├── .gitignore ├── CHANGELOG.md ├── LICENSE ├── README.md ├── android ├── .classpath ├── .gitignore ├── .idea │ └── misc.xml ├── .project ├── .settings │ └── org.eclipse.buildship.core.prefs ├── build.gradle ├── gradle.properties ├── settings.gradle └── src │ └── main │ ├── AndroidManifest.xml │ └── java │ └── sq │ └── flutter │ └── tflite │ └── TflitePlugin.java ├── example ├── .gitignore ├── .metadata ├── README.md ├── android │ ├── .gitignore │ ├── .project │ ├── .settings │ │ └── org.eclipse.buildship.core.prefs │ ├── app │ │ ├── .classpath │ │ ├── .project │ │ ├── .settings │ │ │ └── org.eclipse.buildship.core.prefs │ │ ├── build.gradle │ │ └── src │ │ │ └── main │ │ │ ├── AndroidManifest.xml │ │ │ ├── java │ │ │ └── sq │ │ │ │ └── flutter │ │ │ │ └── tfliteexample │ │ │ │ └── MainActivity.java │ │ │ └── res │ │ │ ├── drawable │ │ │ └── launch_background.xml │ │ │ ├── mipmap-hdpi │ │ │ └── ic_launcher.png │ │ │ ├── mipmap-mdpi │ │ │ └── ic_launcher.png │ │ │ ├── mipmap-xhdpi │ │ │ └── ic_launcher.png │ │ │ ├── mipmap-xxhdpi │ │ │ └── ic_launcher.png │ │ │ ├── mipmap-xxxhdpi │ │ │ └── ic_launcher.png │ │ │ └── values │ │ │ └── styles.xml │ ├── build.gradle │ ├── gradle.properties │ ├── gradle │ │ └── wrapper │ │ │ ├── gradle-wrapper.jar │ │ │ └── gradle-wrapper.properties │ ├── gradlew │ ├── gradlew.bat │ ├── settings.gradle │ └── settings_aar.gradle ├── assets │ ├── deeplabv3_257_mv_gpu.tflite │ ├── deeplabv3_257_mv_gpu.txt │ ├── mobilenet_v1_1.0_224.tflite │ ├── mobilenet_v1_1.0_224.txt │ ├── posenet_mv1_075_float_from_checkpoints.tflite │ ├── ssd_mobilenet.tflite │ ├── ssd_mobilenet.txt │ ├── yolov2_tiny.tflite │ └── yolov2_tiny.txt ├── ios │ ├── .gitignore │ ├── Flutter │ │ ├── AppFrameworkInfo.plist │ │ ├── Debug.xcconfig │ │ └── Release.xcconfig │ ├── Podfile │ ├── Podfile.lock │ ├── Runner.xcodeproj │ │ ├── project.pbxproj │ │ ├── project.xcworkspace │ │ │ └── contents.xcworkspacedata │ │ └── xcshareddata │ │ │ └── xcschemes │ │ │ └── Runner.xcscheme │ ├── Runner.xcworkspace │ │ ├── contents.xcworkspacedata │ │ └── xcshareddata │ │ │ └── IDEWorkspaceChecks.plist │ └── Runner │ │ ├── AppDelegate.h │ │ ├── AppDelegate.m │ │ ├── Assets.xcassets │ │ ├── AppIcon.appiconset │ │ │ ├── Contents.json │ │ │ ├── Icon-App-1024x1024@1x.png │ │ │ ├── Icon-App-20x20@1x.png │ │ │ ├── Icon-App-20x20@2x.png │ │ │ ├── Icon-App-20x20@3x.png │ │ │ ├── Icon-App-29x29@1x.png │ │ │ ├── Icon-App-29x29@2x.png │ │ │ ├── Icon-App-29x29@3x.png │ │ │ ├── Icon-App-40x40@1x.png │ │ │ ├── Icon-App-40x40@2x.png │ │ │ ├── Icon-App-40x40@3x.png │ │ │ ├── Icon-App-60x60@2x.png │ │ │ ├── Icon-App-60x60@3x.png │ │ │ ├── Icon-App-76x76@1x.png │ │ │ ├── Icon-App-76x76@2x.png │ │ │ └── Icon-App-83.5x83.5@2x.png │ │ └── LaunchImage.imageset │ │ │ ├── Contents.json │ │ │ ├── LaunchImage.png │ │ │ ├── LaunchImage@2x.png │ │ │ ├── LaunchImage@3x.png │ │ │ └── README.md │ │ ├── Base.lproj │ │ ├── LaunchScreen.storyboard │ │ └── Main.storyboard │ │ ├── Info.plist │ │ └── main.m ├── lib │ └── main.dart ├── pubspec.yaml ├── test │ └── widget_test.dart └── yolo.jpg ├── ios ├── .gitignore ├── Assets │ └── .gitkeep ├── Classes │ ├── TflitePlugin.h │ ├── TflitePlugin.mm │ ├── ios_image_load.h │ └── ios_image_load.mm └── tflite.podspec ├── lib └── tensorflow_lite_flutter.dart ├── pubspec.yaml └── test └── tflite_test.dart /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .dart_tool/ 3 | 4 | .packages 5 | .pub/ 6 | pubspec.lock 7 | 8 | build/ 9 | 10 | # IntelliJ 11 | *.iml 12 | .idea/workspace.xml 13 | .idea/tasks.xml 14 | .idea/gradle.xml 15 | .idea/assetWizardSettings.xml 16 | .idea/dictionaries 17 | .idea/libraries 18 | .idea/caches -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | ## 3.0.0 (2025-04-27) 2 | 3 | * Updated to support Flutter 3.16+ and Dart 3.2+ 4 | * Improved documentation for all API methods with detailed parameter descriptions 5 | * Fixed lint errors and improved code formatting 6 | * Added comprehensive API documentation with examples for each ML task 7 | * Enhanced README with better installation and usage instructions 8 | * Updated dependencies to their latest versions 9 | * Added repository and issue tracker URLs to pubspec.yaml 10 | 11 | ## 2.0.1 12 | 13 | * Fixed compatibility issues with newer Flutter versions 14 | * Minor bug fixes and improvements 15 | 16 | ## 2.0.0 17 | 18 | * Initial release after fixing all issues from old plugin. 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2018 Qian Sha 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy 4 | of this software and associated documentation files (the "Software"), to deal 5 | in the Software without restriction, including without limitation the rights 6 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | copies of the Software, and to permit persons to whom the Software is 8 | furnished to do so, subject to the following conditions: 9 | 10 | The above copyright notice and this permission notice shall be included in all 11 | copies or substantial portions of the Software. 12 | 13 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 14 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 15 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 16 | IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, 17 | DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR 18 | OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE 19 | OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TensorFlow Lite Flutter 2 | 3 | [![pub package](https://img.shields.io/pub/v/tensorflow_lite_flutter.svg)](https://pub.dev/packages/tensorflow_lite_flutter) 4 | 5 | A comprehensive Flutter plugin for accessing TensorFlow Lite API. This plugin provides a Dart interface to TensorFlow Lite models, allowing Flutter apps to perform on-device machine learning with high performance and low latency. 6 | 7 | ## Features 8 | 9 | Supports multiple ML tasks on both iOS and Android: 10 | 11 | - ✅ Image Classification 12 | - ✅ Object Detection (SSD MobileNet and YOLO) 13 | - ✅ Pix2Pix Image-to-Image Translation 14 | - ✅ Semantic Segmentation (Deeplab) 15 | - ✅ Pose Estimation (PoseNet) 16 | 17 | ## Table of Contents 18 | 19 | - [Installation](#installation) 20 | - [Setup](#setup) 21 | - [Android Configuration](#android-configuration) 22 | - [iOS Configuration](#ios-configuration) 23 | - [Usage](#usage) 24 | - [Loading Models](#loading-models) 25 | - [Image Classification](#image-classification) 26 | - [Object Detection](#object-detection) 27 | - [Pix2Pix](#pix2pix) 28 | - [Semantic Segmentation](#semantic-segmentation) 29 | - [Pose Estimation](#pose-estimation) 30 | - [Advanced Usage](#advanced-usage) 31 | - [GPU Acceleration](#gpu-acceleration) 32 | - [Performance Optimization](#performance-optimization) 33 | - [Examples](#examples) 34 | - [Troubleshooting](#troubleshooting) 35 | 36 | ## Version History 37 | 38 | ### v3.0.0 (Latest) 39 | 40 | - Updated to support Flutter 3.16+ and Dart 3.2+ 41 | - Improved documentation and examples 42 | - Performance optimizations 43 | 44 | ### v2.0.1 45 | 46 | - iOS TensorFlow Lite library upgraded to TensorFlowLiteObjC 2.x 47 | - Changes to native code are denoted with `TFLITE2` 48 | 49 | ### v1.0.0 50 | 51 | - Updated to TensorFlow Lite API v1.12.0 52 | - No longer accepts parameter `inputSize` and `numChannels` (retrieved from input tensor) 53 | - `numThreads` moved to `Tflite.loadModel` 54 | 55 | ## Installation 56 | 57 | Add `tensorflow_lite_flutter` as a dependency in your `pubspec.yaml` file: 58 | 59 | ```yaml 60 | dependencies: 61 | flutter: 62 | sdk: flutter 63 | tensorflow_lite_flutter: ^3.0.0 64 | ``` 65 | 66 | Then run: 67 | 68 | ```bash 69 | flutter pub get 70 | ``` 71 | 72 | ## Setup 73 | 74 | ### Android Configuration 75 | 76 | 1. In `android/app/build.gradle`, add the following setting in the `android` block to ensure TensorFlow Lite model files aren't compressed: 77 | 78 | ```gradle 79 | aaptOptions { 80 | noCompress 'tflite' 81 | noCompress 'lite' 82 | } 83 | ``` 84 | 85 | 2. If you're using models larger than 100MB, you may need to enable split APKs by adding the following to your `android/app/build.gradle` file: 86 | 87 | ```gradle 88 | android { 89 | // Other settings... 90 | defaultConfig { 91 | // Other settings... 92 | ndk { 93 | abiFilters 'armeabi-v7a', 'arm64-v8a' 94 | } 95 | } 96 | 97 | splits { 98 | abi { 99 | enable true 100 | reset() 101 | include 'armeabi-v7a', 'arm64-v8a' 102 | universalApk false 103 | } 104 | } 105 | } 106 | ``` 107 | 108 | ### iOS Configuration 109 | 110 | Solutions to common build errors on iOS: 111 | 112 | 1. **'vector' file not found** 113 | 114 | Open `ios/Runner.xcworkspace` in Xcode, click Runner > Targets > Runner > Build Settings, search for `Compile Sources As`, and change the value to `Objective-C++` 115 | 116 | 2. **'tensorflow/lite/kernels/register.h' file not found** 117 | 118 | The plugin assumes the TensorFlow header files are located in path "tensorflow/lite/kernels". 119 | 120 | For earlier versions of TensorFlow, the header path may be "tensorflow/contrib/lite/kernels". 121 | 122 | Use `CONTRIB_PATH` to toggle the path. Uncomment `//#define CONTRIB_PATH` in the iOS implementation if needed. 123 | 124 | 3. **Memory usage issues** 125 | 126 | For large models, you may need to increase the memory available to your app. Add the following to your `ios/Runner/Info.plist`: 127 | 128 | ```xml 129 | NSAppTransportSecurity 130 | 131 | NSAllowsArbitraryLoads 132 | 133 | 134 | ``` 135 | 136 | ## Usage 137 | 138 | ### Getting Started 139 | 140 | 1. Create an `assets` folder and place your model and label files in it. Add them to your `pubspec.yaml`: 141 | 142 | ```yaml 143 | assets: 144 | - assets/labels.txt 145 | - assets/mobilenet_v1_1.0_224.tflite 146 | ``` 147 | 148 | 2. Import the library in your Dart code: 149 | 150 | ```dart 151 | import 'package:tensorflow_lite_flutter/tensorflow_lite_flutter.dart'; 152 | ``` 153 | 154 | ### Loading Models 155 | 156 | Before using any TensorFlow Lite model, you need to load it into memory: 157 | 158 | ```dart 159 | Future loadModel() async { 160 | try { 161 | String? result = await Tflite.loadModel( 162 | model: "assets/mobilenet_v1_1.0_224.tflite", 163 | labels: "assets/labels.txt", 164 | numThreads: 2, // Number of threads to use (default: 1) 165 | isAsset: true, // Is the model file an asset or a file? (default: true) 166 | useGpuDelegate: false // Use GPU acceleration? (default: false) 167 | ); 168 | print('Model loaded successfully: $result'); 169 | } catch (e) { 170 | print('Failed to load model: $e'); 171 | } 172 | } 173 | ``` 174 | 175 | ### Releasing Resources 176 | 177 | When you're done using the model, release the resources to free up memory: 178 | 179 | ```dart 180 | Future disposeModel() async { 181 | await Tflite.close(); 182 | print('Model resources released'); 183 | } 184 | ``` 185 | 186 | ### GPU Acceleration 187 | 188 | To use GPU acceleration for faster inference: 189 | 190 | 1. Set `useGpuDelegate: true` when loading the model 191 | 2. For optimal performance in release mode, follow the [TensorFlow Lite GPU delegate optimization guide](https://www.tensorflow.org/lite/performance/gpu#step_5_release_mode) 192 | 193 | ```dart 194 | // Example with GPU acceleration enabled 195 | await Tflite.loadModel( 196 | model: "assets/model.tflite", 197 | labels: "assets/labels.txt", 198 | useGpuDelegate: true // Enable GPU acceleration 199 | ); 200 | ``` 201 | 202 | > **Note**: GPU acceleration works best for floating-point models and may not improve performance for quantized models. 203 | 204 | ### Image Classification 205 | 206 | #### Overview 207 | 208 | Image classification identifies what's in an image from a predefined set of categories. This plugin supports various image classification models like MobileNet, EfficientNet, and custom TensorFlow Lite models. 209 | 210 | #### Output Format 211 | 212 | The model returns a list of classifications with their confidence scores: 213 | 214 | ```json 215 | [ 216 | { 217 | "index": 0, 218 | "label": "person", 219 | "confidence": 0.629 220 | }, 221 | { 222 | "index": 1, 223 | "label": "dog", 224 | "confidence": 0.324 225 | } 226 | ] 227 | ``` 228 | 229 | #### Classifying Images 230 | 231 | **From a file path:** 232 | 233 | ```dart 234 | Future classifyImage(String imagePath) async { 235 | try { 236 | // Run inference 237 | List? recognitions = await Tflite.runModelOnImage( 238 | path: imagePath, // Required: Path to the image file 239 | imageMean: 127.5, // Default: 117.0 (depends on your model) 240 | imageStd: 127.5, // Default: 1.0 (depends on your model) 241 | numResults: 5, // Default: 5 (maximum number of results) 242 | threshold: 0.2, // Default: 0.1 (minimum confidence threshold) 243 | asynch: true // Default: true (run in background) 244 | ); 245 | 246 | // Process results 247 | if (recognitions != null) { 248 | for (var result in recognitions) { 249 | print('${result["label"]} - ${(result["confidence"] * 100).toStringAsFixed(2)}%'); 250 | } 251 | } 252 | } catch (e) { 253 | print('Error classifying image: $e'); 254 | } 255 | } 256 | ``` 257 | 258 | **From binary data (useful for camera frames):** 259 | 260 | ```dart 261 | Future classifyImageBinary(Uint8List imageBytes, int inputSize) async { 262 | try { 263 | // Process image data to match model input requirements 264 | Uint8List processedData = imageToByteListFloat32(imageBytes, inputSize, 127.5, 127.5); 265 | 266 | // Run inference 267 | List? recognitions = await Tflite.runModelOnBinary( 268 | binary: processedData, // Required: Processed image data 269 | numResults: 5, // Default: 5 270 | threshold: 0.1, // Default: 0.1 271 | asynch: true // Default: true 272 | ); 273 | 274 | // Process results 275 | if (recognitions != null) { 276 | for (var result in recognitions) { 277 | print('${result["label"]} - ${(result["confidence"] * 100).toStringAsFixed(2)}%'); 278 | } 279 | } 280 | } catch (e) { 281 | print('Error classifying binary image: $e'); 282 | } 283 | } 284 | 285 | // Helper function to prepare image data 286 | Uint8List imageToByteListFloat32(Uint8List imageBytes, int inputSize, double mean, double std) { 287 | var convertedBytes = Float32List(1 * inputSize * inputSize * 3); 288 | var buffer = Float32List.view(convertedBytes.buffer); 289 | int pixelIndex = 0; 290 | 291 | // Process image data to match model input format 292 | // ... (implementation depends on your image processing needs) 293 | 294 | return convertedBytes.buffer.asUint8List(); 295 | } 296 | 297 | Uint8List imageToByteListFloat32( 298 | img.Image image, int inputSize, double mean, double std) { 299 | var convertedBytes = Float32List(1 * inputSize * inputSize * 3); 300 | var buffer = Float32List.view(convertedBytes.buffer); 301 | int pixelIndex = 0; 302 | for (var i = 0; i < inputSize; i++) { 303 | for (var j = 0; j < inputSize; j++) { 304 | var pixel = image.getPixel(j, i); 305 | buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std; 306 | buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std; 307 | buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std; 308 | } 309 | } 310 | return convertedBytes.buffer.asUint8List(); 311 | } 312 | 313 | Uint8List imageToByteListUint8(img.Image image, int inputSize) { 314 | var convertedBytes = Uint8List(1 * inputSize * inputSize * 3); 315 | var buffer = Uint8List.view(convertedBytes.buffer); 316 | int pixelIndex = 0; 317 | for (var i = 0; i < inputSize; i++) { 318 | for (var j = 0; j < inputSize; j++) { 319 | var pixel = image.getPixel(j, i); 320 | buffer[pixelIndex++] = img.getRed(pixel); 321 | buffer[pixelIndex++] = img.getGreen(pixel); 322 | buffer[pixelIndex++] = img.getBlue(pixel); 323 | } 324 | } 325 | return convertedBytes.buffer.asUint8List(); 326 | } 327 | ``` 328 | 329 | - Run on image stream (video frame): 330 | 331 | > Works with [camera plugin 4.0.0](https://pub.dartlang.org/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888. 