OpenCvSharp
ML.NET
Our great sponsors
OpenCvSharp | ML.NET | |
---|---|---|
9 | 17 | |
5,116 | 8,838 | |
- | 0.8% | |
7.8 | 8.9 | |
about 2 months ago | 4 days ago | |
C# | C# | |
Apache License 2.0 | MIT License |
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.
OpenCvSharp
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Image Feature Detection and Matching using OpenCvSharp - First Steps Towards VinylEye
In a previous post, I have gone through the process for building a Docker Image for OpenCVSharp that supports multiple processor architectures.
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Does there exist an API accessible from C# that detects faces in images?
Alternatively, you can get into the nitty gritty of face detection yourself. OpenCV is a massive, open source project for all kinds computer vision tasks. Without jumping into the proprietary world, this is one of the most popular and capable computer vision libraries available. While powerful, OpenCV is comparatively low level; giving you the tools you need to accomplish your task, rather than a direct, single method to call. This tutorial goes into depth on how you'd go about training facial models to create your own detection methods. You'll notice that the example is written in C++. The con with this option (aside from it being more complex) is that OpenCV is largely used in the context of C++ or Python. However, it does have C# wrappers. Emgu CV is probably the most popular .NET wrapper for OpenCV, though there are other wrappers available. Here is a nice tutorial using OpenCVSharp, which is a bit closer to the native C++ API OpenCV uses.
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Compress/resize images
Another option for more advanced image manipulation - use OpenCV wrapper like opencvsharp. I haven't done much benchmarking, but from my understanding this is considerably faster option than anything made in managed code (like ImageSharp), but coming at the cost of native dependency and a bit more C++-style API and other quirks of OpenCV.
- Creating a NuGet package with static dependencies AND a native assembly wrapper?
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Universal UI testing based on image and text recognition
In order to do so, I had to learn about the basics of image processing. I was able to find an image within another with a technique called template matching, and I used an OpenCV wrapper made for .NET. I will not go through the technical details here, but you can find the relevant source code in my GitHub repository.
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Docker multi-architecture, .NET 6.0 and OpenCVSharp
FROM debian:bullseye-slim AS build-native-env ARG TARGETPLATFORM ENV DEBIAN_FRONTEND=noninteractive # 4.5.5: released 25 Dec 2021 ENV OPENCV_VERSION=4.5.5 # 4.5.3.20211228: released 28 Dec 2021 ENV OPENCVSHARP_VERSION=4.5.3.20211228 WORKDIR / # install dependencies required for building OpenCV and OpenCvSharpExtern RUN apt-get update && apt-get -y install --no-install-recommends \ # details omitted \ libgdiplus # Get OpenCV and opencv-contrib sources using the specified release. RUN wget https://github.com/opencv/opencv/archive/${OPENCV_VERSION}.zip && \ unzip ${OPENCV_VERSION}.zip && \ rm ${OPENCV_VERSION}.zip && \ mv opencv-${OPENCV_VERSION} opencv RUN wget https://github.com/opencv/opencv_contrib/archive/${OPENCV_VERSION}.zip && \ unzip ${OPENCV_VERSION}.zip && \ rm ${OPENCV_VERSION}.zip && \ mv opencv_contrib-${OPENCV_VERSION} opencv_contrib # configure and build OpenCV optionally specifying architecture related cmake options. RUN if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \ ADDITIONAL_FLAGS='' ; \ elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ ADDITIONAL_FLAGS='-D ENABLE_NEON=ON -D CPU_BASELINE=NEON ' ; \ elif [ "$TARGETPLATFORM" = "linux/arm/v7" ]; then \ ADDITIONAL_FLAGS='-D CPU_BASELINE=NEON -D ENABLE_NEON=ON ' ; \ fi && cd opencv && mkdir build && cd build && \ cmake $ADDITIONAL_FLAGS \ # additional flags omitted for clarity \ && make -j$(nproc) \ && make install \ && ldconfig # Download OpenCvSharp to build OpenCvSharpExtern native library RUN git clone https://github.com/shimat/opencvsharp.git RUN cd opencvsharp && git fetch --all --tags --prune && git checkout ${OPENCVSHARP_VERSION} WORKDIR /opencvsharp/src RUN mkdir /opencvsharp/make \ && cd /opencvsharp/make \ && cmake -D CMAKE_INSTALL_PREFIX=/opencvsharp/make /opencvsharp/src \ && make -j$(nproc) \ && make install \ && cp /opencvsharp/make/OpenCvSharpExtern/libOpenCvSharpExtern.so /usr/lib/ \ && ldconfig # Copy the library and dependencies to /artifacts (to be used by images consuming this build) # cpld.sh will copy the library we specify (./libOpenCvSharpExtern.so) and any dependencies # to the /artifacts directory. This is useful for sharing the library with other images # consuming this build. # credits: Hemanth.HM -> https://h3manth.com/content/copying-shared-library-dependencies WORKDIR /opencvsharp/make/OpenCvSharpExtern COPY cpld.sh . RUN chmod +x cpld.sh && \ mkdir /artifacts && \ ./cpld.sh ./libOpenCvSharpExtern.so /artifacts/ RUN cp ./libOpenCvSharpExtern.so /artifacts/ # Publish the artefacts using a clean image FROM debian:bullseye-slim AS final RUN mkdir /artifacts COPY --from=build-native-env /artifacts/ /artifacts WORKDIR /artifacts
- Why we can’t use C++ in WPF?
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What's the fastest way to get pixel data from a Bitmap?
Maybe opencv will be faster? https://github.com/shimat/opencvsharp
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image classification in .net with webcam
hello. the quickest way to access webcam in .NET would be use this package shimat/opencvsharp: OpenCV wrapper for .NET (github.com) and give feedback about using the lobe.OpenCVSharp binding to help you classify the images
ML.NET
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ML.net image classification, poor GPU accuracy
You can direct your question to https://github.com/dotnet/machinelearning/issues. Perhaps it is already documented.
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Building a File Analysis Dataset with Python
Here I'm analyzing all projects in the src and test directories of the ML.NET repository. I chose to include these as separate paths because they represent two different groupings of projects in this repository.
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Extracting git repository data with PyDriller
Important Note: looping over repository commits takes a long time for large repositories. It took 52 minutes to analyze the ML.NET repository this code example refers to, which had 2,681 commits at the time of analysis on February 25th, 2023.
- Can we please be allowed to do machine learning object detection model training locally?
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ML.NET: can Microsoft's machine learning be trusted?
We checked the ML.NET 1.7.1 version. The source code of this project's version is available on GitHub.
- Stable Diffusion converted to ONNX (Demo usage, optimized to CPU)
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Why is there a lack of cool repos?
machine learning? https://github.com/dotnet/machinelearning
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what is the future of ML.NET?
You can follow some of our plans by taking a look at our roadmap which we'll be updating shortly to more accurately reflect the areas we're investing in.
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Does anyone actually use ML.NET?
Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!
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Requesting Senior Project Ideas
Good clarification, I think using something like ML.NET could be cool but I have some experience with Blazor that might be fun to use as well, I think generally performance monitoring or optimizing systems seems interesting to me, and I'm really open to other ideas as well. Let me know if any of that helps narrow my question down!
What are some alternatives?
Emgu CV - Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library.
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
ImageSharp - :camera: A modern, cross-platform, 2D Graphics library for .NET
Accord.NET
Magick.NET - The .NET library for ImageMagick
FaceRecognitionDotNet - The world's simplest facial recognition api for .NET on Windows, MacOS and Linux
ImageProcessor - :camera: A fluent wrapper around System.Drawing for the processing of image files.
Catalyst - 🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
MetadataExtractor - Extracts Exif, IPTC, XMP, ICC and other metadata from image, video and audio files
Deedle - Easy to use .NET library for data and time series manipulation and for scientific programming
DotImaging - Minimalistic .NET imaging portable platform
AForge.NET - AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc.