Proof of concept script to analyze video using ML via beholder and pythia services

auto-analyze.sc

#!/usr/bin/env -S scala-cli shebang
//> using scala "3.3.0"
//> using dep "com.lihaoyi::requests:0.8.0"
//> using dep "com.lihaoyi::mainargs:0.5.0"
//> using dep "com.lihaoyi::upickle:3.1.0"

import requests.*
import mainargs.{main, arg, ParserForMethods, Flag}
import upickle.default.*
import java.nio.file.{Files, Paths}


object Main:

  @main def run(
      @arg(
        short = 'v',
        name = "videoUrl",
        doc = "URL of the video to extract a frame from"
      )
      videoUrl: String,
      beholderUrl: String = "http://singularity.shore.mbari.org:8088/capture", 
      xApiKey: String = "foo"): Unit =

    var ok = true
    var elapsedTimeMillis = 0
    while (ok) {
      try
        val imageBytes = requestJpegImage(videoUrl, elapsedTimeMillis, beholderUrl, xApiKey)
        predict(imageBytes)
        elapsedTimeMillis += 1000
      catch
        case e: Exception => 
          println("Stopping")
          ok = false
    }
    

  def requestJpegImage(videoUrl: String, 
      elapsedTimeMillis: Int, 
      beholderUrl: String = "http://singularity.shore.mbari.org:8088/capture", 
      xApiKey: String = "foo"): Array[Byte] =
    
    val bodyData = Map(
        "videoUrl" -> videoUrl,
        "elapsedTimeMillis" -> elapsedTimeMillis.toString
    )
    val body = write(bodyData)

    val res = requests.post(beholderUrl, data = body, headers = Map(
        "x-api-key" -> xApiKey
      ))

    // Files.write(Paths.get("test.jpg"), res.contents)
    println("-- Millis " + elapsedTimeMillis)
    res.contents

  def predict(jpeg: Array[Byte], 
    pythiaUrl: String = "http://perceptron.shore.mbari.org:8080/predict"): String = 
    val res = requests.post(pythiaUrl, 
        data = requests.MultiPart(requests.MultiItem("file", jpeg)))
    println(res.text())
    res.text()

  def main(args: Array[String]): Unit = ParserForMethods(this).runOrExit(args)