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Migrate NNM Settings to SQLITE (#1894)

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Co-authored-by: Gold856 <117957790+Gold856@users.noreply.github.com>
Co-authored-by: Chris Gerth <gerth2@users.noreply.github.com>
This commit is contained in:
Sam Freund
2025-06-30 22:02:44 -05:00
committed by GitHub
parent cc7923eeb4
commit 4a648b302a
30 changed files with 1443 additions and 495 deletions
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357
photon-server/src/main/java/org/photonvision/server/RequestHandler.java
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@@ -25,8 +25,10 @@ import java.io.*;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.nio.file.StandardCopyOption;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.Optional;
import javax.imageio.ImageIO;
import org.apache.commons.io.FileUtils;
@@ -37,6 +39,7 @@ import org.opencv.imgcodecs.Imgcodecs;
import org.photonvision.common.configuration.ConfigManager;
import org.photonvision.common.configuration.NetworkConfig;
import org.photonvision.common.configuration.NeuralNetworkModelManager;
import org.photonvision.common.configuration.NeuralNetworkPropertyManager.ModelProperties;
import org.photonvision.common.dataflow.DataChangeDestination;
import org.photonvision.common.dataflow.DataChangeService;
import org.photonvision.common.dataflow.events.IncomingWebSocketEvent;
@@ -50,6 +53,7 @@ import org.photonvision.common.logging.Logger;
import org.photonvision.common.networking.NetworkManager;
import org.photonvision.common.util.ShellExec;
import org.photonvision.common.util.TimedTaskManager;
import org.photonvision.common.util.file.JacksonUtils;
import org.photonvision.common.util.file.ProgramDirectoryUtilities;
import org.photonvision.vision.calibration.CameraCalibrationCoefficients;
import org.photonvision.vision.camera.CameraQuirk;
@@ -550,55 +554,108 @@ public class RequestHandler {
public static void onImportObjectDetectionModelRequest(Context ctx) {
try {
// Retrieve the uploaded files
var modelFile = ctx.uploadedFile("rknn");
var labelsFile = ctx.uploadedFile("labels");
var modelFile = ctx.uploadedFile("modelFile");
if (modelFile == null || labelsFile == null) {
// Strip any whitespaces on either side of the commas
LinkedList<String> labels = new LinkedList<>();
String rawLabels = ctx.formParam("labels");
if (rawLabels != null) {
for (String label : rawLabels.split(",")) {
labels.add(label.trim());
}
}
int width = Integer.parseInt(ctx.formParam("width"));
int height = Integer.parseInt(ctx.formParam("height"));
NeuralNetworkModelManager.Version version =
switch (ctx.formParam("version").toString()) {
case "YOLOv5" -> NeuralNetworkModelManager.Version.YOLOV5;
case "YOLOv8" -> NeuralNetworkModelManager.Version.YOLOV8;
case "YOLO11" -> NeuralNetworkModelManager.Version.YOLOV11;
// Add more versions as necessary for new models
default -> {
ctx.status(400);
ctx.result("The provided version was not valid");
logger.error("The provided version was not valid");
yield null;
}
};
if (modelFile == null) {
ctx.status(400);
ctx.result(
"No File was sent with the request. Make sure that the model and labels files are sent at the keys 'rknn' and 'labels'");
"No File was sent with the request. Make sure that the model file is sent at the key 'modelFile'");
logger.error(
"No File was sent with the request. Make sure that the model and labels files are sent at the keys 'rknn' and 'labels'");
"No File was sent with the request. Make sure that the model file is sent at the key 'modelFile'");
return;
}
if (!modelFile.extension().contains("rknn") || !labelsFile.extension().contains("txt")) {
if (labels == null || labels.isEmpty()) {
ctx.status(400);
ctx.result(
"The uploaded files were not of type 'rknn' and 'txt'. The uploaded files should be a .rknn and .txt file.");
logger.error(
"The uploaded files were not of type 'rknn' and 'txt'. The uploaded files should be a .rknn and .txt file.");
ctx.result("The provided labels were malformed");
logger.error("The provided labels were malformed");
return;
}
// verify naming convention
if (width < 0 || height < 0 || width != Math.floor(width) || height != Math.floor(height)) {
ctx.status(400);
ctx.result(
"The provided width and height were malformed. They must be integers greater than one.");
logger.error(
"The provided width and height were malformed. They must be integers greater than one.");
return;
}
// throws IllegalArgumentException if the model name is invalid
NeuralNetworkModelManager.verifyRKNNNames(modelFile.filename(), labelsFile.filename());
// If adding additional platforms, check platform matches
if (!modelFile.extension().contains("rknn")) {
ctx.status(400);
ctx.result(
"The uploaded file was not of type 'rknn'. The uploaded file should be a .rknn file.");
logger.error(
"The uploaded file was not of type 'rknn'. The uploaded file should be a .rknn file.");
