creek
The AI Image Editor of 2030git clone https://github.com/maydayv7/creek
commit f9da70050ce82ec6ffec8e3365a7d099799c9223 parent eb038cb8050e02f99fc80904b9985f717ee7bafd Author: Sanjeebani Parida <sanjeebani14@gmail.com> Date: Thu, 27 Nov 2025 01:16:21 +0530 emotions,lighting embeddings model deployed
Diffstat:
| M | .gitignore | | | 2 | ++ |
| A | assets/emotion_centroids.json | | | 2 | ++ |
| A | assets/lighting_centroids.json | | | 2 | ++ |
| A | lib/services/emotional_embeddings_service.dart | | | 132 | +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
| M | lib/services/image_analyzer_service.dart | | | 72 | ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++---------- |
| A | lib/services/lighting_service.dart | | | 132 | +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
| A | lib/utils/clip_image_processor.dart | | | 64 | ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
| M | linux/flutter/generated_plugins.cmake | | | 1 | + |
| M | pubspec.lock | | | 50 | +++++++++++++++++++++++++++++++++++++++++++++++++- |
| M | pubspec.yaml | | | 8 | ++++++++ |
| M | windows/flutter/generated_plugins.cmake | | | 1 | + |
11 files changed, 455 insertions(+), 11 deletions(-)
diff --git a/.gitignore b/.gitignore @@ -41,3 +41,4 @@ app.*.map.json /android/app/release # Python virtual environment python_env/ +assets/clip_vitb32_image.onnx +\ No newline at end of file diff --git a/assets/emotion_centroids.json b/assets/emotion_centroids.json @@ -0,0 +1 @@ +{"classes": ["sensual", "playful", "edgy", "bold", "luxurious", "cheeky", "ironic", "mysterious"], "centroids": [[-0.00713348388671875, 0.0291748046875, -0.011749267578125, 0.012481689453125, -0.01349639892578125, -0.00443267822265625, 0.00031876564025878906, -0.0853271484375, 0.030609130859375, -0.006866455078125, -0.0013837814331054688, -0.0204620361328125, -0.031829833984375, -0.00605010986328125, -0.005802154541015625, -0.01053619384765625, -0.034820556640625, -0.00031447410583496094, -0.031341552734375, 0.033447265625, -0.02508544921875, -0.004711151123046875, -0.0018825531005859375, -0.0009975433349609375, 0.0216064453125, 0.0179901123046875, 0.0166473388671875, 0.034881591796875, -0.00482940673828125, 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lib/services/style_analyzer_service.dart + +import 'dart:convert'; +import 'dart:math'; +import 'dart:io'; +import 'package:flutter/foundation.dart'; +import 'package:flutter/services.dart'; +import 'package:onnxruntime/onnxruntime.dart'; +import 'package:path_provider/path_provider.dart'; +import '../utils/clip_image_processor.dart'; + +class EmotionalEmbeddingsService { + static OrtSession? _session; + static List<List<double>>? _centroids; + static List<String>? _classes; + + static Future<void> initialize() async { + if (_session != null) return; + + debugPrint("Initializing AI..."); + + // 1. Setup ONNX Environment + OrtEnv.instance.init(); + + // 2. Load Model from Assets + final sessionOptions = OrtSessionOptions(); + final rawAsset = await rootBundle.load('assets/clip_vitb32_image.onnx'); + final bytes = rawAsset.buffer.asUint8List(); + + // Store temp file to load into ONNX + final dir = await getTemporaryDirectory(); + final modelFile = File('${dir.path}/clip_model.onnx'); + await modelFile.writeAsBytes(bytes); + + _session = OrtSession.fromFile(modelFile, sessionOptions); + + // 3. Load Embeddings JSON + final jsonString = await rootBundle.loadString( + 'assets/emotion_centroids.json', + ); + final jsonData = json.decode(jsonString); + + _classes = List<String>.from(jsonData['classes']); + _centroids = + (jsonData['centroids'] as List) + .map((e) => List<double>.from(e)) + .toList(); + + debugPrint("Loaded ${_classes!.length} emotions."); + } + + static Future<Map<String, dynamic>?