332 | 333 | ```dart 334 | var recognitions = await Tflite.runModelOnFrame( 335 | bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required 336 | imageHeight: img.height, 337 | imageWidth: img.width, 338 | imageMean: 127.5, // defaults to 127.5 339 | imageStd: 127.5, // defaults to 127.5 340 | rotation: 90, // defaults to 90, Android only 341 | numResults: 2, // defaults to 5 342 | threshold: 0.1, // defaults to 0.1 343 | asynch: true // defaults to true 344 | ); 345 | ``` 346 | 347 | ### Object Detection 348 | 349 | #### Overview 350 | 351 | Object detection identifies and locates objects within an image. This plugin supports two popular object detection architectures: 352 | 353 | 1. **SSD MobileNet** - Fast and efficient for mobile devices 354 | 2. **YOLO** (You Only Look Once) - Higher accuracy but more computationally intensive 355 | 356 | #### SSD MobileNet 357 | 358 | **Output Format:** 359 | 360 | ```json 361 | [ 362 | { 363 | "detectedClass": "hot dog", 364 | "confidenceInClass": 0.923, 365 | "rect": { 366 | "x": 0.15, // Normalized coordinates (0-1) 367 | "y": 0.33, // Normalized coordinates (0-1) 368 | "w": 0.80, // Width as percentage of image width 369 | "h": 0.27 // Height as percentage of image height 370 | } 371 | }, 372 | { 373 | "detectedClass": "person", 374 | "confidenceInClass": 0.845, 375 | "rect": { 376 | "x": 0.52, 377 | "y": 0.18, 378 | "w": 0.35, 379 | "h": 0.75 380 | } 381 | } 382 | ] 383 | ``` 384 | 385 | **Detecting Objects from an Image File:** 386 | 387 | ```dart 388 | Future detectObjectsOnImage(String imagePath) async { 389 | try { 390 | // Run inference 391 | List? detections = await Tflite.detectObjectOnImage( 392 | path: imagePath, // Required: Path to the image file 393 | model: "SSDMobileNet", // Default: "SSDMobileNet" 394 | imageMean: 127.5, // Default: 127.5 395 | imageStd: 127.5, // Default: 127.5 396 | threshold: 0.4, // Default: 0.1 (confidence threshold) 397 | numResultsPerClass: 2, // Default: 5 (max detections per class) 398 | asynch: true // Default: true (run in background) 399 | ); 400 | 401 | // Process results 402 | if (detections != null) { 403 | for (var detection in detections) { 404 | final rect = detection["rect"]; 405 | print('${detection["detectedClass"]} - ${(detection["confidenceInClass"] * 100).toStringAsFixed(2)}%'); 406 | print('Location: x=${rect["x"]}, y=${rect["y"]}, w=${rect["w"]}, h=${rect["h"]}'); 407 | } 408 | } 409 | } catch (e) { 410 | print('Error detecting objects: $e'); 411 | } 412 | } 413 | ``` 414 | 415 | **Detecting Objects from Binary Data:** 416 | 417 | ```dart 418 | Future detectObjectsOnBinary(Uint8List imageBytes) async { 419 | try { 420 | List? detections = await Tflite.detectObjectOnBinary( 421 | binary: imageBytes, // Required: Binary image data 422 | model: "SSDMobileNet", // Default: "SSDMobileNet" 423 | threshold: 0.4, // Default: 0.1 424 | numResultsPerClass: 2, // Default: 5 425 | asynch: true // Default: true 426 | ); 427 | 428 | // Process results 429 | if (detections != null) { 430 | for (var detection in detections) { 431 | print('${detection["detectedClass"]} - ${(detection["confidenceInClass"] * 100).toStringAsFixed(2)}%'); 432 | } 433 | } 434 | } catch (e) { 435 | print('Error detecting objects from binary: $e'); 436 | } 437 | } 438 | ``` 439 | 440 | **Detecting Objects from Camera Frames:** 441 | 442 | > Works with [camera plugin](https://pub.dev/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888. 443 | 444 | ```dart 445 | Future detectObjectsOnFrame(CameraImage cameraImage) async { 446 | try { 447 | List? detections = await Tflite.detectObjectOnFrame( 448 | bytesList: cameraImage.planes.map((plane) => plane.bytes).toList(), // Required 449 | model: "SSDMobileNet", // Default: "SSDMobileNet" 450 | imageHeight: cameraImage.height, 451 | imageWidth: cameraImage.width, 452 | imageMean: 127.5, // Default: 127.5 453 | imageStd: 127.5, // Default: 127.5 454 | rotation: 90, // Default: 90, Android only 455 | numResults: 5, // Default: 5 456 | threshold: 0.4, // Default: 0.1 457 | asynch: true // Default: true 458 | ); 459 | 460 | // Process results 461 | if (detections != null) { 462 | for (var detection in detections) { 463 | print('${detection["detectedClass"]} - ${(detection["confidenceInClass"] * 100).toStringAsFixed(2)}%'); 464 | } 465 | } 466 | } catch (e) { 467 | print('Error detecting objects on frame: $e'); 468 | } 469 | } 470 | ``` 471 | 472 | #### YOLO (You Only Look Once) 473 | 474 | YOLO is another popular object detection model that's more accurate but slightly more computationally intensive than SSD MobileNet. 475 | 476 | **Using YOLO for Object Detection:** 477 | 478 | ```dart 479 | Future detectObjectsWithYOLO(String imagePath) async { 480 | // YOLO-specific anchors (can be customized based on your model) 481 | final List anchors = [ 482 | 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 483 | 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 484 | ]; 485 | 486 | try { 487 | List? detections = await Tflite.detectObjectOnImage( 488 | path: imagePath, // Required: Path to the image file 489 | model: "YOLO", // Use YOLO model 490 | imageMean: 0.0, // Default: 127.5 (but YOLO typically uses 0.0) 491 | imageStd: 255.0, // Default: 127.5 (but YOLO typically uses 255.0) 492 | threshold: 0.3, // Default: 0.1 493 | numResultsPerClass: 2, // Default: 5 494 | anchors: anchors, // YOLO-specific parameter 495 | blockSize: 32, // Default: 32 496 | numBoxesPerBlock: 5, // Default: 5 497 | asynch: true // Default: true 498 | ); 499 | 500 | // Process results (same format as SSD MobileNet) 501 | if (detections != null) { 502 | for (var detection in detections) { 503 | final rect = detection["rect"]; 504 | print('${detection["detectedClass"]} - ${(detection["confidenceInClass"] * 100).toStringAsFixed(2)}%'); 505 | print('Location: x=${rect["x"]}, y=${rect["y"]}, w=${rect["w"]}, h=${rect["h"]}'); 506 | } 507 | } 508 | } catch (e) { 509 | print('Error detecting objects with YOLO: $e'); 510 | } 511 | } 512 | ``` 513 | 514 | - Run on binary: 515 | 516 | ```dart 517 | var recognitions = await Tflite.detectObjectOnBinary( 518 | binary: imageToByteListFloat32(resizedImage, 416, 0.0, 255.0), // required 519 | model: "YOLO", 520 | threshold: 0.3, // defaults to 0.1 521 | numResultsPerClass: 2,// defaults to 5 522 | anchors: anchors, // defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828] 523 | blockSize: 32, // defaults to 32 524 | numBoxesPerBlock: 5, // defaults to 5 525 | asynch: true // defaults to true 526 | ); 527 | ``` 528 | 529 | - Run on image stream (video frame): 530 | 531 | > Works with [camera plugin 4.0.0](https://pub.dartlang.org/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888. 532 | 533 | ```dart 534 | var recognitions = await Tflite.detectObjectOnFrame( 535 | bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required 536 | model: "YOLO", 537 | imageHeight: img.height, 538 | imageWidth: img.width, 539 | imageMean: 0, // defaults to 127.5 540 | imageStd: 255.0, // defaults to 127.5 541 | numResults: 2, // defaults to 5 542 | threshold: 0.1, // defaults to 0.1 543 | numResultsPerClass: 2,// defaults to 5 544 | anchors: anchors, // defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828] 545 | blockSize: 32, // defaults to 32 546 | numBoxesPerBlock: 5, // defaults to 5 547 | asynch: true // defaults to true 548 | ); 549 | ``` 550 | 551 | ### Pix2Pix 552 | 553 | > Thanks to [RP](https://github.com/shaqian/flutter_tflite/pull/18) from [Green Appers](https://github.com/GreenAppers) 554 | 555 | - Output format: 556 | 557 | The output of Pix2Pix inference is Uint8List type. Depending on the `outputType` used, the output is: 558 | 559 | - (if outputType is png) byte array of a png image 560 | 561 | - (otherwise) byte array of the raw output 562 | 563 | - Run on image: 564 | 565 | ```dart 566 | var result = await runPix2PixOnImage( 567 | path: filepath, // required 568 | imageMean: 0.0, // defaults to 0.0 569 | imageStd: 255.0, // defaults to 255.0 570 | asynch: true // defaults to true 571 | ); 572 | ``` 573 | 574 | - Run on binary: 575 | 576 | ```dart 577 | var result = await runPix2PixOnBinary( 578 | binary: binary, // required 579 | asynch: true // defaults to true 580 | ); 581 | ``` 582 | 583 | - Run on image stream (video frame): 584 | 585 | ```dart 586 | var result = await runPix2PixOnFrame( 587 | bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required 588 | imageHeight: img.height, // defaults to 1280 589 | imageWidth: img.width, // defaults to 720 590 | imageMean: 127.5, // defaults to 0.0 591 | imageStd: 127.5, // defaults to 255.0 592 | rotation: 90, // defaults to 90, Android only 593 | asynch: true // defaults to true 594 | ); 595 | ``` 596 | 597 | ### Deeplab 598 | 599 | > Thanks to [RP](https://github.com/shaqian/flutter_tflite/pull/22) from [see--](https://github.com/see--) for Android implementation. 600 | 601 | - Output format: 602 | 603 | The output of Deeplab inference is Uint8List type. Depending on the `outputType` used, the output is: 604 | 605 | - (if outputType is png) byte array of a png image 606 | 607 | - (otherwise) byte array of r, g, b, a values of the pixels 608 | 609 | - Run on image: 610 | 611 | ```dart 612 | var result = await runSegmentationOnImage( 613 | path: filepath, // required 614 | imageMean: 0.0, // defaults to 0.0 615 | imageStd: 255.0, // defaults to 255.0 616 | labelColors: [...], // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219 617 | outputType: "png", // defaults to "png" 618 | asynch: true // defaults to true 619 | ); 620 | ``` 621 | 622 | - Run on binary: 623 | 624 | ```dart 625 | var result = await runSegmentationOnBinary( 626 | binary: binary, // required 627 | labelColors: [...], // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219 628 | outputType: "png", // defaults to "png" 629 | asynch: true // defaults to true 630 | ); 631 | ``` 632 | 633 | - Run on image stream (video frame): 634 | 635 | ```dart 636 | var result = await runSegmentationOnFrame( 637 | bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required 638 | imageHeight: img.height, // defaults to 1280 639 | imageWidth: img.width, // defaults to 720 640 | imageMean: 127.5, // defaults to 0.0 641 | imageStd: 127.5, // defaults to 255.0 642 | rotation: 90, // defaults to 90, Android only 643 | labelColors: [...], // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219 644 | outputType: "png", // defaults to "png" 645 | asynch: true // defaults to true 646 | ); 647 | ``` 648 | 649 | ### PoseNet 650 | 651 | > Model is from [StackOverflow thread](https://stackoverflow.com/a/55288616). 652 | 653 | - Output format: 654 | 655 | `x, y` are between [0, 1]. You can scale `x` by the width and `y` by the height of the image. 656 | 657 | ``` 658 | [ // array of poses/persons 659 | { // pose #1 660 | score: 0.6324902, 661 | keypoints: { 662 | 0: { 663 | x: 0.250, 664 | y: 0.125, 665 | part: nose, 666 | score: 0.9971070 667 | }, 668 | 1: { 669 | x: 0.230, 670 | y: 0.105, 671 | part: leftEye, 672 | score: 0.9978438 673 | } 674 | ...... 675 | } 676 | }, 677 | { // pose #2 678 | score: 0.32534285, 679 | keypoints: { 680 | 0: { 681 | x: 0.402, 682 | y: 0.538, 683 | part: nose, 684 | score: 0.8798978 685 | }, 686 | 1: { 687 | x: 0.380, 688 | y: 0.513, 689 | part: leftEye, 690 | score: 0.7090239 691 | } 692 | ...... 693 | } 694 | }, 695 | ...... 