OpenCV Jobs

I need a Python application that takes a pretrained YOLO model and turns it into a real-time desktop solution capable of recognising industrial, hand and power tools as they appear in a live camera feed. The workflow should be straightforward for a non-technical operator: launch the program, select the camera (USB or built-in), and immediately see bounding boxes with class names and confidence scores updating on-screen at 30 fps or better. Key details • Model: start with an existing YOLO checkpoint (v5, v7, v8 or YOLO-NAS—whatever you feel offers the best speed / accuracy trade-off). Feel free to fine-tune if that improves precision, but the core must stay YOLO. • Language & libs: Python 3.x, OpenCV for video capture/rendering, torch or ultralytics for inference. ...

I need to build an AI-powered video creator that leans heavily on computer-vision techniques. The goal is a tool that takes raw clips or even still images and, through smart scene understanding, automatically assembles polished videos—essentially an AI video maker that handles the tedious parts of editing and enhancement for me. Core objectives • Train or adapt a computer-vision model that can detect key moments, faces, objects, and scene changes, then use that information to sequence footage intelligently. • Implement automated video generation logic—transitions, basic trimming, and soundtrack alignment—so the user can go from assets to finished output with minimal clicks. • Include real-time or near-real-time preview plus the option for manual twea...

J’ai une séquence vidéo de 1 minute en définition standard dans laquelle la plaque d’immatriculation d’un véhicule reste illisible. Mon unique objectif est d’en extraire, si possible, les caractères exacts de cette plaque. Je fournirai le fichier original (format MP4) ainsi que le time-code approximatif où le véhicule apparaît. J’attends en retour : • l’image ou le court extrait optimisé où la plaque est lisible, • la transcription claire des caractères, • un court mémo sur les techniques utilisées (par exemple : stabilisation, upscaling IA via Topaz Video Enhance AI, filtres OpenCV, traitement Adobe After Effects, etc.) afin que je comprenne...

Title: AI-Powered Document Intelligence Demo – 7-Day Delivery Budget: ₹10k Timeline: 7 Days Location: Remote ⸻ Project Overview We are building a proof-of-concept Doc Intelligence demo for medical perfusion sheets. The goal is to extract structured vitals from uploaded images/PDFs and display them in a simple real-time dashboard with alerts. This is a demo only, not a full MVP — the focus is on speed, accuracy, and visual clarity. ⸻ Key Deliverables 1. OCR / Text Extraction • Input: scanned perfusion sheet (PNG/JPG) or PDF. • Output: structured JSON (BP, ACT, Flow, Temp, Time). • Include basic validation (range checks, missing values). 2. Backend API • Python + FastAPI (or equivalent). • Receives uploaded file → returns structured JS...

I want an app that scans the phone’s camera roll, recognises the same person across different shots, and then lets me decide exactly which images to forward. The core flow is simple: • First, the app analyses every photo, clusters faces that it believes belong to the same individual and shows me those groupings. • From there, I can pick pictures one-by-one or bulk-select them in three distinct ways: individually, by date range, or by geolocation. This mirrors the way I already think about my library, so the interface needs to surface those three filters prominently and let me combine them if I wish. • Once I have a final selection, the app should open the standard share sheet so I can send the chosen files through any installed channel (Messages, WhatsApp, AirDrop...

The goal is to build a proof-of-concept that embeds additional image data inside the JPEG photographs captured by my drones, using the classic Least Significant Bit (LSB) method. Once hidden, the pictures travel through a FANET where each UAV joins a lightweight consensus round to decide which unit forwards the package to the ground station. By the time the file reaches the base, I want to be able to demonstrate that the payload is intact, immutable, and directly traceable to the specific drone that collected it. Core expectations • A clean, well-commented module that hides and later extracts image-based payloads inside JPEG frames via LSB or other types required. • A simple simulation (or lightweight real-world test) showing at least two drones/nodes running a consensus rou...

