Incompatibility tflite in "live_object_detection_ssd_mobilenet"

[muamal2]

Hello..

I hope you are well, I am training the file and configuring it on teachable machine

I suffer from a problem that the file does not work according to the written code and the application does not perform the required purpose.

I trained the images, prepared the files, and uploaded them to the project files. I encountered the following problems, as in the picture

[qu-ngx]

Can you please kindly copy and paste down the "full log" instead of just taking a screenshot of your IDE?

[muamal2]

Sure :)

Launching lib/main.dart on 22101320G in debug mode...
Running Gradle task 'assembleDebug'...
✓ Built build/app/outputs/flutter-apk/app-debug.apk.
Installing build/app/outputs/flutter-apk/app-debug.apk...
Debug service listening on ws://127.0.0.1:49474/eIPV7vQ946U=/ws
Syncing files to device 22101320G...
I/CameraManagerGlobal( 8569): Connecting to camera service
D/CameraExtStub( 8569): init android.hardware.camera2.impl.CameraExtImplXiaoMi
D/CameraInjector( 8569): updateCloudCameraControllerInfoAsync: E
D/CameraInjector( 8569): updateCloudCameraControllerInfoAsync: X
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 2
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 3
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 4
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 5
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 6
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): [soar.cts] ignore the status update of camera: 7
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 2
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 3
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 4
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 5
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 6
E/libEGL ( 8569): pre_cache appList: ,,
W/libc ( 8569): Access denied finding property "ro.hardware.chipname"
I/tflite ( 8569): Created TensorFlow Lite XNNPACK delegate for CPU.
I/tflite ( 8569): Initialized TensorFlow Lite runtime.
I/tflite ( 8569): Replacing 71 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions.
E/LB ( 8569): fail to open node: No such file or directory
D/CompatibilityChangeReporter( 8569): Compat change id reported: 206033068; UID 10180; state: ENABLED
D/VRIMainActivity: vri.Setup new sync id=0 syncSeqId=0
D/n_ssd_mobilenet( 8569): MiuiProcessManagerServiceStub setSchedFifo
I/MiuiProcessManagerImpl( 8569): setSchedFifo pid:8569, mode:3
E/perf_hint( 8569): Session creation failed, mPreferredRateNanos: -1
D/VRIMainActivity: vri.reportDrawFinished syncSeqId=0 android.view.ViewRootImpl.lambda$createSyncIfNeeded$4$android-view-ViewRootImpl:3852 android.view.ViewRootImpl$$ExternalSyntheticLambda1.run:6 android.os.Handler.handleCallback:942 android.os.Handler.dispatchMessage:99 android.os.Looper.loopOnce:211
W/Camera ( 8569): The selected imageFormatGroup is not supported by Android. Defaulting to yuv420
W/libc ( 8569): Access denied finding property "persist.vendor.camera.privapp.list"
D/CameraExtImplXiaoMi( 8569): initCameraDevice: 0
D/MiuiMultiWindowUtils( 8569): freeform resolution args raw data:{ "liuqin": { "freeform_args": { "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.3334,"original_scale":0.835,"top_margin":0.049,"left_margin":0.2775}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.3334,"original_scale":0.835,"top_margin":-1,"left_margin":0.6525}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":0.049,"left_margin":-1}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":-1,"left_margin":0.4976} }, "mini_freeform_args":{ "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1278}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1278}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":0.1778}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":0.1778} } }, "pipa": { "freeform_args": { "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.3334,"original_scale":0.835,"top_margin":0.049,"left_margin":0.2775}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.3334,"original_scale":0.835,"top_margin":-1,"left_margin":0.6525}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":0.049,"left_margin":-1}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":-1,"left_margin":0.4976} }, "mini_freeform_args":{ "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1278}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1278}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":0.1778}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":0.1778} } }, "yunluo": { "freeform_args": { "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.48,"original_scale":0.6133,"top_margin":0.049,"left_margin":0.2775}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.48,"original_scale":0.6133,"top_margin":-1,"left_margin":0.6525}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":0.049,"left_margin":-1}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":1,"original_scale":0.468,"top_margin":-1,"left_margin":0.4976} }, "mini_freeform_args":{ "vertical_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1917}, "horizontal_portrait":{"aspect_ratio":0.5625, "original_ratio":0.1917}, "vertical_landscape":{"aspect_ratio":-1, "original_ratio":0.2667}, "horizontal_landscape":{"aspect_ratio":-1, "original_ratio":0.2667} } }, "zizhan":{ "freeform_args": { "inner":{ "vertical_portrait":{"aspect_ratio":0.626, "original_ratio":0.5643,"original_scale":0.74,"top_margin":0.168,"left_margin":0.484}, "horizontal_portrait":{"aspect_ratio":0.626, "original_ratio":0.5643,"original_scale":0.74,"top_margin":0.1222,"left_margin":0.59745}, "vertical_landscape":{"aspect_ratio":1.6, "original_ratio":1,"original_scale":0.604,"top_margin":0.2596,"left_margin":0.2624}, "horizontal_landscape":{"aspect_ratio":1.6, "original_ratio":1,"original_scale":0.604,"top_margin":0.213,"left_margin":0.3758} }, "outer":{ "vertical_portrait":{"aspect_ratio":0.626, "original_ratio":1,"original_scale":0.74,"top_margin":0.0753,"left_margin":-1}, "horizontal_portrait":{"aspect_ratio":0.626, "original_ratio":1,"original_scale":0.5756,"top_margin":-1,"left_margin":0.0753}, "vertical_landscape":{"aspect_ratio":1.6, "original_ratio":0.6847,"original_scale":0.587,"top_margin":0.0753,"left_margin":-1}, "horizontal_landscape":{"aspect_ratio":1.6, "original_ratio":0.6847,"original_scale":0.587,"top_margin":-1,"left_margin":0.0753}} }, "mini_freeform_args":{ "inner":{ "vertical_portrait":{"aspect_ratio":0.626, "original_ratio":0.147}, "horizontal_portrait":{
E/MiuiMultiWindowUtils( 8569): initFreeFormResolutionArgs failed, device is redwood
D/IS_CTS_MODE( 8569): false
D/MULTI_WINDOW_SWITCH_ENABLED( 8569): false
E/CameraManagerGlobal( 8569): Camera 4 is not available. Ignore physical camera status change
E/CameraManagerGlobal( 8569): Camera 5 is not available. Ignore physical camera status change
E/CameraManagerGlobal( 8569): Camera 6 is not available. Ignore physical camera status change
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 2
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 3
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 4
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 5
W/libc ( 8569): Access denied finding property "vendor.camera.aux.packagelist"
W/CameraManagerGlobal( 8569): ignore the torch status update of camera: 6
I/Camera ( 8569): startPreview
D/DecorView: onWindowFocusChanged hasWindowFocus true
I/HandWritingStubImpl( 8569): refreshLastKeyboardType: 1
I/HandWritingStubImpl( 8569): getCurrentKeyboardType: 1
D/CameraDevice-JV-0( 8569): waitUntilIdle: E. id = 0
D/CameraDevice-JV-0( 8569): waitUntilIdle: X
I/HandWritingStubImpl( 8569): getCurrentKeyboardType: 1
I/Camera ( 8569): CameraCaptureSession onConfigured
I/Camera ( 8569): Updating builder settings
D/Camera ( 8569): Updating builder with feature: ExposureLockFeature
D/Camera ( 8569): Updating builder with feature: ExposurePointFeature
D/Camera ( 8569): Updating builder with feature: ZoomLevelFeature
D/Camera ( 8569): Updating builder with feature: AutoFocusFeature
D/Camera ( 8569): Updating builder with feature: NoiseReductionFeature
I/Camera ( 8569): updateNoiseReduction | currentSetting: fast
D/Camera ( 8569): Updating builder with feature: FocusPointFeature
D/Camera ( 8569): Updating builder with feature: ResolutionFeature
D/Camera ( 8569): Updating builder with feature: SensorOrientationFeature
D/Camera ( 8569): Updating builder with feature: FlashFeature
D/Camera ( 8569): Updating builder with feature: ExposureOffsetFeature
D/Camera ( 8569): Updating builder with feature: FpsRangeFeature
I/Camera ( 8569): refreshPreviewCaptureSession
D/CameraDevice-JV-0( 8569): waitUntilIdle: E. id = 0
W/BpBinder( 8569): PerfMonitor binderTransact : time=289ms interface=android.hardware.camera2.ICameraDeviceUser code=14
D/CameraDevice-JV-0( 8569): waitUntilIdle: X
I/Camera ( 8569): startPreviewWithImageStream
W/n_ssd_mobilenet( 8569): Long monitor contention with owner main (8569) at void android.hardware.camera2.impl.CameraDeviceImpl.waitUntilIdle()(CameraDeviceImpl.java:1445) waiters=0 in void android.hardware.camera2.impl.CameraDeviceImpl$4.run() for 451ms
I/Camera ( 8569): CameraCaptureSession onConfigured
I/Camera ( 8569): Updating builder settings
W/n_ssd_mobilenet( 8569): Long monitor contention with owner main (8569) at void android.hardware.camera2.impl.CameraDeviceImpl.waitUntilIdle()(CameraDeviceImpl.java:1445) waiters=1 in void android.hardware.camera2.impl.CameraDeviceImpl$CameraDeviceCallbacks.onResultReceived(android.hardware.camera2.impl.CameraMetadataNative, android.hardware.camera2.impl.CaptureResultExtras, android.hardware.camera2.impl.PhysicalCaptureResultInfo[]) for 337ms
D/Camera ( 8569): Updating builder with feature: ExposureLockFeature
D/Camera ( 8569): Updating builder with feature: ExposurePointFeature
D/Camera ( 8569): Updating builder with feature: ZoomLevelFeature
D/Camera ( 8569): Updating builder with feature: AutoFocusFeature
D/Camera ( 8569): Updating builder with feature: NoiseReductionFeature
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/Camera ( 8569): updateNoiseReduction | currentSetting: fast
I/Camera ( 8569): CameraCaptureSession onClosed
D/Camera ( 8569): Updating builder with feature: FocusPointFeature
D/Camera ( 8569): Updating builder with feature: ResolutionFeature
D/Camera ( 8569): Updating builder with feature: SensorOrientationFeature
D/Camera ( 8569): Updating builder with feature: FlashFeature
D/Camera ( 8569): Updating builder with feature: ExposureOffsetFeature
D/Camera ( 8569): Updating builder with feature: FpsRangeFeature
I/Camera ( 8569): refreshPreviewCaptureSession
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/tflite ( 8569): Replacing 71 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions.
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
I/n_ssd_mobilenet( 8569): Background concurrent copying GC freed 67811(4589KB) AllocSpace objects, 113(61MB) LOS objects, 75% free, 3668KB/14MB, paused 42us,17us total 139.197ms
W/System ( 8569): A resource failed to call Surface.release.
E/flutter ( 8569): [ERROR:flutter/runtime/dart_isolate.cc(1097)] Unhandled exception:
E/flutter ( 8569): Invalid argument(s): Output object shape mismatch, interpreter returned output of shape: [1, 2] while shape of output provided as argument in run is: [1, 10, 4]
E/flutter ( 8569): #0 Tensor._duplicateList (package:tflite_flutter/src/tensor.dart:233:7)
E/flutter ( 8569): #1 Tensor.copyTo (package:tflite_flutter/src/tensor.dart:203:7)
E/flutter ( 8569): #2 Interpreter.runForMultipleInputs (package:tflite_flutter/src/interpreter.dart:183:24)
E/flutter ( 8569): #3 _DetectorServer._runInference (package:live_object_detection_ssd_mobilenet/service/detector_service.dart:260:19)
E/flutter ( 8569): #4 _DetectorServer.analyseImage (package:live_object_detection_ssd_mobilenet/service/detector_service.dart:196:20)
E/flutter ( 8569): #5 _DetectorServer._convertCameraImage (package:live_object_detection_ssd_mobilenet/service/detector_service.dart:162:23)
E/flutter ( 8569): #6 _DetectorServer._handleCommand (package:live_object_detection_ssd_mobilenet/service/detector_service.dart:145:9)
E/flutter ( 8569): #7 _DetectorServer._run. (package:live_object_detection_ssd_mobilenet/service/detector_service.dart:129:20)
E/flutter ( 8569): #8 _RootZone.runUnaryGuarded (dart:async/zone.dart:1594:10)
E/flutter ( 8569): #9 _BufferingStreamSubscription._sendData (dart:async/stream_impl.dart:339:11)
E/flutter ( 8569): #10 _BufferingStreamSubscription._add (dart:async/stream_impl.dart:271:7)
E/flutter ( 8569): #11 _SyncStreamControllerDispatch._sendData (dart:async/stream_controller.dart:784:19)
E/flutter ( 8569): #12 _StreamController._add (dart:async/stream_controller.dart:658:7)
E/flutter ( 8569): #13 _StreamController.add (dart:async/stream_controller.dart:606:5)
E/flutter ( 8569): #14 _RawReceivePort._handleMessage (dart:isolate-patch/isolate_patch.dart:189:12)
I/n_ssd_mobilenet( 8569): createIfNeeded: Recreate new EGLImage since dataspace changed
D/ProfileInstaller( 8569): Installing profile for com.example.live_object_detection_ssd_mobilenet

