CoronaMask-Detection

Mask Detection in Real-time with 99% Accuracy

Demo Video

Linkedin Post

https://www.linkedin.com/posts/kushal-dulani-28145a189_artificialintelligence-ai-covid19-activity-6659385898809073664-mEB7

After Lot's of request i'm making this as an open source , if i found this used in monitized by any type , all the legal action will done by me

I will publish blog on it that How to make these type of object detection from Scratch and also share labeled images

Please Give Star to My Repository For My Work and I will upload all industrial/Real-Life projects for you, Thank You

Subscribe My YouTube Channel - AI Developer Kushal and Follow me on Instagram @ai.developer_kushal , After 1000 Subscriber and Followers I'll make tutorial videos from scratch in English/Hindi for lot's of project and it'll be absolutely FREE, hurry up!

i can't upload files on github because of memory limit that's why i'm sharing my drive link

drive Link uploading soon

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

How to Use

if you are using anaconda then create new environment for easy of use

1)conda create --name mask

2)conda activate mask

then install requirments

Prerequisites

opencv, matplotlib, Cython, contextlib2, pillow, lxml, tensorflow, jupyter notebook

Installing

pip install opencv-python

pip install opencv-contrib-python

pip install Cython

pip install contextlib2

pip install pillow

pip install lxml

pip/conda install jupyter

pip/conda install matplotlib

pip/conda install tensorflow-gpu==1.15 <-- "Note i used 1.15 version for cuda enable gpu"

if you don't have Nvidia Graphics which compatiblity >5.0 or AMD Graphics

you can use cpu version

pip/conda install tensorflow==1.15 <-- "Note i used 1.15 version for cpu"

(step-1) Download tensorflow object detection api model from github "https://github.com/tensorflow/models"

(step-2) Goto models/research/object_detection and change protos folder with my protos folder then run in terminal " ./bin/protoc object_detection/protos/*.proto --python_out=. " <-- without inverted comma

(step-3) Goto models/research and run command "python setup.py" build then run "python setup.py install" <-- without inverted comma

(step-4) Goto models/research/slim change file name BUILD to BUILDD

(step-5) Goto models/research/slim and run command "python setup.py build" then run "python setup.py install" <-- without inverted comma

(step-6) Goto models/research/object_detection and paste inference_graph,training,run.py,train.record,test.record and finalmask.mp4

(step-7) open your terminal goto models/research/object_detection and run python run.py

(step-8) if you want to use your own video then change videofile in run.py at line no 89 and you want to use your ip/rtsp camera chnage http/rtsp link at line no 90 and comment line no 89

(optional)

if you want to export tflite graph and you can use this command

Goto models/research/object_detection and run

python export_tflite_ssd_graph.py --input_type image_tensor --pipeline_config_path training/ssd_mobilenet_v2_coco.config --trained_checkpoint_prefix training/model.ckpt-43979 --output_directory inference_graph2

now you can check in models/research/object_detection/inference_graph2 in this folder you can fild tflite_graph.pb and tflite_graph.pbtxt files , you can use in micro-computers like rasberrypie/nvidia jetson nano etc

Author

Kushal Dulani