> analyzeImage(String imagePath) async { + if (_session == null) await initialize(); + + try { + // 1. Preprocess Image + final float32Input = await ClipImageProcessor.preprocess(imagePath); + if (float32Input == null) return null; + + // 2. Create Tensor [1, 3, 224, 224] + final inputTensor = OrtValueTensor.createTensorWithDataList( + float32Input, + [1, 3, 224, 224], + ); + + // 3. Run Inference + final inputs = { + 'image': inputTensor, + }; // Check input name in Netron.app if 'image' fails (might be 'input') + final runOptions = OrtRunOptions(); + final outputs = _session!.run(runOptions, inputs); + + // 4. Get Embedding [1, 512] + // ONNX Runtime usually returns a List<List<double>> for 2D output + final rawOutput = outputs[0]?.value as List; + // Flatten if necessary, or access [0] + List<double> imgFeat = + (rawOutput[0] as List).map((e) => e as double).toList(); + + // Cleanup + inputTensor.release(); + runOptions.release(); + outputs.forEach((element) => element?.release()); + + // 5. Math: Normalize & Dot Product + imgFeat = _l2Normalize(imgFeat); + + List<Map<String, dynamic>> scores = []; + for (int i = 0; i < _classes!.length; i++) { + double score = _dotProduct(imgFeat, _centroids![i]); + scores.add({ + "name": _classes![i], + "score": score * 100.0, // Scale for display + }); + } + + // 6. Sort & Return + scores.sort( + (a, b) => (b['score'] as double).compareTo(a['score'] as double), + ); + + return { + "success": true, + "label": scores.first['name'], + "top5": scores.take(5).toList(), + "scores": {for (var e in scores) e['name']: e['score']}, + }; + } catch (e) { + debugPrint("Analysis Error: $e"); + return null; + } + } + + // --- MATH HELPERS --- + + static List<double> _l2Normalize(List<double> vec) { + double sum = 0; + for (var v in vec) { + sum += v * v; + } + double norm = sqrt(sum); + return vec.map((v) => v / norm).toList(); + } + + static double _dotProduct(List<double> a, List<double> b) { + double sum = 0; + for (int i = 0; i < a.length; i++) { + sum += a[i] * b[i]; + } + return sum; + } +} diff --git a/lib/services/image_analyzer_service.dart b/lib/services/image_analyzer_service.dart @@ -1,30 +1,82 @@ import 'package:flutter/foundation.dart'; import 'package:flutter/services.dart'; import 'dart:convert'; +import 'emotional_embeddings_service.dart'; +import 'lighting_service.dart'; class ImageAnalyzerService { + // Channel for Python/Chaquopy (OpenCV) static const MethodChannel _channel = MethodChannel( 'com.example.adobe/image_analyzer', ); static Future<Map<String, dynamic>?> analyzeImage(String imagePath) async { try { - final String? result = await _channel.invokeMethod('analyzeImage', { - 'imagePath': imagePath, - }); + // 1. Run BOTH analyzers in parallel (Faster performance) + final results = await Future.wait([ + // Task A: Python OpenCV (Composition, Colors, Geometry) + _channel.invokeMethod('analyzeImage', {'imagePath': imagePath}), - if (result != null) { - final Map<String, dynamic> jsonResult = json.decode(result); - return jsonResult; + // Task C: Emotion detection + EmotionalEmbeddingsService.analyzeImage(imagePath), + + // Task D: Lighting style detection + LightingEmbeddingsService.analyzeImage(imagePath), + + + ]); + + // 2. Extract Results + final String? pyResultJson = results[0] as