[muamal2]

This condition occurs only when adding a model that is different from the model being added by sh ./scripts/download_model.sh

Note : I am training with teachablemachine

[qu-ngx]

@muamal2 Your model won't work with the code since the code was implemented for that model specifically. You have to modify the example code to match your model's output tensor. Read the following line:

E/flutter ( 8569): Invalid argument(s): Output object shape mismatch, interpreter returned output of shape: [1, 2] while shape of output provided as argument in run is: [1, 10, 4]

I have never used teachablemachine so I don't know what kind of model they use. Upload your model on netron, and check your output tensor's shape carefully. Try modifying the code to match the shape of the output tensor with your model.
If you want me to walk you through every step, please kindly provide me your tflite file.

[muamal2]

Yes, please guide me. This is a preliminary file. I trained a few images

converted_tflite (1).zip

[qu-ngx]

@muamal2 Since your model only returns the label, change output of your model in detector_service.dart, and remove all the lines that do not relate to labels. See the following changes:

class _DetectorServer {
  /// Input size of image (height = width = 224)
  static const int mlModelInputSize = 224;

  /// Result confidence threshold
  static const double confidence = 0.5;
  Interpreter? _interpreter;
  List<String>? _labels;

  _DetectorServer(this._sendPort);

  final SendPort _sendPort;

  // ----------------------------------------------------------------------
  // Here the plugin is used from the background isolate.
  // ----------------------------------------------------------------------

  /// The main entrypoint for the background isolate sent to [Isolate.spawn].
  static void _run(SendPort sendPort) {
    ReceivePort receivePort = ReceivePort();
    final _DetectorServer server = _DetectorServer(sendPort);
    receivePort.listen((message) async {
      final _Command command = message as _Command;
      await server._handleCommand(command);
    });
    // receivePort.sendPort - used by UI isolate to send commands to the service receiverPort
    sendPort.send(_Command(_Codes.init, args: [receivePort.sendPort]));
  }

