OpenCV Jobs

Looking for an experienced developer to build an AI-based UAV detection and tracking system for a custom ArduPilot quadcopter using Raspberry Pi 5 and AI HAT+ 2. Scope includes real-time drone detection, object tracking, MAVLink integration, safe autonomous follow/stand-off navigation, telemetry logging, GCS interface, failsafes, SITL testing, and complete source-code handover.

I have a fully-working Android Studio project (Java) that photographs urine test strips and reads the colours, but its interpretation layer is letting us down: colours are detected correctly, yet the final numbers it returns are wrong. I need someone to dig into the existing code, locate the flaw in the result-interpretation logic, and at the same time refine the overall algorithm so the readings become clinically reliable. Because I’m interested in both fixing the bug and improving the core method, you will be free to refactor, recalibrate, or even redesign parts of the analysis pipeline provided the camera capture and colour-extraction steps remain intact. OpenCV and native Android APIs are already integrated, so you will be working inside that framework. Key deliverables: &bul...

I need a robust automation bot for an Android game that can do three core things: 1. Detect every relevant UI element on multiple screens through image recognition, then tap the right spots to claim the daily rewards without fail. 2. Autonomously solve the in-game maze and pick-pair minigames with human-like speed and accuracy. 3. Run hands-free once launched, logging what it collected or solved so I can review a simple report afterward. Deliverables • Full Android-ready build (APK or script + instructions to run with ADB/Emulator) • Source code with clear comments • Image libraries or model files used for recognition • Quick user guide and a short demo video that shows the bot clearing both minigames and collecting a day’s rewards Acceptance criter...

I’m building a computer-vision pipeline focused on reliable detection and recognition of human faces and full-body figures. The core of the job is a clean, well-documented Python implementation that can take still images or short video clips and return bounding boxes, class labels, and confidence scores for each detected person. I already have test media and the computing environment; what’s missing is the detection logic itself—ideally leveraging familiar libraries such as OpenCV, TensorFlow, PyTorch, or a proven YOLO/SSD variant. Accuracy on varied lighting and crowded scenes is more important to me than sheer speed, but the code should still run in real time on a modern GPU. Deliverables • Python source code with clear inline comments • Pre-trained weig...

Tengo un sistema de automatización ya operativo y necesito a alguien que lo lleve al siguiente nivel. La base está construida en Python y OpenCV, comunicándose con un ESP32; todo corre sin errores, pero falta completar la lógica de detección de objetos y la contabilización de productos. Lo que requiero: • Ajustar y optimizar el modelo de detección de productos (actualmente uso OpenCV + Python). • Implementar el conteo automático de cada producto que aparezca en la cámara. • Enviar el resultado al ESP32 a través de la interfaz existente para que el microcontrolador lo procese. • Dejar el código limpio, documentado y con instrucciones rápidas para volver a entrenar o cambiar el ...

I need a complete mobile app that runs smoothly on both iOS and Android and showcases advanced facial-recognition capabilities. The core of the build is a camera workflow that can: • Face identification • Emotion detection • Age and gender estimation Those three features must fire quickly on-device (or via a lightweight cloud microservice if latency is acceptable) and return an easy-to-parse JSON result I can feed into future modules. The interface should feel modern and sleek with clean typography, subtle animations and dark-mode support. You’ll own the full stack—from selecting the best SDK (e.g., Apple Vision, Google ML Kit, OpenCV, or a custom model in TensorFlow Lite / Core ML) to wiring up the UI in Swift/Kotlin or a unified framework like Flutte...

I run several AI initiatives that revolve around computer-vision pipelines, and I need an adaptable hand to keep our image data in perfect shape before it ever touches a model. Your main focus will be classic machine-learning groundwork: pulling raw image assets from cloud storage, auditing their quality, handling augmentations, labeling inconsistencies, and packaging everything into tidy, well-documented datasets that flow straight into our training scripts. Most tasks live in the data-preprocessing and cleaning stage, so you should be comfortable writing reproducible code for resizing, normalization, class-balancing, and automated sanity checks. We currently use Python with Pandas, NumPy, OpenCV, and sometimes Albumentations; if you have a favorite toolkit that speeds things up, I am op...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed. What we need: Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame. Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator. ...

Necesito desarrollar una aplicación para Linux que automatice el procesamiento de imágenes. El objetivo principal es la automatización de tareas, por lo que no requiero una interfaz gráfica compleja; basta con un flujo por línea de comandos o una API sencilla. Alcance funcional: • La app debe tomar imágenes de una carpeta (o ruta indicada) y aplicar una serie de operaciones configurables: redimensionar, convertir formato y ejecutar filtros básicos. • El núcleo de las rutinas de alto rendimiento podrá escribirse en C++ (por ejemplo, usando OpenCV), mientras que la orquestación, configuración y llamadas al sistema estarán en Python. • Quiero un sistema modular para poder añadir nuev...

I’m looking for a Python-based workflow that takes my equirectangular photo collection and, for any two images I select, confirms whether they were shot from the same angle. Beyond the yes/no decision, the script must also: - check whether they are connected • calculate the scale ratio between the pair, - the angle yaw and pitch they connected • assign a reliability/confidence score to its assessment. sample dataset : i run the progam using cli, json output is fine All results should be written to a concise text report that I can easily parse or forward—feel free to suggest the most convenient plain-text structure. You’re free to use OpenCV, scikit-image, NumPy, or any other well-supported libraries so long as installation remains straightforward (p...

Security footage requires a detailed forensic examination so the individual captured on-camera can be clearly identified. The raw video contains several moments where the face is partially visible, yet heavy compression, low light, and motion blur currently obscure any reliable match. The task is to enhance the relevant segments, extract the sharpest stills, and annotate each frame with time-codes and clarity notes. Where possible, apply frame-by-frame stabilization, noise reduction, and colour correction so distinguishing facial or clothing features become unmistakable. Deliverables • Up-to-date forensic report (PDF) summarising techniques, enhancement settings, and confidence level for identification • A folder of high-resolution still images (PNG or TIFF) keyed to th...