Discover Computer Vision Projects Efficiently

We are seeking an experienced developer to build a computer vision system that analyzes foot orthotics for quality control purposes. The ideal candidate will have a robust understanding of computer vision techniques and be able to implement a solution that identifies defects and ensures compliance with quality standards. This project requires creativity and expertise in machine learning algorithms, image processing, and data analysis to effectively evaluate orthotic products. If you have a passion for improving healthcare technology and a strong background in this field, we invite you to apply. Please see attached video and pdf.

We are seeking a skilled developer to create a web application that captures streaming video input, such as from a camera. The application should include functionality to detect individuals in the video feed and overlay virtual accessories, like glasses or other items. The ideal candidate will have experience with video processing, computer vision, and real-time application development. If you're passionate about creating engaging applications and have a strong understanding of the necessary technologies, we want to hear from you!

a perfect example of the solution would be that the user is taking an ipad or similar tablet and he/she sees herself with a (different) pair of glasses or earrings or any other improvement of her face straight away.

I am hiring an expert-level ComfyUI engineer with deep experience in high-fidelity image-to-image replication.
The task is to take an ultrasound image as input and produce an output that is a direct replica of the same image, rendered as a highly realistic, photo-like image.
This is not a reinterpretation, redesign, enhancement, or “inspired by” result. The output must clearly and consistently match the original image’s structure, proportions, facial features, and positioning, with realism being the only transformation.
I will be uploading before-and-after images to verify accuracy. If the output does not closely replicate the input, it will be rejected.
Deliverables
A production-ready API JSON pipeline compatible with RunPod deployment
Not a standard ComfyUI workflow file
The pipeline must be designed for programmatic execution (API-driven, not manual)
Reliability & Success Criteria (Mandatory)
The image generation process must be fully repeatable and deterministic
Across varied ultrasound inputs, the pipeline must achieve a minimum 90% success rate
“Success” is defined as producing a clearly recognizable, high-fidelity replica of the input image
No manual prompt tuning, node adjustment, or intervention per run
One-off results or pipelines that only work under ideal conditions will not be accepted
Required Expertise (Do Not Apply If You Don’t Meet These)
Expert-level ComfyUI knowledge
Proven experience with image-to-image replication, not creative or stylized generation
Strong understanding of model conditioning, control, and consistency
Experience deploying API-based pipelines (RunPod or similar)
Timeline & Availability
This is a 24–48 hour task
You must be able to work diligently and focus exclusively on this project
Applicants who are juggling multiple projects and cannot commit focused time should not apply
Contract Terms
I will not start the contract until you can:
Demonstrate a completed, working pipeline
Show actual before-and-after results
If you cannot already execute this at a high level, do not apply
Proposal Requirements
Include:
Examples of high-accuracy image-to-image replication work
A brief explanation of how you ensure repeatability and reliability

Hi, I’m reaching out because I’m looking for an automation specialist who’s comfortable with Make.com, webhooks, and API integrations.
I’m building AI receptionist workflows for real businesses and I’m looking for someone to help design and maintain the backend systems.
I think there’s an untapped market here and I’m open to discussing ongoing work or a partnership, not just one-off tasks.
If you’re interested, let’s schedule a Zoom call and talk through the idea.

@everyone ┇ BIG REQUEST ┇ NO UPFRONT ┇ PAY ON FULL DELIVERY ONLY

Helios 2 + Titan Two │ Complete Private Discord Dashboard + Full AI Suite + ALL Scripts From Scratch │ NBA 2K26 & Universal FPS

Need ONE real top-tier dev (or small duo) who can deliver the entire package 100% finished. Focus on the best AI tools like YOLO/OpenCV/TensorFlow for god-tier detection, with dead-simple beginner modes (plug-and-play presets, auto-setup wizards, tooltips) and advanced customization (raw variable tweaking, custom model training, script editing IDE in Discord).

