Python Object Detection Demo App

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@quan9824

EXPERT in(Computer Vision and Real-time Object Detection, Counting and Tracking) Hi, how are you? I checked your detail carefully. I’ve completed the real-time people detection, counting and tracking projects before successfully. Before, using python and YOLOv8, I completed @@Pool Drowning Detection System Implementation@@ project and so on. You can check my works history on my portfolio. I am sure this field and I will do my best. I always thought "It is your job, it is also my job". Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@SmartCodeDev

Hello, I can build a clean and lightweight Python desktop application that performs real-time object detection on video using a pre-trained YOLO model. The app will load a local video file, process it frame by frame, and draw bounding boxes with labels for Person and Mobile in real time using OpenCV. I will keep the code simple and well structured, using standard libraries such as OpenCV and a YOLOv5 or YOLOv8 pretrained model for reliable detection without any need for training. The output will be displayed in a basic desktop window with FPS shown for performance visibility, exactly as you described. I will also provide fully commented source code, a clear README with setup and run instructions, and a short demo video or GIF showing the detection in action. The final solution will be easy to run on Windows with minimal dependencies and designed for clarity and learning purposes.

@mayankinnovative

Hi, As per my understanding: You need a simple Python desktop app that processes a local video, detects only Person and Mobile (cell phone) using a pre-trained YOLO model, draws bounding boxes with labels, and displays the result with FPS—keeping the code clean, readable, and easy to run on Windows. Implementation approach: I will build the app using Python with OpenCV and YOLOv8 (Ultralytics) for reliable detection and simpler setup. The script will load your video, run frame-by-frame inference, filter detections to only “person” and “cell phone,” and render bounding boxes with labels and FPS in a lightweight window. The code will be modular and well-commented for clarity. I’ll include a minimal dependency setup (pip install), and optionally provide an executable using PyInstaller. A short demo recording and a concise README will explain the pipeline (model load → frame capture → inference → filtering → rendering). A few quick questions: 1. Do you prefer a Python script only or also a .exe build? 2. Any specific YOLO version preference (v5/v8)? 3. Will you provide the sample video or should I include one? 4. Do you want GPU support or CPU-only is fine? 5. Target Python version (e.g., 3.10/3.11)?

@KarthikTechCon

Hi, I’m Karthik with 15+ years of experience in Python development, computer vision, OpenCV pipelines, and AI-based object detection systems. I can build your lightweight Python desktop demo app that detects Person and Mobile objects in video using OpenCV and YOLO models with clean, easy-to-understand code. What I can deliver: ✔ Python desktop application for Windows ✔ YOLOv5/YOLOv8-based object detection ✔ Person & mobile phone detection with bounding boxes ✔ Real-time frame-by-frame inference ✔ FPS display overlay ✔ MP4/local video support ✔ Clean commented source code ✔ Simple readable architecture for learning/demo purposes Technical Stack: • Python • OpenCV • PyTorch / ONNX Runtime • YOLOv5 or YOLOv8 • Lightweight dependency setup Deliverables: ✔ Fully working Python script/executable ✔ Source code with inline comments ✔ README with setup/run instructions ✔ Screen-recorded demo/GIF ✔ Basic pipeline explanation I’ll ensure the implementation remains lightweight, easy to run, and beginner-friendly while maintaining reliable detection performance. Ready to start immediately and deliver quickly. Regards, Karthik

@Web1devloper

As someone who has spent over eight years turning complex datasets into actionable business insights, I am confident in my ability to produce the clean and easy-to-understand solution your project requires. I am familiar with the powerful combination of OpenCV and YOLO models and have successfully implemented them in various data processing pipelines previously. My experience with deploying robust and efficient machine learning (ML) models using Python is unmatched. I can ensure that every step, from loading the video to real-time inference, will be smooth, reliable, and well-optimized for performance without compromising on accuracy. My background in data storytelling and dashboard development significantly aligns with your project needs. I am skilled in using various data visualization tools such as Power BI and Looker which mirror the basic requirements you have laid out - a neat representation of the streaming frames with their respective bounding boxes, class names, and a clear FPS readout. Pouring my expertise into this project would mean you get not only a functional desktop application but also a solid codebase backed by clear inline comments - facilitating future customization or changes to fit your evolving needs.