  /// Handle the [command] received from the [ReceivePort].
  Future<void> _handleCommand(_Command command) async {
    switch (command.code) {
      case _Codes.init:
        // ----------------------------------------------------------------------
        // The [RootIsolateToken] is required for
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] and must be
        // obtained on the root isolate and passed into the background isolate via
        // a [SendPort].
        // ----------------------------------------------------------------------
        RootIsolateToken rootIsolateToken =
            command.args?[0] as RootIsolateToken;
        // ----------------------------------------------------------------------
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] for each
        // background isolate that will use plugins. This sets up the
        // [BinaryMessenger] that the Platform Channels will communicate with on
        // the background isolate.
        // ----------------------------------------------------------------------
        BackgroundIsolateBinaryMessenger.ensureInitialized(rootIsolateToken);
        _interpreter = Interpreter.fromAddress(command.args?[1] as int);
        _labels = command.args?[2] as List<String>;
        _sendPort.send(const _Command(_Codes.ready));
      case _Codes.detect:
        _sendPort.send(const _Command(_Codes.busy));
        _convertCameraImage(command.args?[0] as CameraImage);
      default:
        debugPrint('_DetectorService unrecognized command ${command.code}');
    }
  }

  void _convertCameraImage(CameraImage cameraImage) {
    var preConversionTime = DateTime.now().millisecondsSinceEpoch;

    convertCameraImageToImage(cameraImage).then((image) {
      if (image != null) {
        if (Platform.isAndroid) {
          image = image_lib.copyRotate(image, angle: 90);
        }

        final results = analyseImage(image, preConversionTime);
        _sendPort.send(_Command(_Codes.result, args: [results]));
      }
    });
  }

  Map<String, dynamic> analyseImage(
      image_lib.Image? image, int preConversionTime) {
    var conversionElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    var preProcessStart = DateTime.now().millisecondsSinceEpoch;

    /// Pre-process the image
    /// Resizing image for model [224, 224]
    final imageInput = image_lib.copyResize(
      image!,
      width: mlModelInputSize,
      height: mlModelInputSize,
    );

    // Creating matrix representation, [224, 224, 3]
    final imageMatrix = List.generate(
      imageInput.height,
      (y) => List.generate(
        imageInput.width,
        (x) {
          final pixel = imageInput.getPixel(x, y);
          return [pixel.r, pixel.g, pixel.b];
        },
      ),
    );

    var preProcessElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preProcessStart;

    var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;

    final output = _runInference(imageMatrix);

    // Classes
    final classesRaw = output.elementAt(0).first as List<double>;
    final classes = classesRaw.map((value) => value.toString()).toList();

    // Number of detections
    final numberOfDetectionsRaw = output.last.first as double;
    final numberOfDetections = numberOfDetectionsRaw.toInt();

    final List<String> classification = [];
    for (var i = 0; i < numberOfDetections; i++) {
      classification.add(_labels![classes[i]]);
    }

    /// Generate recognitions
    List<Recognition> recognitions = [];
    for (int i = 0; i < numberOfDetections; i++) {
      // Label string
      var label = classification[i];

      if (score > confidence) {
        recognitions.add(
          Recognition(label),
        );
      }
    }

    var inferenceElapsedTime =
        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;

    var totalElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    return {
      "recognitions": recognitions,
      "stats": <String, String>{
        'Conversion time:': conversionElapsedTime.toString(),
        'Pre-processing time:': preProcessElapsedTime.toString(),
        'Inference time:': inferenceElapsedTime.toString(),
        'Total prediction time:': totalElapsedTime.toString(),
        'Frame': '${image.width} X ${image.height}',
      },
    };
  }

  /// Object detection main function
  List<List<Object>> _runInference(
    List<List<List<num>>> imageMatrix,
  ) {
    // Set input tensor [1, 224, 224, 3]
    final input = [imageMatrix];

    // Set output tensor
    // Classes: [1,2],

    final output = {
      0: [List<num>.filled(2, 0)],
    };

    _interpreter!.runForMultipleInputs([input], output);
    return output.values.toList();
  }
}

I did not consider other files so pls lemme know if something does not run accordingly.