Everything included from zero:
- Private Discord bot + pro-level interactive dashboard (embeds that look like Atlas/Phoenix but better – responsive, no lag)
- ALL Titan Two GPC scripts written fresh (anti-recoil patterns, rapid fire mods, perfect release combos, aim injection logic, toggle macros – everything optimized for low detection)
- Helios 2 handling all CV/AI processing
- Full integration: Discord controls Helios → Helios commands Titan Two in real time
- Beginner-friendly: One-tap setups, guided tutorials in bot; Advanced: Unlock Python hooks, threshold sliders, dataset uploads for retraining

Discord Dashboard Details (Scalable UI)
- Clean, modern embeds: sliders for timings/sensitivity, buttons for modes/toggles, dropdowns for profiles/shot types, live graphs for stats (green %, aim drift, recoil curves)
- One-click save/load/share named profiles (export as .gpc or .json)
- Real-time feedback: Detection confidence %, hit/miss logs, visual overlays (preview aim lock or shot meter in Discord)
- Password-locked tester channels + auto-update notifier for new models/scripts
- Beginner mode: Auto-optimize everything with presets; Advanced mode: Full raw access to variables, GPC code editor, AI hyperparams

NBA 2K26 (Current + Next Gen) – Ultimate AI Shot God
- 99-100% green release every single shot with meter AND no-meter modes
- Triple+ detection: Shot meter zones (green/white/yellow), player animations (release point prediction), audio cues (swish/net sounds for feedback), ball trajectory tracking
- Every shot type covered: Jumpers, fades, hops, post spins, stepbacks, dribble pull-ups, catch-and-shoot, off-dribble, leaners, floaters, dunks/layups timing
- Adaptive AI: Detect defender proximity/contest levels (heavy/light/open), auto-adjust release based on stamina/fatigue, badge activation (e.g., Deadeye, Limitless Range)
- Game mode support: MyCareer, Pro-Am, Rec Center, Park – auto-switch profiles based on detected mode
- Patch-proof: One-click retrain button with easy dataset upload (beginners: Bot guides you; pros: Full PyTorch/TensorFlow access for custom models)
- Extra features: Auto-dribble combos for space creation, stamina management (slow down when low), green window expansion via AI prediction
- Zero input lag on HOF difficulty – integrate best tools like PegasusVision CV for pixel-perfect timing

Universal FPS (Warzone, Apex, Valorant, Xdefiant, etc.) – Elite AI Aim Lock Suite
- YOLOv8/v9 (or better) running on Helios 2 for enemy/head/body detection – pre-trained models for instant start, custom training for pros
- All aim modes: Legit (subtle smoothing), Rage (instant snap), Hybrid (toggleable), Silent aim, Triggerbot, Memory/Pixel aim (safest per game)
- Advanced tracking: Wall prediction (track through smoke/walls via movement patterns), aimbot with humanized curves (random offsets, bezier smoothing)
- Live editable recoil tables for every weapon (drag-drop patterns for beginners, script raw values for pros), anti-recoil compensation
- FOV circle customization, priority targeting (closest/low-health/head-first), bone selection (head/neck/chest toggle)
- Extra features: Auto-fire on lock, no-scope assist, quickscope timing, movement prediction for moving targets, anti-ban randomization (variable delays, miss % sliders)
- Stays undetected every season: Auto-update hooks for new anti-cheat patches, mode switcher based on game meta
- Game-specific tweaks: CoD bloom control, Apex legend ability detection, Valorant agent outlines, Xdefiant recoil variance

Tech Requirements (Best-in-Class AI)
- All computer vision/AI on Helios 2 (zero lag, high FPS capture via Elgato/Remote Play)
- You write and deliver ALL Titan Two GPC scripts from scratch (I have nothing – make them modular and easy to flash via Gtuner IV)
- 100% local & encrypted, no cloud dependency – advanced: OpenCV/Python/TensorFlow hooks for custom builds
- Compatibility: PS5/Xbox/PC, future-proof for 2K27+ and new FPS titles

Payment
- ZERO upfront, zero milestones
- Full payment ONLY when I flash the scripts, join the Discord, and everything works perfectly in-game on my console (test beginner + advanced modes)

DM me with:
- Short clip or screenshots of your previous full Helios 2 + Titan Two packages (must show dashboard + working scripts + AI features)
- How long until I get the complete, ready-to-use version
- Your price (paid only on delivery)

New talented devs are 100% welcome — if you’re good with Python/OpenCV, can write clean GPC, and know how to make things easy for beginners while keeping it powerful for pros, hit my DMs. I’ll give you a shot if your proof looks legit.