696 | ] 697 | ``` 698 | 699 | - Run on image: 700 | 701 | ```dart 702 | var result = await runPoseNetOnImage( 703 | path: filepath, // required 704 | imageMean: 125.0, // defaults to 125.0 705 | imageStd: 125.0, // defaults to 125.0 706 | numResults: 2, // defaults to 5 707 | threshold: 0.7, // defaults to 0.5 708 | nmsRadius: 10, // defaults to 20 709 | asynch: true // defaults to true 710 | ); 711 | ``` 712 | 713 | - Run on binary: 714 | 715 | ```dart 716 | var result = await runPoseNetOnBinary( 717 | binary: binary, // required 718 | numResults: 2, // defaults to 5 719 | threshold: 0.7, // defaults to 0.5 720 | nmsRadius: 10, // defaults to 20 721 | asynch: true // defaults to true 722 | ); 723 | ``` 724 | 725 | - Run on image stream (video frame): 726 | 727 | ```dart 728 | var result = await runPoseNetOnFrame( 729 | bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required 730 | imageHeight: img.height, // defaults to 1280 731 | imageWidth: img.width, // defaults to 720 732 | imageMean: 125.0, // defaults to 125.0 733 | imageStd: 125.0, // defaults to 125.0 734 | rotation: 90, // defaults to 90, Android only 735 | numResults: 2, // defaults to 5 736 | threshold: 0.7, // defaults to 0.5 737 | nmsRadius: 10, // defaults to 20 738 | asynch: true // defaults to true 739 | ); 740 | ``` 741 | 742 | ## Example 743 | 744 | ### Prediction in Static Images 745 | 746 | Refer to the [example](https://github.com/draz26648/tensorflow_lite_flutter/tree/master/example). 747 | 748 | ## Run test cases 749 | 750 | `flutter test test/tflite_test.dart` -------------------------------------------------------------------------------- /android/.classpath: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /android/.gitignore: -------------------------------------------------------------------------------- 1 | *.iml 2 | .gradle 3 | /local.properties 4 | /.idea/workspace.xml 5 | /.idea/libraries 6 | .DS_Store 7 | /build 8 | /captures 9 | -------------------------------------------------------------------------------- /android/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | -------------------------------------------------------------------------------- /android/.project: -------------------------------------------------------------------------------- 1 | 2 | 3 | tflite 4 | Project tflite created by Buildship. 5 | 6 | 7 | 8 | 9 | org.eclipse.jdt.core.javabuilder 10 | 11 | 12 | 13 | 14 | org.eclipse.buildship.core.gradleprojectbuilder 15 | 16 | 17 | 18 | 19 | 20 | org.eclipse.jdt.core.javanature 21 | org.eclipse.buildship.core.gradleprojectnature 22 | 23 | 24 | 25 | 1703491490494 26 | 27 | 30 28 | 29 | org.eclipse.core.resources.regexFilterMatcher 30 | node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__ 31 | 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /android/.settings/org.eclipse.buildship.core.prefs: -------------------------------------------------------------------------------- 1 | arguments=--init-script C\:\\Users\\Draz\\AppData\\Roaming\\Code\\User\\globalStorage\\redhat.java\\1.25.1\\config_win\\org.eclipse.osgi\\55\\0\\.cp\\gradle\\init\\init.gradle --init-script C\:\\Users\\Draz\\AppData\\Roaming\\Code\\User\\globalStorage\\redhat.java\\1.25.1\\config_win\\org.eclipse.osgi\\55\\0\\.cp\\gradle\\protobuf\\init.gradle 2 | auto.sync=false 3 | build.scans.enabled=false 4 | connection.gradle.distribution=GRADLE_DISTRIBUTION(VERSION(8.1.1)) 5 | connection.project.dir= 6 | eclipse.preferences.version=1 7 | gradle.user.home= 8 | java.home=C\:/Users/Draz/.vscode/extensions/redhat.java-1.25.1-win32-x64/jre/17.0.9-win32-x86_64 9 | jvm.arguments= 10 | offline.mode=false 11 | override.workspace.settings=true 12 | show.console.view=true 13 | show.executions.view=true 14 | -------------------------------------------------------------------------------- /android/build.gradle: -------------------------------------------------------------------------------- 1 | group 'sq.flutter.tflite' 2 | version '1.0-SNAPSHOT' 3 | 4 | buildscript { 5 | repositories { 6 | google() 7 | jcenter() 8 | } 9 | 10 | dependencies { 11 | classpath 'com.android.tools.build:gradle:3.6.3' 12 | } 13 | } 14 | 15 | rootProject.allprojects { 16 | repositories { 17 | google() 18 | jcenter() 19 | } 20 | } 21 | 22 | apply plugin: 'com.android.library' 23 | 24 | android { 25 | compileSdkVersion 31 26 | 27 | defaultConfig { 28 | minSdkVersion 23 29 | testInstrumentationRunner 'androidx.test.runner.AndroidJUnitRunner' 30 | } 31 | lintOptions { 32 | disable 'InvalidPackage' 33 | } 34 | 35 | dependencies { 36 | implementation 'org.tensorflow:tensorflow-lite:+' 37 | implementation 'org.tensorflow:tensorflow-lite-gpu:+' 38 | } 39 | } 40 | -------------------------------------------------------------------------------- /android/gradle.properties: -------------------------------------------------------------------------------- 1 | org.gradle.jvmargs=-Xmx1536M 2 | android.useAndroidX=true 3 | android.enableJetifier=true 4 | android.enableR8=true -------------------------------------------------------------------------------- /android/settings.gradle: -------------------------------------------------------------------------------- 1 | rootProject.name = 'tflite' 2 | -------------------------------------------------------------------------------- /android/src/main/AndroidManifest.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | -------------------------------------------------------------------------------- /example/.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .dart_tool/ 3 | 4 | .packages 5 | .pub/ 6 | 7 | build/ 8 | 9 | .flutter-plugins 10 | .flutter-plugins-dependencies 11 | 12 | flutter_export_environment.sh 13 | Flutter.podspec 14 | 15 | # IntelliJ 16 | *.iml 17 | .idea/workspace.xml 18 | .idea/tasks.xml 19 | .idea/gradle.xml 20 | .idea/assetWizardSettings.xml 21 | .idea/dictionaries 22 | .idea/libraries 23 | .idea/caches -------------------------------------------------------------------------------- /example/.metadata: -------------------------------------------------------------------------------- 1 | # This file tracks properties of this Flutter project. 2 | # Used by Flutter tool to assess capabilities and perform upgrades etc. 3 | # 4 | # This file should be version controlled and should not be manually edited. 5 | 6 | version: 7 | revision: 3b309bda072a6b326e8aa4591a5836af600923ce 8 | channel: beta 9 | -------------------------------------------------------------------------------- /example/README.md: -------------------------------------------------------------------------------- 1 | # tflite_example 2 | 3 | Use tflite plugin to run model on images. The image is captured by camera or selected from gallery (with the help of [image_picker](https://pub.dartlang.org/packages/image_picker) plugin). 4 | 5 | ![](yolo.jpg) 6 | 7 | ## Prerequisites 8 | 9 | Create a `assets` folder. From https://github.com/shaqian/flutter_tflite/tree/master/example/assets 10 | dowload the following files and place them in `assets` folder. 11 | - mobilenet_v1_1.0_224.tflite 12 | - mobilenet_v1_1.0_224.txt 13 | - ssd_mobilenet.tflite 14 | - ssd_mobilenet.txt 15 | - yolov2_tiny.tflite 16 | - yolov2_tiny.txt 17 | - deeplabv3_257_mv_gpu.tflite 18 | - deeplabv3_257_mv_gpu.txt 19 | - posenet_mv1_075_float_from_checkpoints.tflite 20 | 21 | ## Install 22 | 23 | ``` 24 | flutter packages get 25 | ``` 26 | 27 | ## Run 28 | 29 | ``` 30 | flutter run 31 | ``` 32 | 33 | ## Caveat 34 | 35 | ```recognizeImageBinary(image)``` (sample code for ```runModelOnBinary```) is slow on iOS when decoding image due to a [known issue](https://github.com/brendan-duncan/image/issues/55) with image package. 36 | -------------------------------------------------------------------------------- /example/android/.gitignore: -------------------------------------------------------------------------------- 1 | *.iml 2 | *.class 3 | .gradle 4 | /local.properties 5 | /.idea/workspace.xml 6 | /.idea/libraries 7 | .DS_Store 8 | /build 9 | /captures 10 | GeneratedPluginRegistrant.java 11 | -------------------------------------------------------------------------------- /example/android/.project: -------------------------------------------------------------------------------- 1 | 2 | 3 | android 4 | Project android created by Buildship. 5 | 6 | 7 | 8 | 9 | org.eclipse.buildship.core.gradleprojectbuilder 10 | 11 | 12 | 13 | 14 | 15 | org.eclipse.buildship.core.gradleprojectnature 16 | 17 | 18 | -------------------------------------------------------------------------------- /example/android/.settings/org.eclipse.buildship.core.prefs: -------------------------------------------------------------------------------- 1 | connection.project.dir= 2 | eclipse.preferences.version=1 3 | -------------------------------------------------------------------------------- /example/android/app/.classpath: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /example/android/app/.project: -------------------------------------------------------------------------------- 1 | 2 | 3 | app 4 | Project app created by Buildship. 5 | 6 | 7 | 8 | 9 | org.eclipse.jdt.core.javabuilder 10 | 11 | 12 | 13 | 14 | org.eclipse.buildship.core.gradleprojectbuilder 15 | 16 | 17 | 18 | 19 | 20 | org.eclipse.jdt.core.javanature 21 | org.eclipse.buildship.core.gradleprojectnature 22 | 23 | 24 | 25 | 1703492759744 26 | 27 | 30 28 | 29 | org.eclipse.core.resources.regexFilterMatcher 30 | node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__ 31 | 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /example/android/app/.settings/org.eclipse.buildship.core.prefs: -------------------------------------------------------------------------------- 1 | connection.project.dir=.. 2 | eclipse.preferences.version=1 3 | -------------------------------------------------------------------------------- /example/android/app/build.gradle: -------------------------------------------------------------------------------- 1 | def localProperties = new Properties() 2 | def localPropertiesFile = rootProject.file('local.properties') 3 | if (localPropertiesFile.exists()) { 4 | localPropertiesFile.withReader('UTF-8') { reader -> 5 | localProperties.load(reader) 6 | } 7 | } 8 | 9 | def flutterRoot = localProperties.getProperty('flutter.sdk') 10 | if (flutterRoot == null) { 11 | throw new GradleException("Flutter SDK not found. Define location with flutter.sdk in the local.properties file.") 12 | } 13 | 14 | def flutterVersionCode = localProperties.getProperty('flutter.versionCode') 15 | if (flutterVersionCode == null) { 16 | flutterVersionCode = '1' 17 | } 18 | 19 | def flutterVersionName = localProperties.getProperty('flutter.versionName') 20 | if (flutterVersionName == null) { 21 | flutterVersionName = '1.0' 22 | } 23 | 24 | apply plugin: 'com.android.application' 25 | apply from: "$flutterRoot/packages/flutter_tools/gradle/flutter.gradle" 26 | 27 | android { 28 | compileSdkVersion 28 29 | 30 | lintOptions { 31 | disable 'InvalidPackage' 32 | } 33 | 34 | aaptOptions { 35 | noCompress 'tflite' 36 | } 37 | 38 | defaultConfig { 39 | // TODO: Specify your own unique Application ID (https://developer.android.com/studio/build/application-id.html). 40 | applicationId "sq.flutter.tfliteexample" 41 | minSdkVersion 19 42 | targetSdkVersion 28 43 | versionCode flutterVersionCode.toInteger() 44 | versionName flutterVersionName 45 | testInstrumentationRunner 'androidx.test.runner.AndroidJUnitRunner' 46 | } 47 | 48 | buildTypes { 49 | release { 50 | // TODO: Add your own signing config for the release build. 51 | // Signing with the debug keys for now, so `flutter run --release` works. 52 | signingConfig signingConfigs.debug 53 | } 54 | } 55 | } 56 | 57 | flutter { 58 | source '../..' 59 | } 60 | 61 | dependencies { 62 | testImplementation 'junit:junit:4.12' 63 | androidTestImplementation 'androidx.test.ext:junit:1.1.1' 64 | androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.0' 65 | } 66 | -------------------------------------------------------------------------------- /example/android/app/src/main/AndroidManifest.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 8 | 9 | 10 | 15 | 19 | 26 | 30 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | -------------------------------------------------------------------------------- /example/android/app/src/main/java/sq/flutter/tfliteexample/MainActivity.java: -------------------------------------------------------------------------------- 1 | package sq.flutter.tfliteexample; 2 | 3 | import android.os.Bundle; 4 | import io.flutter.app.FlutterActivity; 5 | import io.flutter.plugins.GeneratedPluginRegistrant; 6 | 7 | public class MainActivity extends FlutterActivity { 8 | @Override 9 | protected void onCreate(Bundle savedInstanceState) { 10 | super.onCreate(savedInstanceState); 11 | GeneratedPluginRegistrant.registerWith(this); 12 | } 13 | } 14 | -------------------------------------------------------------------------------- /example/android/app/src/main/res/drawable/launch_background.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 12 | 13 | -------------------------------------------------------------------------------- /example/android/app/src/main/res/mipmap-hdpi/ic_launcher.png: 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-------------------------------------------------------------------------------- /example/android/app/src/main/res/values/styles.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 8 | 9 | -------------------------------------------------------------------------------- /example/android/build.gradle: -------------------------------------------------------------------------------- 1 | buildscript { 2 | repositories { 3 | google() 4 | jcenter() 5 | } 6 | 7 | dependencies { 8 | classpath 'com.android.tools.build:gradle:3.6.1' 9 | } 10 | } 11 | 12 | allprojects { 13 | repositories { 14 | google() 15 | jcenter() 16 | } 17 | } 18 | 19 | rootProject.buildDir = '../build' 20 | subprojects { 21 | project.buildDir = "${rootProject.buildDir}/${project.name}" 22 | } 23 | subprojects { 24 | project.evaluationDependsOn(':app') 25 | } 26 | 27 | task clean(type: Delete) { 28 | delete rootProject.buildDir 29 | } 30 | -------------------------------------------------------------------------------- /example/android/gradle.properties: -------------------------------------------------------------------------------- 1 | org.gradle.jvmargs=-Xmx1536M 2 | target-platform=android-arm64 3 | android.useAndroidX=true 4 | android.enableJetifier=true 5 | -------------------------------------------------------------------------------- /example/android/gradle/wrapper/gradle-wrapper.