@chiragardeshna

Hi there, Strong alignment with this project comes from experience building Python-based computer vision demos, YOLO object-detection applications, and OpenCV-powered real-time video processing systems focused on clean and understandable implementation. Clear understanding of the requirement to create a lightweight desktop application that processes local video files, detects Person and Mobile classes using a pre-trained YOLO model, and displays live bounding boxes with FPS monitoring. Hands-on expertise with Python, OpenCV, YOLOv5/YOLOv8 workflows, Torch and ONNX inference pipelines, video-frame processing, and real-time object detection ensures reliable execution and easy-to-follow project structure. Risk is minimized through modular code organization, lightweight dependency management, optimized inference handling, structured inline comments, and concise setup documentation for smooth Windows execution. Available to start immediately happy to discuss the preferred YOLO version, packaging approach, or share similar computer-vision and detection-based demo projects. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@BhavinOndhiya

Hi, I can build this clean and simple Python object detection demo exactly as you described. I’ll use OpenCV with a pre-trained YOLO model (v8 or v5) to detect **Person** and **Mobile Phone** in a sample video, with real-time frame processing, bounding boxes, and FPS display in a lightweight desktop window. What I’ll deliver: • Fully working Python script (Windows-ready) • Clean, well-commented code for easy understanding • Real-time detection (Person + Mobile) using video input • Simple OpenCV window with bounding boxes + labels + FPS • Short demo video/GIF showing it working • README with setup (pip install), run steps, and clear explanation of the pipeline Approach: * Load YOLO model (pre-trained, no training needed) * Process video frame-by-frame via OpenCV * Filter only required classes (person, phone) * Render detections with labels and confidence * Keep code minimal and beginner-friendly Timeline: 1–2 days If you have a sample video, you can share it, or I can include one for testing. Ready to start immediately.

@manoval09

Dear Client, Thank you for posting this project. I have extensive experience in AI, Python. I'm confident I can deliver high-quality results: - Technical expertise: Proven track record with similar automation projects - Timeline: Efficient delivery with minimal delays - Support: Full testing and debugging included I'm ready to start immediately and am flexible with communication. I would appreciate the opportunity to discuss your specific requirements in detail. Best regards, Petrovich

@indreshpnk

Hello, I understand you need a simple Python-based desktop application that can detect **Person and Mobile** objects in a sample video using a pre-trained YOLO model and display real-time bounding boxes with labels. The goal is to keep the implementation clean, easy to understand, and suitable for learning/demo purposes. Here’s what I can provide: • YOLO-based object detection (v5/v8) with pre-trained weights for Person & Mobile classes • Real-time video processing using OpenCV with bounding boxes and FPS display • Simple desktop viewer window for playback with minimal dependencies and clean code structure I bring over 4+ years of experience in Python development, OpenCV, and deep learning-based computer vision projects, including object detection and real-time video analytics using YOLO models. My focus is always on writing readable, well-structured, and production-ready code even for demo-level applications. Just to clarify a few things: • Do you have a preferred YOLO version (v5, v8, or ONNX format)? • Should the final deliverable include a Windows executable (PyInstaller) or only the Python script? I will also provide a clear README with setup steps, run instructions, and a short explanation of the detection pipeline, along with a screen recording/GIF of the working demo. Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@arshkumar213

Hi, I can build your Python OpenCV + YOLO app to detect Person and Mobile in video with real-time bounding boxes and FPS display. I’ll keep the code clean and well-commented, using a pre-trained YOLO model for easy setup.

@tannia05

Hi, I am a CSE student and an AI enthusiast. I have experience working with Python and OpenCV. I can build this desktop application for you using a pre-trained YOLOv8 model to accurately detect 'Person' and 'Mobile' in real-time. I will provide clean, well-commented source code, a setup guide (README), and a demo video of the app in action. I am ready to start immediately and ensure a high-quality delivery.