Let’s build the cleanest, most user-friendly, and strongest 2026 setup ever made.

Skills: Python, User Interface / IA, Software Testing, Machine Learning (ML), Software Development, Computer Vision, Game Testing, Discord API, YOLO, AI Development

Hello,
CCTV-based vehicle tracking system for construction sites. Cameras at entry/exit gates will log vehicles, identify number plates, detect material type (sand, debris, etc.), and estimate whether the vehicle is empty, half, or fully loaded.



There are two scopes—CCTV integration (live feed setup) and AI analytics (vehicle detection & reporting). You can handle either or both based on your expertise. Let’s connect to discuss.

We are seeking an experienced AI/Deep Learning developer to help improve our machine learning model for super-resolution image reconstruction. The goal is to enhance the quality and resolution of microscopy images, enabling more precise analysis and visualization.

Responsibilities:
- Capable of suggesting a proper ML model for the task of transforming fluorescence microscopy images to super-resolution.
- Capable of collecting and preparing the necessary training sets (links to public repositories will be provided).
- Convert and optimize the model for Core ML deployment on Apple devices.
- Document methodology and suggest future improvements.

Requirements:
- Strong experience in Python and deep learning frameworks (PyTorch, TensorFlow, or similar).
- Proven background in image processing, super-resolution, or computer vision.
- Experience converting ML models to Core ML format and optimizing for Apple devices.
- Familiarity with microscopy or biomedical imaging is a plus, but not mandatory.
- Ability to optimize models for performance, accuracy, and cross-device compatibility.
- Clear problem-solving skills and efficient communication.

We are building an Event Intelligence Platform that connects to existing venue CCTV systems and provides real-time operational insights for events (sports, venues, live events).

We are looking for a Computer Vision Engineer to implement and configure camera-based AI modules, working closely with our Head of R&D.

Scope of work (MVP – 30 days)

You will work on practical, deployable AI, not research.

Primary modules:
• People counting without double-count
• Directional line crossing
• Entry/exit counts at gates
• Zone footfall
• Queue estimation
• Queue length
• Approximate wait time
• Crowd density
• Zone heatmaps
• Threshold alerts
• Basic presence / completion detection
• “Object present / missing” (for install verification)

Technical requirements
• Strong experience with computer vision in production
• Experience with:
• Python
• OpenCV
• YOLO (v5/v8 or similar)
• Object tracking (ByteTrack, DeepSORT, etc.)
• Experience handling video streams (RTSP / CCTV / IP cameras)
• Ability to tune models for real environments (lighting, occlusion, crowds)
• Focus on accuracy + stability, not over-engineering

What you will NOT do
• No facial recognition
• No identity tracking
• No academic research
• No building dashboards (handled by another developer)

Deliverables
• Working AI modules that expose:
• Counts
• Densities
• Queue metrics
• Clear API outputs for integration into our platform
• Configuration instructions for different camera angles

Nice to have
• Retail analytics, smart city, or transport analytics experience
• Experience deploying on edge or lightweight servers

Engagement
• Remote
• Fast-paced MVP
• Immediate start
• High chance of long-term collaboration as platform grows

I am looking to hire an experienced Titan Two developer who can create high-quality, reliable scripts compatible with every new NBA 2K release as well as all major FPS games. The goal is to have scripts that work seamlessly across multiple game titles, with consistent performance and full support for modern gameplay mechanics. I also need these scripts to integrate smoothly with Helios 2, whether through computer vision or Python-based input systems, so experience with AI-driven automation is a major plus.

The ideal candidate should have strong knowledge of GPC scripting, controller input logic, anti-recoil systems, timing optimization, and cross-game compatibility. If you have worked on advanced integrations, automated input systems, or multi-game solutions before, I would love to hear from you.

This is a paid opportunity, and compensation will reflect skill, reliability, and the quality of your work. Please reach out with your experience, portfolio, or examples of previous Titan Two projects.
Hiring a Titan Two developer to build scripts for all new 2K titles and all FPS games, fully compatible with Helios 2/Python, plus a customizable in-game menu to adjust settings, controls, and features for each game. Paid project for reliable, expert-level work.