[muamal2]

Sorry to bother you with my problem, but I encountered problems here, the picture below

I use this solution for the purpose of improving definition and accuracy and it works perfectly #137

[qu-ngx]

@muamal2 I am sorry for the 3rd, 4th, 5th bug. They are my mistakes. Unfortunately, I am afraid this model is an image classification real time detection. I might need to take a look at it more carefully. I will come back later for you with a better solution. I am sorry if I could't reply soon to you.

[muamal2]

Don't worry, I'm waiting for you and please keep in mind that I made this change #137

[muamal2]

@qu-ngx Hello, I hope you are well. Did you find a solution?

[qu-ngx]

@muamal2 I am sorry. I was busy last week. Did you figure it out or you still need my help with it?

[muamal2]

@qu-ngx
I did not find a solution. Please help me. I am confused

[qu-ngx]

Can you please let me know what you trying to use the model to predict or simply provide me an image so I could test your model? Try the code below and see if it works. I am still lack in context. Can I have your debug log to see all the other issues?

class _DetectorServer {
  /// Input size of image (height = width = 224)
  static const int mlModelInputSize = 224;

  /// Result confidence threshold
  static const double confidence = 0.5;
  Interpreter? _interpreter;
  List<String>? _labels;

  _DetectorServer(this._sendPort);

  final SendPort _sendPort;

  // ----------------------------------------------------------------------
  // Here the plugin is used from the background isolate.
  // ----------------------------------------------------------------------

  /// The main entrypoint for the background isolate sent to [Isolate.spawn].
  static void _run(SendPort sendPort) {
    ReceivePort receivePort = ReceivePort();
    final _DetectorServer server = _DetectorServer(sendPort);
    receivePort.listen((message) async {
      final _Command command = message as _Command;
      await server._handleCommand(command);
    });
    // receivePort.sendPort - used by UI isolate to send commands to the service receiverPort
    sendPort.send(_Command(_Codes.init, args: [receivePort.sendPort]));
  }

  /// Handle the [command] received from the [ReceivePort].
  Future<void> _handleCommand(_Command command) async {
    switch (command.code) {
      case _Codes.init:
        // ----------------------------------------------------------------------
        // The [RootIsolateToken] is required for
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] and must be
        // obtained on the root isolate and passed into the background isolate via
        // a [SendPort].
        // ----------------------------------------------------------------------
        RootIsolateToken rootIsolateToken =
            command.args?[0] as RootIsolateToken;
        // ----------------------------------------------------------------------
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] for each
        // background isolate that will use plugins. This sets up the
        // [BinaryMessenger] that the Platform Channels will communicate with on
        // the background isolate.
        // ----------------------------------------------------------------------
        BackgroundIsolateBinaryMessenger.ensureInitialized(rootIsolateToken);
        _interpreter = Interpreter.fromAddress(command.args?[1] as int);
        _labels = command.args?[2] as List<String>;
        _sendPort.send(const _Command(_Codes.ready));
      case _Codes.detect:
        _sendPort.send(const _Command(_Codes.busy));
        _convertCameraImage(command.args?[0] as CameraImage);
      default:
        debugPrint('_DetectorService unrecognized command ${command.code}');
    }
  }

  void _convertCameraImage(CameraImage cameraImage) {
    var preConversionTime = DateTime.now().millisecondsSinceEpoch;

    convertCameraImageToImage(cameraImage).then((image) {
      if (image != null) {
        if (Platform.isAndroid) {
          image = image_lib.copyRotate(image, angle: 90);
        }

        final results = analyseImage(image, preConversionTime);
        _sendPort.send(_Command(_Codes.result, args: [results]));
      }
    });
  }

