lib folder added

Frontend/LookAI/lib/main.dart

+import 'package:emotion_detection/services/face_detection_service.dart';
+import 'package:emotion_detection/views/views.dart';
+import 'package:flutter/material.dart';
+import 'package:provider/provider.dart';
+
+void main(List<String> args) {
+  WidgetsFlutterBinding.ensureInitialized();
+  runApp(const EmotionDetectionApp());
+}
+
+
+class EmotionDetectionApp extends StatefulWidget {
+  const EmotionDetectionApp({super.key});
+
+  @override
+  State<EmotionDetectionApp> createState() => _EmotionDetectionStateApp();
+}
+
+class _EmotionDetectionStateApp extends State<EmotionDetectionApp> {
+  @override
+  Widget build(BuildContext context) {
+    return MultiProvider(
+      providers: [
+        ChangeNotifierProvider<FaceDetectionsService>(create: (_) => FaceDetectionsService()),
+      ],
+      child: MaterialApp(
+        title: "Look AI",
+        theme: ThemeData(
+          colorScheme: ColorScheme.fromSeed(seedColor: Colors.blue),
+          elevatedButtonTheme: ElevatedButtonThemeData(
+            style: ElevatedButton.styleFrom(
+              shape: const StadiumBorder(),
+            ),
+          ),
+        ),
+        themeMode: ThemeMode.light,
+        home: const HomePage(),
+      ),
+    );
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/models/face_data_model.dart

+import 'package:google_mlkit_face_detection/google_mlkit_face_detection.dart';
+import 'package:image/image.dart' as img;
+import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
+
+class FaceData {
+  Face face;
+  img.Image? image;
+  Category gender;
+  Category age;
+  Category emotion;
+  double? smilingProbability;
+  FaceData({
+    required this.face,
+    this.image,
+    required this.gender,
+    required this.age,
+    required this.emotion,
+    this.smilingProbability,
+  });
+
+  String getDescription() {
+    String sentence = "";
+    sentence += _agePart();
+    sentence += " ${_genderPart()}";
+    sentence += " ${_lookPart()}";
+    return sentence;
+  }
+
+  String _agePart() {
+    if (age.score < 1) {
+      return "Unknown Years Old";
+    } else if (age.score < 2) {
+      return "One Year Old";
+    } else {
+      return "${age.label} Years Old";
+    }
+  }
+
+  String _genderPart() {
+    if (age.score < 1) {
+      if (gender.label.toLowerCase() == "male") {
+        return "Male";
+      } else {
+        return "Female";
+      }
+    } else if (age.score < 12) {
+      if (gender.label.toLowerCase() == "male") {
+        return "Child Boy";
+      } else {
+        return "Child Girl";
+      }
+    } else if (age. score < 18) {
+      if (gender.label.toLowerCase() == "male") {
+        return "Teenage Boy";
+      } else {
+        return "Teenage Girl";
+      }
+    } else {
+      if (gender.label.toLowerCase() == "male") {
+        return "Men";
+      } else {
+        return "Woman";
+      }
+    }
+  }
+
+  String _lookPart() {
+    return "looking ${emotion.label.toLowerCase()}";
+  }
+}

