node-red-contrib-deepstack
Simple Node-RED nodes for interacting with the Deepstack API.
Node Face Recognition
Sends an image to the Deepstack Face Recognition API and outputs the predictions.
Inputs
The input message should contain the image to process.
msg.payload: Image buffer to process.
Outputs
The first output always contain all predictions made by the Face Recognition API. If filters are configured, the corresponding outputs 2..* will contain predictions matching that particular filter. If there is no match, there will be no message on that output.
msg.payload: Deepstack Face Recognition predictions.
msg.success: Deepstack call status.
msg.duration: Deepstack call duration.
msg.originalImage: The image buffer processed.
msg.outlinedImage: Image buffer with rectangular outline around the faces. Only if config option drawPredictions is true.
Node Face Registration
Sends an image to the Deepstack Face Registration API and outputs the result.
Inputs
The input message should contain the image to send for registration.
msg.payload: Image buffer to process.
Outputs
The registration result.
msg.payload: Deepstack Face Registration result.
msg.success: Deepstack call status.
msg.duration: Deepstack call duration.
Node Object Detection
Sends an image to the Deepstack Object Detection API and outputs the predictions.
Inputs
The input message should contain the image to process.
msg.payload: Image buffer to process.
Outputs
The first output always contain all predictions made by the Object Detection API. If filters are configured, the corresponding outputs 2..* will contain predictions matching that particular filter. If there is no match, there will be no message on that output.
msg.payload: Deepstack Object Detection predictions.
msg.success: Deepstack call status.
msg.duration: Deepstack call duration.
msg.originalImage: The image buffer processed.
msg.outlinedImage: Image buffer with rectangular outline around detected objects. Only if config option drawPredictions is true.
Node Custom Model
Sends an image to the Deepstack Custom Model API and outputs the predictions.
Inputs
The input message should contain the image to process.
msg.payload: Image buffer to process.
Outputs
The output contains all predictions made by the Custom Model API.
msg.payload: Deepstack Custom Model predictions.
msg.success: Deepstack call status.
msg.duration: Deepstack call duration.
msg.originalImage: The image buffer processed.
msg.outlinedImage: Image buffer with rectangular outline around detected objects. Only if config option drawPredictions is true.
Troubleshooting
Common errors and fixes
Problem with Sharp & libvips on Alpine on Raspberry Pi
The version of Sharp used for this node requires a version of libvips not provided in the current Alpine base image used for the official node-red image.
The error
```bash $ npm install node-red-contrib-deepstack > sharp@0.26.3 install /usr/src/node-red/node_modules/sharp > (node install/libvips && node install/dll-copy && prebuild-install) || (node-gyp rebuild && node install/dll-copy) info sharp Downloading https://github.com/lovell/sharp-libvips/releases/download/v8.10.0/libvips-8.10.0-linuxmusl-armv7.tar.br ERR! sharp Prebuilt libvips 8.10.0 binaries are not yet available for linuxmusl-armv7 info sharp Attempting to build from source via node-gyp but this may fail due to the above error info sharp Please see https://sharp.pixelplumbing.com/install for required dependencies make: Entering directory '/usr/src/node-red/node_modules/sharp/build' CC(target) Release/obj.target/nothing/../node-addon-api/nothing.o AR(target) Release/obj.target/../node-addon-api/nothing.a COPY Release/nothing.a TOUCH Release/obj.target/libvips-cpp.stamp CXX(target) Release/obj.target/sharp/src/common.o ../src/common.cc:24:10: fatal error: vips/vips8: No such file or directory 24 | #includeThe fix
Build a [custom libvips](https://sharp.pixelplumbing.com/install#custom-libvips) before installation of this node. Running Docker, you can use this Dockerfile: ```Dockerfile FROM nodered/node-red:latest-12 USER root ARG LIBVIPS_VERSION_MAJOR_MINOR=8.10 ARG LIBVIPS_VERSION_PATCH=5 ARG MOZJPEG_VERSION="v3.3.1" ENV CPATH /usr/local/include ENV LIBRARY_PATH /usr/local/lib ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:$PKG_CONFIG_PATH RUN apk update && \ apk upgrade && \ apk add --update \ zlib libxml2 libxslt glib ca-certificates \ expat cairo orc libjpeg-turbo libwebp libexif lcms2 librsvg \ poppler-glib fftw giflib libpng tiff && \ apk add --no-cache --virtual \ .build-dependencies autoconf automake build-base cmake \ git libtool nasm zlib-dev libxml2-dev libxslt-dev wget \ expat-dev cairo-dev orc-dev libjpeg-turbo-dev libwebp-dev \ libexif-dev lcms2-dev librsvg-dev poppler-dev fftw-dev \ giflib-dev libpng-dev tiff-dev glib-dev && \ \ echo 'Install mozjpeg' && \ cd /tmp && \ git clone git://github.com/mozilla/mozjpeg.git && \ cd /tmp/mozjpeg && \ git checkout ${MOZJPEG_VERSION} && \ autoreconf -fiv && ./configure --prefix=/usr && make install && \ \ echo 'Install libvips' && \ wget -O- https://github.com/libvips/libvips/releases/download/v${LIBVIPS_VERSION_MAJOR_MINOR}.${LIBVIPS_VERSION_PATCH}/vips-${LIBVIPS_VERSION_MAJOR_MINOR}.${LIBVIPS_VERSION_PATCH}.tar.gz | tar xzC /tmp && \ cd /tmp/vips-${LIBVIPS_VERSION_MAJOR_MINOR}.${LIBVIPS_VERSION_PATCH} && \ ./configure --prefix=/usr \ --without-gsf \ --enable-debug=no \ --disable-dependency-tracking \ --disable-static \ --enable-silent-rules && \ make -s install-strip && \ cd /data/ && \ echo 'Install Node-RED modules' && \ npm install node-red-contrib-deepstack && \ \ echo 'Cleanup' && \ rm -rf /tmp/vips-${LIBVIPS_VERSION_MAJOR_MINOR}.${LIBVIPS_VERSION_PATCH} && \ rm -rf /tmp/mozjpeg && \ apk del --purge .build-dependencies && \ rm -rf /var/cache/apk/* ```Credits
Credits should go to Deepstack for providing such an awesome service!
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