String?; + final Map<String, dynamic>? emotionMap = + results[1] as Map<String, dynamic>?; + final Map<String, dynamic>? lightingMap = + results[2] as Map<String, dynamic>?; + // 3. Parse Python Result (This is the Base) + Map<String, dynamic> finalResult = {}; + + if (pyResultJson != null) { + try { + finalResult = json.decode(pyResultJson); + } catch (e) { + debugPrint("Error decoding Python JSON: $e"); + // Continue even if python fails, so we can try to show AI results + finalResult = { + 'success': true, + 'error_partial': 'Python analysis failed', + }; + } + } else { + finalResult = {'success': true}; + } + + // 4. Merge AI Style Result into the Base JSON + if (emotionMap != null && emotionMap['success'] == true) { + finalResult['emotion_label'] = emotionMap['label']; + finalResult['emotion_scores'] = + emotionMap['scores']; // Map of style probabilities + } + + if (lightingMap != null && lightingMap['success'] == true) { + finalResult['lighting_label'] = lightingMap['label']; + finalResult['lighting_scores'] = + lightingMap['scores']; // Map of style probabilities } - return null; + + debugPrint( + "Platform analyzing image finalResult[\"emotion_label\"]: ${finalResult["emotion_label"]}, finalResult[\"emotion_scores\"]: ${finalResult["emotion_scores"]}", + ); + debugPrint( + "Platform analyzing image finalResult[\"lighting_label\"]: ${finalResult["lighting_label"]}, finalResult[\"lighting_scores\"]: ${finalResult["lighting_scores"]}", + ); + + return finalResult; } on PlatformException catch (e) { - debugPrint("Error analyzing image: ${e.message}"); - return {'success': false, 'error': e.message ?? 'Unknown error occurred'}; + debugPrint("Platform Error analyzing image: ${e.message}"); + return {'success': false, 'error': e.message ?? 'Unknown error'}; } catch (e) { debugPrint("Unexpected error: $e"); return {'success': false, 'error': e.toString()}; } } } - diff --git a/lib/services/lighting_service.dart b/lib/services/lighting_service.dart @@ -0,0 +1,132 @@ +// lib/services/style_analyzer_service.dart + +import 'dart:convert'; +import 'dart:math'; +import 'dart:io'; +import 'package:flutter/foundation.dart'; +import 'package:flutter/services.dart'; +import 'package:onnxruntime/onnxruntime.dart'; +import 'package:path_provider/path_provider.dart'; +import '../utils/clip_image_processor.dart'; + +class LightingEmbeddingsService { + static OrtSession? _session; + static List<List<double>>? _centroids; + static List<String>? _classes; + + static Future<void> initialize() async { + if (_session != null) return; + + debugPrint("Initializing AI..."); + + // 1. Setup ONNX Environment + OrtEnv.instance.init(); + + // 2. Load Model from Assets + final sessionOptions = OrtSessionOptions(); + final rawAsset = await rootBundle.load('assets/clip_vitb32_image.onnx'); + final bytes = rawAsset.buffer.asUint8List(); + + // Store temp file to load into ONNX + final dir = await getTemporaryDirectory(); + final modelFile = File('${dir.path}/clip_model.onnx'); + await modelFile.writeAsBytes(bytes); + + _session = OrtSession.fromFile(modelFile, sessionOptions); + + // 3. Load Embeddings JSON + final jsonString = await rootBundle.loadString( + 'assets/lighting_centroids.json', + ); + final jsonData = json.decode(jsonString); + + _classes = List<String>.from(jsonData['classes']); + _centroids = + (jsonData['centroids'] as List) + .map((e) => List<double>.from(e)) + .toList(); + + debugPrint("Loaded ${_classes!.length} lighting styles."); + } + + static Future<Map<String, dynamic>?