Add training script
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Training/train.sh
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Training/train.sh
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#!/bin/bash
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########### SUDO COMMANDS WITHOUT PASSWORD #############################################
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#
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# Specific commands must be executable without sudo
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#
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# Add to sudoers file (sudo visudo):
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#
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# Disable password for specific commands
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# pi ALL=(ALL:ALL) NOPASSWD: /usr/bin/chmod -R 755 /data/public/capserver/images
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# pi ALL=(ALL:ALL) NOPASSWD: /usr/bin/mv /home/pi/classifier.mlmodel /data/public/capserver/
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# pi ALL=(ALL:ALL) NOPASSWD: /usr/bin/mv /home/pi/classifier.version /data/public/capserver/
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# pi ALL=(ALL:ALL) NOPASSWD: /usr/bin/chown -R www-data\:www-data /data/public/capserver/
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#
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########################################################################################
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########### EXPLANATION OF RSYNC FLAGS #################################################
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#
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# -h human readable output
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# -v verbose output
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# -r recursive
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# -P print information about long-running transfers, keep partial files (—-progress --partial)
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# -t preserves modification times
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# -u only update if newer
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#
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########################################################################################
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########### PATHS ######################################################################
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WORK_DIR="/Users/imac/Projects/CapCollectorData"
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BACKUP_DIR="${WORK_DIR}/Backup"
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IMAGE_DIR="${WORK_DIR}/images"
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VERSION_FILE="${WORK_DIR}/classifier.version"
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MODEL_FILE="${WORK_DIR}/classifier.mlmodel"
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TRAINING_ITERATIONS="17"
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SSH_PORT="5432"
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SERVER="pi@pi4"
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SERVER_ROOT_PATH="/data/public/capserver"
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########################################################################################
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echo "[INFO] Working in directory ${WORK_DIR}"
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echo "[INFO] Getting classifier version from server..."
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scp -P $SSH_PORT ${SERVER}:/${SERVER_ROOT_PATH}/classifier.version $WORK_DIR
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# Read classifier version from file
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OLD_VERSION=$(< $VERSION_FILE)
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NEW_VERSION=$(($OLD_VERSION + 1))
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echo "[INFO] Creating classifier version ${NEW_VERSION}"
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echo "[INFO] Ensuring permissions for images on server..."
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ssh -p $SSH_PORT ${SERVER} "sudo chmod -R 755 ${SERVER_ROOT_PATH}/images"
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echo "[INFO] Transferring images from server..."
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rsync -hvrPut -e "ssh -p ${SSH_PORT}" ${SERVER}:/${SERVER_ROOT_PATH}/images/ "${IMAGE_DIR}"
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echo "[INFO] Training the model..."
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swift train.swift $IMAGE_DIR $TRAINING_ITERATIONS $MODEL_FILE
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echo "[INFO] Backing up model..."
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cp $MODEL_FILE "${BACKUP_DIR}/classifier${NEW_VERSION}.mlmodel"
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echo "[INFO] Incrementing version file..."
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echo "${NEW_VERSION}" > $VERSION_FILE
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echo "[INFO] Copying the files to the server..."
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scp -P $SSH_PORT $MODEL_FILE $VERSION_FILE ${SERVER}:~/
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echo "[INFO] Moving files into public directory..."
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ssh -p ${SSH_PORT} ${SERVER} "sudo mv /home/pi/classifier.* ${SERVER_ROOT_PATH}/"
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echo "[INFO] Updating permissions..."
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ssh -p ${SSH_PORT} ${SERVER} "sudo chown -R www-data\:www-data ${SERVER_ROOT_PATH}/"
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echo "[INFO] Cleaning up..."
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rm $MODEL_FILE $VERSION_FILE
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echo "[INFO] Process finished"
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Training/train.swift
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Training/train.swift
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import Cocoa
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import CreateML
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let defaultIterations = 17
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let defaultWorkingDirectory = URL(fileURLWithPath: "/Users/imac/Development/CapCollectorData")
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let defaultTrainDirectory = defaultWorkingDirectory.appendingPathComponent("images")
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let defaultClassifierFile = defaultWorkingDirectory.appendingPathComponent("classifier.mlmodel")
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func readArguments() -> (images: URL, classifier: URL, iterations: Int) {
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let count = CommandLine.argc
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guard count > 1 else {
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// No arguments
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return (defaultTrainDirectory, defaultClassifierFile, defaultIterations)
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}
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// First argument is the image directory
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let imageDir = URL(fileURLWithPath: CommandLine.arguments[1])
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let classifier = imageDir.deletingLastPathComponent().appendingPathComponent("classifier.mlmodel")
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guard count > 2 else {
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// Single argument is the image directory
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return (imageDir, classifier, defaultIterations)
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}
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// Second argument is the iteration count
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guard let iterations = Int(CommandLine.arguments[2]) else {
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print("[ERROR] Invalid iterations argument '\(CommandLine.arguments[2])'")
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exit(-1)
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}
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guard count > 3 else {
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return (imageDir, classifier, iterations)
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}
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// Third argument is the classifier path
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let classifierPath = URL(fileURLWithPath: CommandLine.arguments[3])
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if count > 4 {
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print("[WARNING] Ignoring additional arguments")
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}
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return (imageDir, classifierPath, iterations)
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}
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let arguments = readArguments()
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print("[INFO] Using images in \(arguments.images.path)")
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print("[INFO] Training for \(arguments.iterations) iterations")
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print("[INFO] Classifier path set to \(arguments.classifier.path)")
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var params = MLImageClassifier.ModelParameters(augmentation: [])
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params.maxIterations = arguments.iterations
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let model = try MLImageClassifier(
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trainingData: .labeledDirectories(at: arguments.images),
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parameters: params)
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print("[INFO] Writing classifier...")
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try model.write(to: arguments.classifier)
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/*
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let evaluation = model.evaluation(on: .labeledDirectories(at: trainDirectory))
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print("Printing evaluation:")
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print(evaluation)
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print("Finished evaluation")
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*/
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