  Map<String, dynamic> analyseImage(
      image_lib.Image? image, int preConversionTime) {
    var conversionElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    var preProcessStart = DateTime.now().millisecondsSinceEpoch;

    /// Pre-process the image
    /// Resizing image for model [224, 224]
    final imageInput = image_lib.copyResize(
      image!,
      width: mlModelInputSize,
      height: mlModelInputSize,
    );

    // Creating matrix representation, [224, 224, 3]
    final imageMatrix = List.generate(
      imageInput.height,
      (y) => List.generate(
        imageInput.width,
        (x) {
          final pixel = imageInput.getPixel(x, y);
          return [pixel.r, pixel.g, pixel.b];
        },
      ),
    );

    var preProcessElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preProcessStart;

    var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;

    final output = _runInference(imageMatrix);

    // Classes
    final classesRaw = output.elementAt(0).first as List<double>;
    final classes = classesRaw.map((value) => value.toString()).toList();

    // Number of detections
    final numberOfDetectionsRaw = output.last.first as double;
    final numberOfDetections = numberOfDetectionsRaw.toInt();

    final List<String> classification = [];
    for (var i = 0; i < numberOfDetections; i++) {
      classification.add(_labels![classes[i]]);
    }

    /// Generate recognitions
    List<Recognition> recognitions = [];
    for (int i = 0; i < numberOfDetections; i++) {
      // Label string
      var label = classification[i];

      recognitions.add(
          Recognition(label),
        );
    }

    var inferenceElapsedTime =
        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;

    var totalElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    return {
      "recognitions": recognitions,
      "stats": <String, String>{
        "Conversion time:": conversionElapsedTime.toString(),
        "Pre-processing time:": preProcessElapsedTime.toString(),
        "Inference time:": inferenceElapsedTime.toString(),
        "Total prediction time:": totalElapsedTime.toString(),
        "Frame": '${image.width} X ${image.height}',
      },
    };
  }

  /// Object detection main function
  List<List<Object>> _runInference(
    List<List<List<num>>> imageMatrix,
  ) {
    // Set input tensor [1, 224, 224, 3]
    final input = [imageMatrix];

    // Set output tensor
    // Classes: [1,2],

    final output = {
      0: [List<num>.filled(2, 0)],
    };

    _interpreter!.runForMultipleInputs([input], output);
    return output.values.toList();
  }
}

[muamal2]

These are the problems that stand out to me

[qu-ngx]

I'm not sure about line 291 and 301. Can you paste the code where it shows error or take screenshots of to where it says String cannot be assigned to "int"?

[muamal2]

 ` 291 => classification.add(_labels![classes[i]]);`
`  301 =>  Recognition(label),`

[qu-ngx]

Gotcha. Wait me a little bit

[muamal2]

I'm waiting , Thanks

[qu-ngx]

class _DetectorServer {
  /// Input size of image (height = width = 224)
  static const int mlModelInputSize = 224;

  /// Result confidence threshold
  static const double confidence = 0.5;
  Interpreter? _interpreter;
  List<String>? _labels;

  _DetectorServer(this._sendPort);

  final SendPort _sendPort;

  // ----------------------------------------------------------------------
  // Here the plugin is used from the background isolate.
  // ----------------------------------------------------------------------

  /// The main entrypoint for the background isolate sent to [Isolate.spawn].
  static void _run(SendPort sendPort) {
    ReceivePort receivePort = ReceivePort();
    final _DetectorServer server = _DetectorServer(sendPort);
    receivePort.listen((message) async {
      final _Command command = message as _Command;
      await server._handleCommand(command);
    });
    // receivePort.sendPort - used by UI isolate to send commands to the service receiverPort
    sendPort.send(_Command(_Codes.init, args: [receivePort.sendPort]));
  }