We need an Android developer to add iPhone-style panorama to the Open Camera app (open source: ). Goal: Implement panorama in Open Camera so it works like iPhone: guided on-screen motion, smooth horizontal panning, and one stitched panorama image. • Works seamlessly on both the front and back cameras. • Guides the user with the same arrow-line UI Apple uses, keeping motion smooth and level. • Captures a continuous series of frames, stitches them on-device, and outputs one high-quality image without pushing memory limits. You’ll work only against the official Open Camera codebase (or a clean fork you create). I’ll take your branch and merge it into our own app later.

Every morning my iPhone has to be woken, a six-digit PIN entered, a work app opened, credentials typed, several form fields completed and a digital signature placed. I want that entire routine removed from my hands by a small desktop robot. Core flow • Wake the phone from sleep by pressing the side button. • Physically tap the screen to enter the PIN code—no Bluetooth, Face ID or Touch ID, just precise mechanical interaction. • Launch the target app, wait for it to load, populate the login screen and navigate onward until the signature page is reached and signed. I imagine a compact gantry or articulated arm driven by servos or steppers, guided by a microcontroller (Arduino, Raspberry Pi, or similar). Because the touch targets are fixed, a predefined set of c...

I’m building a cross-platform mobile app that acts as a personal styling and beauty assistant for both men and women. The core of the product is an AI engine that delivers highly-personalized outfit recommendations. Key functionality • The assistant studies three user-supplied inputs—body measurements, skin tone and face shape—and combines them with each person’s stated style preferences, their body type and real-time fashion trend data. • For every request it returns a clear set of outfit suggestions, indicating why each piece flatters the user’s proportions, complexion and current tastes. • A lightweight dashboard lets me update trend data, brand catalogs and style rules without redeploying the app. Technology expectations The re...

I want an app that scans the phone’s camera roll, recognises the same person across different shots, and then lets me decide exactly which images to forward. The core flow is simple: • First, the app analyses every photo, clusters faces that it believes belong to the same individual and shows me those groupings. • From there, I can pick pictures one-by-one or bulk-select them in three distinct ways: individually, by date range, or by geolocation. This mirrors the way I already think about my library, so the interface needs to surface those three filters prominently and let me combine them if I wish. • Once I have a final selection, the app should open the standard share sheet so I can send the chosen files through any installed channel (Messages, WhatsApp, AirDrop...

Quiero implementar en mi servidor Linux un sistema OMR escrito en Python que me permita elaborar, procesar y calificar hojas de respuesta de 1 a 100 preguntas. Necesito que desde la propia interfaz gráfica pueda: • Diseñar plantillas personalizadas: definir número de preguntas, incluir nombre de la institución y cargar el logo. • Reservar cuatro columnas de dígitos (0-9) que el alumno sombreará para generar su código interno; por ejemplo, 1101 identificará “PRIMERO A – Estudiante 01”. • Escanear la hoja PDF o imagen, identificar automáticamente al estudiante mediante esas columnas y leer cada burbuja para asignar la nota. • Guardar los resultados directamente en MySQL y permitirme co...

I need a fully-packaged proctoring add-on that plugs straight into Moodle quiz pages. Before the candidate even starts, the page must request camera permission, show a brief consent notice, then take and store an initial snapshot. While the quiz is running, the script should capture images at a configurable interval and run face-detection logic that immediately raises alerts for four specific situations: no face, multiple faces, noticeable face movement, and any visible gadgets. Each alert increments a violation counter; when the counter reaches a limit I set, the attempt should submit automatically. Inside Moodle’s admin area I want a settings screen where I can toggle the feature, choose snapshot frequency, set violation limits, and enable or disable each alert type. After each e...

I need a Python-based tool that can scan video files, detect Trees, Road, Infrastructure—and leave room for many more object classes as the project grows—then save every relevant frame straight into a MySQL database. Each stored image must be linked to the corresponding frame number and any detection metadata so I can later query, filter, and analyse the results. Once the data is stored, I want a lightweight viewer that steps through the saved frames in order, overlaying the detection boxes so I can visually confirm accuracy. OpenCV for frame extraction and display is acceptable; YOLO, TensorFlow, or another modern model is fine so long as the code is clean, well-commented, and easy for me to retrain with additional classes. To keep the hand-off smooth, please include: •...