Search Results for "deep learning toolbox"

Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu and Ali to complete text recognition locally. ...

The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials!...

...Unlike traditional speech translation systems that rely on multi-stage pipelines, Transcoder directly translates one speaker’s video into another language while preserving facial expressions, lip-sync, and vocal identity. Designed for real-time use and production-grade pipelines, Transcoder combines advanced deep learning models with GPU acceleration to deliver high-quality translations across languages. It’s built with researchers and developers in mind, offering tools for testing, evaluating, and deploying AI-driven media localization.

...Upscaling is done entirely on the CPU. Blender renders a low-resolution image. The Real-ESRGAN Upscaler upscales the low-resolution image to a higher-resolution image. Real-ESRGAN is a deep learning upscaler that uses neural networks to achieve excellent results by adding in detail when it upscales.

Sharpen your low-resolution pictures with the power of AI upscaling. SuperImage is a neural network-based image upscaling build with the MNN deep learning framework and the Real-ESRGAN algorithm. By leveraging the power of your device's GPU, SuperImage is able to upscale and restore the details of your images without uploading them to the internet, keeping your data secure. SuperImage is a neural network-based image upscaling application for Android built with the MNN deep learning framework and Real-ESRGAN. ...

BasicSR is a deep learning framework designed for advanced video restoration tasks such as video super-resolution, deblurring, and denoising. Unlike single-image restoration models, EDVR addresses the temporal dimension by aligning multiple video frames using deformable convolutional layers in a coarse-to-fine manner, allowing it to effectively handle large motion and complex scene dynamics.

Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a...

IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware:

...DeepFaceLab is an open-source deepfake system that enables users to swap the faces on images and on video. It offers an imperative and easy-to-use pipeline that even those without a comprehensive understanding of the deep learning framework or model implementation can use; and yet also provides a flexible and loose coupling structure for those who want to strengthen their own pipeline with other features without having to write complicated boilerplate code. DeepFaceLab can achieve results with high fidelity that are indiscernible by mainstream forgery detection approaches. ...

...The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference.