OpenCV Jobs

Poker Vision and GTO Analysis Tool — Developer Specification Objective Develop an offline application that analyzes recorded poker footage, screenshots, or manually entered hand data to detect the game state and generate GTO-based recommendations for post-session review and training. The system is intended for study and hand analysis only. It must not provide live in-game assistance. Scope The application should: • Detect hole cards, community cards, player positions, pot size, bet sizing, and approximate stack sizes from recorded media. • Reconstruct the full hand state in a structured format. • Pass the reconstructed state into a GTO analysis module or solver-based recommendation engine. • Display recommendations, alternative lines, and relevant metrics...

Project Overview: I am seeking a highly skilled n8n Automation Expert to develop and deploy a sophisticated, end-to-end content generation engine on my own n8n instance. The goal is to automate SEO-optimized article writing, AI image creation, and multi-platform social media distribution while using Google Sheets as a central database for content management and duplication prevention. Required Workflow & Technical Logic: Smart Trigger & Duplicate Prevention (Google Sheets): The workflow must run on a Cron Job (scheduled). Before generating any content, the system must query a specific Google Sheets file to ensure the topic or title has not been used previously. After a successful run, the workflow must append the new Title, Date, and WordPress Post URL to the sheet to prevent ...

I want to see a working proof-of-concept that can watch a live webcam feed in an indoor setting and reliably decide whether someone is merely holding a phone or actively using it. The prototype must process the video stream in real time, recognise the presence of a smartphone, then look for behavioural cues—hand placement, posture and, ideally, gaze direction—to confirm active usage. Whenever the model judges that the phone is being used, it should trigger an audible or visible alarm on the host machine instantly; no other logging or alert channels are required for this first iteration. I am happy for you to choose your preferred computer-vision stack (e.g. OpenCV, MediaPipe, PyTorch, TensorFlow, ONNX) as long as the end result runs on a typical workstation without specialise...

I want to see a working proof-of-concept that can watch a live webcam feed in an indoor setting and reliably decide whether someone is merely holding a phone or actively using it. The prototype must process the video stream in real time, recognise the presence of a smartphone, then look for behavioural cues—hand placement, posture and, ideally, gaze direction—to confirm active usage. Whenever the model judges that the phone is being used, it should trigger an audible or visible alarm on the host machine instantly; no other logging or alert channels are required for this first iteration. I am happy for you to choose your preferred computer-vision stack (e.g. OpenCV, MediaPipe, PyTorch, TensorFlow, ONNX) as long as the end result runs on a typical workstation without specia...

I’m building a proof-of-concept layer that intercepts any image or video delivered through a web browser and automatically replaces detected humans or animals with a neutral “shadow” placeholder. The aim is to strip away elements that might provoke irrational responses while keeping the surrounding media unchanged. Here’s what I need you to deliver: • A working browser-based solution (extension, local proxy, or another approach you propose) that processes both static images and streaming video in real time. • Reliable detection of people and animals using modern computer-vision tools (e.g., TensorFlow, OpenCV, YOLO, or alternatives) and seamless substitution with a non-identifying silhouette or blurred overlay. • A simple toggle UI so users can...

I want to see a working proof-of-concept that can watch a live webcam feed in an indoor setting and reliably decide whether someone is merely holding a phone or actively using it. The prototype must process the video stream in real time, recognise the presence of a smartphone, then look for behavioural cues—hand placement, posture and, ideally, gaze direction—to confirm active usage. Whenever the model judges that the phone is being used, it should trigger an audible or visible alarm on the host machine instantly; no other logging or alert channels are required for this first iteration. I am happy for you to choose your preferred computer-vision stack (e.g. OpenCV, MediaPipe, PyTorch, TensorFlow, ONNX) as long as the end result runs on a typical workstation without specia...

I’m building an MVP that inspects coal quality from both photos and short videos. The system must return a simple breakdown—e.g., “Coal 75 % / Stone 25 %”—and lay the groundwork for optional size-distribution analysis in later releases. What you’ll have to work with • A batch of truck-loading videos and corresponding lab quality reports. Phased scope Phase 1 – Extract still frames from the supplied videos and organise them into a clean, labelled image set. Phase 2 – Train a computer-vision model (YOLO or a comparable CNN in Python) able to distinguish coal from stone. Baseline accuracy is sufficient for now; we can iterate later. Both real-time and batch inference modes should be supported. Phase 3 – Wrap the model in...

We are looking for an experienced full-stack developer to build a scalable AI-powered web platform that provides multiple online image processing tools. The platform should allow users to upload images and process them using different tools such as background removal, image compression, upscaling, format conversion, and enhancement. The goal is to build a high-quality platform comparable to popular online image tools, with a focus on good performance, scalability, and search engine visibility. Core Tools (Phase 1): • Background remover • Image compressor • Image upscaler • Photo enhancer • PNG to JPG converter • JPG to PNG converter • Image resizer • Crop image tool • Background blur tool Processing Architecture: The platfo...

I need a small Windows-only application that can take a folder of 20–100 TIFF files, apply a unique pixel shift and crop to each image, then save the processed set to disk. After that, the program should average the processed images into a single composite frame and finally assemble every frame into a short video clip. All parameters—image size, batch size, per-file pixel shift, crop dimensions, output frame rate and any other relevant image settings—must be editable from a clean, graphical interface that carries my company logo. No multilingual support is required, so the UI can stay in English. To speed things up I can supply an existing Lua script that demonstrates the core image calculations; feel free to translate or rewrite it in the language you are most comforta...