Frontend/LookAI/lib/models/models.dart

+export 'face_data_model.dart';
\ No newline at end of file

Frontend/LookAI/lib/services/custom/age_classifier.dart

+import 'dart:developer';
+import 'dart:math' hide log;
+
+import 'package:image/image.dart';
+import 'package:tflite_flutter/tflite_flutter.dart';
+import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
+
+class AgeClassifier {
+  late Interpreter interpreter;
+  late InterpreterOptions _interpreterOptions;
+
+  late List<int> _inputShape;
+  late List<int> _outputShape;
+
+  late TensorImage _inputImage;
+  late TensorBuffer _outputBuffer;
+
+  late TfLiteType _inputType;
+  late TfLiteType _outputType;
+
+  late SequentialProcessor<TensorBuffer> _probabilityProcessor;
+
+  late String _modelName;
+  late NormalizeOp _postProcessNormalizeOp; 
+
+  String get modelName => _modelName;
+
+  NormalizeOp get postProcessNormalizeOp => _postProcessNormalizeOp;
+
+  AgeClassifier({int? numThreads}) {
+    _interpreterOptions = InterpreterOptions();
+
+    if (numThreads != null) {
+      _interpreterOptions.threads = numThreads;
+    }
+
+    _modelName = 'models/age_detection.tflite';
+    _postProcessNormalizeOp = NormalizeOp(0, 1);
+
+    loadModel();
+  }
+
+  Future<void> loadModel() async {
+    try {
+      interpreter = await Interpreter.fromAsset(modelName, options: _interpreterOptions);
+      _inputShape = interpreter.getInputTensor(0).shape;
+      _inputType = interpreter.getInputTensor(0).type;
+      _outputShape = interpreter.getOutputTensor(0).shape;
+      _outputType = interpreter.getOutputTensor(0).type;
+
+      _outputBuffer = TensorBuffer.createFixedSize(_outputShape, _outputType);
+      _probabilityProcessor = TensorProcessorBuilder().add(postProcessNormalizeOp).build();
+    } catch (e) {
+      log('Unable to create interpreter, Caught Exception: ${e.toString()}');
+    }
+  }
+
+  TensorImage _preProcess() {
+    int cropSize = min(_inputImage.height, _inputImage.width);
+    return ImageProcessorBuilder()
+      .add(ResizeWithCropOrPadOp(cropSize, cropSize))
+      .add(ResizeOp(_inputShape[1], _inputShape[2], ResizeMethod.NEAREST_NEIGHBOUR))
+      .build()
+      .process(_inputImage);
+  }
+
+  Category predict(Image image) {
+    _inputImage = TensorImage(_inputType);
+    _inputImage.loadImage(image);
+    _inputImage = _preProcess();
+    TensorBuffer inputBuffer = normalizeInputBuffer(_inputImage.getTensorBuffer());
+    interpreter.run(inputBuffer.buffer, _outputBuffer.getBuffer());
+
+    double result = _probabilityProcessor.process(_outputBuffer).getDoubleList().first;
+    int age = result.toInt();
+    return Category("${age == 0.0 ? "Unknown" : age}", result);
+  }
+
+  void close() {
+    interpreter.close();
+  }
+
+  TensorBuffer normalizeInputBuffer(TensorBuffer input) {
+    TensorBuffer output;
+    List<double> values = List.filled(input.getFlatSize(), 0.0, growable: false);
+    for (int i = 0; i < input.getFlatSize(); i++) {
+      values[i] = input.getDoubleValue(i) / 255.0;
+    }
+    if (input.isDynamic) {
+      output = TensorBuffer.createDynamic(_inputType);
+    } else {
+      output = TensorBuffer.createFixedSize(_inputShape, _inputType);
+    }
+    output.loadList(values, shape: input.getShape());
+    return output;
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/services/custom/custom.dart

+export 'gender_classifier.dart';
+export 'age_classifier.dart';
+export 'emotion_classifier.dart';
\ No newline at end of file

