Update README.md

Author: IcodeG00D

README.md

@@ -1,27 +1,77 @@
-# Brute Force Image Search
 
-This project is a real-time brute force image search application that searches for related images of objects captured by a camera. 
-It leverages computer vision techniques to find matching images from a predefined dataset.
+
+# Object Matching with CLIP & OpenCV
+
+## Overview
+
+This project implements a simple object matching system using OpenCV for webcam capture and OpenAI's CLIP model for image similarity detection. The goal is to open the camera feed, detect when an object from a small dataset is presented, and display the most similar image side-by-side in real time.
+
+---
 
 ## Features
 
-- Real-time object recognition using computer vision.
-- Brute force search to find similar images in a dataset.
-- Integration with camera feed for live searches.
-- Simple and easy-to-use.
+* Real-time webcam preview
+* Efficient object matching using CLIP’s pretrained image embeddings
+* Displays the closest matching dataset image next to the live camera feed when a match is found
+* Lightweight solution tailored for small datasets (e.g., 5 images)
+* Threshold tuning for balancing accuracy and detection sensitivity
+
+---
+
+## Technologies Used
+
+| Technology       | Purpose                                                                                                        |
+| ---------------- | -------------------------------------------------------------------------------------------------------------- |
+| **OpenCV**       | Capture video from the webcam, process frames, and display results.                                            |
+| **OpenAI CLIP**  | Compute semantic embeddings for images and measure similarity between the live camera feed and dataset images. |
+| **PyTorch**      | Deep learning framework to run the CLIP model efficiently on CPU or GPU.                                       |
+| **PIL (Pillow)** | Image preprocessing before feeding images to CLIP.                                                             |
+| **NumPy**        | Handle array operations for image processing and similarity computations.                                      |
+
+---
 
-## Getting Started
+## Why These Technologies?
 
-To get started with the project, follow these steps:
+* **OpenCV** is the de facto standard for real-time video and image processing in Python, providing easy access to camera streams and visualization tools.
+* **CLIP** (Contrastive Language–Image Pretraining) is a state-of-the-art model that produces rich, semantic image embeddings without requiring additional training. This makes it ideal for small projects where retraining large models isn’t feasible.
+* Using **CLIP** allows comparing visual similarity at a semantic level rather than just pixel matching or local features, improving robustness to lighting, angle, and minor variations.
+* **PyTorch** provides a flexible backend for running CLIP efficiently, taking advantage of GPUs if available, but also running on CPU for smaller-scale projects.
+* **PIL** is used to preprocess images to the format CLIP expects.
+* **NumPy** enables efficient numerical operations needed for similarity scoring.
 
-1. **Prerequisites**: Make sure you have the required libraries and dependencies installed.
+---
 
-2. **Configuration**: Configure the project settings and dataset for image search.
+## Setup and Usage
 
-3. **Run the Application**: Execute the application to start live image searching.
+### Requirements
 
-   ALL ABOVE POINTS ARE SO IMPORTANT TO EXECUTE,PLEASE KEEP A NOTE OF THEM !!
-   ALL THE BEST !
+* Python 3.7+
+* pip packages: `opencv-python`, `torch`, `openai-clip`, `numpy`, `Pillow`
+
+### Installation
 
 ```bash
+pip install opencv-python torch openai-clip numpy Pillow
+```
+
+### Running the Program
+
+1. Place your dataset images in a folder (e.g., `testimg/`).
+2. Run the main script:
+
+   ```bash
+   python BFSearch.py
+   ```
+3. The camera window will open.
+4. Place an object from your dataset in front of the camera.
+5. When a matching object is detected, its image will be displayed side-by-side with the camera feed.
+
+---
+
+## Limitations & Future Work
+
+* The matching threshold might require tuning depending on lighting and dataset quality.
+* Works best with small datasets; larger datasets may require optimization.
+* Future improvements may include more advanced object detection or real-time bounding boxes.
+* Integration with GUI frameworks for better user interaction.