return;
}
// TODO move into neural network manager
var modelPath =
Path modelPath =
Paths.get(
ConfigManager.getInstance().getModelsDirectory().toString(), modelFile.filename());
var labelsPath =
Paths.get(
ConfigManager.getInstance().getModelsDirectory().toString(), labelsFile.filename());
if (modelPath.toFile().exists()) {
ctx.status(400);
ctx.result(
"The model file already exists. Please delete the existing model file before uploading a new one.");
logger.error(
"The model file already exists. Please delete the existing model file before uploading a new one.");
return;
}
try (FileOutputStream out = new FileOutputStream(modelPath.toFile())) {
modelFile.content().transferTo(out);
}
try (FileOutputStream out = new FileOutputStream(labelsPath.toFile())) {
labelsFile.content().transferTo(out);
}
ConfigManager.getInstance()
.getConfig()
.neuralNetworkPropertyManager()
.addModelProperties(
new ModelProperties(
modelPath,
modelFile.filename().replaceAll(".rknn", ""),
labels,
width,
height,
NeuralNetworkModelManager.Family.RKNN, // This can be determined by platform if
// additional platforms are
// supported
version));
NeuralNetworkModelManager.getInstance()
.discoverModels(ConfigManager.getInstance().getModelsDirectory());
logger.debug(
ConfigManager.getInstance().getConfig().neuralNetworkPropertyManager().toString());
NeuralNetworkModelManager.getInstance().discoverModels();
ctx.status(200).result("Successfully uploaded object detection model");
} catch (Exception e) {
ctx.status(500).result("Error processing files: " + e.getMessage());
logger.error("Error processing new object detection model", e);
}
DataChangeService.getInstance()
@@ -608,6 +665,258 @@ public class RequestHandler {
UIPhotonConfiguration.programStateToUi(ConfigManager.getInstance().getConfig())));
}
public static void onExportObjectDetectionModelsRequest(Context ctx) {
logger.info("Exporting Object Detection Models to ZIP Archive");
try {
var zip = ConfigManager.getInstance().getObjectDetectionExportAsZip();
var stream = new FileInputStream(zip);
logger.info("Uploading object detection models with size " + stream.available());
ctx.contentType("application/zip");
ctx.header(
"Content-Disposition",
"attachment; filename=\"photonvision-object-detection-models-export.zip\"");
ctx.result(stream);
ctx.status(200);
} catch (IOException e) {
logger.error("Unable to export object detection models archive, bad recode from zip to byte");
ctx.status(500);
ctx.result("There was an error while exporting the object detection models archive");
}
}
public static void onExportIndividualObjectDetectionModelRequest(Context ctx) {
logger.info("Exporting Individual Object Detection Model");
try {
String modelPath = ctx.queryParam("modelPath");
if (modelPath == null || modelPath.isEmpty()) {
ctx.status(400);
ctx.result("The provided model path was malformed");
logger.error("The provided model path was malformed");
return;
}
File modelFile = NeuralNetworkModelManager.getInstance().exportSingleModel(modelPath);
var stream = new FileInputStream(modelFile);
logger.info("Uploading object detection model with size " + stream.available());
ctx.contentType("application/octet-stream");
ctx.header("Content-Disposition", "attachment; filename=" + modelFile.getName());
ctx.result(stream);
ctx.status(200);
} catch (IOException e) {
logger.error("Unable to export object detection model, " + e);
ctx.status(500);
ctx.result("There was an error while exporting the object detection model");
}
}
public static void onBulkImportObjectDetectionModelRequest(Context ctx) {
var file = ctx.uploadedFile("data");
if (file == null) {
ctx.status(400);
ctx.result(
"No File was sent with the request. Make sure that the object detection zip is sent at the key 'data'");
logger.error(
"No File was sent with the request. Make sure that the object detection zip file is sent at the key 'data'");
return;
}
if (!file.extension().contains("zip")) {
ctx.status(400);
ctx.result(
"The uploaded file was not of type 'zip'. The uploaded file should be a .zip file.");
logger.error(
"The uploaded file was not of type 'zip'. The uploaded file should be a .zip file.");
return;
}
// Create a temp file
var tempFilePath = handleTempFileCreation(file);
if (tempFilePath.isEmpty()) {
ctx.status(500);
ctx.result("There was an error while creating a temporary copy of the file");
logger.error("There was an error while creating a temporary copy of the file");
return;
}
Path tempDir = null;
// Extract .rknn files from zip and move to models directory
try {
tempDir = Files.createTempDirectory("photonvision-od-models");
ZipUtil.unpack(tempFilePath.get(), tempDir.toFile());
Path targetModelsDir = ConfigManager.getInstance().getModelsDirectory().toPath();
// Copy all files from the source models directory to the target models
// directory
try (var stream = Files.list(tempDir)) {
for (Path modelFile : stream.toList()) {