Frontend/LookAI/lib/services/custom/emotion_classifier.dart

+import 'dart:developer';
+import 'dart:math' hide log;
+
+import 'package:image/image.dart';
+import 'package:tflite_flutter/tflite_flutter.dart';
+import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
+
+class EmotionClassifier {
+  late Interpreter interpreter;
+  late InterpreterOptions _interpreterOptions;
+
+  late List<int> _inputShape;
+  late List<int> _outputShape;
+
+  late TensorImage _inputImage;
+  late TensorBuffer _outputBuffer;
+
+  late TfLiteType _inputType;
+  late TfLiteType _outputType;
+
+  late SequentialProcessor<TensorBuffer> _probabilityProcessor;
+
+  late String _modelName;
+  late NormalizeOp _postProcessNormalizeOp; 
+
+  String get modelName => _modelName;
+
+  NormalizeOp get postProcessNormalizeOp => _postProcessNormalizeOp;
+
+  EmotionClassifier({int? numThreads}) {
+    _interpreterOptions = InterpreterOptions();
+
+    if (numThreads != null) {
+      _interpreterOptions.threads = numThreads;
+    }
+
+    _modelName = 'models/emotion_detection.tflite';
+    _postProcessNormalizeOp = NormalizeOp(0, 1);
+
+    loadModel();
+  }
+
+  Future<void> loadModel() async {
+    try {
+      interpreter = await Interpreter.fromAsset(modelName, options: _interpreterOptions);
+      _inputShape = interpreter.getInputTensor(0).shape;
+      _inputType = interpreter.getInputTensor(0).type;
+      _outputShape = interpreter.getOutputTensor(0).shape;
+      _outputType = interpreter.getOutputTensor(0).type;
+
+      _outputBuffer = TensorBuffer.createFixedSize(_outputShape, _outputType);
+      _probabilityProcessor = TensorProcessorBuilder().add(postProcessNormalizeOp).build();
+    } catch (e) {
+      log('Unable to create interpreter, Caught Exception: ${e.toString()}');
+    }
+  }
+
+  TensorImage _preProcess() {
+    int cropSize = min(_inputImage.height, _inputImage.width);
+    return ImageProcessorBuilder()
+      .add(ResizeWithCropOrPadOp(cropSize, cropSize))
+      .add(ResizeOp(_inputShape[1], _inputShape[2], ResizeMethod.NEAREST_NEIGHBOUR))
+      .build()
+      .process(_inputImage);
+  }
+
+  Category predict(Image image) {
+    _inputImage = TensorImage(_inputType);
+    _inputImage.loadImage(image);
+    _inputImage = _preProcess();
+    TensorBuffer inputBuffer = reshapeImageTensorBuffer(_inputImage.getTensorBuffer());
+    inputBuffer = normalizeInputBuffer(inputBuffer);
+    interpreter.run(inputBuffer.buffer, _outputBuffer.getBuffer());
+
+    Map<String, double> labeledProb = TensorLabel.fromList(['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise'], _probabilityProcessor.process(_outputBuffer)).getMapWithFloatValue();
+    final sortedProb = labeledProb.entries.toList()..sort((e1, e2) => e2.value.compareTo(e1.value));
+    return Category(sortedProb[0].key, sortedProb[0].value);
+  }
+
+  void close() {
+    interpreter.close();
+  }
+
+  // Reshape Image Tensor Buffer to Gray Scale
+  TensorBuffer reshapeImageTensorBuffer(TensorBuffer input) {
+    List<int> shape = input.getShape();
+    
+    if (shape.last == 1) {
+      TensorImage image = TensorImage(TfLiteType.float32);
+      image.loadTensorBuffer(input);
+      return image.getTensorBuffer();
+    } else if (shape.last != 3) {
+      throw Exception('Input TensorBuffer shape is not supported.');
+    }
+
+    int flatSize = input.getFlatSize();
+    List<double> values = List.filled(1 * shape[0] * shape[1] * 1, 0.0, growable: false);
+    for (int i = 0; i < flatSize; i += 3) {
+      values[i ~/ 3] = (input.getDoubleValue(i) * 0.299 + input.getDoubleValue(i + 1) * 0.587 + input.getDoubleValue(i + 2) * 0.114);
+    }
+    TensorBuffer output;
+    if (input.isDynamic) {
+      output = TensorBuffer.createDynamic(_inputType);
+    } else {
+      output = TensorBuffer.createFixedSize(_inputShape, _inputType);
+    }
+    output.loadList(values, shape: [1, shape[0], shape[1], 1]);
+    return output;
+  }
+
+  TensorBuffer normalizeInputBuffer(TensorBuffer input) {
+    TensorBuffer output;
+    List<double> values = List.filled(input.getFlatSize(), 0.0, growable: false);
+    for (int i = 0; i < input.getFlatSize(); i++) {
+      values[i] = input.getDoubleValue(i) / 255.0;
+    }
+    if (input.isDynamic) {
+      output = TensorBuffer.createDynamic(_inputType);
+    } else {
+      output = TensorBuffer.createFixedSize(_inputShape, _inputType);
+    }
+    output.loadList(values, shape: input.getShape());
+    return output;
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/services/custom/gender_classifier.dart

+import 'dart:developer';
+import 'dart:math' hide log;
+
+import 'package:image/image.dart';
+import 'package:tflite_flutter/tflite_flutter.dart';
+import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
+
+class GenderClassifier {
+  late Interpreter interpreter;
+  late InterpreterOptions _interpreterOptions;
+
+  late List<int> _inputShape;
+  late List<int> _outputShape;
+
+  late TensorImage _inputImage;
+  late TensorBuffer _outputBuffer;
+
+  late TfLiteType _inputType;
+  late TfLiteType _outputType;
+
+  late SequentialProcessor<TensorBuffer> _probabilityProcessor;
+
+  late String _modelName;
+  late NormalizeOp _postProcessNormalizeOp; 
+
+  String get modelName => _modelName;
+
+  NormalizeOp get postProcessNormalizeOp => _postProcessNormalizeOp;
+
+  GenderClassifier({int? numThreads}) {
+    _interpreterOptions = InterpreterOptions();
+
+    if (numThreads != null) {
+      _interpreterOptions.threads = numThreads;
+    }
+
+    _modelName = 'models/gender_detection.tflite';
+    _postProcessNormalizeOp = NormalizeOp(0, 1);
+
+    loadModel();
+  }
+
+  Future<void> loadModel() async {
+    try {
+      interpreter = await Interpreter.fromAsset(modelName, options: _interpreterOptions);
+      _inputShape = interpreter.getInputTensor(0).shape;
+      _inputType = interpreter.getInputTensor(0).type;
+      _outputShape = interpreter.getOutputTensor(0).shape;
+      _outputType = interpreter.getOutputTensor(0).type;
+
+      _outputBuffer = TensorBuffer.createFixedSize(_outputShape, _outputType);
+      _probabilityProcessor = TensorProcessorBuilder().add(postProcessNormalizeOp).build();
+    } catch (e) {
+      log('Unable to create interpreter, Caught Exception: ${e.toString()}');
+    }
+  }
+
+  TensorImage _preProcess() {
+    int cropSize = min(_inputImage.height, _inputImage.width);
+    return ImageProcessorBuilder()
+      .add(ResizeWithCropOrPadOp(cropSize, cropSize))
+      .add(ResizeOp(_inputShape[1], _inputShape[2], ResizeMethod.NEAREST_NEIGHBOUR))
+      .build()
+      .process(_inputImage);
+  }
+
+  Category predict(Image image) {
+    _inputImage = TensorImage(_inputType);
+    _inputImage.loadImage(image);
+    _inputImage = _preProcess();
+    TensorBuffer inputBuffer = normalizeInputBuffer(_inputImage.getTensorBuffer());
+    interpreter.run(inputBuffer.buffer, _outputBuffer.getBuffer());
+    List<double> result = _probabilityProcessor.process(_outputBuffer).getDoubleList();
+    log('Gender Result: $result');
+    double prediction = double.parse(result.first.toStringAsFixed(2));
+
+    if (prediction >= 0.5) {
+      return Category("Female", prediction);
+    } else {
+      return Category("Male", prediction);
+    }
+  }
+
+  void close() {
+    interpreter.close();
+  }
+
+  TensorBuffer normalizeInputBuffer(TensorBuffer input) {
+    TensorBuffer output;
+    List<double> values = List.filled(input.getFlatSize(), 0.0, growable: false);
+    for (int i = 0; i < input.getFlatSize(); i++) {
+      values[i] = input.getDoubleValue(i) / 255.0;
+    }
+    if (input.isDynamic) {
+      output = TensorBuffer.createDynamic(_inputType);
+    } else {
+      output = TensorBuffer.createFixedSize(_inputShape, _inputType);
+    }
+    output.loadList(values, shape: input.getShape());
+    return output;
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/services/face_detection_service.dart

