Everything is very professional, the specifications are fully completed and even exceeded. I recommend.
16
0
Budget: 20000 UAH Deadline: 13 days
Good day. I will set up video processing optimization on Node.js using FFmpeg, ensuring pipeline acceleration without loss of quality. I am ready to start now. I would appreciate the collaboration.
1 294
1
Budget: 20000 UAH Deadline: 7 days
Hello. I work with Python. I am ready to collaborate. Please contact me. Can I get acquainted with the project?
4
0
Budget: 20000 UAH Deadline: 6 days
Hello, Anatoliy!
We are doing exactly such pipelines in Python — video processing through FFmpeg + OpenCV, extracting key points through MediaPipe, with an emphasis on speed. The levers we usually use to increase throughput: hardware decoding in FFmpeg (nvdec/vaapi), smart frame sampling instead of full decoding, batching MediaPipe inference, and parallelization across cores — depending on where the real bottleneck is right now.
Stack: Python, OpenCV, FFmpeg, MediaPipe. Price 20,000 UAH, estimated time 4-6 days after agreeing on the technical specifications.
One clarification to assess accurately: where is the speed currently dropping the most — during decoding, MediaPipe inference, or dataset recording? And what is the approximate volume and resolution of the input videos, and what should be the output — frames, landmarks, annotations?
I am ready to demonstrate the approach on a short sample so you can see the real gain even before the solution.
Petro Pankov, BotCraft Group
8
0
Budget: 20000 UAH Deadline: 5 days
Good day! I worked on Python video processing pipelines — FFmpeg for batch decoding and cutting, OpenCV for frames, heavy operations were queued so that it could handle not 3 files, but thousands. To be precise: is MediaPipe here for labeling (pose/face) or as a frame filter? And is there already code that needs to be optimized, or a pipeline from scratch? Please provide a couple of typical videos and the desired dataset format — I will complete the first working stage in 5 days and show the real speed on your data.
34
0
Budget: 20000 UAH Deadline: 6 days
Good day! I have completed similar projects, ready to collaborate, write to me - we will discuss the details.
32
0
Budget: 20000 UAH Deadline: 5 days
20,000 UAH is a realistic budget for the first stage if we need to audit the current approach, identify bottlenecks, and assemble a working pipeline for preparing the video dataset.
We would start by checking the processing speed on your actual videos, after which we would optimize decoding, batch processing, OpenCV, FFmpeg, and MediaPipe. We can offload heavy operations to a more stable scheme so that the result works not only on 3 test files but on a normal volume of data.
> We need to see a sample video and the desired format of the output dataset.
> It is important to know whether there is already code that needs to be accelerated or if the pipeline needs to be built from scratch.
Look, there’s a nuance here - without this data, calculating the full implementation accurately is risky. However, the first technical stage can be completed within your budget in 5 days, after which we can provide a calm assessment of the entire system.
Similar logic cases from Ingello:
> https://business.ingello.com/vorfahr - automation of complex processes and data processing
> https://business.ingello.com/fractal - systematic automation where the architecture of processing is important
> https://systems-fl.ingello.com/ua - a brief overview of our approach to complex systems
We can start after reviewing the materials. It's better to measure seven times and launch properly once =)
5
0
Budget: 20000 UAH Deadline: 7 days
Hello, I worked on a Video Processing System for e-commerce — we automated the processing pipeline for 10,000+ videos, speeding up the process by 340% using FFmpeg and OpenCV parallel processing.
Do you already have an understanding of the dataset size and what specific metadata needs to be extracted through MediaPipe — positions, facial expressions, or something else?
I suggest we get in touch; I will provide you with free technical consultation and we can outline a development plan + I will tell you about my team!
49
0
Budget: 20000 UAH Deadline: 10 days
Hello.
I can help with optimizing video processing speed in Python.
I work with OpenCV, FFmpeg, MediaPipe, and tasks where it's necessary to speed up frame processing, reduce CPU/GPU load, and eliminate unnecessary delays in the pipeline.