  /// Handle the [command] received from the [ReceivePort].
  Future<void> _handleCommand(_Command command) async {
    switch (command.code) {
      case _Codes.init:
        // ----------------------------------------------------------------------
        // The [RootIsolateToken] is required for
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] and must be
        // obtained on the root isolate and passed into the background isolate via
        // a [SendPort].
        // ----------------------------------------------------------------------
        RootIsolateToken rootIsolateToken =
            command.args?[0] as RootIsolateToken;
        // ----------------------------------------------------------------------
        // [BackgroundIsolateBinaryMessenger.ensureInitialized] for each
        // background isolate that will use plugins. This sets up the
        // [BinaryMessenger] that the Platform Channels will communicate with on
        // the background isolate.
        // ----------------------------------------------------------------------
        BackgroundIsolateBinaryMessenger.ensureInitialized(rootIsolateToken);
        _interpreter = Interpreter.fromAddress(command.args?[1] as int);
        _labels = command.args?[2] as List<String>;
        _sendPort.send(const _Command(_Codes.ready));
      case _Codes.detect:
        _sendPort.send(const _Command(_Codes.busy));
        _convertCameraImage(command.args?[0] as CameraImage);
      default:
        debugPrint('_DetectorService unrecognized command ${command.code}');
    }
  }

  void _convertCameraImage(CameraImage cameraImage) {
    var preConversionTime = DateTime.now().millisecondsSinceEpoch;

    convertCameraImageToImage(cameraImage).then((image) {
      if (image != null) {
        if (Platform.isAndroid) {
          image = image_lib.copyRotate(image, angle: 90);
        }

        final results = analyseImage(image, preConversionTime);
        _sendPort.send(_Command(_Codes.result, args: [results]));
      }
    });
  }

  Map<String, dynamic> analyseImage(
      image_lib.Image? image, int preConversionTime) {
    var conversionElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    var preProcessStart = DateTime.now().millisecondsSinceEpoch;

    /// Pre-process the image
    /// Resizing image for model [224, 224]
    final imageInput = image_lib.copyResize(
      image!,
      width: mlModelInputSize,
      height: mlModelInputSize,
    );

    // Creating matrix representation, [224, 224, 3]
    final imageMatrix = List.generate(
      imageInput.height,
      (y) => List.generate(
        imageInput.width,
        (x) {
          final pixel = imageInput.getPixel(x, y);
          return [pixel.r, pixel.g, pixel.b];
        },
      ),
    );

    var preProcessElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preProcessStart;

    var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;

    final output = _runInference(imageMatrix);

    // get first output tensor
    final result = output.first;
    int maxScore = result.reduce((a, b) => a+b);
    
   List<Recognition> recognitions = [];
    // Set classifciation map {labels: points}
    var classification = <String, double>{};
    for (var i = 0; i < numberOfDetections; i++) {
        if (result[i] != 0) {
            // Set label: points
            classification[_labels[i]] = result[i].toDouble() / maxScore.toDouble();
            recognitions.add(Recognition(classification));
        }
   }

    var inferenceElapsedTime =
        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;

    var totalElapsedTime =
        DateTime.now().millisecondsSinceEpoch - preConversionTime;

    return {
      "recognitions": recognitions,
      "stats": <String, String>{
        "Conversion time:": conversionElapsedTime.toString(),
        "Pre-processing time:": preProcessElapsedTime.toString(),
        "Inference time:": inferenceElapsedTime.toString(),
        "Total prediction time:": totalElapsedTime.toString(),
        "Frame": '${image.width} X ${image.height}',
      },
    };
  }

  /// Object detection main function
  List<List<Object>> _runInference(
    List<List<List<num>>> imageMatrix,
  ) {
    // Set input tensor [1, 224, 224, 3]
    final input = [imageMatrix];

    // Set output tensor
    // Classes: [1,2],

    final output = {
      0: [List<num>.filled(2, 0)],
    };

    _interpreter!.runForMultipleInputs([input], output);
    return output.values.toList();
   
  }
}

[muamal2]

[zyrridian]

@muamal2 Have you figured it out? I can't use the model that I made in teachable machine