object counting from video

This is an open-source video-based object counting software for tallying pretty much anything (vehicles, people, animals — you name it).

Requirements

• Python 3 (tested with version 3.7)

Setup

• Clone this repo.
• Install the dependencies in requirements.txt pip install -r requirements.txt.
• Choose a detector and install its dependencies where necessary (if you're not sure what to pick, we recommend you start with yolo).

Detector	Description	Dependencies
yolo	Perform detection using models created with the YOLO (You Only Look Once) neural net. https://pjreddie.com/darknet/yolo/
tfoda	Perform detection using models created with the Tensorflow Object Detection API. https://github.com/tensorflow/models/tree/master/research/object_detection	CPU: pip install tensorflow-cpu GPU: pip install tensorflow-gpu
detectron2	Perform detection using models created with FAIR's Detectron2 framework. https://github.com/facebookresearch/detectron2	python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' (https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)
haarcascade	Perform detection using Haar feature-based cascade classifiers. https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html

Run

• Create a .env file (based on .env.example) in the project's root directory and edit as appropriate.
• Run python -m main.
• Run using Docker docker build -t adrian-krol/ivy ..

Debug

By default, runs in "debug mode" which provides you a window to monitor the object counting process. You can:

• press the p key to pause/play the counting process
• press the s key to capture a screenshot
• press the q key to quit the program
• click any point on the window to log the coordinates of the pixel in that position