First, I will review the current code and determine where time is being lost: reading video, processing frames, MediaPipe model, recording results, or FFmpeg commands. After that, we can optimize the logic: reduce unnecessary operations, adjust FPS/resolution, offload some work to FFmpeg, add multithreading, or implement other solutions depending on the task.
I am ready to look at the code, a sample video, and the desired result; after that, I can assess the timeline and cost.
20
0
Budget: 20000 UAH Deadline: 5 days
Зрозумів завдання: потрібно оптимізувати швидкість обробки відео в існуючому Python-пайплайні для підготовки датасету.
Основна моя зона — Python-бекенд і асинхронна обробка даних, з ffmpeg, OpenCV і mediapipe напряму так щільно не працював, але це стандартні бібліотеки для такого типу задачі — розберуся швидко і знаю куди дивитися в першу чергу: паралелізація по кадрах або файлах через multiprocessing, винесення важких операцій в ffmpeg напряму замість Python-обгорток, профайлінг вузьких місць перед оптимізацією замість здогадок.
Скиньте, будь ласка, поточний скрипт і приклад відеофайлу з датасету — подивлюся де саме просідає швидкість (I/O, CPU-bound обробка кадрів чи сама модель mediapipe) і дам конкретний план з оцінкою приросту швидкості.
Термін залежить від того що покаже профайлінг, але для точкової оптимізації типово це кілька днів.
8
0
Budget: 20000 UAH Deadline: 10 days
Hello! I can optimize video processing speed by up to 50% through effective implementation of a pipeline using OpenCV and FFmpeg. My approach includes building asynchronous data processing and using MediaPipe for real-time video analysis. Please send me video examples or project specifications in private messages so I can prepare a detailed implementation plan.
15000 UAH
56
0
Budget: 21111 UAH Deadline: 1 day
Hello! I am an experienced developer, capable of creating bots of any complexity, ranking in the TOP-4 in the "Bot Development" category. I also develop websites and API documentation in Python, where I rank in the TOP-2.
Regarding your project, I have some clarifying questions that will affect the assessment of your task and help understand what you want. Please write to me to clarify all the details!
You can review my skills in my resume 👉 
Freelancehunt
My work is also published in my portfolio 👉 Freelancehunt
💻 I am also the SEO of a team consisting of a FrontEnd developer and a BackEnd developer! So if necessary, our team of three can organize a turnkey development for you!
I look forward to your response, thank you.
4
0
Budget: 20000 UAH Deadline: 1 day
Good day! I have experience with Python and video processing (OpenCV/FFmpeg), ready to take on the task. Message me privately, and we will discuss the details.
52
1
Budget: 20000 UAH Deadline: 4 days
Good day
I am ready to complete your work
Feel free to write, I will be happy to help with your task
I have experience working with these libraries
18
3
Budget: 20000 UAH Deadline: 7 days
Good day. I have extensive experience working with the necessary technologies in Python/C++. I can help you with the development. I would like to discuss the project in more detail for an accurate assessment of the workload, please write.
15
0
Budget: 20000 UAH Deadline: 14 days
Hello, Anatoly!
I am a senior developer with ten years of experience. I love optimizing speed — it's my specialty.
FFmpeg — I have edited and cut videos directly through it (orchestration, fragmentation, synchronization of tracks), also in Python. OpenCV + pandas — frame processing and datasets, I have extensive experience.
Regarding speed — this is the main headache of such heavy pipelines, and I have repeatedly tackled it (parallel processing on hundreds of processes with streaming and backpressure, batch on rented GPUs via VastAI). That is, optimizing throughput, not just "making it work."
About the models: I have worked a lot with local HuggingFace / Ollama vision models on images and videos.
Feel free to message me to discuss the pipeline, bottlenecks (decoding, frame processing, model inference on CPU, GPU, recording, etc.)
I am ready to start immediately.
