -c menpo opencv (For opencv)
2. conda install pandas (For Pandas)

Installation

git clone https://github.com/nakul-shahdadpuri/narknet.git
cd narknet/
cd Weights/
chmod u+x GetWeights.sh
./GetWeights.sh

Running narknet

Image Classification

import sys
import cv2

from narknet.classify import image

Path = 'Path to an image'

#loads model
net,classes,output_layers,layer_names = image.load_model()
#predicts output
output,data = image.predict(Path,net,classes,output_layers,layer_names)

print(data)
cv2.imshow('Image', output)
cv2.waitKey(0)

Resources

1. Non Max Suppression 'https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c'
2. YOLOv3 model 'https://pjreddie.com/darknet/yolo/'
3. cv2.BlobFromImage 'https://www.pyimagesearch.com/2017/11/06/deep-learning-opencvs-blobfromimage-works/'
4. OpenCv Documentation 'https://docs.opencv.org/2.4/'
5. DeepSort Repo 'https://github.com/nwojke/deep_sort'
6. SORT Paper 'https://arxiv.org/abs/1602.00763'
7. Deep Sort 'https://medium.com/analytics-vidhya/yolo-v3-real-time-object-tracking-with-deep-sort-4cb1294c127f'
8. Open Cv wiki: https://en.wikipedia.org/wiki/OpenCV

Licensing

MIT License