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/android/gradle/wrapper/gradle-wrapper.jar -------------------------------------------------------------------------------- /example/android/gradle/wrapper/gradle-wrapper.properties: -------------------------------------------------------------------------------- 1 | #Sat Mar 28 00:33:22 ICT 2020 2 | distributionBase=GRADLE_USER_HOME 3 | distributionPath=wrapper/dists 4 | zipStoreBase=GRADLE_USER_HOME 5 | zipStorePath=wrapper/dists 6 | distributionUrl=https\://services.gradle.org/distributions/gradle-5.6.4-all.zip 7 | -------------------------------------------------------------------------------- /example/android/gradlew: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | ############################################################################## 4 | ## 5 | ## Gradle start up script for UN*X 6 | ## 7 | ############################################################################## 8 | 9 | # Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. 10 | DEFAULT_JVM_OPTS="" 11 | 12 | APP_NAME="Gradle" 13 | APP_BASE_NAME=`basename "$0"` 14 | 15 | # Use the maximum available, or set MAX_FD != -1 to use that value. 16 | MAX_FD="maximum" 17 | 18 | warn ( ) { 19 | echo "$*" 20 | } 21 | 22 | die ( ) { 23 | echo 24 | echo "$*" 25 | echo 26 | exit 1 27 | } 28 | 29 | # OS specific support (must be 'true' or 'false'). 30 | cygwin=false 31 | msys=false 32 | darwin=false 33 | case "`uname`" in 34 | CYGWIN* ) 35 | cygwin=true 36 | ;; 37 | Darwin* ) 38 | darwin=true 39 | ;; 40 | MINGW* ) 41 | msys=true 42 | ;; 43 | esac 44 | 45 | # Attempt to set APP_HOME 46 | # Resolve links: $0 may be a link 47 | PRG="$0" 48 | # Need this for relative symlinks. 49 | while [ -h "$PRG" ] ; do 50 | ls=`ls -ld "$PRG"` 51 | link=`expr "$ls" : '.*-> \(.*\)$'` 52 | if expr "$link" : '/.*' > /dev/null; then 53 | PRG="$link" 54 | else 55 | PRG=`dirname "$PRG"`"/$link" 56 | fi 57 | done 58 | SAVED="`pwd`" 59 | cd "`dirname \"$PRG\"`/" >/dev/null 60 | APP_HOME="`pwd -P`" 61 | cd "$SAVED" >/dev/null 62 | 63 | CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar 64 | 65 | # Determine the Java command to use to start the JVM. 66 | if [ -n "$JAVA_HOME" ] ; then 67 | if [ -x "$JAVA_HOME/jre/sh/java" ] ; then 68 | # IBM's JDK on AIX uses strange locations for the executables 69 | JAVACMD="$JAVA_HOME/jre/sh/java" 70 | else 71 | JAVACMD="$JAVA_HOME/bin/java" 72 | fi 73 | if [ ! -x "$JAVACMD" ] ; then 74 | die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME 75 | 76 | Please set the JAVA_HOME variable in your environment to match the 77 | location of your Java installation." 78 | fi 79 | else 80 | JAVACMD="java" 81 | which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. 82 | 83 | Please set the JAVA_HOME variable in your environment to match the 84 | location of your Java installation." 85 | fi 86 | 87 | # Increase the maximum file descriptors if we can. 88 | if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then 89 | MAX_FD_LIMIT=`ulimit -H -n` 90 | if [ $? -eq 0 ] ; then 91 | if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then 92 | MAX_FD="$MAX_FD_LIMIT" 93 | fi 94 | ulimit -n $MAX_FD 95 | if [ $? -ne 0 ] ; then 96 | warn "Could not set maximum file descriptor limit: $MAX_FD" 97 | fi 98 | else 99 | warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT" 100 | fi 101 | fi 102 | 103 | # For Darwin, add options to specify how the application appears in the dock 104 | if $darwin; then 105 | GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\"" 106 | fi 107 | 108 | # For Cygwin, switch paths to Windows format before running java 109 | if $cygwin ; then 110 | APP_HOME=`cygpath --path --mixed "$APP_HOME"` 111 | CLASSPATH=`cygpath --path --mixed "$CLASSPATH"` 112 | JAVACMD=`cygpath --unix "$JAVACMD"` 113 | 114 | # We build the pattern for arguments to be converted via cygpath 115 | ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null` 116 | SEP="" 117 | for dir in $ROOTDIRSRAW ; do 118 | ROOTDIRS="$ROOTDIRS$SEP$dir" 119 | SEP="|" 120 | done 121 | OURCYGPATTERN="(^($ROOTDIRS))" 122 | # Add a user-defined pattern to the cygpath arguments 123 | if [ "$GRADLE_CYGPATTERN" != "" ] ; then 124 | OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)" 125 | fi 126 | # Now convert the arguments - kludge to limit ourselves to /bin/sh 127 | i=0 128 | for arg in "$@" ; do 129 | CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -` 130 | CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option 131 | 132 | if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition 133 | eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"` 134 | else 135 | eval `echo args$i`="\"$arg\"" 136 | fi 137 | i=$((i+1)) 138 | done 139 | case $i in 140 | (0) set -- ;; 141 | (1) set -- "$args0" ;; 142 | (2) set -- "$args0" "$args1" ;; 143 | (3) set -- "$args0" "$args1" "$args2" ;; 144 | (4) set -- "$args0" "$args1" "$args2" "$args3" ;; 145 | (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;; 146 | (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;; 147 | (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;; 148 | (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;; 149 | (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;; 150 | esac 151 | fi 152 | 153 | # Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules 154 | function splitJvmOpts() { 155 | JVM_OPTS=("$@") 156 | } 157 | eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS 158 | JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME" 159 | 160 | exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@" 161 | -------------------------------------------------------------------------------- /example/android/gradlew.bat: -------------------------------------------------------------------------------- 1 | @if "%DEBUG%" == "" @echo off 2 | @rem ########################################################################## 3 | @rem 4 | @rem Gradle startup script for Windows 5 | @rem 6 | @rem ########################################################################## 7 | 8 | @rem Set local scope for the variables with windows NT shell 9 | if "%OS%"=="Windows_NT" setlocal 10 | 11 | @rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. 12 | set DEFAULT_JVM_OPTS= 13 | 14 | set DIRNAME=%~dp0 15 | if "%DIRNAME%" == "" set DIRNAME=. 16 | set APP_BASE_NAME=%~n0 17 | set APP_HOME=%DIRNAME% 18 | 19 | @rem Find java.exe 20 | if defined JAVA_HOME goto findJavaFromJavaHome 21 | 22 | set JAVA_EXE=java.exe 23 | %JAVA_EXE% -version >NUL 2>&1 24 | if "%ERRORLEVEL%" == "0" goto init 25 | 26 | echo. 27 | echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. 28 | echo. 29 | echo Please set the JAVA_HOME variable in your environment to match the 30 | echo location of your Java installation. 31 | 32 | goto fail 33 | 34 | :findJavaFromJavaHome 35 | set JAVA_HOME=%JAVA_HOME:"=% 36 | set JAVA_EXE=%JAVA_HOME%/bin/java.exe 37 | 38 | if exist "%JAVA_EXE%" goto init 39 | 40 | echo. 41 | echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% 42 | echo. 43 | echo Please set the JAVA_HOME variable in your environment to match the 44 | echo location of your Java installation. 45 | 46 | goto fail 47 | 48 | :init 49 | @rem Get command-line arguments, handling Windowz variants 50 | 51 | if not "%OS%" == "Windows_NT" goto win9xME_args 52 | if "%@eval[2+2]" == "4" goto 4NT_args 53 | 54 | :win9xME_args 55 | @rem Slurp the command line arguments. 56 | set CMD_LINE_ARGS= 57 | set _SKIP=2 58 | 59 | :win9xME_args_slurp 60 | if "x%~1" == "x" goto execute 61 | 62 | set CMD_LINE_ARGS=%* 63 | goto execute 64 | 65 | :4NT_args 66 | @rem Get arguments from the 4NT Shell from JP Software 67 | set CMD_LINE_ARGS=%$ 68 | 69 | :execute 70 | @rem Setup the command line 71 | 72 | set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar 73 | 74 | @rem Execute Gradle 75 | "%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS% 76 | 77 | :end 78 | @rem End local scope for the variables with windows NT shell 79 | if "%ERRORLEVEL%"=="0" goto mainEnd 80 | 81 | :fail 82 | rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of 83 | rem the _cmd.exe /c_ return code! 84 | if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1 85 | exit /b 1 86 | 87 | :mainEnd 88 | if "%OS%"=="Windows_NT" endlocal 89 | 90 | :omega 91 | -------------------------------------------------------------------------------- /example/android/settings.gradle: -------------------------------------------------------------------------------- 1 | include ':app' 2 | 3 | def flutterProjectRoot = rootProject.projectDir.parentFile.toPath() 4 | 5 | def plugins = new Properties() 6 | def pluginsFile = new File(flutterProjectRoot.toFile(), '.flutter-plugins') 7 | if (pluginsFile.exists()) { 8 | pluginsFile.withReader('UTF-8') { reader -> plugins.load(reader) } 9 | } 10 | 11 | plugins.each { name, path -> 12 | def pluginDirectory = flutterProjectRoot.resolve(path).resolve('android').toFile() 13 | include ":$name" 14 | project(":$name").projectDir = pluginDirectory 15 | } 16 | -------------------------------------------------------------------------------- /example/android/settings_aar.gradle: -------------------------------------------------------------------------------- 1 | include ':app' 2 | -------------------------------------------------------------------------------- /example/assets/deeplabv3_257_mv_gpu.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/assets/deeplabv3_257_mv_gpu.tflite -------------------------------------------------------------------------------- /example/assets/deeplabv3_257_mv_gpu.txt: -------------------------------------------------------------------------------- 1 | background 2 | aeroplane 3 | biyclce 4 | bird 5 | boat 6 | bottle 7 | bus 8 | car 9 | cat 10 | chair 11 | cow 12 | diningtable 13 | dog 14 | horse 15 | motorbike 16 | person 17 | potted plant 18 | sheep 19 | sofa 20 | train 21 | tv-monitor 22 | -------------------------------------------------------------------------------- /example/assets/mobilenet_v1_1.0_224.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/assets/mobilenet_v1_1.0_224.tflite -------------------------------------------------------------------------------- /example/assets/mobilenet_v1_1.0_224.txt: -------------------------------------------------------------------------------- 1 | background 2 | tench 3 | goldfish 4 | great white shark 5 | tiger shark 6 | hammerhead 7 | electric ray 8 | stingray 9 | cock 10 | hen 11 | ostrich 12 | brambling 13 | goldfinch 14 | house finch 15 | junco 16 | indigo bunting 17 | robin 18 | bulbul 19 | jay 20 | magpie 21 | chickadee 22 | water ouzel 23 | kite 24 | bald eagle 25 | vulture 26 | great grey owl 27 | European fire salamander 28 | common newt 29 | eft 30 | spotted salamander 31 | axolotl 32 | bullfrog 33 | tree frog 34 | tailed frog 35 | loggerhead 36 | leatherback turtle 37 | mud turtle 38 | terrapin 39 | box turtle 40 | banded gecko 41 | common iguana 42 | American chameleon 43 | whiptail 44 | agama 45 | frilled lizard 46 | alligator lizard 47 | Gila monster 48 | green lizard 49 | African chameleon 50 | Komodo dragon 51 | African crocodile 52 | American alligator 53 | triceratops 54 | thunder snake 55 | ringneck snake 56 | hognose snake 57 | green snake 58 | king snake 59 | garter snake 60 | water snake 61 | vine snake 62 | night snake 63 | boa constrictor 64 | rock python 65 | Indian cobra 66 | green mamba 67 | sea snake 68 | horned viper 69 | diamondback 70 | sidewinder 71 | trilobite 72 | harvestman 73 | scorpion 74 | black and gold garden spider 75 | barn spider 76 | garden spider 77 | black widow 78 | tarantula 79 | wolf spider 80 | tick 81 | centipede 82 | black grouse 83 | ptarmigan 84 | ruffed grouse 85 | prairie chicken 86 | peacock 87 | quail 88 | partridge 89 | African grey 90 | macaw 91 | sulphur-crested cockatoo 92 | lorikeet 93 | coucal 94 | bee eater 95 | hornbill 96 | hummingbird 97 | jacamar 98 | toucan 99 | drake 100 | red-breasted merganser 101 | goose 102 | black swan 103 | tusker 104 | echidna 105 | platypus 106 | wallaby 107 | koala 108 | wombat 109 | jellyfish 110 | sea anemone 111 | brain coral 112 | flatworm 113 | nematode 114 | conch 115 | snail 116 | slug 117 | sea slug 118 | chiton 119 | chambered nautilus 120 | Dungeness crab 121 | rock crab 122 | fiddler crab 123 | king crab 124 | American lobster 125 | spiny lobster 126 | crayfish 127 | hermit crab 128 | isopod 129 | white stork 130 | black stork 131 | spoonbill 132 | flamingo 133 | little blue heron 134 | American egret 135 | bittern 136 | crane 137 | limpkin 138 | European gallinule 139 | American coot 140 | bustard 141 | ruddy turnstone 142 | red-backed sandpiper 143 | redshank 144 | dowitcher 145 | oystercatcher 146 | pelican 147 | king penguin 148 | albatross 149 | grey whale 150 | killer whale 151 | dugong 152 | sea lion 153 | Chihuahua 154 | Japanese spaniel 155 | Maltese dog 156 | Pekinese 157 | Shih-Tzu 158 | Blenheim spaniel 159 | papillon 160 | toy terrier 161 | Rhodesian ridgeback 162 | Afghan hound 163 | basset 164 | beagle 165 | bloodhound 166 | bluetick 167 | black-and-tan coonhound 168 | Walker hound 169 | English foxhound 170 | redbone 171 | borzoi 172 | Irish wolfhound 173 | Italian greyhound 174 | whippet 175 | Ibizan hound 176 | Norwegian elkhound 177 | otterhound 178 | Saluki 179 | Scottish deerhound 180 | Weimaraner 181 | Staffordshire bullterrier 182 | American Staffordshire terrier 183 | Bedlington terrier 184 | Border terrier 185 | Kerry blue terrier 186 | Irish terrier 187 | Norfolk terrier 188 | Norwich terrier 189 | Yorkshire terrier 190 | wire-haired fox terrier 191 | Lakeland terrier 192 | Sealyham terrier 193 | Airedale 194 | cairn 195 | Australian terrier 196 | Dandie Dinmont 197 | Boston bull 198 | miniature schnauzer 199 | giant schnauzer 200 | standard schnauzer 201 | Scotch terrier 202 | Tibetan terrier 203 | silky terrier 204 | soft-coated wheaten terrier 205 | West Highland white terrier 206 | Lhasa 207 | flat-coated