OpenCV
Boost.GIL
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OpenCV | Boost.GIL | |
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187 | 0 | |
71,271 | 166 | |
1.4% | 3.0% | |
9.6 | 0.0 | |
3 days ago | about 1 month ago | |
C++ | C++ | |
Apache License 2.0 | Boost Software License 1.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
OpenCV
- VidCutter: A program for lossless video cutting
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Looking to recreate a cool AI assistant project with free tools
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!
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[Question] I'd like to find out about how the x, y, w, h values retrieved by detectMultiScale() (for the rectangle boundary during face detection) and how it is calculated in the Haar Cascade OpenCV library. Does anyone know where I can find the code?
Glancing at the code, I think it's detectMultiScaleNoGrouping and then the operator() of CascadeClassifierInvoker gets called. It will probably help you to put a breakpoint and step through that bit of the code.
On GitHub https://github.com/opencv/opencv
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OpenCV VS ppmpp - a user suggested alternative
2 projects | 22 Jun 2023
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Analyze defects and errors in the created images
OpenCV
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What are the limits of blueprints?
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++.
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how to fix failed to fetch error when installing OpenCV on raspberry pi?
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.0.zip
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Interesting problem trying to solve.
Another option is using the OpenCV library with Python to extract features from images and compare them using feature matching algorithms like SIFT, SURF, or ORB. This approach is more computationally intensive but might be more accurate in identifying near-duplicates. You can find more information about OpenCV here: https://opencv.org/
Boost.GIL
We haven't tracked posts mentioning Boost.GIL yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
libvips - A fast image processing library with low memory needs.
VTK - Mirror of Visualization Toolkit repository
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
CImg - The CImg Library is a small and open-source C++ toolkit for image processing
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
scikit-image - Image processing in Python
SimpleCV - The Open Source Framework for Machine Vision
imagick - Go binding to ImageMagick's MagickWand C API
ITK - Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
Kornia - Computer Vision and Robotics Library for AI
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
OpenImageIO - Reading, writing, and processing images in a wide variety of file formats, using a format-agnostic API, aimed at VFX applications.