This project is an object detection system implemented using Flask and OpenCV. It utilizes computer vision techniques to identify and localize objects within an image or video stream. The system is designed to be deployed as a web application, allowing users to upload images or provide a video feed for real-time object detection.

The project was developed by a team called "Brogrammers" for the Asian Hack 2022 hackathon, where it secured the third position. The team members worked collaboratively to create a robust and efficient object detection solution within the given time frame.

• Object Detection: The system uses OpenCV's pre-trained deep learning models, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), to detect various objects in an image or video stream.
• Web Interface: The project integrates Flask, a Python web framework, to provide a user-friendly web interface. Users can upload images or provide a video URL to perform object detection.
• Real-time Detection: For video input, the system continuously analyzes frames to provide real-time object detection. This feature allows users to monitor live video feeds for object identification.
• Bounding Box Visualization: Detected objects are highlighted using bounding boxes, providing visual cues to identify the location of each object within the image or video.
• Object Labels: The system also labels the detected objects, providing textual information about the identified objects.

• Clone the repository from the GitHub link: https://github.com/initialrise/AsianHack2022.git
• Install the required dependencies by running the following command:
  ``` pip install -r requirements.txt ```
• Run the Flask web application using the following command:
  ``` python main.py ```
• Access the web application by opening a web browser and navigating to http://localhost:5000.
• Upload an image or provide a video URL to perform object detection.
• View the detected objects with bounding boxes and corresponding labels.

Hackathon: Asian Hack 2022
Position: 3rd

1. Rabindra Man Bajracharya
2. Yugesh Raj Shilakar
3. Ojash Devkota
4. Amir Shrestha

This project demonstrates the implementation of an object detection system using Flask and OpenCV. With its intuitive web interface and real-time detection capabilities, the system provides an efficient solution for identifying and localizing objects in images and video streams. The project was developed by the "Brogrammers" team during the Asian Hack 2022 hackathon, where it secured the third position.