opencv

Personal Assistant built using python libraries. It does almost anything which includes sending emails, Optical Text Recognition, Dynamic News Reporting at any time with API integration, Todo list generator, Opens any website with just a voice command, Plays Music, Wikipedia searching, Dictionary with Intelligent Sensing i.e. auto spell checking, Weather Reporting i.e. temp, wind speed, humidity, YouTube searching, Google Map searching, Youtube Downloading, etc.

AI-Video-Cropper is a Python-based tool that leverages the power of GPT-4 (OpenAI's language model) to automatically analyze videos, extract the most interesting sections, and crop them for improved viewing experience. This project combines the capabilities of GPT-4, FFmpeg, and OpenCV to automate the process of identifying highlights in videos

Creating a software for automatic monitoring in online proctoring

Real-time football video analytics: player/ball/referee tracking and pitch keypoints. YOLO, HRNet, OSNet, ByteTrack, FastAPI. Game state recognition with lightweight CLIP/VLM validation. Python, OpenCV, CUDA/MPS.

A lightweight toolkit for quantitatively scoring LeRobot episodes.

An SDK for Transformers + YOLO and other SSD family models

OpenCV Spatial AI Competition 3rd prize winner project "Automatic Lawn Mower Navigation"

Extract the most viral clips from a random youtube video.

High-accuracy Russian ANPR system built with YOLOv8 for detection and an optimized, custom-trained PyTorch CRNN for OCR.

Circuitry.ai is an open-source tool that combines computer vision and large language models to detect, analyze, and explain electronic circuit diagrams. It leverages YOLOv8 for component detection and LLaMA 3 for generating intelligent textual explanations of how the circuit works.

Video Summarization - Summarized a video lecture and converted it to a slideshow using Speech-to-text, Keyword extraction and OpenCV Shot detection.

PyFrame splits GIFs into equal time windows and picks the frame with the highest motion delta from each one. This way you get good scene coverage and catch peak frames without sending every frame to AWS Rekognition cuts costs by ~93% with minimal accuracy loss.

The aim of this project is to develop an efficient computer vision model capable of real-time wildlife detection.

An end-to-end tennis video analysis system leveraging Ultralytics YOLOv8 for player and ball detection, multi-object tracking across frames, and a custom CNN for court keypoint extraction, providing data-driven insights into player movement, ball trajectories, and match dynamics using advanced computer vision and deep learning techniques.