+import 'dart:async';
+import 'dart:developer';
+import 'dart:io';
+import 'dart:ui' as ui;
+
+import 'package:emotion_detection/models/models.dart';
+import 'package:flutter/material.dart';
+import 'package:google_mlkit_face_detection/google_mlkit_face_detection.dart';
+
+import 'custom/custom.dart';
+import 'image_picker_service.dart';
+
+class FaceDetectionsService with ChangeNotifier {
+  final FaceDetectorOptions _options = FaceDetectorOptions(
+    enableClassification: true,
+    minFaceSize: 0.5,
+  );
+  File? _imageFile;
+  Size _imageSize = Size.zero;
+  final List<FaceData> _detectedFaces = List.empty(growable: true);
+
+  final StreamController<List<FaceData>> _onFaceDetect = StreamController<List<FaceData>>.broadcast();
+
+  late AgeClassifier _ageClassifier;
+  late GenderClassifier _genderClassifier;
+  late EmotionClassifier _emotionClassifier;
+
+  FaceDetectionsService() {
+    _ageClassifier = AgeClassifier();
+    _genderClassifier = GenderClassifier();
+    _emotionClassifier = EmotionClassifier();
+  }
+
+  File? get selectedImage => _imageFile;
+  Size get imageSize => _imageSize;
+  List<FaceData> get detectedFaces => _detectedFaces;
+  Stream<List<FaceData>> get onFaceDetect => _onFaceDetect.stream;
+
+  Future loadImage(File imageFile) async {
+    _imageFile = imageFile;
+    _detectedFaces.clear();
+    _imageSize = await _getImageResolution();
+    notifyListeners();
+    await processImage();
+  }
+
+  void clearImage({bool notify = false}) {
+    _imageFile = null;
+    _detectedFaces.clear();
+    _onFaceDetect.add(List.empty());
+    _imageSize = Size.zero;
+    if (notify) notifyListeners();
+  }
+
+  InputImage _getInputImage() {
+    if (_imageFile != null) {
+      return InputImage.fromFile(_imageFile!);
+    }
+    throw Exception("Load Image File before calling this method!");
+  }
+
+  Future<Size> _getImageResolution() async {
+    if (_imageFile == null) {
+      return Size.zero;
+    }
+    final Completer<ui.Image> completer = Completer();
+    ui.decodeImageFromList(_imageFile!.readAsBytesSync(), (ui.Image img) {
+      return completer.complete(img);
+    });
+    return completer.future.then((value) => Size(value.width.toDouble(), value.height.toDouble()));
+  }
+
+  Future processImage() async {
+    FaceDetector faceDetector = FaceDetector(options: _options);
+    try {
+      InputImage inputImage = _getInputImage();
+      final List<Face> faces = await faceDetector.processImage(inputImage);
+      log("Total Faces Detected: ${faces.length}");
+      for (var element in faces) {
+        log("Detected FaceData: ${element.boundingBox.toString()}");
+        log("Detected smilingProbability: ${element.smilingProbability}");
+        final image = await ImagePickerService.getCroppedImageData(_imageFile!, element.boundingBox);
+        if (image != null) {
+          final ageCategory = _ageClassifier.predict(image);
+          final genderCategory = _genderClassifier.predict(image);
+          final emotionCategory = _emotionClassifier.predict(image);
+          log("Predicted Label: ${ageCategory.label} (${ageCategory.score})");
+          log("Predicted Label: ${genderCategory.label} (${genderCategory.score})");
+          log("Predicted Label: ${emotionCategory.label} (${emotionCategory.score})");
+          _detectedFaces.add(
+            FaceData(face: element, image: image, gender: genderCategory, age: ageCategory, emotion: emotionCategory, smilingProbability: element.smilingProbability)
+          );
+        } else {
+          log("Image Error");
+        }
+      }
+      notifyListeners();
+      _onFaceDetect.add(_detectedFaces);
+      faceDetector.close();
+    } catch (e) {
+      log(e.toString());
+      _detectedFaces.clear();
+      _onFaceDetect.add(List.empty());
+      notifyListeners();
+      faceDetector.close();
+    }
+  } 
+}
\ No newline at end of file