retriever 208 | curly-coated retriever 209 | golden retriever 210 | Labrador retriever 211 | Chesapeake Bay retriever 212 | German short-haired pointer 213 | vizsla 214 | English setter 215 | Irish setter 216 | Gordon setter 217 | Brittany spaniel 218 | clumber 219 | English springer 220 | Welsh springer spaniel 221 | cocker spaniel 222 | Sussex spaniel 223 | Irish water spaniel 224 | kuvasz 225 | schipperke 226 | groenendael 227 | malinois 228 | briard 229 | kelpie 230 | komondor 231 | Old English sheepdog 232 | Shetland sheepdog 233 | collie 234 | Border collie 235 | Bouvier des Flandres 236 | Rottweiler 237 | German shepherd 238 | Doberman 239 | miniature pinscher 240 | Greater Swiss Mountain dog 241 | Bernese mountain dog 242 | Appenzeller 243 | EntleBucher 244 | boxer 245 | bull mastiff 246 | Tibetan mastiff 247 | French bulldog 248 | Great Dane 249 | Saint Bernard 250 | Eskimo dog 251 | malamute 252 | Siberian husky 253 | dalmatian 254 | affenpinscher 255 | basenji 256 | pug 257 | Leonberg 258 | Newfoundland 259 | Great Pyrenees 260 | Samoyed 261 | Pomeranian 262 | chow 263 | keeshond 264 | Brabancon griffon 265 | Pembroke 266 | Cardigan 267 | toy poodle 268 | miniature poodle 269 | standard poodle 270 | Mexican hairless 271 | timber wolf 272 | white wolf 273 | red wolf 274 | coyote 275 | dingo 276 | dhole 277 | African hunting dog 278 | hyena 279 | red fox 280 | kit fox 281 | Arctic fox 282 | grey fox 283 | tabby 284 | tiger cat 285 | Persian cat 286 | Siamese cat 287 | Egyptian cat 288 | cougar 289 | lynx 290 | leopard 291 | snow leopard 292 | jaguar 293 | lion 294 | tiger 295 | cheetah 296 | brown bear 297 | American black bear 298 | ice bear 299 | sloth bear 300 | mongoose 301 | meerkat 302 | tiger beetle 303 | ladybug 304 | ground beetle 305 | long-horned beetle 306 | leaf beetle 307 | dung beetle 308 | rhinoceros beetle 309 | weevil 310 | fly 311 | bee 312 | ant 313 | grasshopper 314 | cricket 315 | walking stick 316 | cockroach 317 | mantis 318 | cicada 319 | leafhopper 320 | lacewing 321 | dragonfly 322 | damselfly 323 | admiral 324 | ringlet 325 | monarch 326 | cabbage butterfly 327 | sulphur butterfly 328 | lycaenid 329 | starfish 330 | sea urchin 331 | sea cucumber 332 | wood rabbit 333 | hare 334 | Angora 335 | hamster 336 | porcupine 337 | fox squirrel 338 | marmot 339 | beaver 340 | guinea pig 341 | sorrel 342 | zebra 343 | hog 344 | wild boar 345 | warthog 346 | hippopotamus 347 | ox 348 | water buffalo 349 | bison 350 | ram 351 | bighorn 352 | ibex 353 | hartebeest 354 | impala 355 | gazelle 356 | Arabian camel 357 | llama 358 | weasel 359 | mink 360 | polecat 361 | black-footed ferret 362 | otter 363 | skunk 364 | badger 365 | armadillo 366 | three-toed sloth 367 | orangutan 368 | gorilla 369 | chimpanzee 370 | gibbon 371 | siamang 372 | guenon 373 | patas 374 | baboon 375 | macaque 376 | langur 377 | colobus 378 | proboscis monkey 379 | marmoset 380 | capuchin 381 | howler monkey 382 | titi 383 | spider monkey 384 | squirrel monkey 385 | Madagascar cat 386 | indri 387 | Indian elephant 388 | African elephant 389 | lesser panda 390 | giant panda 391 | barracouta 392 | eel 393 | coho 394 | rock beauty 395 | anemone fish 396 | sturgeon 397 | gar 398 | lionfish 399 | puffer 400 | abacus 401 | abaya 402 | academic gown 403 | accordion 404 | acoustic guitar 405 | aircraft carrier 406 | airliner 407 | airship 408 | altar 409 | ambulance 410 | amphibian 411 | analog clock 412 | apiary 413 | apron 414 | ashcan 415 | assault rifle 416 | backpack 417 | bakery 418 | balance beam 419 | balloon 420 | ballpoint 421 | Band Aid 422 | banjo 423 | bannister 424 | barbell 425 | barber chair 426 | barbershop 427 | barn 428 | barometer 429 | barrel 430 | barrow 431 | baseball 432 | basketball 433 | bassinet 434 | bassoon 435 | bathing cap 436 | bath towel 437 | bathtub 438 | beach wagon 439 | beacon 440 | beaker 441 | bearskin 442 | beer bottle 443 | beer glass 444 | bell cote 445 | bib 446 | bicycle-built-for-two 447 | bikini 448 | binder 449 | binoculars 450 | birdhouse 451 | boathouse 452 | bobsled 453 | bolo tie 454 | bonnet 455 | bookcase 456 | bookshop 457 | bottlecap 458 | bow 459 | bow tie 460 | brass 461 | brassiere 462 | breakwater 463 | breastplate 464 | broom 465 | bucket 466 | buckle 467 | bulletproof vest 468 | bullet train 469 | butcher shop 470 | cab 471 | caldron 472 | candle 473 | cannon 474 | canoe 475 | can opener 476 | cardigan 477 | car mirror 478 | carousel 479 | carpenter's kit 480 | carton 481 | car wheel 482 | cash machine 483 | cassette 484 | cassette player 485 | castle 486 | catamaran 487 | CD player 488 | cello 489 | cellular telephone 490 | chain 491 | chainlink fence 492 | chain mail 493 | chain saw 494 | chest 495 | chiffonier 496 | chime 497 | china cabinet 498 | Christmas stocking 499 | church 500 | cinema 501 | cleaver 502 | cliff dwelling 503 | cloak 504 | clog 505 | cocktail shaker 506 | coffee mug 507 | coffeepot 508 | coil 509 | combination lock 510 | computer keyboard 511 | confectionery 512 | container ship 513 | convertible 514 | corkscrew 515 | cornet 516 | cowboy boot 517 | cowboy hat 518 | cradle 519 | crane 520 | crash helmet 521 | crate 522 | crib 523 | Crock Pot 524 | croquet ball 525 | crutch 526 | cuirass 527 | dam 528 | desk 529 | desktop computer 530 | dial telephone 531 | diaper 532 | digital clock 533 | digital watch 534 | dining table 535 | dishrag 536 | dishwasher 537 | disk brake 538 | dock 539 | dogsled 540 | dome 541 | doormat 542 | drilling platform 543 | drum 544 | drumstick 545 | dumbbell 546 | Dutch oven 547 | electric fan 548 | electric guitar 549 | electric locomotive 550 | entertainment center 551 | envelope 552 | espresso maker 553 | face powder 554 | feather boa 555 | file 556 | fireboat 557 | fire engine 558 | fire screen 559 | flagpole 560 | flute 561 | folding chair 562 | football helmet 563 | forklift 564 | fountain 565 | fountain pen 566 | four-poster 567 | freight car 568 | French horn 569 | frying pan 570 | fur coat 571 | garbage truck 572 | gasmask 573 | gas pump 574 | goblet 575 | go-kart 576 | golf ball 577 | golfcart 578 | gondola 579 | gong 580 | gown 581 | grand piano 582 | greenhouse 583 | grille 584 | grocery store 585 | guillotine 586 | hair slide 587 | hair spray 588 | half track 589 | hammer 590 | hamper 591 | hand blower 592 | hand-held computer 593 | handkerchief 594 | hard disc 595 | harmonica 596 | harp 597 | harvester 598 | hatchet 599 | holster 600 | home theater 601 | honeycomb 602 | hook 603 | hoopskirt 604 | horizontal bar 605 | horse cart 606 | hourglass 607 | iPod 608 | iron 609 | jack-o'-lantern 610 | jean 611 | jeep 612 | jersey 613 | jigsaw puzzle 614 | jinrikisha 615 | joystick 616 | kimono 617 | knee pad 618 | knot 619 | lab coat 620 | ladle 621 | lampshade 622 | laptop 623 | lawn mower 624 | lens cap 625 | letter opener 626 | library 627 | lifeboat 628 | lighter 629 | limousine 630 | liner 631 | lipstick 632 | Loafer 633 | lotion 634 | loudspeaker 635 | loupe 636 | lumbermill 637 | magnetic compass 638 | mailbag 639 | mailbox 640 | maillot 641 | maillot 642 | manhole cover 643 | maraca 644 | marimba 645 | mask 646 | matchstick 647 | maypole 648 | maze 649 | measuring cup 650 | medicine chest 651 | megalith 652 | microphone 653 | microwave 654 | military uniform 655 | milk can 656 | minibus 657 | miniskirt 658 | minivan 659 | missile 660 | mitten 661 | mixing bowl 662 | mobile home 663 | Model T 664 | modem 665 | monastery 666 | monitor 667 | moped 668 | mortar 669 | mortarboard 670 | mosque 671 | mosquito net 672 | motor scooter 673 | mountain bike 674 | mountain tent 675 | mouse 676 | mousetrap 677 | moving van 678 | muzzle 679 | nail 680 | neck brace 681 | necklace 682 | nipple 683 | notebook 684 | obelisk 685 | oboe 686 | ocarina 687 | odometer 688 | oil filter 689 | organ 690 | oscilloscope 691 | overskirt 692 | oxcart 693 | oxygen mask 694 | packet 695 | paddle 696 | paddlewheel 697 | padlock 698 | paintbrush 699 | pajama 700 | palace 701 | panpipe 702 | paper towel 703 | parachute 704 | parallel bars 705 | park bench 706 | parking meter 707 | passenger car 708 | patio 709 | pay-phone 710 | pedestal 711 | pencil box 712 | pencil sharpener 713 | perfume 714 | Petri dish 715 | photocopier 716 | pick 717 | pickelhaube 718 | picket fence 719 | pickup 720 | pier 721 | piggy bank 722 | pill bottle 723 | pillow 724 | ping-pong ball 725 | pinwheel 726 | pirate 727 | pitcher 728 | plane 729 | planetarium 730 | plastic bag 731 | plate rack 732 | plow 733 | plunger 734 | Polaroid camera 735 | pole 736 | police van 737 | poncho 738 | pool table 739 | pop bottle 740 | pot 741 | potter's wheel 742 | power drill 743 | prayer rug 744 | printer 745 | prison 746 | projectile 747 | projector 748 | puck 749 | punching bag 750 | purse 751 | quill 752 | quilt 753 | racer 754 | racket 755 | radiator 756 | radio 757 | radio telescope 758 | rain barrel 759 | recreational vehicle 760 | reel 761 | reflex camera 762 | refrigerator 763 | remote control 764 | restaurant 765 | revolver 766 | rifle 767 | rocking chair 768 | rotisserie 769 | rubber eraser 770 | rugby ball 771 | rule 772 | running shoe 773 | safe 774 | safety pin 775 | saltshaker 776 | sandal 777 | sarong 778 | sax 779 | scabbard 780 | scale 781 | school bus 782 | schooner 783 | scoreboard 784 | screen 785 | screw 786 | screwdriver 787 | seat belt 788 | sewing machine 789 | shield 790 | shoe shop 791 | shoji 792 | shopping basket 793 | shopping cart 794 | shovel 795 | shower cap 796 | shower curtain 797 | ski 798 | ski mask 799 | sleeping bag 800 | slide rule 801 | sliding door 802 | slot 803 | snorkel 804 | snowmobile 805 | snowplow 806 | soap dispenser 807 | soccer ball 808 | sock 809 | solar dish 810 | sombrero 811 | soup bowl 812 | space bar 813 | space heater 814 | space shuttle 815 | spatula 816 | speedboat 817 | spider web 818 | spindle 819 | sports car 820 | spotlight 821 | stage 822 | steam locomotive 823 | steel arch bridge 824 | steel drum 825 | stethoscope 826 | stole 827 | stone wall 828 | stopwatch 829 | stove 830 | strainer 831 | streetcar 832 | stretcher 833 | studio couch 834 | stupa 835 | submarine 836 | suit 837 | sundial 838 | sunglass 839 | sunglasses 840 | sunscreen 841 | suspension bridge 842 | swab 843 | sweatshirt 844 | swimming trunks 845 | swing 846 | switch 847 | syringe 848 | table lamp 849 | tank 850 | tape player 851 | teapot 852 | teddy 853 | television 854 | tennis ball 855 | thatch 856 | theater curtain 857 | thimble 858 | thresher 859 | throne 860 | tile roof 861 | toaster 862 | tobacco shop 863 | toilet seat 864 | torch 865 | totem pole 866 | tow truck 867 | toyshop 868 | tractor 869 | trailer truck 870 | tray 871 | trench coat 872 | tricycle 873 | trimaran 874 | tripod 875 | triumphal arch 876 | trolleybus 877 | trombone 878 | tub 879 | turnstile 880 | typewriter keyboard 881 | umbrella 882 | unicycle 883 | upright 884 | vacuum 885 | vase 886 | vault 887 | velvet 888 | vending machine 889 | vestment 890 | viaduct 891 | violin 892 | volleyball 893 | waffle iron 894 | wall clock 895 | wallet 896 | wardrobe 897 | warplane 898 | washbasin 899 | washer 900 | water bottle 901 | water jug 902 | water tower 903 | whiskey jug 904 | whistle 905 | wig 906 | window screen 907 | window shade 908 | Windsor tie 909 | wine bottle 910 | wing 911 | wok 912 | wooden spoon 913 | wool 914 | worm fence 915 | wreck 916 | yawl 917 | yurt 918 | web site 919 | comic book 920 | crossword puzzle 921 | street sign 922 | traffic light 923 | book jacket 924 | menu 925 | plate 926 | guacamole 927 | consomme 928 | hot pot 929 | trifle 930 | ice cream 931 | ice lolly 932 | French loaf 933 | bagel 934 | pretzel 935 | cheeseburger 936 | hotdog 937 | mashed potato 938 | head cabbage 939 | broccoli 940 | cauliflower 941 | zucchini 942 | spaghetti squash 943 | acorn squash 944 | butternut squash 945 | cucumber 946 | artichoke 947 | bell pepper 948 | cardoon 949 | mushroom 950 | Granny Smith 951 | strawberry 952 | orange 953 | lemon 954 | fig 955 | pineapple 956 | banana 957 | jackfruit 958 | custard apple 959 | pomegranate 960 | hay 961 | carbonara 962 | chocolate sauce 963 | dough 964 | meat loaf 965 | pizza 966 | potpie 967 | burrito 968 | red wine 969 | espresso 970 | cup 971 | eggnog 972 | alp 973 | bubble 974 | cliff 975 | coral reef 976 | geyser 977 | lakeside 978 | promontory 979 | sandbar 980 | seashore 981 | valley 982 | volcano 983 | ballplayer 984 | groom 985 | scuba diver 986 | rapeseed 987 | daisy 988 | yellow lady's slipper 989 | corn 990 | acorn 991 | hip 992 | buckeye 993 | coral fungus 994 | agaric 995 | gyromitra 996 | stinkhorn 997 | earthstar 998 | hen-of-the-woods 999 | bolete 1000 | ear 1001 | toilet tissue 1002 | -------------------------------------------------------------------------------- /example/assets/posenet_mv1_075_float_from_checkpoints.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/assets/posenet_mv1_075_float_from_checkpoints.tflite -------------------------------------------------------------------------------- /example/assets/ssd_mobilenet.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/assets/ssd_mobilenet.tflite -------------------------------------------------------------------------------- /example/assets/ssd_mobilenet.txt: -------------------------------------------------------------------------------- 1 | ??? 2 | person 3 | bicycle 4 | car 5 | motorcycle 6 | airplane 7 | bus 8 | train 9 | truck 10 | boat 11 | traffic light 12 | fire hydrant 13 | ??? 14 | stop sign 15 | parking meter 16 | bench 17 | bird 18 | cat 19 | dog 20 | horse 21 | sheep 22 | cow 23 | elephant 24 | bear 25 | zebra 26 | giraffe 27 | ??? 28 | backpack 29 | umbrella 30 | ??? 31 | ??? 32 | handbag 33 | tie 34 | suitcase 35 | frisbee 36 | skis 37 | snowboard 38 | sports ball 39 | kite 40 | baseball bat 41 | baseball glove 42 | skateboard 43 | surfboard 44 | tennis racket 45 | bottle 46 | ??? 