Vidformer is a drop-in replacement for OpenCV's (cv2) VideoCapture class that significantly accelerates video annotation scripts by leveraging hardware decoding. It maintains API compatibility with existing cv2 code, making integration simple, while offering a substantial performance boost, particularly for I/O-bound annotation tasks. By efficiently utilizing GPU or specialized hardware decoders when available, Vidformer reduces CPU load and speeds up video processing without requiring significant code changes.
Taner Şener, the creator of FFmpegKit, a commercial wrapper around FFmpeg for mobile development, announced that he's ceasing development and support. Due to complexities in maintaining FFmpeg across various architectures and operating systems, increasing maintenance burden, and inadequate revenue to justify continued development, he's chosen to shut down. Existing clients can continue using their purchased licenses, but future updates and support are discontinued. The core issue is the difficulty of sustainably supporting a complex project like FFmpegKit, even as a paid product, given the rapid pace of mobile development and the substantial engineering effort required for compatibility. While acknowledging the disappointment this will cause some users, Şener emphasizes the unsustainable nature of the project's current trajectory and thanks users for their support over the years.
Hacker News users discuss the author's decision to discontinue FFmpegKit, an iOS/Android FFmpeg library. Several commenters express disappointment, highlighting FFmpegKit's ease of use compared to alternatives like MobileFFmpeg. Some suggest the decision stems from the difficulty of maintaining cross-platform compatibility and the complex build process involved with FFmpeg. Others speculate about the author's motivation, including burnout or lack of financial viability. A few offer alternative solutions or express hope for a successor project. The lack of clear documentation for building FFmpeg directly is also a recurring concern, reinforcing the value of projects like FFmpegKit.
The open-source "Video Starter Kit" allows users to edit videos using natural language prompts. It leverages large language models and other AI tools to perform actions like generating captions, translating audio, creating summaries, and even adding music. The project aims to simplify video editing, making complex tasks accessible to anyone, regardless of technical expertise. It provides a foundation for developers to build upon and contribute to a growing ecosystem of AI-powered video editing tools.
Hacker News users discussed the potential and limitations of the open-source AI video editor. Some expressed excitement about the possibilities, particularly for tasks like automated video editing and content creation. Others were more cautious, pointing out the current limitations of AI in creative fields and questioning the practical applicability of the tool in its current state. Several commenters brought up copyright concerns related to AI-generated content and the potential misuse of such tools. The discussion also touched on the technical aspects, including the underlying models used and the need for further development and refinement. Some users requested specific features or improvements, such as better integration with existing video editing software. Overall, the comments reflected a mix of enthusiasm and skepticism, acknowledging the project's potential while also recognizing the challenges it faces.
FFmpeg by Example provides practical, copy-pasteable command-line examples for common FFmpeg tasks. The site organizes examples by specific goals, such as converting between formats, manipulating audio and video streams, applying filters, and working with subtitles. It emphasizes concise, easily understood commands and explains the function of each parameter, making it a valuable resource for both beginners learning FFmpeg and experienced users seeking quick solutions to everyday encoding and processing challenges.
Hacker News users generally praised "FFmpeg by Example" for its clear explanations and practical approach. Several commenters pointed out its usefulness for beginners, highlighting the simple, reproducible examples and the focus on solving specific problems rather than exhaustive documentation. Some suggested additional topics, like hardware acceleration and subtitles, while others shared their own FFmpeg struggles and appreciated the resource. One commenter specifically praised the explanation of filters, a notoriously complex aspect of FFmpeg. The overall sentiment was positive, with many finding the resource valuable and readily applicable to their own projects.
The author recreated the "Bad Apple!!" animation within Vim using an incredibly unconventional method: thousands of regular expressions. Instead of manipulating images directly, they constructed 6,500 unique regex searches, each designed to highlight specific character patterns within a specially prepared text file. When run sequentially, these searches effectively "draw" each frame of the animation by selectively highlighting characters that visually approximate the shapes and shading. This process is exceptionally slow and resource-intensive, pushing Vim to its limits, but results in a surprisingly accurate, albeit flickering, rendition of the iconic video entirely within the text editor.
Hacker News commenters generally expressed amusement and impressed disbelief at the author's feat of rendering Bad Apple!! in Vim using thousands of regex searches. Several pointed out the inefficiency and absurdity of the method, highlighting the vast difference between text manipulation and video rendering. Some questioned the practical applications, while others praised the creativity and dedication involved. A few commenters delved into the technical aspects, discussing Vim's handling of complex regex operations and the potential performance implications. One commenter jokingly suggested using this technique for machine learning, training a model on regexes to generate animations. Another thread discussed the author's choice of lossy compression for the regex data, debating whether a lossless approach would have been more appropriate for such an unusual project.
Summary of Comments ( 10 )
https://news.ycombinator.com/item?id=43257704
HN users generally expressed interest in Vidformer, praising its ease of use with existing OpenCV scripts and potential for significant speed improvements in video processing tasks like annotation. Several commenters pointed out the cleverness of using a generator for frame processing, allowing for seamless integration with existing code. Some questioned the benchmarks and the choice of using
multiprocessingover other parallelization methods, suggesting potential further optimizations. Others expressed a desire for more details, like hardware specifications and broader compatibility information beyond the provided examples. A few users also suggested alternative approaches for video processing acceleration, including GPU utilization and different Python libraries. Overall, the reception was positive, with the project seen as a practical tool for a common problem.The Hacker News post titled "Show HN: Vidformer – Drop-In Acceleration for Cv2 Video Annotation Scripts" sparked a small discussion with a few noteworthy comments.
One commenter questioned the performance comparison, pointing out that using OpenCV directly for video loading and processing might not be the most efficient approach. They suggested that a library like PyAV, which leverages hardware acceleration, could be significantly faster and might even outperform Vidformer. This comment raises a valid concern about the benchmark used and suggests a more robust comparison would be beneficial.
Another commenter appreciated the simplicity and potential of Vidformer, particularly for tasks involving object detection on videos. They highlighted the convenience of being able to accelerate existing OpenCV scripts without significant code changes. This positive feedback emphasizes the ease of use and potential applicability of the tool.
A subsequent reply to the performance concern clarified the project's focus: it's primarily aimed at simplifying the integration of hardware acceleration into existing OpenCV-based video annotation workflows, rather than achieving absolute peak performance. They acknowledge that specialized libraries like PyAV can be faster for raw video decoding and processing but reiterate that Vidformer's goal is ease of integration for annotation tasks.
Another commenter asked about specific hardware support and if Vidformer leverages CUDA. The original poster confirmed CUDA support.
The conversation remains focused on performance and ease of use. While acknowledging that other libraries might offer faster raw video processing, the comments highlight Vidformer's value proposition: simplifying the integration of hardware acceleration for video annotation tasks using OpenCV. The relatively small number of comments suggests moderate interest in the project at the time of this summary.