Frontend/LookAI/lib/services/image_picker_service.dart

+import 'dart:developer';
+import 'dart:io';
+import 'dart:ui';
+
+import 'package:image_picker/image_picker.dart';
+import 'package:image/image.dart' as img;
+
+class ImagePickerService {
+  ImagePickerService._();
+  static final ImagePicker _imagePicker = ImagePicker();
+
+  static Future<File?> pickImage({ImageSource imageSource = ImageSource.gallery}) async {
+    try {
+      final XFile? image = await _imagePicker.pickImage(source: imageSource);
+      if (image == null) {
+        return null;
+      }
+      return File(image.path);
+    } catch(e) {
+      log(e.toString());
+      return null;
+    }
+  }
+  
+  static Future<File?> pickVideo({ImageSource imageSource = ImageSource.gallery}) async {
+    try {
+      final XFile? image = await _imagePicker.pickVideo(source: imageSource);
+      if (image == null) {
+        return null;
+      }
+      return File(image.path);
+    } catch(e) {
+      log(e.toString());
+      return null;
+    }
+  }
+
+  static Future<img.Image?> getCroppedImageData(File imageFile, Rect boundBox) async {
+    final image = img.decodeImage(imageFile.readAsBytesSync());
+    if (image == null) {
+      return null;
+    }
+    final croppedImage = img.copyCrop(image, boundBox.left.toInt(), boundBox.top.toInt(), boundBox.width.toInt(), boundBox.height.toInt());
+    return croppedImage;
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/services/services.dart

+export 'face_detection_service.dart';
+export 'image_picker_service.dart';
+export 'custom/custom.dart';
\ No newline at end of file

Frontend/LookAI/lib/views/home.dart

+import 'package:emotion_detection/services/services.dart';
+import 'package:emotion_detection/views/image_classification_page.dart';
+import 'package:emotion_detection/views/video_classification_page.dart';
+import 'package:flutter/cupertino.dart';
+import 'package:flutter/material.dart';
+import 'package:provider/provider.dart';
+
+class HomePage extends StatefulWidget {
+  const HomePage({super.key});
+
+  @override
+  State<HomePage> createState() => _HomePageState();
+}
+
+class _HomePageState extends State<HomePage> {
+  int currentIndex = 0;
+  late PageController _pageController;
+
+  @override
+  void initState() {
+    _pageController = PageController(initialPage: currentIndex);
+    super.initState();
+  }
+
+  @override
+  void dispose() {
+    _pageController.dispose();
+    super.dispose();
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    final faceDetectionsService = Provider.of<FaceDetectionsService>(context);
+    return Scaffold(
+      appBar: AppBar(
+        title: const Text("Look AI"),
+      ),
+      body: Column(
+        children: [
+          Container(
+            padding: const EdgeInsets.symmetric(vertical: 16, horizontal: 16),
+            width: double.infinity,
+            child: CupertinoSlidingSegmentedControl(
+              groupValue: currentIndex,
+              onValueChanged: (value) {
+                faceDetectionsService.clearImage();
+                _pageController.animateToPage(value ?? 0, duration: const Duration(milliseconds: 300), curve: Curves.easeIn);
+              },
+              children: const {
+                0: Text("Image"),
+                1: Text("Video"),
+              },
+            ),
+          ),
+          Expanded(
+            child: PageView(
+              controller: _pageController,
+              physics: const NeverScrollableScrollPhysics(),
+              onPageChanged: (value) {
+                setState(() {
+                  currentIndex = value;
+                });
+              },
+              children: const [
+                ImageClassificationPage(),
+                VideoClassificationPage(),
+              ],
+            ),
+          ),
+        ],
+      ),
+    );
+  }
+}
\ No newline at end of file

Frontend/LookAI/lib/views/image_classification_page.dart

+import 'dart:async';
+import 'dart:io';
+
+import 'package:adaptive_dialog/adaptive_dialog.dart';
+import 'package:emotion_detection/models/face_data_model.dart';
+import 'package:flutter/material.dart';
+import 'package:flutter_tts/flutter_tts.dart';
+import 'package:image_picker/image_picker.dart';
+import 'package:provider/provider.dart';
+