Shop-floor-Monitoring

Shop-floor monitoring system using YOLOv4-DeepSort. YOLOv4-DeepSort is a object tracking algorithm that uses deep convolutional neural networks. System calculates factory indicators using ID and coordinates of the target object.

Resulting Video

Getting Started

Cloning Yolov4-DeepSort repository and Shop-floor monitoring repository in your computer.
Move 'Object_tracker_SF.py' file into YoloV4-deepsort repository file.

Install the proper dependencies in repository path via Anaconda.
I recommend using GPU for real time.

Conda

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov4-gpu

Pip

# TensorFlow GPU
pip install -r requirements-gpu.txt

Downloading Pre-trained Weights(Custom object)

yolov4.weights
Copy and paste yolov4.weights from your downloads folder into the 'data' folder of Yolov4 repository.

Running the Tracker with YOLOv4-DeepSort

First, convert .weights into TensorFlow model.
Then run the object_tracker_SF.py using camera.

# Convert darknet weights to tensorflow model
python save_model.py --model yolov4 

# Run yolov4 deep sort object tracker on camera (set video flag to 1)
python object_tracker_SF.py --video 1 --model yolov4

--video flag number can be differ.
It will be helpful if you refer to Yolov4-Deepsort repository's README.

Difference from the Original Code

You can initialize variables and change values in ShopFloor.py modules.

I summarize the added parts of the object_tracker_SF.py
line 105~120 - Initialize indicator variables
line 122~123 - Connect GUI client
line 197~218 - Calculate indicators for each class.
line 239~242 - Draw start and end line
line 251~256 - Set boundingbox color
line 262~263 - Calculate the center point
line 265~269 - Send indicators to DB(Updating)
line 271~281 - Track object through the start line
line 284~301 - Check object through the end line and change variables
line 303~315 - Calculate cycletime
line 317~331 - Check Error point
line 333~335 - Function that draws indicators
line 341 - Send indicators to GUI