https://github.com/ashenee123/visionlite-color-and-object-detectation
https://github.com/ashenee123/visionlite-color-and-object-detectation
contours flask imageprocessing opencv python templates thresholding
Last synced: 17 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/ashenee123/visionlite-color-and-object-detectation
- Owner: ASHENEE123
- Created: 2025-09-15T06:58:58.000Z (23 days ago)
- Default Branch: main
- Last Pushed: 2025-09-15T07:35:53.000Z (23 days ago)
- Last Synced: 2025-09-15T09:08:42.049Z (23 days ago)
- Topics: contours, flask, imageprocessing, opencv, python, templates, thresholding
- Language: HTML
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# VISIONLITE
**ColorDetectation** is an educational Python project designed as a learning experiment for exploring OpenCV and its core computer vision techniques. The project provides a hands-on approach to detecting both colors and objects in images, supporting real-time webcam input and image uploads via a simple Flask web interface. Users can analyze images, see how OpenCV processes them, and download the annotated results.
---
## 🚀 Features
- **Dual Input Modes**:
- **Real-Time Detection**: Detects colors and objects from live webcam video.
- **Image Upload**: Users can upload images and analyze them interactively.
- **User-Driven Analysis**:
- Click or select regions in images or video to identify colors and objects at those points.
- **Color & Object Detection**:
- Detects objects using contour detection and identifies their dominant color.
- Annotates detected objects with color and object labels.
- **Flask Web Backend**:
- Simple web UI for uploading images, viewing results, and downloading annotated images.
---
## 🛠️ Techniques & Libraries Used
- **OpenCV**:
- Core library for computer vision tasks.
- Handles image loading, webcam streaming, color space conversion (BGR/HSV), and drawing.
- **Thresholding**:
- Separates objects from the background with binary/adaptive thresholding.
- **Contours & Contour Properties**:
- Uses `cv2.findContours` to detect object outlines.
- Analyzes contour properties (area, centroid) and computes mean color within contours for labeling.
- **Color Detection**:
- Converts pixel/region color to HSV/RGB and maps them to basic color names.
- **Flask**:
- Provides the backend server for image upload, processing, and result download.
- **Image Download**:
- Lets users download processed images with all detected objects and color annotations.
---
## 📚 Educational Purpose
> **Note:**
> This project is a trial model created to learn and experiment with OpenCV and its core techniques. It is intended for educational use, experimentation, and as a starting point for deeper exploration into computer vision.
---
## 📦 Installation
1. **Clone the repository**
```bash
git clone https://github.com/ASHENEE123/ColorDetectation.git
cd ColorDetectation
```
2. **(Optional) Create a virtual environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install requirements**
```bash
pip install -r requirements.txt
```
Or, if `requirements.txt` is missing:
```bash
pip install numpy opencv-python flask
```
---
## ⚡ Usage
### 1. **Run the Web Application**
```bash
python color.py
```
or
```bash
flask run
```
- Visit `http://localhost:5000` in your browser.
### 2. **Analyze Images**
- **Webcam Mode:** Analyze live video feed for object and color detection.
- **Upload Mode:** Upload images, click to analyze regions, and download results.
---
## 🌈 Example
- Upload an image or use the webcam.
- Detected objects will be outlined and labeled with their dominant colors ranges in image and object type.
- Download the processed image from the browser.