47 | wine glass 48 | cup 49 | fork 50 | knife 51 | spoon 52 | bowl 53 | banana 54 | apple 55 | sandwich 56 | orange 57 | broccoli 58 | carrot 59 | hot dog 60 | pizza 61 | donut 62 | cake 63 | chair 64 | couch 65 | potted plant 66 | bed 67 | ??? 68 | dining table 69 | ??? 70 | ??? 71 | toilet 72 | ??? 73 | tv 74 | laptop 75 | mouse 76 | remote 77 | keyboard 78 | cell phone 79 | microwave 80 | oven 81 | toaster 82 | sink 83 | refrigerator 84 | ??? 85 | book 86 | clock 87 | vase 88 | scissors 89 | teddy bear 90 | hair drier 91 | toothbrush 92 | -------------------------------------------------------------------------------- /example/assets/yolov2_tiny.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/assets/yolov2_tiny.tflite -------------------------------------------------------------------------------- /example/assets/yolov2_tiny.txt: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush 81 | -------------------------------------------------------------------------------- /example/ios/.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | .vagrant/ 3 | .sconsign.dblite 4 | .svn/ 5 | 6 | .DS_Store 7 | *.swp 8 | profile 9 | 10 | DerivedData/ 11 | build/ 12 | GeneratedPluginRegistrant.h 13 | GeneratedPluginRegistrant.m 14 | 15 | .generated/ 16 | 17 | *.pbxuser 18 | *.mode1v3 19 | *.mode2v3 20 | *.perspectivev3 21 | 22 | !default.pbxuser 23 | !default.mode1v3 24 | !default.mode2v3 25 | !default.perspectivev3 26 | 27 | xcuserdata 28 | 29 | *.moved-aside 30 | 31 | *.pyc 32 | *sync/ 33 | Icon? 34 | .tags* 35 | 36 | /Flutter/app.flx 37 | /Flutter/app.zip 38 | /Flutter/flutter_assets/ 39 | /Flutter/App.framework 40 | /Flutter/Flutter.framework 41 | /Flutter/Generated.xcconfig 42 | /ServiceDefinitions.json 43 | 44 | Pods/ 45 | .symlinks/ 46 | -------------------------------------------------------------------------------- /example/ios/Flutter/AppFrameworkInfo.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | CFBundleDevelopmentRegion 6 | en 7 | CFBundleExecutable 8 | App 9 | CFBundleIdentifier 10 | io.flutter.flutter.app 11 | CFBundleInfoDictionaryVersion 12 | 6.0 13 | CFBundleName 14 | App 15 | CFBundlePackageType 16 | FMWK 17 | CFBundleShortVersionString 18 | 1.0 19 | CFBundleSignature 20 | ???? 21 | CFBundleVersion 22 | 1.0 23 | MinimumOSVersion 24 | 8.0 25 | 26 | 27 | -------------------------------------------------------------------------------- /example/ios/Flutter/Debug.xcconfig: -------------------------------------------------------------------------------- 1 | #include "Pods/Target Support Files/Pods-Runner/Pods-Runner.debug.xcconfig" 2 | #include "Generated.xcconfig" 3 | -------------------------------------------------------------------------------- /example/ios/Flutter/Release.xcconfig: -------------------------------------------------------------------------------- 1 | #include "Pods/Target Support Files/Pods-Runner/Pods-Runner.release.xcconfig" 2 | #include "Generated.xcconfig" 3 | -------------------------------------------------------------------------------- /example/ios/Podfile: -------------------------------------------------------------------------------- 1 | # Uncomment this line to define a global platform for your project 2 | # platform :ios, '9.0' 3 | 4 | # CocoaPods analytics sends network stats synchronously affecting flutter build latency. 5 | ENV['COCOAPODS_DISABLE_STATS'] = 'true' 6 | 7 | project 'Runner', { 8 | 'Debug' => :debug, 9 | 'Profile' => :release, 10 | 'Release' => :release, 11 | } 12 | 13 | def flutter_root 14 | generated_xcode_build_settings_path = File.expand_path(File.join('..', 'Flutter', 'Generated.xcconfig'), __FILE__) 15 | unless File.exist?(generated_xcode_build_settings_path) 16 | raise "#{generated_xcode_build_settings_path} must exist. If you're running pod install manually, make sure flutter pub get is executed first" 17 | end 18 | 19 | File.foreach(generated_xcode_build_settings_path) do |line| 20 | matches = line.match(/FLUTTER_ROOT\=(.*)/) 21 | return matches[1].strip if matches 22 | end 23 | raise "FLUTTER_ROOT not found in #{generated_xcode_build_settings_path}. Try deleting Generated.xcconfig, then run flutter pub get" 24 | end 25 | 26 | require File.expand_path(File.join('packages', 'flutter_tools', 'bin', 'podhelper'), flutter_root) 27 | 28 | flutter_ios_podfile_setup 29 | 30 | target 'Runner' do 31 | flutter_install_all_ios_pods File.dirname(File.realpath(__FILE__)) 32 | end 33 | 34 | post_install do |installer| 35 | installer.pods_project.targets.each do |target| 36 | flutter_additional_ios_build_settings(target) 37 | end 38 | end 39 | -------------------------------------------------------------------------------- /example/ios/Podfile.lock: -------------------------------------------------------------------------------- 1 | PODS: 2 | - Flutter (1.0.0) 3 | - image_picker (0.0.1): 4 | - Flutter 5 | - TensorFlowLiteC (2.2.0) 6 | - tflite (1.1.2): 7 | - Flutter 8 | - TensorFlowLiteC 9 | 10 | DEPENDENCIES: 11 | - Flutter (from `Flutter`) 12 | - image_picker (from `.symlinks/plugins/image_picker/ios`) 13 | - tflite (from `.symlinks/plugins/tflite/ios`) 14 | 15 | SPEC REPOS: 16 | trunk: 17 | - TensorFlowLiteC 18 | 19 | EXTERNAL SOURCES: 20 | Flutter: 21 | :path: Flutter 22 | image_picker: 23 | :path: ".symlinks/plugins/image_picker/ios" 24 | tflite: 25 | :path: ".symlinks/plugins/tflite/ios" 26 | 27 | SPEC CHECKSUMS: 28 | Flutter: 434fef37c0980e73bb6479ef766c45957d4b510c 29 | image_picker: a211f28b95a560433c00f5cd3773f4710a20404d 30 | TensorFlowLiteC: b3ab9e867b0b71052ca102a32a786555b330b02e 31 | tflite: f0403a894740019d63ab5662253bba5b2dd37296 32 | 33 | PODFILE CHECKSUM: 8e679eca47255a8ca8067c4c67aab20e64cb974d 34 | 35 | COCOAPODS: 1.10.1 36 | -------------------------------------------------------------------------------- /example/ios/Runner.xcodeproj/project.pbxproj: -------------------------------------------------------------------------------- 1 | // !$*UTF8*$! 2 | { 3 | archiveVersion = 1; 4 | classes = { 5 | }; 6 | objectVersion = 46; 7 | objects = { 8 | 9 | /* Begin PBXBuildFile section */ 10 | 1498D2341E8E89220040F4C2 /* GeneratedPluginRegistrant.m in Sources */ = {isa = PBXBuildFile; fileRef = 1498D2331E8E89220040F4C2 /* GeneratedPluginRegistrant.m */; }; 11 | 3B3967161E833CAA004F5970 /* AppFrameworkInfo.plist in Resources */ = {isa = PBXBuildFile; fileRef = 3B3967151E833CAA004F5970 /* AppFrameworkInfo.plist */; }; 12 | 9740EEB41CF90195004384FC /* Debug.xcconfig in Resources */ = {isa = PBXBuildFile; fileRef = 9740EEB21CF90195004384FC /* Debug.xcconfig */; }; 13 | 978B8F6F1D3862AE00F588F7 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 7AFFD8EE1D35381100E5BB4D /* AppDelegate.m */; }; 14 | 97C146F31CF9000F007C117D /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = 97C146F21CF9000F007C117D /* main.m */; }; 15 | 97C146FC1CF9000F007C117D /* Main.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 97C146FA1CF9000F007C117D /* Main.storyboard */; }; 16 | 97C146FE1CF9000F007C117D /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 97C146FD1CF9000F007C117D /* Assets.xcassets */; }; 17 | 97C147011CF9000F007C117D /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 97C146FF1CF9000F007C117D /* LaunchScreen.storyboard */; }; 18 | A8FCB07931B147D0C738D807 /* libPods-Runner.a in Frameworks */ = {isa = PBXBuildFile; fileRef = A4A034B01AB21E851714E03C /* libPods-Runner.a */; }; 19 | /* End PBXBuildFile section */ 20 | 21 | /* Begin PBXCopyFilesBuildPhase section */ 22 | 9705A1C41CF9048500538489 /* Embed Frameworks */ = { 23 | isa = PBXCopyFilesBuildPhase; 24 | buildActionMask = 2147483647; 25 | dstPath = ""; 26 | dstSubfolderSpec = 10; 27 | files = ( 28 | ); 29 | name = "Embed Frameworks"; 30 | runOnlyForDeploymentPostprocessing = 0; 31 | }; 32 | /* End PBXCopyFilesBuildPhase section */ 33 | 34 | /* Begin PBXFileReference section */ 35 | 1498D2321E8E86230040F4C2 /* GeneratedPluginRegistrant.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = GeneratedPluginRegistrant.h; sourceTree = ""; }; 36 | 1498D2331E8E89220040F4C2 /* GeneratedPluginRegistrant.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = GeneratedPluginRegistrant.m; sourceTree = ""; }; 37 | 3B3967151E833CAA004F5970 /* AppFrameworkInfo.plist */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.plist.xml; name = AppFrameworkInfo.plist; path = Flutter/AppFrameworkInfo.plist; sourceTree = ""; }; 38 | 7AFA3C8E1D35360C0083082E /* Release.xcconfig */ = {isa = PBXFileReference; lastKnownFileType = text.xcconfig; name = Release.xcconfig; path = Flutter/Release.xcconfig; sourceTree = ""; }; 39 | 7AFFD8ED1D35381100E5BB4D /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = ""; }; 40 | 7AFFD8EE1D35381100E5BB4D /* AppDelegate.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = AppDelegate.m; sourceTree = ""; }; 41 | 864E0E2308AE5F3A9409E901 /* Pods-Runner.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-Runner.release.xcconfig"; path = "Pods/Target Support Files/Pods-Runner/Pods-Runner.release.xcconfig"; sourceTree = ""; }; 42 | 9740EEB21CF90195004384FC /* Debug.xcconfig */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.xcconfig; name = Debug.xcconfig; path = Flutter/Debug.xcconfig; sourceTree = ""; }; 43 | 9740EEB31CF90195004384FC /* Generated.xcconfig */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.xcconfig; name = Generated.xcconfig; path = Flutter/Generated.xcconfig; sourceTree = ""; }; 44 | 97C146EE1CF9000F007C117D /* Runner.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = Runner.app; sourceTree = BUILT_PRODUCTS_DIR; }; 45 | 97C146F21CF9000F007C117D /* main.m */ = {isa = PBXFileReference; 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includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-Runner.debug.xcconfig"; path = "Pods/Target Support Files/Pods-Runner/Pods-Runner.debug.xcconfig"; sourceTree = ""; }; 52 | /* End PBXFileReference section */ 53 | 54 | /* Begin PBXFrameworksBuildPhase section */ 55 | 97C146EB1CF9000F007C117D /* Frameworks */ = { 56 | isa = PBXFrameworksBuildPhase; 57 | buildActionMask = 2147483647; 58 | files = ( 59 | A8FCB07931B147D0C738D807 /* libPods-Runner.a in Frameworks */, 60 | ); 61 | runOnlyForDeploymentPostprocessing = 0; 62 | }; 63 | /* End PBXFrameworksBuildPhase section */ 64 | 65 | /* Begin PBXGroup section */ 66 | 7670CC45CF9B055E20C18D9C /* Frameworks */ = { 67 | isa = PBXGroup; 68 | children = ( 69 | A4A034B01AB21E851714E03C /* libPods-Runner.a */, 70 | ); 71 | name = Frameworks; 72 | sourceTree = ""; 73 | }; 74 | 8EE3D73475BA2048B61051C2 /* Pods */ = { 75 | isa = PBXGroup; 76 | children = ( 77 | E0C0C115F9024C6ADB3B2DB5 /* Pods-Runner.debug.xcconfig */, 78 | 864E0E2308AE5F3A9409E901 /* Pods-Runner.release.xcconfig */, 79 | ); 80 | name = Pods; 81 | sourceTree = ""; 82 | }; 83 | 9740EEB11CF90186004384FC /* Flutter */ = { 84 | isa = PBXGroup; 85 | children = ( 86 | 3B3967151E833CAA004F5970 /* AppFrameworkInfo.plist */, 87 | 9740EEB21CF90195004384FC /* Debug.xcconfig */, 88 | 7AFA3C8E1D35360C0083082E /* Release.xcconfig */, 89 | 9740EEB31CF90195004384FC /* Generated.xcconfig */, 90 | ); 91 | name = Flutter; 92 | sourceTree = ""; 93 | }; 94 | 97C146E51CF9000F007C117D = { 95 | isa = PBXGroup; 96 | children = ( 97 | 9740EEB11CF90186004384FC /* Flutter */, 98 | 97C146F01CF9000F007C117D /* Runner */, 99 | 97C146EF1CF9000F007C117D /* Products */, 100 | 8EE3D73475BA2048B61051C2 /* Pods */, 101 | 7670CC45CF9B055E20C18D9C /* Frameworks */, 102 | ); 103 | sourceTree = ""; 104 | }; 105 | 97C146EF1CF9000F007C117D /* Products */ = { 106 | isa = PBXGroup; 107 | children = ( 108 | 97C146EE1CF9000F007C117D /* Runner.app */, 109 | ); 110 | name = Products; 111 | sourceTree = ""; 112 | }; 113 | 97C146F01CF9000F007C117D /* Runner */ = { 114 | isa = PBXGroup; 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143 | buildPhases = ( 144 | FAEC5F0CFA3366178E53C4C5 /* [CP] Check Pods Manifest.lock */, 145 | 9740EEB61CF901F6004384FC /* Run Script */, 146 | 97C146EA1CF9000F007C117D /* Sources */, 147 | 97C146EB1CF9000F007C117D /* Frameworks */, 148 | 97C146EC1CF9000F007C117D /* Resources */, 149 | 9705A1C41CF9048500538489 /* Embed Frameworks */, 150 | 3B06AD1E1E4923F5004D2608 /* Thin Binary */, 151 | ); 152 | buildRules = ( 153 | ); 154 | dependencies = ( 155 | ); 156 | name = Runner; 157 | productName = Runner; 158 | productReference = 97C146EE1CF9000F007C117D /* Runner.app */; 159 | productType = "com.apple.product-type.application"; 160 | }; 161 | /* End PBXNativeTarget section */ 162 | 163 | /* Begin PBXProject section */ 164 | 97C146E61CF9000F007C117D /* Project object */ = { 165 | isa = PBXProject; 166 | attributes = { 167 | LastUpgradeCheck = 0910; 168 | ORGANIZATIONNAME = "The Chromium Authors"; 169 | TargetAttributes = { 170 | 97C146ED1CF9000F007C117D = { 171 | CreatedOnToolsVersion = 7.3.1; 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238 | }; 239 | FAEC5F0CFA3366178E53C4C5 /* [CP] Check Pods Manifest.lock */ = { 240 | isa = PBXShellScriptBuildPhase; 241 | buildActionMask = 2147483647; 242 | files = ( 243 | ); 244 | inputPaths = ( 245 | "${PODS_PODFILE_DIR_PATH}/Podfile.lock", 246 | "${PODS_ROOT}/Manifest.lock", 247 | ); 248 | name = "[CP] Check Pods Manifest.lock"; 249 | outputPaths = ( 250 | "$(DERIVED_FILE_DIR)/Pods-Runner-checkManifestLockResult.txt", 251 | ); 252 | runOnlyForDeploymentPostprocessing = 0; 253 | shellPath = /bin/sh; 254 | shellScript = "diff \"${PODS_PODFILE_DIR_PATH}/Podfile.lock\" \"${PODS_ROOT}/Manifest.lock\" > /dev/null\nif [ $? != 0 ] ; then\n # print error to STDERR\n echo \"error: The sandbox is not in sync with the Podfile.lock. 