@dilyavera

Hi, I see exactly what you need – a clean, no‑excess demo that detects only Person and Mobile in a sample MP4, shows boxes + labels, and displays FPS. No webcam, no extra classes. My approach: Python + OpenCV + YOLOv8 (pre‑trained on COCO, filtered to class 0 = person, class 67 = cell phone) Process frame‑by‑frame, draw boxes, overlay FPS counter Output: simple OpenCV window Deliverables: Single script (runs on Windows, few lines) – or packaged .exe if you prefer Inline comments explaining model → frame → boxes 30‑second video/GIF of the demo README: high‑level pipeline (loading model, detection loop, rendering) + install/run steps Why this fits: Person + Mobile exactly as requested Works from local MP4 (you provide or I use sample) Easy to read – made for learning

@adrian1a

Hi there. I don't know whether the teacher or the student, but I am glad to think that this helps with learning. As an AI developer, it clearly indicates what you want. I can develop what you want in 2 days. Thank you.

@zhangm24

As an experienced algorithm engineer specialized in AI and data-driven methods like yourself, I'm uniquely positioned to deliver what you need for this project. With solid knowledge of Python and proficiency in influential libraries like OpenCV, my capabilities encompass efficient use of pre-trained deep learning models such as YOLO, evident from my previous work on GNSS based atmospheric remote sensing and environmental monitoring projects. In addition to being adept with Python and machine learning algorithms, I have extensive experience with web development and data management, which adds complementary value to the neat UI Minimalist approach needed for the project. I'm committed to delivering robust, easy-to-understand, well-commented code that meets your requirements precisely while providing a comprehensive README for smooth use of the app I believe my versatile background in data analysis and problem-solving allows me to approach tasks systematically with precision, thoroughness, and speed. With these skills along with my significant ability for quick adaptation to new technology and research tasks, I am confident that I could meet your expectations diligently and efficiently.

@MthokoD

I'll handle it, don't worry. Let's engage further on your vision for a clean user-centric demo app that spotlights Person and Mobile classes in video via a modern pre-trained YOLO model. My approach involves keeping the implementation straightforward yet effective and leveraging OpenCV with PyTorch or ONNX for efficient real-time inference. I'll also include a lightweight desktop window that shows live FPS and bounding boxes. Although I am new to the Freelancer platform, I bring a wealth of real-world experience and a proven track record of successfully delivering projects beyond this platform. In a recent project I built a similar Python application that detected multiple object classes in live video streams while balancing speed and clarity with well-documented code and a concise Readme. Let's connect and explore how we can bring your vision to life. Regards, Ahead Solutions P.S. If you're not happy — which we doubt — you don't pay.

@pushkarp58

Hi, I’ll build you a clean, easy-to-understand Python desktop app that detects Person and Mobile Phone in video using a pre-trained YOLO model, with real-time bounding boxes and labels—focused purely on clarity and demonstration, not over-engineering.

@TARUN0998

Hi, I can build a simple, clean, and reliable Python desktop application that detects Person and Mobile in video using OpenCV and a pre-trained YOLO model. I’ll use OpenCV together with a lightweight YOLO implementation such as YOLOv8 (via PyTorch or ONNX) to ensure fast and accurate real-time detection. The application will load your sample video, process it frame-by-frame, and display bounding boxes with class labels and FPS in a minimal desktop window. What I’ll deliver: Fully working Python script (Windows-ready) Clean, well-commented code for easy understanding Real-time detection of Person and Mobile Phone with stable performance Simple UI (video window + FPS display, no unnecessary complexity) Short demo video/GIF showing the output Concise README with setup (pip install), run instructions, and a clear explanation of the pipeline My approach: Use pre-trained YOLO weights for accurate detection Optimize inference for smooth FPS on standard systems Keep dependencies minimal and code modular for easy learning and reuse I’ve worked with object detection pipelines and focus on making implementations both practical and easy to understand, especially for demo and educational use. Timeline: 1–3 days for full delivery with documentation. Let’s get this running quickly and cleanly.

@Rachit1704

I have build a similar project for my University Fest and won Gold medal for two consecutive years. The project was heavily inspired by Pranav Mistry's wearable Tech. We initially started with a Laptop and colors markers to detect hand gestures then moved to machine learning to detect hands. We also shipped the code to a Raspberry PI 5, webcam and Battery to make it truly wearable prototype.