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Vision Counter: Object Counting Library using Deep Learning

VisionCounter is a powerful Python library that provides a collection of deep learning models for accurately counting objects in input images. Whether you need to count people in a crowded street, cars in a parking lot, or any other objects in a scene, VisionCounter has got you covered.

Key Features

• State-of-the-Art Models: VisionCounter comes with pre-trained state-of-the-art deep learning models, trained on large-scale datasets, to deliver high accuracy in counting various objects.

• Easy-to-Use API: We believe in simplicity and ease of use. VisionCounter provides a straightforward API, allowing you to seamlessly integrate object counting capabilities into your Python projects.

• Customizable: While the default pre-trained models work well in many scenarios, we understand that every project is unique. VisionCounter allows you to fine-tune models or even train your custom models on your specific datasets.

• Fast and Efficient: Our models are designed for speed and efficiency, making them suitable for real-time applications or processing large batches of images.

• Wide Range of Applications: VisionCounter is versatile and can be used in various domains, including surveillance, traffic monitoring, inventory management, and more.

Quick Start

Install the library using pip

pip install vision_counter

Usage

from sys import argv
from vision_counter import CounTR

counter = CounTR()
bboxes = [[[156, 112], [173, 129]]]  # x1, y1, x2, y2

image_path = argv[1]
object_count = counter.count(image_path, bboxes)
print("Estimated object count:", object_count)

Contributing

We welcome contributions from the community! Whether it's bug fixes, feature enhancements, or new models, feel free to submit pull requests.

Citation

@article{liu2022countr,
  author = {Chang, Liu and Yujie, Zhong and Andrew, Zisserman and Weidi, Xie},
  title = {CounTR: Transformer-based Generalised Visual Counting},
  journal = {arXiv:2208.13721},
  year = {2022}
}