dotnet-docker VS OpenCV

This cool method lets you whip up optimized apps that get along swimmingly with Docker. Neat, huh? --platform values. Take a peek at this sample to see the pattern in action!

You can also use the .NET Docker images to run (or build from source and then run) .NET applications.

Sadly, lots of dotnet apps still out there. Luckily, there's a container for that too!

A search of "alpine 3.17 ssl" brings up this link that shows the same error.

Actually, it looks like I am a bit out of the loop, and there is an official .NET runtime for Linux, so you can use it like any other Linux docker images. Looks like there are examples here: https://github.com/dotnet/dotnet-docker

It is good that you are thinking about security 😊. there is an issue in dotnet repo here https://github.com/dotnet/dotnet-docker/issues/1772 that have hardened examples.

You can use .NET without using ASP.NET Core (yes, the names are confusing right now, they dropped the "Core" suffix for the platform but did not drop the suffix for the web framework). Microsoft publishes a Docker image for .NET for when you just want to run some compiled CIL, but they also provide a Docker image where it includes .NET and ASP.NET Core for when you run a compiled ASP.NET Core web app. (https://hub.docker.com/_/microsoft-dotnet/)

Keeping the whole Maritime theme alive with Docker (and Kubernetes), I jumped into the sea of Docker Hub with millions of containers and found out that Microsoft hosts all the .NET related container images as .NET by Microsoft registry.

ARG OPENCV_SHARP_BUILD_TAG=2 ARG SDK_VERSION=6.0.202-bullseye-slim-amd64 ARG RUNTIME_VERSION=6.0.4-bullseye-slim FROM syamaner/opencvsharp-build:$OPENCV_SHARP_BUILD_TAG AS opencv # Given we are building a .Net application, the build does not have to be in the target architecture. # Reference: https://github.com/dotnet/dotnet-docker/issues/1537#issuecomment-755351628 FROM mcr.microsoft.com/dotnet/sdk:$SDK_VERSION as build ARG TARGETPLATFORM WORKDIR /src COPY . . # Select the correct RID for the target architecture. # run dotnet publish as usual and pass the RID. RUN if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \ RID=linux-x64 ; \ elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ RID=linux-arm64 ; \ elif [ "$TARGETPLATFORM" = "linux/arm/v7" ]; then \ RID=linux-arm ; \ fi && \ dotnet publish -c release -o /app -r $RID --self-contained false # Copy the application as well as native dependencies to the final stage and build the final image without any unnecessary files. FROM mcr.microsoft.com/dotnet/runtime:$RUNTIME_VERSION as final WORKDIR /app # Copy opencv sharp native binding and runtime dependencies. COPY --from=opencv /artifacts/ /usr/lib/ RUN ldconfig COPY --from=build /app/ /app/ ENTRYPOINT [ "dotnet", "/app/OpenCVSharpBenchmarkApp.dll" ]

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.