0
0
Budget: 20000 UAH Deadline: 14 days
Hello, Anatoliy! Everyone here has already written about FFmpeg, batching, and multiprocessing, but there is a nuance that can easily spoil all the speed gains: MediaPipe itself creates its own thread pool internally, and if you simply distribute the processing across all cores using multiprocessing, these internal threads will start competing for the CPU, and the speed may even drop instead of increasing. I would first profile the actual load on your videos, and then adjust the number of processes along with limiting the threads inside each MediaPipe instance so that parallelism actually works instead of eating itself. Please send an example video and let me know if there is already some Python code that needs to be accelerated, or if the pipeline needs to be built from scratch, and what hardware will be used (number of cores, whether there is a GPU).
Budget: 20000 UAH Deadline: 5 days
Good day!
I see that the essence is not just to "process the video," but to make it work quickly. This is precisely where pipelines on OpenCV + MediaPipe often lag, usually for predictable reasons.
Where the bottleneck mostly hides:
- frame-by-frame loop in pure Python instead of batch processing;
- software video decoding where it can be handed off to FFmpeg with hardware acceleration (NVDEC / VAAPI);
- MediaPipe inference at full resolution when a reduced frame is sufficient for the task;
- unnecessary copying of frames between memory and format conversions;
- a single thread where decoding, processing, and writing can be done in parallel.
How I would act specifically: first, I would profile the current pipeline to see the real bottleneck, not just guess. Then I would remove the most expensive parts: hardware decoding via FFmpeg, batching frames, downscaling before inference, parallel queues for reading, processing, and writing. After profiling, I would name a realistic speed increase, without making unrealistic promises.
Python and accelerating heavy pipelines is my main work. Please send a piece of code or a description of the current process: resolution and FPS of the video, what exactly you are calculating through MediaPipe, and on what hardware it runs. I will immediately point out where the speed drops and what I would start with.
I am ready to start right away.
31
0
Budget: 20000 UAH Deadline: 10 days
A pipeline is needed that takes raw videos and outputs a ready-labeled dataset with minimal manual intervention.
Here’s how I build it: first, an FFmpeg layer for cutting, resizing, and normalizing FPS with parallel execution through multiprocessing.Pool. Next, MediaPipe for extracting key points or segmentation, with the result written into structured annotations. The critical speed point here is batching OpenCV operations and avoiding unnecessary decodings: we read the frame once, perform all transformations in memory, and only then write to disk.
What is the target throughput: how many hours of video need to be processed per session and what hardware is available (CPU core count, is there a GPU for MediaPipe)?
3
0
Budget: 20000 UAH Deadline: 8 days
Hello! I am ready to take on the development and optimization of your video pipeline.
I have extensive experience in building complex data processing systems (ETL pipelines) in Python, where it is critically important to optimize speed, memory usage, and eliminate bottlenecks. Working with arrays of video data fully falls under this architectural paradigm.
How I will address the speed optimization task:
FFmpeg and OpenCV: I will set up the fastest possible extraction and transformation of frames without unnecessary re-encoding. Instead of sequential processing, I will implement parallelism (through multiprocessing or asynchronous queues) so that FFmpeg does not idle and utilizes system resources at 100%.
MediaPipe: The use of computer vision models often blocks the main thread. I will optimize the transfer of frames to MediaPipe using batching and proper memory buffer management, which will significantly reduce I/O operation time.
Architecture: You will receive not just a "raw" script, but a reliable, object-oriented pipeline that is easily scalable and ready for quick deployment via Docker.
I am ready to review your current code or discuss technical requirements for building the pipeline from scratch. Feel free to message me privately!
0
0
Budget: 19000 UAH Deadline: 10 days
Hello! Optimizing video processing speed for datasets is a great architectural challenge. Preparing clean data is the most crucial stage in machine learning, and my experience in developing pipelines, backed by the Associate Data Scientist in Python certification, allows me to tackle this issue without "workarounds."
My vision for the technical solution (Speed Optimization):
FFmpeg instead of OpenCV for I/O: Reading and writing frames through the standard cv2.VideoCapture often becomes the main "bottleneck." I propose delegating video decoding to low-level FFmpeg (using NVENC/CUDA hardware acceleration if a GPU is available), while keeping OpenCV exclusively for lightweight matrix transformations of frames.