> analyzeImage(String imagePath) async { + if (_session == null) await initialize(); + + try { + // 1. Preprocess Image + final float32Input = await ClipImageProcessor.preprocess(imagePath); + if (float32Input == null) return null; + + // 2. Create Tensor [1, 3, 224, 224] + final inputTensor = OrtValueTensor.createTensorWithDataList( + float32Input, + [1, 3, 224, 224], + ); + + // 3. Run Inference + final inputs = { + 'image': inputTensor, + }; // Check input name in Netron.app if 'image' fails (might be 'input') + final runOptions = OrtRunOptions(); + final outputs = _session!.run(runOptions, inputs); + + // 4. Get Embedding [1, 512] + // ONNX Runtime usually returns a List<List<double>> for 2D output + final rawOutput = outputs[0]?.value as List; + // Flatten if necessary, or access [0] + List<double> imgFeat = + (rawOutput[0] as List).map((e) => e as double).toList(); + + // Cleanup + inputTensor.release(); + runOptions.release(); + outputs.forEach((element) => element?.release()); + + // 5. Math: Normalize & Dot Product + imgFeat = _l2Normalize(imgFeat); + + List<Map<String, dynamic>> scores = []; + for (int i = 0; i < _classes!.length; i++) { + double score = _dotProduct(imgFeat, _centroids![i]); + scores.add({ + "name": _classes![i], + "score": score * 100.0, // Scale for display + }); + } + + // 6. Sort & Return + scores.sort( + (a, b) => (b['score'] as double).compareTo(a['score'] as double), + ); + + return { + "success": true, + "label": scores.first['name'], + "top5": scores.take(5).toList(), + "scores": {for (var e in scores) e['name']: e['score']}, + }; + } catch (e) { + debugPrint("Analysis Error: $e"); + return null; + } + } + + // --- MATH HELPERS --- + + static List<double> _l2Normalize(List<double> vec) { + double sum = 0; + for (var v in vec) { + sum += v * v; + } + double norm = sqrt(sum); + return vec.map((v) => v / norm).toList(); + } + + static double _dotProduct(List<double> a, List<double> b) { + double sum = 0; + for (int i = 0; i < a.length; i++) { + sum += a[i] * b[i]; + } + return sum; + } +} diff --git a/lib/utils/clip_image_processor.dart b/lib/utils/clip_image_processor.dart @@ -0,0 +1,64 @@ +// lib/utils/clip_image_processor.dart + +import 'dart:typed_data'; +import 'dart:io'; +import 'package:image/image.dart' as img; + +class ClipImageProcessor { + // CLIP Standard Constants + static const List<double> mean = [0.48145466, 0.4578275, 0.40821073]; + static const List<double> std = [0.26862954, 0.26130258, 0.27577711]; + + /// Reads file, resizes, crops, normalizes, and returns Float32List [1, 3, 224, 224] + static Future<Float32List?> preprocess(String filePath) async { + final bytes = await File(filePath).readAsBytes(); + final image = img.decodeImage(bytes); + + if (image == null) return null; + + // 1. Resize (Shortest side to 224) + int w = image.width; + int h = image.height; + int target = 224; + + img.Image resized; + if (w < h) { + resized = img.copyResize(image, width: target); + } else { + resized = img.copyResize(image, height: target); + } + + // 2. Center Crop (224x224) + final cropped = img.copyResizeCropSquare(resized, size: target); + + // 3. Convert to Float32 List (NCHW format: Batch, Channels, Height, Width) + // Size: 1 * 3 * 224 * 224 = 150,528 float values + final Float32List inputData = Float32List(1 * 3 * 224 * 224); + + int pixelIndex = 0; + // Iterate pixels and separate channels + // Planar format (RRRR... GGGG... BBBB...) + int rOffset = 0; + int gOffset = 224 * 224; + int bOffset = 224 * 224 * 2; + + for (var y = 0; y < 224; y++) { + for (var x = 0; x < 224; x++) { + final pixel = cropped.getPixel(x, y); + + // Normalize: (Value/255 - Mean) / Std + double r = (pixel.r / 255.0 - mean[0]) / std[0]; + double g = (pixel.g / 255.0 - mean[1]) / std[1]; + double b = (pixel.b / 255.0 - mean[2]) / std[2]; + + inputData[rOffset + pixelIndex] = r; + inputData[gOffset + pixelIndex] = g; + inputData[bOffset + pixelIndex] = b; + + pixelIndex++; + } + } + + return inputData; + } +} diff --git a/linux/flutter/generated_plugins.cmake b/linux/flutter/generated_plugins.cmake @@ -7,6 +7,7 @@ list(APPEND FLUTTER_PLUGIN_LIST ) list(APPEND FLUTTER_FFI_PLUGIN_LIST + onnxruntime ) set(PLUGIN_BUNDLED_LIBRARIES) diff --git a/pubspec.lock b/pubspec.lock @@ -1,6 +1,14 @@ # Generated by pub # See https://dart.dev/tools/pub/glossary#lockfile packages: + archive: + dependency: transitive + description: + name: archive + sha256: "2fde1607386ab523f7a36bb3e7edb43bd58e6edaf2ffb29d8a6d578b297fdbbd" + url: "https://pub.dev" + source: hosted + version: "4.0.7" async: dependency: transitive description: @@ -184,6 +192,14 @@ packages: url: "https://pub.dev" source: hosted version: "4.1.2" + image: + dependency: "direct main" + description: + name: image + sha256: "4e973fcf4caae1a4be2fa0a13157aa38a8f9cb049db6529aa00b4d71abc4d928" + url: "https://pub.dev" + source: hosted + version: "4.5.4" image_picker: dependency: "direct main" description: @@ -312,6 +328,14 @@ packages: url: "https://pub.dev" source: hosted version: "2.0.0" + onnxruntime: + dependency: "direct main" + description: + name: onnxruntime + sha256: e77ec05acafc135cc5fe7bcdf11b101b39f06513c9d5e9fa02cb1929f6bac72a + url: "https://pub.dev" + source: hosted + version: "1.4.1" path: dependency: "direct main" description: @@ -368,6 +392,14 @@ packages: url: "https://pub.dev" source: hosted version: "2.3.0" + petitparser: + dependency: transitive + description: + name: petitparser + sha256: "1a97266a94f7350d30ae522c0af07890c70b8e62c71e8e3920d1db4d23c057d1" + url: "https://pub.dev" + source: hosted + version: "7.0.1" platform: dependency: transitive description: @@ -384,6 +416,14 @@ packages: url: "https://pub.dev" source: hosted version: "2.1.8" + posix: + dependency: transitive + description: + name: posix + sha256: "6323a5b0fa688b6a010df4905a56b00181479e6d10534cecfecede2aa55add61" + url: "https://pub.dev" + source: hosted + version: "6.0.3" receive_sharing_intent: dependency: "direct main" description: @@ -541,6 +581,14 @@ packages: url: "https://pub.dev" source: hosted version: "1.1.0" + xml: + dependency: transitive + description: + name: xml + sha256: "971043b3a0d3da28727e40ed3e0b5d18b742fa5a68665cca88e74b7876d5e025" + url: "https://pub.dev" + source: hosted + version: "6.6.1" sdks: - dart: ">=3.8.0-0 <4.0.0" + dart: ">=3.8.0 <4.0.0" flutter: ">=3.29.0" diff --git a/pubspec.yaml b/pubspec.yaml @@ -43,6 +43,9 @@ dependencies: http: ^1.6.0 html: ^0.15.6 + onnxruntime: ^1.2.0 + image: ^4.5.4 + dev_dependencies: flutter_test: sdk: flutter @@ -65,6 +68,11 @@ flutter: # the material Icons class. uses-material-design: true + assets: + - assets/clip_vitb32_image.onnx + - assets/emotion_centroids.json + - assets/lighting_centroids.json + # To add assets to your application, add an assets section, like this: # assets: # - images/a_dot_burr.jpeg diff --git a/windows/flutter/generated_plugins.cmake b/windows/flutter/generated_plugins.cmake @@ -7,6 +7,7 @@ list(APPEND FLUTTER_PLUGIN_LIST ) list(APPEND FLUTTER_FFI_PLUGIN_LIST + onnxruntime ) set(PLUGIN_BUNDLED_LIBRARIES)