https://github.com/scorleos773/snap-scan

https://github.com/scorleos773/snap-scan

Last synced: 25 days ago
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• Host: GitHub
• URL: https://github.com/scorleos773/snap-scan
• Owner: SCORLEOs773
• Created: 2025-01-11T03:04:20.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-01-11T03:37:48.000Z (about 1 month ago)
• Last Synced: 2025-01-11T04:29:14.782Z (about 1 month ago)
• Language: Python
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Snap-Scan: Object Detection with YOLOv8

This project demonstrates object detection using the YOLOv8 model. The program detects objects in an input image, identifies sub-objects (e.g., helmets for people, tires for cars, etc.), and saves the results, including cropped sub-object images and annotated visuals.

---

## Features

- Detect objects in an image using YOLOv8.

- Identify sub-objects for specific detected objects:

  - Helmet for "person"

  - Tire for "car"

  - "Unknown" for other objects

- Save annotated images with bounding boxes.

- Generate a JSON file containing object and sub-object details.

- Save cropped images of detected sub-objects.

---

## Folder Structure

ENV/

├── Include/

├── Lib/

├── Scripts/

├── share/

├── sub_objects/             # Cropped sub-object images are saved here

├── annotated_sample.jpg     # Annotated image with bounding boxes

├── detect_objects.py        # Main Python script for object detection

├── output.json              # JSON file with object and sub-object details

├── README.md                # Project documentation

├── sample.jpeg              # Input image for object detection

├── yolov8n.pt               # Pre-trained YOLOv8 model weights

---

## Setup Instructions

1. **Clone the Repository**

   ```bash

   git clone 

   cd 

2. **Setup the Python Environment**

  ```bash

    python -m venv ENV

    source ENV/Scripts/activate  # On Windows

    source ENV/bin/activate      # On macOS/Linux

    pip install -r requirements.txt

  ```

3. **Place Input Image**

   Replace ```sample.jpeg``` with your input image.

## Usage

1. Run the Script

```bash

python detect_objects.py

```

2. Results

   a. Annotated Image: ```annotated_sample.jpg```

   b. JSON Output: ```output.json```

   c. Cropped Sub-Objects: Saved in the ```sub_objects/``` folder.

## Sample JSON Output

```json

[

    {

        "object": "person",

        "id": 1,

        "bbox": [50.34, 100.56, 200.45, 300.67],

        "subobject": {

            "object": "Helmet",

            "id": 1,

            "bbox": [60.34, 110.56, 190.45, 290.67]

        }

    }

]

```

## Notes

1. The yolov8n.pt model is pre-trained on the COCO dataset.

2. Sub-object detection is simulated and depends on predefined rules.

3. The JSON output includes bounding box coordinates for both objects and sub-objects.

## Acknowledgments

1. Ultralytics YOLO

2. COCO Dataset