https://github.com/nickorzha/video_objcount

video-based object counting software for tallying pretty much anything
https://github.com/nickorzha/video_objcount

computer-vision object-counter object-counting video-analysis video-processing

Last synced: 10 months ago
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video-based object counting software for tallying pretty much anything

• Host: GitHub
• URL: https://github.com/nickorzha/video_objcount
• Owner: nickorzha
• License: mit
• Created: 2023-05-19T08:41:50.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-05-19T08:58:08.000Z (about 3 years ago)
• Last Synced: 2025-06-06T12:45:38.168Z (about 1 year ago)
• Topics: computer-vision, object-counter, object-counting, video-analysis, video-processing
• Language: Python
• Homepage:
• Size: 47.6 MB
• Stars: 19
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 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).

![](object_counting.jpg)

## 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