mrtj

{
    "name": "interface",
    "category": "business_logic",
    "asset": "",
    "inputs": [
        {
            "name": "video_in",
            "type": "media"
        },
        {

mrtj

FROM public.ecr.aws/panorama/panorama-application
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir opencv-python numpy
COPY src /panorama

mrtj

{
    "mlModelDescriptor": {
        "envelopeVersion": "2021-01-01",
        "framework": "PYTORCH",
        "inputs": [
            {
                "name": "images",
                "shape": [1, 3, 640, 640]
            }
        ]

mrtj

{
    "nodePackage": {
        ...
        "interfaces": [
            {
                "name": "interface",
                "category": "ml_model",
                "asset": "yolox_s_asset",
                ...
            }

mrtj

{
    "name": "model_node",
    "interface": "<AWS_ACCOUNT_ID>::yolox_s.interface",
    "overridable": false,
    "launch": "onAppStart"
}

mrtj

AWS_ACCOUNT_ID=$(aws sts get-caller-identity --output text --query 'Account')
panorama-cli add-raw-model \
    --model-asset-name yolox_s_asset \
    --model-local-path [model_archive_path]/yolox_s_torchscript.tar.gz \
    --descriptor-path packages/${AWS_ACCOUNT_ID}-yolox_s-1.0/descriptor.json \
    --packages-path packages/${AWS_ACCOUNT_ID}-yolox_s-1.0

mrtj

"edges": [
    {
        "producer": "camera_node.video_out",
        "consumer": "code_node.video_in"
    },
    {
        "producer": "code_node.video_out",
        "consumer": "display_node.video_in"
    },
    {

mrtj

{
    "name": "detection_threshold",
    "interface": "float32",
    "value": 20.0,
    "overridable": true,
    "decorator": {
        "title": "Threshold",
        "description": "The minimum confidence percentage for a positive classification."
    }
}

mrtj

{
    "name": "code_node",
    "interface": "123456789012::application_logic.interface"
},
{
    "name": "model_node",
    "interface": "123456789012::yolox_s.interface"
}

mrtj

#!/usr/bin/env bash
cat train.csv | sed 's/ /_/g' | sed 's/,/ /g' | awk '{print $1}' | grep "$1" | sort | uniq