We are a drone software company with a fully built 2D & 3D web viewer frontend. We are now looking for an expert Computer Vision Engineer to architect a custom, high-performance 3D reconstruction engine to power the backend.
The Goal: We want to build a proprietary processing pipeline that surpasses OpenDroneMap (ODM/WebODM) in quality. While ODM is a great starting point, our goal is "commercial-grade" output: sharper edges on buildings, denser point clouds, and superior texture blending without the "fuzziness" often seen in open-source wrappers.
The Challenge (Backend Only): You will architect a custom pipeline (likely leveraging COLMAP + OpenMVS or similar high-end libraries) to achieve:

Superior Dense Matching: Higher density point clouds than standard OpenSfM.

Engineering-Grade Accuracy: Robust bundle adjustment and camera calibration handling (self-calibration).
High-Quality Texturing: Advanced seam leveling and color correction to avoid texture artifacts.
Performance: GPU-accelerated processing (CUDA) to handle 1000+ image datasets efficiently.

Technical Requirements:

Deep expertise in Photogrammetry: Structure from Motion (SfM) and Multi-View Stereo (MVS).
C++ & Python: Ability to write performant C++ code and wrap it for a Python backend.
Libraries: Extensive experience with COLMAP, OpenMVS, AliceVision, or Ceres Solver.
Math: Understanding of Bundle Adjustment, Camera Intrinsic/Extrinsic calibration, and coordinate system transformations (EPSG).

Nice to Have:
Experience with NeRF (Neural Radiance Fields) or Gaussian Splatting (as a future R&D path).
Experience optimizing large LAS/LAZ files for web streaming (Entwine/Potree).

We are seeking a skilled developer to create Titan Two scripts compatible with computer vision or Helios 2. The ideal candidate should have experience in programming scripts specifically for Titan Two devices and a strong understanding of vision processing systems. Communication will be conducted through Upwork, and we do not accept payments or discussions via Discord. If you have a passion for gaming technology and can deliver high-quality scripts, we encourage you to apply!

**Relevant Skills: **
- Titan Two Scripting
- Computer Vision
- Helios 2 Integration
- Programming (C, C++)
- Problem-Solving
- Attention to Detail
I want to give you an example it a discord name input sense I want to learn this and be successful I just want my own willing to learn and be friends https://discord.gg/6TRJ95k3

• Describe your recent experience with similar projects
• Please list any certifications related to this project

We are looking for an experienced Native Mobile App Developer (iOS and Android) with strong expertise in AR-based measurement tools. The goal is to build an application that can measure real-world objects accurately without relying on flat surfaces or controlled backgrounds—similar in functionality to the Apple Measure app.
Project Requirements:
Develop native iOS and Android applications (Swift for iOS and Kotlin/Java for Android).
Ability to measure objects in the real world using AR technology, computer vision, and device sensors.
Measurements should work without constraints such as plain surfaces or clean background conditions.
Accuracy and stability must be comparable to the Apple Measure app.
Experience working with ARKit, ARCore, or similar frameworks is essential.
Clean, maintainable, and well-documented code.
Provide guidance on technical feasibility, edge cases, and measurement accuracy.
Ideal Candidate:
Proven experience building AR measurement or similar CV-based apps.
Strong understanding of 3D space mapping, depth estimation, and object tracking.
Experience with sensor fusion, LiDAR , and spatial computing.
Ability to deliver a reliable and user-friendly UI/UX.

Front-End React Developer (Design-First Engineer) — Long-Term Role

$500–$800 per month (depending on experience)
Confidential Startup Project

We’re looking for a front-end React engineer with exceptional design instincts to join our team on a long-term basis. This role is ideal for someone who has a sharp design eye, cares about detail, and can turn premium Figma designs into polished, production-ready interfaces for web and mobile.


