# <div align="center">Peoplce Counting Project</div>
## Overview
The People Counting Project is designed to detect and count the number of people entering and exiting a specified area using computer vision techniques. This project can be used in various settings such as retail stores, offices, and events to monitor foot traffic and gather valuable data.
## Features
- **People Detection**: Detects people in a video feed or image using a deep learning model.
- **Bidirectional Counting**: Tracks and counts people entering and exiting a specific area.
- **Estimate person appear time**: Tracks and estimate time per person appear in video
## Installation
### Prerequisites
- Python 3.x (x >= 10)
- pip or pip3
- A GPU (optional but recommended for faster processing)
### Steps
1. **Clone the repository:**
```bash
git clone gitlab_url
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Download the pre-trained model (if applicable):**
The model can download from model section
## Usage
```python
from smartoscreid import PeopleCounting
# Your model path
model_path = 'smartoscreid/model/yolov8m.pt'
pc = PeopleCounting(model_path)
# List video from multiple camera
videos = ["input/Single1.mp4"]
pc.run(videos)
```
Result will generate in output folder
## <div align="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
<details open><summary>Detection (COCO)</summary>
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
</details>
## <div align="center">Torchreid</div>
Torchreid is a library for deep-learning person re-identification, written in [PyTorch](https://pytorch.org/) and developed for our ICCV'19 project, [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1905.00953).
It features:
- Multi-GPU training
- Support both image- and video-reid
- End-to-end training and evaluation
- Incredibly easy preparation of reid datasets
- Multi-dataset training
- Cross-dataset evaluation
- Standard protocol used by most research papers
- Highly extensible (easy to add models, datasets, training methods, etc.)
- Implementations of state-of-the-art deep reid models
- Access to pretrained reid models
- Advanced training techniques
- Visualization tools (tensorboard, ranks, etc.)
Code: https://github.com/KaiyangZhou/deep-person-reid.
Documentation: https://kaiyangzhou.github.io/deep-person-reid/.
How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.
Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.
Tech report: https://arxiv.org/abs/1910.10093.
You can find some research projects that are built on top of Torchreid [here](https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects).
## <div align="center">License</div>
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# <div align=\"center\">Peoplce Counting Project</div>\n\n## Overview\n\nThe People Counting Project is designed to detect and count the number of people entering and exiting a specified area using computer vision techniques. This project can be used in various settings such as retail stores, offices, and events to monitor foot traffic and gather valuable data.\n\n## Features\n\n- **People Detection**: Detects people in a video feed or image using a deep learning model.\n- **Bidirectional Counting**: Tracks and counts people entering and exiting a specific area.\n\n- **Estimate person appear time**: Tracks and estimate time per person appear in video\n\n## Installation\n\n### Prerequisites\n\n- Python 3.x (x >= 10)\n- pip or pip3\n- A GPU (optional but recommended for faster processing)\n\n\n### Steps\n\n1. **Clone the repository:**\n\n ```bash\n git clone gitlab_url \n ```\n\n2. **Install dependencies:**\n\n ```bash\n pip install -r requirements.txt\n ```\n\n3. **Download the pre-trained model (if applicable):**\n The model can download from model section\n\n\n\n## Usage\n\n```python \nfrom smartoscreid import PeopleCounting\n\n# Your model path\nmodel_path = 'smartoscreid/model/yolov8m.pt'\n\npc = PeopleCounting(model_path)\n\n# List video from multiple camera\nvideos = [\"input/Single1.mp4\"]\n\npc.run(videos)\n```\n\nResult will generate in output folder\n\n## <div align=\"center\">Models</div>\n\nYOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.\n\nAll [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.\n\n<details open><summary>Detection (COCO)</summary>\n\nSee [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.\n\n| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |\n| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |\n| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |\n| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |\n| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |\n| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |\n| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |\n\n\n</details>\n\n\n## <div align=\"center\">Torchreid</div>\nTorchreid is a library for deep-learning person re-identification, written in [PyTorch](https://pytorch.org/) and developed for our ICCV'19 project, [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1905.00953).\n\nIt features:\n\n- Multi-GPU training\n- Support both image- and video-reid\n- End-to-end training and evaluation\n- Incredibly easy preparation of reid datasets\n- Multi-dataset training\n- Cross-dataset evaluation\n- Standard protocol used by most research papers\n- Highly extensible (easy to add models, datasets, training methods, etc.)\n- Implementations of state-of-the-art deep reid models\n- Access to pretrained reid models\n- Advanced training techniques\n- Visualization tools (tensorboard, ranks, etc.)\n\n\nCode: https://github.com/KaiyangZhou/deep-person-reid.\n\nDocumentation: https://kaiyangzhou.github.io/deep-person-reid/.\n\nHow-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.\n\nModel zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.\n\nTech report: https://arxiv.org/abs/1910.10093.\n\nYou can find some research projects that are built on top of Torchreid [here](https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects).\n\n## <div align=\"center\">License</div>\nThis project is licensed under the MIT License - see the LICENSE file for details.\n",
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