OpenCV Jobs

I want to replace paper timesheets with an automatic clock-in / clock-out system that identifies staff by face the moment they pass our main entrance camera. As soon as a recognised face crosses the frame, the system should log the arrival or departure time against that employee’s profile and store the record in an attendance database I can export to CSV or Excel. Architecture is open: I’m comfortable with a cloud, on-prem, or hybrid deployment—whichever you feel offers the best balance of accuracy, speed and privacy given a single entrance camera. No links to my HR or payroll software are needed for now, so a self-contained dashboard or lightweight web portal that lets me view, edit and download daily, weekly and monthly reports is perfect. Key deliverables &bull...

We are looking for a Senior Developer (or technical team) to develop an MVP (Minimum Viable Product) in the field of educational technology (EdTech). The project consists of a mobile application designed to automate administrative and pedagogical processes in educational institutions, using real-time image processing and data analysis through Artificial Intelligence. Responsibilities include developing the mobile app (Android/iOS) with advanced camera integration, implementing computer vision algorithms for processing physical documents, configuring a cloud architecture with support for offline work and subsequent synchronization, and integrating Language Models (LLMs) for generating intelligent reports. The suggested technology stack is: Frontend: Flutter (preferred), Backend/Vision: Py...

I need a custom-built Zynga Poker bot that can reliably run on Windows and handle the entire gameplay loop for me. The core tasks are straightforward: automatically play hands with solid, rules-based or AI-driven decisions; hop between tables to find the best seats; and sweep all available daily bonuses, gifts, and chip offers so my stack keeps growing without manual effort. Because I intend to run the tool on my main account, strong anti-detection measures are mandatory. The solution must emulate human-like input patterns, respect timing randomness, and avoid any easily flagged memory edits or packet injection tactics. Typical tech stacks for this kind of work include Python, C++, or C# with computer-vision libraries such as OpenCV, and I’m open to whichever you believe will be th...

I need a complete Automatic Number Plate Recognition solution that accurately reads both car and motorcycle plates during daylight hours. Once a plate is captured, the system must do two things automatically: log the full plate data (image, text, timestamp) into a database and trigger an immediate alert through a simple webhook or REST call. My ideal stack is Python with OpenCV or a comparable vision framework, paired with a modern detection model such as YOLOv8-seg, but I am open to alternatives if you can demonstrate equal or better accuracy. You are free to use an off-the-shelf OCR engine (Tesseract, EasyOCR, PaddleOCR) or a custom-trained model, as long as you meet the accuracy target. Key deliverables • Optimized plate-detection and OCR pipeline tuned for daylight footage &bu...

Computer Vision Developer – Sports Video Analysis (Mobile-Based) Description: We are looking for a skilled computer vision developer to assist with building a prototype system that analyses sports video captured from smartphones. The goal is to detect and track fast-moving objects, identify key events within video footage, and generate structured outputs (e.g. timestamps, event detection, basic classifications). This is an early-stage prototype, so we are looking for someone who can work quickly, think practically, and help shape the technical approach. Key Responsibilities: Develop object detection and tracking models for fast-moving objects (e.g. ball tracking) Process video input from 1–2 mobile camera angles Detect key events (e.g. impacts, direction changes, motion pa...

I’m assembling a small, highly-skilled development squad to finish a platform that sees the world, talks to external services that never published their docs, and then deploys itself at scale. Python sits at the heart of the codebase, so deep confidence with modern Python, virtual environments and dependency management is essential. Most of the heavy lifting in computer vision happens through OpenCV and complementary deep-learning libraries; the pipeline must handle object detection, image processing and facial recognition with equal reliability and speed. If you also think in C++ or Java when performance demands it, that flexibility will be welcome. Beyond vision, we have to collect data from third-party applications that do not provide friendly endpoints. That means reverse-e...

I have a proprietary image dataset and I need a seasoned computer-vision engineer to turn it into an end-to-end solution that can 1) recognise each image, 2) detect all relevant objects inside it, and 3) optionally segment those objects when that adds value to the overall result. The primary goal is accurate detection and classification, but I’d like the system to be flexible enough to switch on pixel-level segmentation whenever it boosts performance. Here is what I’m after: • A clean Python pipeline (PyTorch or TensorFlow preferred, with OpenCV for preprocessing) that ingests my custom images, trains suitable models, and exposes an easy inference script. • Clear evaluation: confusion matrices, mAP/IoU scores, and plots of loss/accuracy over epochs so I can judge...

I need a lightweight, computer-vision bot that relies on on-screen image recognition rather than Selenium or similar browser-automation libraries. The workflow is straightforward: the bot launches a local browser window, navigates to a specific site I will provide, and—through visual cues—automatically clicks the required elements, fills in form fields, and finally extracts the resulting text, images, and tabular data that the page returns. The process must loop continuously so it can watch for page changes and react in real time. A small delay between cycles is fine as long as the interaction remains smooth and does not overload the system or the target site. Robust error handling is important; if a button is missing or the page layout shifts, the bot should retry gracefull...

This project is a full stack system built around a vision based browser automation bot. The bot visits three target websites, fills multi-step forms on each of them using data provided by an operator, scrapes the results returned after each submission, and stores everything in a database. The operator interacts with the system through a web interface and never touches the bot or the code directly. The system has five parts that all need to be built and connected. A React frontend is where the operator submits input data and monitors what the bot is doing in real time. A FastAPI backend sits behind the frontend and handles communication between the operator, the bot, and the database. Redis acts as the message layer between the backend and the bot, passing events like run triggers, status u...

I want a lightweight, browser-based application that automatically straightens converging verticals in real-estate photos. Accuracy of correction is the top priority; I need the result to look as if the building was photographed with a perfectly level camera. ShiftN () comes close, but often leaves residual tilt or unnatural stretching, so the new tool must be noticeably more reliable. Core workflow • User drags one or many images onto the page. • The app detects vanishing points, computes the homography, and warps each photo so the main verticals are perfectly parallel. • A side-by-side before/after viewer lets the user verify the fix. • Corrected files are downloadable individually or in a zip when batch mode is used. Key requirements 1. Accuracy first: th...

I want a lean, lightning-fast Python script that sits on Windows, continuously grabs a small, predefined rectangle of my BlueStacks window, performs real-time blob detection with OpenCV, and the instant a qualifying blob appears fires off a single, specific key press back to the emulator. The key points that matter to me are low latency capture, accurate blob filtering (size, colour or contour thresholding we can fine-tune together) and rock-solid keystroke delivery so the command never misses its timing window in-game. Please code the solution in plain Python with OpenCV as the imaging backbone; feel free to pull in lightweight helpers such as mss, pywin32, pynput, or pyautogui for screen capture and keyboard output as long as the overall loop stays responsive. I’d like the script...

Project Overview: I am looking for a developer or team to build a dual-mode educational grading system. The system consists of: An iOS App: Capable of real-time scanning and grading of both standard OMR bubble sheets AND unique orientation-based cards (Plickers-style). A PHP Web Backend: A tool to generate customized PDF test sheets and student cards using Chromium (via Spatie/Browsershot). Privacy is paramount. All student scanning and grading must occur on-device to ensure data security. Module 1: The iOS App (Swift/SwiftUI) The app must feature a toggle or auto-detection to switch between two modes: Mode A: OMR Bubble Sheets: Detects standard multiple-choice grids. Real-time detection of "filled" vs. "empty" bubbles using local image thresholding. Mode B: Pli...