if (Files.isRegularFile(modelFile)
&& !modelFile.getFileName().toString().endsWith(".json")) {
logger.debug("Copying model file: " + modelFile.getFileName());
Files.copy(
modelFile,
Path.of(targetModelsDir.toString(), modelFile.getFileName().toString()),
StandardCopyOption.REPLACE_EXISTING);
}
}
}
logger.info("Successfully copied models from " + tempDir + " to " + targetModelsDir);
} catch (Exception e) {
ctx.status(500);
ctx.result("There was an error while extracting and coyping the object detection models");
logger.error(
"There was an error while extracting and copying the object detection models", e);
return;
}
if (ConfigManager.getInstance()
.saveUploadedNeuralNetworkProperties(
Path.of(tempDir.toString(), "photonvision-object-detection-models.json"))) {
ctx.status(200);
ctx.result("Successfully saved the uploaded object detection models, rebooting...");
logger.info("Successfully saved the uploaded object detection models, rebooting...");
restartProgram();
} else {
ctx.status(500);
ctx.result("There was an error while saving the uploaded object detection models");
logger.error("There was an error while saving the uploaded object detection models");
}
}
private record DeleteObjectDetectionModelRequest(String modelPath) {}
public static void onDeleteObjectDetectionModelRequest(Context ctx) {
logger.info("Deleting object detection model");
Path modelPath;
try {
DeleteObjectDetectionModelRequest request =
JacksonUtils.deserialize(ctx.body(), DeleteObjectDetectionModelRequest.class);
modelPath = Path.of(request.modelPath.substring(7));
if (modelPath == null) {
ctx.status(400);
ctx.result("The provided model path was malformed");
logger.error("The provided model path was malformed");
return;
}
if (!modelPath.toFile().exists()) {
ctx.status(400);
ctx.result("The provided model path does not exist");
logger.error("The provided model path does not exist");
return;
}
if (!modelPath.toFile().delete()) {
ctx.status(500);
ctx.result("Unable to delete the model file");
logger.error("Unable to delete the model file");
return;
}
if (!ConfigManager.getInstance()
.getConfig()
.neuralNetworkPropertyManager()
.removeModel(modelPath)) {
ctx.status(400);
ctx.result("The model's information was not found in the config");
logger.error("The model's information was not found in the config");
return;
}
NeuralNetworkModelManager.getInstance().discoverModels();
ctx.status(200).result("Successfully deleted object detection model");
} catch (Exception e) {
ctx.status(500);
ctx.result("Error deleting object detection model: " + e.getMessage());
logger.error("Error deleting object detection model", e);
}
DataChangeService.getInstance()
.publishEvent(
new OutgoingUIEvent<>(
"fullsettings",
UIPhotonConfiguration.programStateToUi(ConfigManager.getInstance().getConfig())));
}
private record RenameObjectDetectionModelRequest(String modelPath, String newName) {}
public static void onRenameObjectDetectionModelRequest(Context ctx) {
try {
RenameObjectDetectionModelRequest request =
JacksonUtils.deserialize(ctx.body(), RenameObjectDetectionModelRequest.class);
Path modelPath = Path.of(request.modelPath);
if (modelPath == null) {
ctx.status(400);
ctx.result("The provided model path was malformed");
logger.error("The provided model path was malformed");
return;
}
if (!modelPath.toFile().exists()) {
ctx.status(400);
ctx.result("The provided model path does not exist");
logger.error("The model path: " + modelPath + " does not exist");
return;
}
if (request.newName == null || request.newName.isEmpty()) {
ctx.status(400);
ctx.result("The provided new name was malformed");
logger.error("The provided new name was malformed");
return;
}
if (!ConfigManager.getInstance()
.getConfig()
.neuralNetworkPropertyManager()
.renameModel(modelPath, request.newName)) {
ctx.status(400);
ctx.result("The model's information was not found in the config");
logger.error("The model's information was not found in the config");
return;
}
NeuralNetworkModelManager.getInstance().discoverModels();
ctx.status(200).result("Successfully renamed object detection model");
} catch (Exception e) {
ctx.status(500);
ctx.result("Error renaming object detection model: " + e.getMessage());
logger.error("Error renaming object detection model", e);
return;
}
}
public static void onNukeObjectDetectionModelsRequest(Context ctx) {
logger.info("Attempting to clear object detection models");
try {
NeuralNetworkModelManager.getInstance().clearModels();
NeuralNetworkModelManager.getInstance().extractModels();
ctx.status(200).result("Successfully cleared and reset object detection models");
} catch (Exception e) {
ctx.status(500);
ctx.result("Error clearing object detection models: " + e.getMessage());
logger.error("Error clearing object detection models", e);
}
}
public static void onDeviceRestartRequest(Context ctx) {
ctx.status(HardwareManager.getInstance().restartDevice() ? 204 : 500);
}