OpenCvSharp VS HoloLens2-Machine-Learning

In a previous post, I have gone through the process for building a Docker Image for OpenCVSharp that supports multiple processor architectures.

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.

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.

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.

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

Maybe opencv will be faster? https://github.com/shimat/opencvsharp

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

HoloLens 2 ML with ONNX