What You’ll Do
• Build beautiful, responsive, and highly polished UI components in React / Next.js
• Implement premium Figma designs with pixel-perfect accuracy
• Work closely with our designer to bring screens, flows, and animations to life
• Create clean, modern UI interactions, transitions, and micro-animations
• Develop reusable UI systems and scalable front-end architecture
• Ensure interfaces feel premium, smooth, and intuitive (Apple-like quality)

🔒 MUST-HAVE EXPERIENCE (required)

Candidates without these will not be considered:
• Strong design eye and proof of premium UI work
• Experience integrating APIs into front-end applications
• Experience working with AI models or AI-driven features
• Experience with Spline 3D or similar 3D interactive frameworks
• Experience building mobile-friendly or mobile-first web apps
• Experience implementing haptic feedback, animations, or high-fidelity interaction states
• Advanced React + Next.js + Tailwind CSS


Nice to Have
• Experience with Lottie, Framer Motion, or custom micro-interactions
• Experience in healthtech or startups
• Ability to collaborate closely with designers
• Experience with scanning interfaces or camera-based flows is a bonus

About the Role
• 30–35 hours per week (long-term)
• Fully remote
• Highly confidential early-stage project
• You’ll be building UI for advanced AI + computer vision experiences
• Must be reliable, proactive, and take ownership

If you take pride in craftsmanship, UI detail, and building beautiful, modern interfaces, we’d love to hear from you.

Invitation to Collaborate on HARPi Project

Subject: Invitation to Collaborate on Medical AI Project - PyTorch, FastAPI, AWS
Dear Aryan,

I came across your profile on Upwork and was extremely impressed with your experience in building full-stack AI applications, particularly your expertise with PyTorch, FastAPI, and AWS. Your projects in computer vision and multi-agent LLM workflows are directly relevant to a critical challenge we are facing.

About Our Project: HARPi

We are developing HARPi, an AI-powered platform designed to assist radiologists in interpreting medical images (CT, MRI, Ultrasound). Our core engine uses a multi-model LLM consensus to analyze studies and generate reports. While it excels at reasoning and language, it has a significant weakness we need to solve.

The Challenge: Low Diagnostic Sensitivity (Perceptual Blindness)

Our core LLM-based system struggles to "see" and correctly identify specific clinical findings in medical images. For example, it has missed critical issues like esophageal inflammation, kidney stones, and compression fractures in CT scans. This has resulted in a low diagnostic sensitivity, which is unacceptable for a clinical product. Our current average accuracy is below 30%, and we need to get it above 90%.

The Solution: Hybrid Perception Architecture

To solve this, we are building a Hybrid Perception Architecture. The plan is to create a separate Model Controller & Processing (MCP) server that will host a suite of specialized, pre-trained medical imaging models (like TotalSegmentator, SAM-Med3D, etc.).

This MCP server will:

1.
Receive a medical imaging study (e.g., a NIfTI file from a CT scan).

2.
Run the appropriate specialized model (e.g., TotalSegmentator) to perform segmentation and analysis.

3.
Return a structured JSON output with the perceptual findings.

4.
This JSON output will then be fed into our core HARPi LLM engine for reasoning and report generation.

The Role & Our Questions:

We are looking for a skilled developer to build this MCP server and integrate these critical perception models. Your profile suggests you are a perfect fit for this task.

We would like to know:

1.
Is this project something you are interested in and confident you can deliver? This involves building a FastAPI-based server, integrating PyTorch models, handling medical image formats (NIfTI, DICOM), and deploying it on AWS with Docker.

2.
How do you see your skills adding value to this specific problem? Based on the challenge described, how would you approach building the MCP server and ensuring the specialized models effectively solve our sensitivity problem?

3.
Are you comfortable collaborating with our lead developer, Goodnews, who is based in Nigeria? This would involve regular communication via Slack/email and coordinating on API specifications to ensure the MCP server integrates smoothly with the core HARPi platform.

We have a detailed technical specification and a prioritized list of models ready for the right person. This is a critical, well-defined project with the potential for significant impact.

We look forward to hearing your thoughts.

Sincerely,

Bayant Dhindsa
CEO, EkonAi Inc.

My team is looking for guidance on what approaches/techniques to try out as we build out human action recognition classifiers for our product.

We currently pass in video segments to LLMs and they predict against our ontology. We have a solid validation dataset + prompt performing at ~70% accuracy.

This will start as a consultation where we present what we are doing, what the videos look like, and then seek feedback on what to try next.

LLMs/VLMs are working well to distinguish segments and high level actions, but lack granularity as we consider slight differences in actions.