Asynchronous Processing and Batching: Processing frames in MediaPipe (especially heavy detection models) heavily burdens the CPU when executed sequentially. Implementing multiprocessing will allow us to utilize all CPU cores by splitting the video into batches and processing them in parallel.
Memory Optimization: Eliminating unnecessary copies of NumPy arrays in memory when transferring frames between FFmpeg, OpenCV, and MediaPipe (using shared memory for multiprocessing).
Main project risk: In multithreaded processing of large video volumes, memory leaks often occur, leading to application crashes (OOM errors). The pipeline will be configured for controlled resource release through forced Garbage Collection after each processed chunk.
8
2
Budget: 20000 UAH Deadline: 5 days
I have 7+ years of commercial experience in Python development. I have worked with image and video processing optimization, OpenCV, FFmpeg, MediaPipe, as well as multithreaded and asynchronous data processing.
I am ready to conduct an audit of the current solution, identify bottlenecks, and optimize performance without sacrificing quality. If necessary, I can redesign the processing pipeline, optimize frame handling, reduce unnecessary operations, utilize hardware acceleration (if available), and speed up the performance of OpenCV/FFmpeg and MediaPipe.
Before starting work, I will perform profiling to determine where performance is being lost, after which I will propose the most effective solution.
Timeline: 5–7 days (after reviewing the project, I will be able to provide a more accurate estimate).
I would be happy to discuss the details and get started.
0
0
Budget: 20000 UAH Deadline: 7 days
I already have a practically ready similar solution for automated video dataset preparation - it can be quickly adapted to your format and launched, I'm in touch =)
The approximate first working stage - 20,000 UAH and 7 days.
In this stage, I would include profiling the current code, optimization through FFmpeg, OpenCV, and MediaPipe, batch processing, caching intermediate results, error logging, and a proper structure for the output dataset.
We can keep it simple - first, identify the main bottlenecks in speed, and only then add parallel processing or GPU if it really provides a gain.
Questions regarding the estimate - what is the approximate volume of video and what is the target processing time for one file?
Also important - is there already existing Python code that needs to be accelerated, or does the process need to be built from scratch?
Similar logic cases from Ingello
- https://business.ingello.com/vorfahr - automation and AI agents for complex processes
- https://business.ingello.com/fractal - architecture of agent automation and task processing
Main profile for FreelanceHunt
- https://systems-fl.ingello.com/ua
Budget: 20000 UAH Deadline: 5 days
"Task type: video processing speed optimization - Python developer with experience in OpenCV, FFmpeg, MediaPipe"
No problem.
6
2
Budget: 19500 UAH Deadline: 12 days
Anatoliy, I understand that you need an efficient pipeline for automated and rapid preparation of video datasets. The main goal here is to significantly speed up video processing using the capabilities of Python, OpenCV, FFmpeg, and MediaPipe.
I will design and implement such a pipeline that will be optimized to work with your video data. The result will be a significant reduction in the time required to prepare datasets and an increase in the overall efficiency of your work.
Are there any specific requirements for the output format of the dataset or the type of metadata that needs to be extracted from the video?
9
0
Budget: 20000 UAH Deadline: 1 day
Good day, Anatoliy!
In general, the task is clear, but to provide an accurate response regarding the deadlines and price, I would like to clarify some questions that arose after analyzing your task.
Please write in private messages – we will discuss the details and your wishes.
10
0
Budget: 20000 UAH Deadline: 1 day
Hello. I will focus on deep optimization through parallel processing of frames and efficient use of GPU when possible, integrating FFmpeg for hardware acceleration of encoding/decoding and OpenCV/MediaPipe for algorithmic optimization. I have significant experience in developing high-performance video analysis systems, particularly using stream processing and custom C++/Cython extensions for Python, which allows achieving maximum speed. My developments include ready-made templates for efficient management of video streams and containerization of computations, ensuring scalability and stability of the solution. I suggest discussing all implementation details, final budget, and deadlines in personal messages.
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