# CrowdCounting Made Easy 🤓 with CNN-based Cascaded Multi-task
[](https://github.com/psf/black)
This is a packaging implementation of the paper [CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting](https://arxiv.org/pdf/1707.09605.pdf) for single image crowd counting which is accepted at [AVSS 2017](http://www.avss2017.org/)
The package is compatible with all operating systems, provides a staggering fast and accurate prediction. It achieves a min of 20 fps on a 6 core intel cpu.
# Installation
```bash
pip install ezcrowdcount
```
# Usage
To run inference on your favorite image/video simply run the following on your terminal/console:
```bash
crowdcount --mode video --path /path/to/video
```
```python
"""
mode (str): Whether to run prediction on video or image
path (str | int): Path to video or image. It can be an index to a camera feed, or a URL also. (Default = 0).
"""
```
The inference will run on your GPU (if available), and will be viewed right in front of you 👀
Also, the number of people during each frame will be printed on your console/terminal.
# Demo
**Input Image:**
**Result Image:**
#### Number of people: 165.8 🎉
Raw data
{
"_id": null,
"home_page": "",
"name": "ezcrowdcount",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "crowd counting,computer vision,object recognition,human counting,pytorch,thread",
"author": "",
"author_email": "Ahmed Heakl <ahmed.heakl@ejust.edu.eg>",
"download_url": "https://files.pythonhosted.org/packages/16/3a/5ddeb232b2cb18eeb16424774144b5b5244398c67f24152ffdd9d863dd86/ezcrowdcount-1.0.0.tar.gz",
"platform": null,
"description": "# CrowdCounting Made Easy \ud83e\udd13 with CNN-based Cascaded Multi-task\n[](https://github.com/psf/black)\nThis is a packaging implementation of the paper [CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting](https://arxiv.org/pdf/1707.09605.pdf) for single image crowd counting which is accepted at [AVSS 2017](http://www.avss2017.org/)\n\nThe package is compatible with all operating systems, provides a staggering fast and accurate prediction. It achieves a min of 20 fps on a 6 core intel cpu.\n\n# Installation \n```bash\npip install ezcrowdcount\n```\n\n# Usage\n\nTo run inference on your favorite image/video simply run the following on your terminal/console:\n\n```bash\ncrowdcount --mode video --path /path/to/video\n```\n\n```python\n\"\"\"\nmode (str): Whether to run prediction on video or image\npath (str | int): Path to video or image. It can be an index to a camera feed, or a URL also. (Default = 0).\n\"\"\"\n```\n\nThe inference will run on your GPU (if available), and will be viewed right in front of you \ud83d\udc40\nAlso, the number of people during each frame will be printed on your console/terminal.\n\n# Demo\n**Input Image:**\n\n\n\n**Result Image:**\n\n\n\n#### Number of people: 165.8 \ud83c\udf89\n",
"bugtrack_url": null,
"license": "",
"summary": "Easy to integrate Crowd Counting Library",
"version": "1.0.0",
"project_urls": {
"repository": "https://github.com/ahmedheakl/crowdcount"
},
"split_keywords": [
"crowd counting",
"computer vision",
"object recognition",
"human counting",
"pytorch",
"thread"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "f8ab27f1f83183cc2f7a3c2762860a56933043a07c3ca93351b196c53026a254",
"md5": "db749e0d9f1f3f4b1d4705b5b9bb48b5",
"sha256": "54b05092dee4e33b37f7a2a34ba76bedc2ed0d53e37ff25faefcdf61aef137bf"
},
"downloads": -1,
"filename": "ezcrowdcount-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "db749e0d9f1f3f4b1d4705b5b9bb48b5",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 8541,
"upload_time": "2023-09-24T03:00:49",
"upload_time_iso_8601": "2023-09-24T03:00:49.879252Z",
"url": "https://files.pythonhosted.org/packages/f8/ab/27f1f83183cc2f7a3c2762860a56933043a07c3ca93351b196c53026a254/ezcrowdcount-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "163a5ddeb232b2cb18eeb16424774144b5b5244398c67f24152ffdd9d863dd86",
"md5": "ddef1a4f825f90a814213bd9b85a3a12",
"sha256": "5f88ff7d7d8690be9f0ca5995407370b0e7ddd2c439c57a119de1ca71c14b1d6"
},
"downloads": -1,
"filename": "ezcrowdcount-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "ddef1a4f825f90a814213bd9b85a3a12",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 8112,
"upload_time": "2023-09-24T03:00:51",
"upload_time_iso_8601": "2023-09-24T03:00:51.405993Z",
"url": "https://files.pythonhosted.org/packages/16/3a/5ddeb232b2cb18eeb16424774144b5b5244398c67f24152ffdd9d863dd86/ezcrowdcount-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-24 03:00:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ahmedheakl",
"github_project": "crowdcount",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "numpy",
"specs": []
},
{
"name": "opencv_python",
"specs": []
},
{
"name": "torch",
"specs": []
},
{
"name": "h5py",
"specs": []
},
{
"name": "googledrivedownloader",
"specs": []
},
{
"name": "requests",
"specs": []
}
],
"lcname": "ezcrowdcount"
}
JSON