I need a fully automated face-and-body swap pipeline that runs inside my Runninghub cloud workspace. The workflow must take a standard-resolution photo of any subject—male, female, or child, including both average and obese body types—and convincingly transplant them into a target image so they retain

• the identical clothing style and fit
• the original pose and background of the target shot
• an exact match of the target person’s facial expression

Reliability is key: every result should look like the same outfit was tailored to the new physique without distortion or mismatched wrinkles. I am open to your preferred deep-learning stack (e.g., Stable Diffusion, ControlNet, LoRA fine-tuning, face-landmark-driven warping, etc.) as long as it installs smoothly in a Python environment and consumes the standard GPU instance already available on Runninghub.

Deliverables
• Source code or notebook with clear, reproducible steps
• Requirements file / Docker snippet so the setup works out-of-the-box on Runninghub
• Simple CLI or REST entry point for batch processing
• A small test set demonstrating success on both average and obese examples, with before-and-after images for verification

I will consider the project complete when the workflow consistently produces swaps that are indistinguishable from an original photo in terms of clothing continuity, pose alignment, and facial-expression accuracy.

Skills: C Programming, Python, Software Architecture, C++ Programming, Image Processing, Computer Vision, Deep Learning, Generative Adversarial Network

Нужен софт на win11, который сможет пакетно генерировать десятки–сотни уникальных версий видео без потери качества, обходя современные системы поиска копий (видео-хэши, нейросетевые эмбеддинги, аудио-фингерпринтинг).

Задача непростая, включает работу сразу на нескольких уровнях:
геометрические трансформации (в т.ч. perspective warp),
цвет и свет (LUT, контраст, локальные изменения),
текстурные изменения (анимированное зерно, SR → downscale),
временные трансформации (interpolation, motion blur, time-warp),
лёгкие AI/ML-эффекты (super-resolution, стиль, depth-эффекты),
адаптивная обработка аудио (если оно есть),
рандомизация параметров и генерация метаданных “устройства”.

Все изменения должны быть незаметны глазу, но достаточно сильны, чтобы ролики считались разными алгоритмами.
Допускается очень длительное кодирование, особенно в Hard-режиме (1–2+ минуты на 1 секунду видео).
Требуется аккуратная архитектура с одним финальным перекодированием и полностью локальной работой.
Можно писать с нуля или переработать существующий скрипт. Любые ЯП, библиотеки, нейронки и прочее - главное результат

Прошу не беспокоить вайбкодеров — зря потратите и моё, и своё время.
4000 грн

Skills: Computer Vision, Deep Learning

For my academic project I need a small, self-contained Python program that can tell, from a single JPEG image, whether a chair is empty or occupied. The workflow I have in mind is simple:

• I place a JPEG file which have multiple chairs in it (or pass its path as an argument).
• Your script runs and prints, logs, or returns how many chairs are occupied.

The input will always be a static image, not a live feed or video stream. Beyond that, I’m flexible: you can rely on classical OpenCV techniques, a lightweight CNN in TensorFlow or PyTorch, or any hybrid approach you feel offers the best accuracy–as long as the code is clean, well commented, and easy for me to explain in class.

Please include in the hand-over:
1. Source code (.py) with in-line comments.
2. A short README describing environment setup, how to run the script, and how the detection logic works.
3. Optional sample images or links to any open dataset you used for testing, so I can reproduce your results.

Accuracy matters, but clarity and reproducibility matter even more for this assignment. If you think a pre-trained model or transfer learning will speed things up, feel free to recommend it as part of your proposal.

Skills: JavaScript, Python, Software Architecture, Machine Learning (ML), C++ Programming, Artificial Intelligence, Image Processing, OpenCV, Computer Vision, Deep Learning

Overview
We are building a night-vision goggle (NVG) calibration rig using two Raspberry Pi camera modules. The system must overlay or compare the two feeds and produce quantitative scores for both alignment and focus (blurriness). We are only interested in candidates who have already built real image-processing systems on Raspberry Pi using Python and OpenCV. If you have not done work of this nature, do not apply.

Project Requirements
• Capture and process two simultaneous Pi camera feeds
• Perform feature detection and alignment scoring
• Compute a repeatable focus/blurriness metric
• Output numeric alignment and focus scores (0–100 scale preferred)
• Provide basic live display of the results
• Deliver deterministic, stable performance on Raspberry Pi hardware
• Code must be clean, documented, and production-ready

Required Experience (non-negotiable)
• Proven work with Raspberry Pi + Python + OpenCV
• Direct experience with multi-camera input on Raspberry Pi
• Prior implementation of feature matching (ORB/SIFT/etc.)
• Prior implementation of focus or blur metrics (Laplacian variance or similar)
• Ability to demonstrate past projects (GitHub, video, screenshots, explanation)
• Ability to take full ownership of the task without hand-holding

Do Not Apply If
• Your experience is limited to tutorials, school projects, or chatGPT-generated code
• You’ve only used OpenCV on desktop machines
• You have never run two cameras simultaneously on a Pi
• You are “willing to learn on the job” (we’re not paying for your education)

Deliverables
• A complete Python application running on the Pi
• All image-processing logic implemented cleanly and efficiently
• Documentation for setup and operation
• Support during integration and testing with our hardware

Budget and Timeline
Flexible. We prefer someone who can start immediately and finish quickly due to prior experience.

I am looking for an experienced Python + Deep Learning developer to build a complete software system for Emotion Recognition from Handwritten Text.

The system will take an image of handwritten text (Hindi or English), process it using deep-learning models, and detect six human emotions:

Happy

Excited

Tender

Sad

Angry

Scared

This application will be used for an academic research project, so the quality of the model, accuracy, and documentation matter a lot.

Key Features Required
1. Image Upload Interface (GUI) – Tkinter

Upload a handwritten image

Preview inside the app

Predict Emotion button

Train / Re-Train Model button

Display predicted emotion + confidence percentage

Save logs of all predictions

2. Preprocessing Steps

The system should perform essential image processing such as:

Convert to grayscale

Binarization (Otsu or adaptive threshold)

Noise removal

Resizing to model input size

Optional: line/word segmentation for cleaner feature extraction

3. Deep Learning-Based Classification

The model should classify handwritten images into 6 emotions.

You may use:

Model Options

TensorFlow / Keras

A custom CNN

Or Transfer Learning such as:

ResNet50

EfficientNet

VGG16

Output

6 emotion classes

Probability score / confidence for each prediction

4. Handwriting Feature Extraction (Optional but Preferred)

In addition to CNN features, the system should optionally extract handcrafted handwriting features such as:

Ink Density

Slant Angle

Stroke Thickness / Pressure

Baseline Stability

Word Spacing

Line Spacing

If possible, fuse these features with CNN features (feature-level fusion) to improve accuracy.

Deliverables

The final delivery should include:

Fully functional Tkinter GUI Application

Training script + pre-trained deep learning model (.h5)

Custom preprocessing pipeline

Handwriting feature extraction module (if included)

Evaluation report (Accuracy, Precision, Recall, Confusion Matrix)

Clean, modular Python code with comments

README or PDF documentation explaining:

How to run the app

How to add new data

How to retrain the model

Dependencies and environment setup

Tech Stack Required

Python

TensorFlow / Keras

NumPy

OpenCV

Pillow

Tkinter

Scikit-learn

Bonus Skills:

Transfer Learning

Model optimization

Feature fusion (CNN + handcrafted features)

Explainability (Grad-CAM)

Description
I’m looking for a talented full-stack developer to help improve an existing system that analyzes complex PDF documents and converts them into structured data. You’ll work across Python, Node.js, React, and modern AI tools on an already running project.

What You’ll Do

Enhance Python code for processing and analyzing PDF documents

Integrate and refine AI/ML models for document understanding and detection

Build and maintain Node.js REST APIs for the processing pipeline

Work on a React UI to visualize and review extracted data

Debug, optimize, and improve accuracy and performance

Required Skills

4+ years full-stack development experience

Strong Python skills (data processing / automation)

Experience using AI/ML in production (e.g. document/image analysis, model integration, or AI APIs)

Solid Node.js and REST API development

Good React knowledge

Experience working with technical/engineering/architectural PDFs or complex documents

Nice to Have

Experience with CAD, vector, or technical drawing formats

Background in engineering, architecture, or similar fields

Experience with computer vision / document processing frameworks (OpenCV, PyTorch, TensorFlow, etc.)

Offer

Long-term collaboration if it’s a good fit

Clear technical challenges and ownership

Competitive compensation, remote