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name = Main.storyboard; 279 | sourceTree = ""; 280 | }; 281 | 97C146FF1CF9000F007C117D /* LaunchScreen.storyboard */ = { 282 | isa = PBXVariantGroup; 283 | children = ( 284 | 97C147001CF9000F007C117D /* Base */, 285 | ); 286 | name = LaunchScreen.storyboard; 287 | sourceTree = ""; 288 | }; 289 | /* End PBXVariantGroup section */ 290 | 291 | /* Begin XCBuildConfiguration section */ 292 | 97C147031CF9000F007C117D /* Debug */ = { 293 | isa = XCBuildConfiguration; 294 | buildSettings = { 295 | ALWAYS_SEARCH_USER_PATHS = NO; 296 | CLANG_ANALYZER_NONNULL = YES; 297 | CLANG_CXX_LANGUAGE_STANDARD = "gnu++0x"; 298 | CLANG_CXX_LIBRARY = "libc++"; 299 | CLANG_ENABLE_MODULES = YES; 300 | CLANG_ENABLE_OBJC_ARC = YES; 301 | CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES; 302 | CLANG_WARN_BOOL_CONVERSION = YES; 303 | CLANG_WARN_COMMA = YES; 304 | CLANG_WARN_CONSTANT_CONVERSION = YES; 305 | CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR; 306 | CLANG_WARN_EMPTY_BODY = YES; 307 | CLANG_WARN_ENUM_CONVERSION 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/* Debug */, 466 | 97C147041CF9000F007C117D /* Release */, 467 | ); 468 | defaultConfigurationIsVisible = 0; 469 | defaultConfigurationName = Release; 470 | }; 471 | 97C147051CF9000F007C117D /* Build configuration list for PBXNativeTarget "Runner" */ = { 472 | isa = XCConfigurationList; 473 | buildConfigurations = ( 474 | 97C147061CF9000F007C117D /* Debug */, 475 | 97C147071CF9000F007C117D /* Release */, 476 | ); 477 | defaultConfigurationIsVisible = 0; 478 | defaultConfigurationName = Release; 479 | }; 480 | /* End XCConfigurationList section */ 481 | }; 482 | rootObject = 97C146E61CF9000F007C117D /* Project object */; 483 | } 484 | -------------------------------------------------------------------------------- /example/ios/Runner.xcodeproj/project.xcworkspace/contents.xcworkspacedata: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /example/ios/Runner.xcodeproj/xcshareddata/xcschemes/Runner.xcscheme: -------------------------------------------------------------------------------- 1 | 2 | 5 | 8 | 9 | 15 | 21 | 22 | 23 | 24 | 25 | 30 | 31 | 37 | 38 | 39 | 40 | 41 | 42 | 52 | 54 | 60 | 61 | 62 | 63 | 69 | 71 | 77 | 78 | 79 | 80 | 82 | 83 | 86 | 87 | 88 | -------------------------------------------------------------------------------- /example/ios/Runner.xcworkspace/contents.xcworkspacedata: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 9 | 10 | 11 | -------------------------------------------------------------------------------- /example/ios/Runner.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | IDEDidComputeMac32BitWarning 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /example/ios/Runner/AppDelegate.h: -------------------------------------------------------------------------------- 1 | #import 2 | #import 3 | 4 | @interface AppDelegate : FlutterAppDelegate 5 | 6 | @end 7 | -------------------------------------------------------------------------------- /example/ios/Runner/AppDelegate.m: -------------------------------------------------------------------------------- 1 | #include "AppDelegate.h" 2 | #include "GeneratedPluginRegistrant.h" 3 | 4 | @implementation AppDelegate 5 | 6 | - (BOOL)application:(UIApplication *)application 7 | didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { 8 | [GeneratedPluginRegistrant registerWithRegistry:self]; 9 | // Override point for customization after application launch. 10 | return [super application:application didFinishLaunchingWithOptions:launchOptions]; 11 | } 12 | 13 | @end 14 | -------------------------------------------------------------------------------- /example/ios/Runner/Assets.xcassets/AppIcon.appiconset/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "images" : [ 3 | { 4 | "size" : "20x20", 5 | "idiom" : "iphone", 6 | "filename" : "Icon-App-20x20@2x.png", 7 | "scale" : "2x" 8 | }, 9 | { 10 | "size" : "20x20", 11 | "idiom" : "iphone", 12 | "filename" : "Icon-App-20x20@3x.png", 13 | "scale" : "3x" 14 | }, 15 | { 16 | "size" : "29x29", 17 | "idiom" : "iphone", 18 | "filename" : "Icon-App-29x29@1x.png", 19 | "scale" : "1x" 20 | }, 21 | { 22 | "size" : "29x29", 23 | "idiom" : "iphone", 24 | "filename" : "Icon-App-29x29@2x.png", 25 | "scale" : "2x" 26 | }, 27 | { 28 | "size" : "29x29", 29 | "idiom" : "iphone", 30 | "filename" : "Icon-App-29x29@3x.png", 31 | "scale" : "3x" 32 | }, 33 | { 34 | "size" : "40x40", 35 | "idiom" : "iphone", 36 | "filename" : "Icon-App-40x40@2x.png", 37 | "scale" : "2x" 38 | }, 39 | { 40 | "size" : "40x40", 41 | "idiom" : "iphone", 42 | "filename" : "Icon-App-40x40@3x.png", 43 | "scale" : "3x" 44 | }, 45 | { 46 | "size" : "60x60", 47 | "idiom" : "iphone", 48 | "filename" : "Icon-App-60x60@2x.png", 49 | "scale" : "2x" 50 | }, 51 | { 52 | "size" : "60x60", 53 | "idiom" : "iphone", 54 | "filename" : "Icon-App-60x60@3x.png", 55 | "scale" : "3x" 56 | }, 57 | { 58 | "size" : "20x20", 59 | "idiom" : "ipad", 60 | "filename" : "Icon-App-20x20@1x.png", 61 | "scale" : "1x" 62 | }, 63 | { 64 | "size" : "20x20", 65 | "idiom" : "ipad", 66 | "filename" : "Icon-App-20x20@2x.png", 67 | "scale" : "2x" 68 | }, 69 | { 70 | "size" : "29x29", 71 | "idiom" : "ipad", 72 | "filename" : "Icon-App-29x29@1x.png", 73 | "scale" : "1x" 74 | }, 75 | { 76 | "size" : "29x29", 77 | "idiom" : "ipad", 78 | "filename" : "Icon-App-29x29@2x.png", 79 | "scale" : "2x" 80 | }, 81 | { 82 | "size" : "40x40", 83 | "idiom" : "ipad", 84 | "filename" : "Icon-App-40x40@1x.png", 85 | "scale" : "1x" 86 | }, 87 | { 88 | "size" : "40x40", 89 | "idiom" : "ipad", 90 | "filename" : "Icon-App-40x40@2x.png", 91 | "scale" : "2x" 92 | }, 93 | { 94 | "size" : 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| 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /example/ios/Runner/Base.lproj/Main.storyboard: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | -------------------------------------------------------------------------------- /example/ios/Runner/Info.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | CFBundleDevelopmentRegion 6 | en 7 | CFBundleExecutable 8 | $(EXECUTABLE_NAME) 9 | CFBundleIdentifier 10 | $(PRODUCT_BUNDLE_IDENTIFIER) 11 | CFBundleInfoDictionaryVersion 12 | 6.0 13 | CFBundleName 14 | tflite_example 15 | CFBundlePackageType 16 | APPL 17 | CFBundleShortVersionString 18 | $(FLUTTER_BUILD_NAME) 19 | CFBundleSignature 20 | ???? 21 | CFBundleVersion 22 | $(FLUTTER_BUILD_NUMBER) 23 | LSRequiresIPhoneOS 24 | 25 | UILaunchStoryboardName 26 | LaunchScreen 27 | UIMainStoryboardFile 28 | Main 29 | UISupportedInterfaceOrientations 30 | 31 | UIInterfaceOrientationPortrait 32 | UIInterfaceOrientationLandscapeLeft 33 | UIInterfaceOrientationLandscapeRight 34 | 35 | UISupportedInterfaceOrientations~ipad 36 | 37 | UIInterfaceOrientationPortrait 38 | UIInterfaceOrientationPortraitUpsideDown 39 | UIInterfaceOrientationLandscapeLeft 40 | UIInterfaceOrientationLandscapeRight 41 | 42 | UIViewControllerBasedStatusBarAppearance 43 | 44 | NSPhotoLibraryUsageDescription 45 | We need your permission to access photo gallery 46 | NSCameraUsageDescription 47 | We need your permission to use phone camera 48 | NSMicrophoneUsageDescription 49 | We need your permission to use microsphone 50 | 51 | 52 | -------------------------------------------------------------------------------- /example/ios/Runner/main.m: -------------------------------------------------------------------------------- 1 | #import 2 | #import 3 | #import "AppDelegate.h" 4 | 5 | int main(int argc, char* argv[]) { 6 | @autoreleasepool { 7 | return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class])); 8 | } 9 | } 10 | -------------------------------------------------------------------------------- /example/lib/main.dart: -------------------------------------------------------------------------------- 1 | // ignore_for_file: unnecessary_null_comparison 2 | 3 | import 'dart:async'; 4 | import 'dart:io'; 5 | import 'dart:math'; 6 | import 'dart:typed_data'; 7 | import 'package:flutter/material.dart'; 8 | import 'package:flutter/services.dart'; 9 | import 'package:image_picker/image_picker.dart'; 10 | import 'package:tensorflow_lite_flutter/tensorflow_lite_flutter.dart'; 11 | import 'package:image/image.dart' as img; 12 | 13 | void main() => runApp(new App()); 14 | 15 | const String mobile = "MobileNet"; 16 | const String ssd = "SSD MobileNet"; 17 | const String yolo = "Tiny YOLOv2"; 18 | const String deeplab = "DeepLab"; 19 | const String posenet = "PoseNet"; 20 | 21 | class App extends StatelessWidget { 22 | @override 23 | Widget build(BuildContext context) { 24 | return MaterialApp( 25 | home: MyApp(), 26 | ); 27 | } 28 | } 29 | 30 | class MyApp extends StatefulWidget { 31 | @override 32 | _MyAppState createState() => new _MyAppState(); 33 | } 34 | 35 | class _MyAppState extends State { 36 | late File _image; 37 | late List _recognitions; 38 | String _model = mobile; 39 | late double _imageHeight; 40 | late double _imageWidth; 41 | bool _busy = false; 42 | 43 | Future predictImagePicker() async { 44 | var imagePicker = ImagePicker(); 45 | var image = await imagePicker.pickImage(source: ImageSource.gallery); 46 | if (image == null) return; 47 | setState(() { 48 | _busy = true; 49 | }); 50 | predictImage(image as File); 51 | } 52 | 53 | Future predictImage(File image) async { 54 | switch (_model) { 55 | case yolo: 56 | await yolov2Tiny(image); 57 | break; 58 | case ssd: 59 | await ssdMobileNet(image); 60 | break; 61 | case deeplab: 62 | await segmentMobileNet(image); 63 | break; 64 | case posenet: 65 | await poseNet(image); 66 | break; 67 | default: 68 | await recognizeImage(image); 69 | // await recognizeImageBinary(image); 70 | } 71 | 72 | new FileImage(image) 73 | .resolve(new ImageConfiguration()) 74 | .addListener(ImageStreamListener((ImageInfo info, bool _) { 75 | setState(() { 76 | _imageHeight = info.image.height.toDouble(); 77 | _imageWidth = info.image.width.toDouble(); 78 | }); 79 | })); 80 | 81 | setState(() { 82 | _image = image; 83 | _busy = false; 84 | }); 85 | } 86 | 87 | @override 88 | void initState() { 89 | super.initState(); 90 | 91 | _busy = true; 92 | 93 | loadModel().then((val) { 94 | setState(() { 95 | _busy = false; 96 | }); 97 | }); 98 | } 99 | 100 | Future loadModel() async { 101 | Tflite.close(); 102 | try { 103 | String? res; 104 | switch (_model) { 105 | case yolo: 106 | res = await Tflite.loadModel( 107 | model: "assets/yolov2_tiny.tflite", 108 | labels: "assets/yolov2_tiny.txt", 109 | // useGpuDelegate: true, 110 | ); 111 | break; 112 | case ssd: 113 | res = await Tflite.loadModel( 114 | model: "assets/ssd_mobilenet.tflite", 115 | labels: "assets/ssd_mobilenet.txt", 116 | // useGpuDelegate: true, 117 | ); 118 | break; 119 | case deeplab: 120 | res = await Tflite.loadModel( 121 | model: "assets/deeplabv3_257_mv_gpu.tflite", 122 | labels: "assets/deeplabv3_257_mv_gpu.txt", 123 | // useGpuDelegate: true, 124 | ); 125 | break; 126 | case posenet: 127 | res = await Tflite.loadModel( 128 | model: "assets/posenet_mv1_075_float_from_checkpoints.tflite", 129 | // useGpuDelegate: true, 130 | ); 131 | break; 132 | default: 133 | res = await Tflite.loadModel( 134 | model: "assets/mobilenet_v1_1.0_224.tflite", 135 | labels: "assets/mobilenet_v1_1.0_224.txt", 136 | // useGpuDelegate: true, 137 | ); 138 | } 139 | print(res); 140 | } on PlatformException { 141 | print('Failed to load model.'); 142 | } 143 | } 144 | 145 | Uint8List imageToByteListFloat32( 146 | img.Image image, int inputSize, double mean, double std) { 147 | var convertedBytes = Float32List(1 * inputSize * inputSize * 3); 148 | var buffer = Float32List.view(convertedBytes.buffer); 149 | int pixelIndex = 0; 150 | for (var i = 0; i < inputSize; i++) { 151 | for (var j = 0; j < inputSize; j++) { 152 | var pixel = image.getPixel(j, i); 153 | buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std; 154 | buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std; 155 | buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std; 156 | } 157 | } 158 | return convertedBytes.buffer.asUint8List(); 159 | } 160 | 161 | Uint8List imageToByteListUint8(img.Image image, int inputSize) { 162 | var convertedBytes = Uint8List(1 * inputSize * inputSize * 3); 163 | var buffer = Uint8List.view(convertedBytes.buffer); 164 | int pixelIndex = 0; 165 | for (var i = 0; i < inputSize; i++) { 166 | for (var j = 0; j < inputSize; j++) { 167 | var pixel = image.getPixel(j, i); 168 | buffer[pixelIndex++] = img.getRed(pixel); 169 | buffer[pixelIndex++] = img.getGreen(pixel); 170 | buffer[pixelIndex++] = img.getBlue(pixel); 171 | } 172 | } 173 | return convertedBytes.buffer.asUint8List(); 174 | } 175 | 176 | Future recognizeImage(File image) async { 177 | int startTime = new DateTime.now().millisecondsSinceEpoch; 178 | var recognitions = await Tflite.runModelOnImage( 179 | path: image.path, 180 | numResults: 6, 181 | threshold: 0.05, 182 | imageMean: 127.5, 183 | imageStd: 127.5, 184 | ); 185 | setState(() { 186 | _recognitions = recognitions!; 187 | }); 188 | int endTime = new DateTime.now().millisecondsSinceEpoch; 189 | print("Inference took ${endTime - startTime}ms"); 190 | } 191 | 192 | Future recognizeImageBinary(File image) async { 193 | int startTime = new DateTime.now().millisecondsSinceEpoch; 194 | var imageBytes = (await rootBundle.load(image.path)).buffer; 195 | img.Image? oriImage = img.decodeJpg(imageBytes.asUint8List()); 196 | img.Image resizedImage = img.copyResize(oriImage!, height: 224, width: 224); 197 | var recognitions = await Tflite.runModelOnBinary( 198 | binary: imageToByteListFloat32(resizedImage, 224, 127.5, 127.5), 199 | numResults: 6, 200 | threshold: 0.05, 201 | ); 202 | setState(() { 203 | _recognitions = recognitions!; 204 | }); 205 | int endTime = new DateTime.now().millisecondsSinceEpoch; 206 | print("Inference took ${endTime - startTime}ms"); 207 | } 208 | 209 | Future yolov2Tiny(File image) async { 210 | int startTime = new DateTime.now().millisecondsSinceEpoch; 211 | var recognitions = await Tflite.detectObjectOnImage( 212 | path: image.path, 213 | model: "YOLO", 214 | threshold: 0.3, 215 | imageMean: 0.0, 216 | imageStd: 255.0, 217 | numResultsPerClass: 1, 218 | ); 219 | // var imageBytes = (await rootBundle.load(image.path)).buffer; 220 | // img.Image oriImage = img.decodeJpg(imageBytes.asUint8List()); 221 | // img.Image resizedImage = img.copyResize(oriImage, 416, 416); 222 | // var recognitions = await Tflite.detectObjectOnBinary( 223 | // binary: imageToByteListFloat32(resizedImage, 416, 0.0, 255.0), 224 | // model: "YOLO", 225 | // threshold: 0.3, 226 | // numResultsPerClass: 1, 227 | // ); 228 | setState(() { 229 | _recognitions = recognitions!; 230 | }); 231 | int endTime = new DateTime.now().millisecondsSinceEpoch; 232 | print("Inference took ${endTime - startTime}ms"); 233 | } 234 | 235 | Future ssdMobileNet(File image) async { 236 | int startTime = new DateTime.now().millisecondsSinceEpoch; 237 | var recognitions = await Tflite.detectObjectOnImage( 238 | path: image.path, 239 | numResultsPerClass: 1, 240 | ); 241 | // var imageBytes = (await rootBundle.load(image.path)).buffer; 242 | // img.Image oriImage = img.decodeJpg(imageBytes.asUint8List()); 243 | // img.Image resizedImage = img.copyResize(oriImage, 300, 300); 244 | // var recognitions = await Tflite.detectObjectOnBinary( 245 | // binary: imageToByteListUint8(resizedImage, 300), 246 | // numResultsPerClass: 1, 247 | // ); 248 | setState(() { 249 | _recognitions = recognitions!; 250 | }); 251 | int endTime = new DateTime.now().millisecondsSinceEpoch; 252 | print("Inference took ${endTime - startTime}ms"); 253 | } 254 | 255 | Future segmentMobileNet(File image) async { 256 | int startTime = new DateTime.now().millisecondsSinceEpoch; 257 | var recognitions = await Tflite.runSegmentationOnImage( 258 | path: image.path, 259 | imageMean: 127.5, 260 | imageStd: 127.5, 261 | ); 262 | 263 | setState(() { 264 | _recognitions = recognitions!; 265 | }); 266 | int endTime = new DateTime.now().millisecondsSinceEpoch; 267 | print("Inference took ${endTime - startTime}"); 268 | } 269 | 270 | Future poseNet(File image) async { 271 | int startTime = new DateTime.now().millisecondsSinceEpoch; 272 | var recognitions = await Tflite.runPoseNetOnImage( 273 | path: image.path, 274 | numResults: 2, 275 | ); 276 | 277 | print(recognitions); 278 | 279 | setState(() { 280 | _recognitions = recognitions!; 281 | }); 282 | int endTime = new DateTime.now().millisecondsSinceEpoch; 283 | print("Inference took ${endTime - startTime}ms"); 284 | } 285 | 286 | onSelect(model) async { 287 | setState(() { 288 | _busy = true; 289 | _model = model; 290 | _recognitions = []; 291 | }); 292 | await loadModel(); 293 | 294 | predictImage(_image); 295 | } 296 | 297 | List renderBoxes(Size screen) { 298 | double factorX = screen.width; 299 | double factorY = _imageHeight / _imageWidth * screen.width; 300 | Color blue = Color.fromRGBO(37, 213, 253, 1.0); 301 | return _recognitions.map((re) { 302 | return Positioned( 303 | left: re["rect"]["x"] * factorX, 304 | top: re["rect"]["y"] * factorY, 305 | width: re["rect"]["w"] * factorX, 306 | height: re["rect"]["h"] * factorY, 307 | child: Container( 308 | decoration: BoxDecoration( 309 | borderRadius: BorderRadius.all(Radius.circular(8.0)), 310 | border: Border.all( 311 | color: blue, 312 | width: 2, 313 | ), 314 | ), 315 | child: Text( 316 | "${re["detectedClass"]} ${(re["confidenceInClass"] * 100).toStringAsFixed(0)}%", 317 | style: TextStyle( 318 | background: Paint()..color = blue, 319 | color: Colors.white, 320 | fontSize: 12.0, 321 | ), 322 | ), 323 | ), 324 | ); 325 | }).toList(); 326 | } 327 | 328 | List renderKeypoints(Size screen) { 329 | double factorX = screen.width; 330 | double factorY = _imageHeight / _imageWidth * screen.width; 331 | 332 | var lists = []; 333 | _recognitions.forEach((re) { 334 | var color = Color((Random().nextDouble() * 0xFFFFFF).toInt() << 0) 335 | .withOpacity(1.0); 336 | var list = re["keypoints"].values.map((k) { 337 | return Positioned( 338 | left: k["x"] * factorX - 6, 339 | top: k["y"] * factorY - 6, 340 | width: 100, 341 | height: 12, 342 | child: Text( 343 | "● ${k["part"]}", 344 | style: TextStyle( 345 | color: color, 346 | fontSize: 12.0, 347 | ), 348 | ), 349 | ); 350 | }).toList(); 351 | 352 | lists..addAll(list); 353 | }); 354 | 355 | return lists; 356 | } 357 | 358 | @override 359 | Widget build(BuildContext context) { 360 | Size size = MediaQuery.of(context).size; 361 | List stackChildren = []; 362 | 363 | if (_model == deeplab) { 364 | stackChildren.add(Positioned( 365 | top: 0.0, 366 | left: 0.0, 367 | width: size.width, 368 | child: _image == null 369 | ? Text('No image selected.') 370 | : Container( 371 | decoration: BoxDecoration( 372 | image: DecorationImage( 373 | alignment: Alignment.topCenter, 374 | image: MemoryImage(Uint8List.fromList( 375 | _recognitions.map((e) => e as int).toList())), 376 | fit: BoxFit.fill)), 377 | child: Opacity(opacity: 0.3, child: Image.file(_image))), 378 | )); 379 | } else { 380 | stackChildren.add(Positioned( 381 | top: 0.0, 382 | left: 0.0, 383 | width: size.width, 384 | child: _image == null ? Text('No image selected.') : Image.file(_image), 385 | )); 386 | } 387 | 388 | if (_model == mobile) { 389 | stackChildren.add(Center( 390 | child: Column( 391 | children: _recognitions != null 392 | ? _recognitions.map((res) { 393 | return Text( 394 | "${res["index"]} - ${res["label"]}: ${res["confidence"].toStringAsFixed(3)}", 395 | style: TextStyle( 396 | color: Colors.black, 397 | fontSize: 20.0, 398 | background: Paint()..color = Colors.white, 399 | ), 400 | ); 401 | }).toList() 402 | : [], 403 | ), 404 | )); 405 | } else if (_model == ssd || _model == yolo) { 406 | stackChildren.addAll(renderBoxes(size)); 407 | } else if (_model == posenet) { 408 | stackChildren.addAll(renderKeypoints(size)); 409 | } 410 | 411 | if (_busy) { 412 | stackChildren.add(const Opacity( 413 | child: ModalBarrier(dismissible: false, color: Colors.grey), 414 | opacity: 0.3, 415 | )); 416 | stackChildren.add(const Center(child: CircularProgressIndicator())); 417 | } 418 | 419 | return Scaffold( 420 | appBar: AppBar( 421 | title: const Text('tflite example app'), 422 | actions: [ 423 | PopupMenuButton( 424 | onSelected: onSelect, 425 | itemBuilder: (context) { 426 | List> menuEntries = [ 427 | const PopupMenuItem( 428 | child: Text(mobile), 429 | value: mobile, 430 | ), 431 | const PopupMenuItem( 432 | child: Text(ssd), 433 | value: ssd, 434 | ), 435 | const PopupMenuItem( 436 | child: Text(yolo), 437 | value: yolo, 438 | ), 439 | const PopupMenuItem( 440 | child: Text(deeplab), 441 | value: deeplab, 442 | ), 443 | const PopupMenuItem( 444 | child: Text(posenet), 445 | value: posenet, 446 | ) 447 | ]; 448 | return menuEntries; 449 | }, 450 | ) 451 | ], 452 | ), 453 | body: Stack( 454 | children: stackChildren, 455 | ), 456 | floatingActionButton: FloatingActionButton( 457 | onPressed: predictImagePicker, 458 | tooltip: 'Pick Image', 459 | child: Icon(Icons.image), 460 | ), 461 | ); 462 | } 463 | } 464 | -------------------------------------------------------------------------------- /example/pubspec.yaml: -------------------------------------------------------------------------------- 1 | name: tflite_example 2 | description: Demonstrates how to use the tflite plugin. 3 | 4 | # The following defines the version and build number for your application. 5 | # A version number is three numbers separated by dots, like 1.2.43 6 | # followed by an optional build number separated by a +. 7 | # Both the version and the builder number may be overridden in flutter 8 | # build by specifying --build-name and --build-number, respectively. 9 | # Read more about versioning at semver.org. 10 | version: 1.0.0+1 11 | 12 | environment: 13 | sdk: ">=3.0.6-dev.68.0 <4.0.0" 14 | 15 | dependencies: 16 | 17 | # The following adds the Cupertino Icons font to your application. 18 | # Use with the CupertinoIcons class for iOS style icons. 19 | cupertino_icons: ^1.0.2 20 | flutter: 21 | sdk: flutter 22 | 23 | dev_dependencies: 24 | flutter_test: 25 | sdk: flutter 26 | image: ^3.0.1 27 | image_picker: ^1.0.5 28 | test: ^1.12.0 29 | tensorflow_lite_flutter: 30 | path: ../ 31 | # For information on the generic Dart part of this file, see the 32 | # following page: https://www.dartlang.org/tools/pub/pubspec 33 | # The following section is specific to Flutter. 34 | flutter: 35 | 36 | # The following line ensures that the Material Icons font is 37 | # included with your application, so that you can use the icons in 38 | # the material Icons class. 39 | uses-material-design: true 40 | 41 | # To add assets to your application, add an assets section, like this: 42 | assets: 43 | - assets/mobilenet_v1_1.0_224.txt 44 | - assets/mobilenet_v1_1.0_224.tflite 45 | - assets/yolov2_tiny.tflite 46 | - assets/yolov2_tiny.txt 47 | - assets/ssd_mobilenet.tflite 48 | - assets/ssd_mobilenet.txt 49 | - assets/deeplabv3_257_mv_gpu.tflite 50 | - assets/deeplabv3_257_mv_gpu.txt 51 | - assets/posenet_mv1_075_float_from_checkpoints.tflite 52 | -------------------------------------------------------------------------------- /example/test/widget_test.dart: -------------------------------------------------------------------------------- 1 | // This is a basic Flutter widget test. 2 | // To perform an interaction with a widget in your test, use the WidgetTester utility that Flutter 3 | // provides. For example, you can send tap and scroll gestures. You can also use WidgetTester to 4 | // find child widgets in the widget tree, read text, and verify that the values of widget properties 5 | // are correct. 6 | 7 | import 'package:flutter/material.dart'; 8 | import 'package:flutter_test/flutter_test.dart'; 9 | 10 | import '../lib/main.dart'; 11 | 12 | void main() { 13 | testWidgets('Verify Platform version', (WidgetTester tester) async { 14 | // Build our app and trigger a frame. 15 | await tester.pumpWidget(new MyApp()); 16 | 17 | // Verify that platform version is retrieved. 18 | expect( 19 | find.byWidgetPredicate( 20 | (Widget widget) => 21 | widget is Text && widget.data!.startsWith('Running on:'), 22 | ), 23 | findsOneWidget); 24 | }); 25 | } 26 | -------------------------------------------------------------------------------- /example/yolo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/example/yolo.jpg -------------------------------------------------------------------------------- /ios/.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | .vagrant/ 3 | .sconsign.dblite 4 | .svn/ 5 | 6 | .DS_Store 7 | *.swp 8 | profile 9 | 10 | DerivedData/ 11 | build/ 12 | GeneratedPluginRegistrant.h 13 | GeneratedPluginRegistrant.m 14 | 15 | .generated/ 16 | 17 | *.pbxuser 18 | *.mode1v3 19 | *.mode2v3 20 | *.perspectivev3 21 | 22 | !default.pbxuser 23 | !default.mode1v3 24 | !default.mode2v3 25 | !default.perspectivev3 26 | 27 | xcuserdata 28 | 29 | *.moved-aside 30 | 31 | *.pyc 32 | *sync/ 33 | Icon? 34 | .tags* 35 | 36 | /Flutter/Generated.xcconfig 37 | -------------------------------------------------------------------------------- /ios/Assets/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/draz26648/tensorflow_lite_flutter/fc55c07816f7c35b354bc401c578fee20972a7b1/ios/Assets/.gitkeep -------------------------------------------------------------------------------- /ios/Classes/TflitePlugin.h: -------------------------------------------------------------------------------- 1 | #import 2 | 3 | @interface TflitePlugin : NSObject 4 | @end 5 | -------------------------------------------------------------------------------- /ios/Classes/ios_image_load.h: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | std::vector LoadImageFromFile(const char* file_name, 4 | int* out_width, 5 | int* out_height, 6 | int* out_channels); 7 | 8 | NSData *CompressImage(NSMutableData*, 9 | int width, 10 | int height, 11 | int bytesPerPixel); 12 | 13 | -------------------------------------------------------------------------------- /ios/Classes/ios_image_load.mm: -------------------------------------------------------------------------------- 1 | #import 2 | #include "ios_image_load.h" 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | #import 10 | #import 11 | 12 | std::vector LoadImageFromFile(const char* file_name, 13 | int* out_width, int* out_height, 14 | int* out_channels) { 15 | FILE* file_handle = fopen(file_name, "rb"); 16 | fseek(file_handle, 0, SEEK_END); 17 | const size_t bytes_in_file = ftell(file_handle); 18 | fseek(file_handle, 0, SEEK_SET); 19 | std::vector file_data(bytes_in_file); 20 | fread(file_data.data(), 1, bytes_in_file, file_handle); 21 | fclose(file_handle); 22 | 23 | CFDataRef file_data_ref = CFDataCreateWithBytesNoCopy(NULL, file_data.data(), 24 | bytes_in_file, 25 | kCFAllocatorNull); 26 | CGDataProviderRef image_provider = CGDataProviderCreateWithCFData(file_data_ref); 27 | 28 | const char* suffix = strrchr(file_name, '.'); 29 | if (!suffix || suffix == file_name) { 30 | suffix = ""; 31 | } 32 | CGImageRef image; 33 | if (strcasecmp(suffix, ".png") == 0) { 34 | image = CGImageCreateWithPNGDataProvider(image_provider, NULL, true, 35 | kCGRenderingIntentDefault); 36 | } else if ((strcasecmp(suffix, ".jpg") == 0) || 37 | (strcasecmp(suffix, ".jpeg") == 0)) { 38 | image = CGImageCreateWithJPEGDataProvider(image_provider, NULL, true, 39 | kCGRenderingIntentDefault); 40 | } else { 41 | CFRelease(image_provider); 42 | CFRelease(file_data_ref); 43 | fprintf(stderr, "Unknown suffix for file '%s'\n", file_name); 44 | out_width = 0; 45 | out_height = 0; 46 | *out_channels = 0; 47 | return std::vector(); 48 | } 49 | 50 | int width = (int)CGImageGetWidth(image); 51 | int height = (int)CGImageGetHeight(image); 52 | const int channels = 4; 53 | CGColorSpaceRef color_space = CGColorSpaceCreateDeviceRGB(); 54 | const int bytes_per_row = (width * channels); 55 | const int bytes_in_image = (bytes_per_row * height); 56 | std::vector result(bytes_in_image); 57 | const int bits_per_component = 8; 58 | 59 | CGContextRef context = CGBitmapContextCreate(result.data(), width, height, 60 | bits_per_component, bytes_per_row, color_space, 61 | kCGImageAlphaPremultipliedLast | kCGBitmapByteOrder32Big); 62 | CGColorSpaceRelease(color_space); 63 | CGContextDrawImage(context, CGRectMake(0, 0, width, height), image); 64 | CGContextRelease(context); 65 | CFRelease(image); 66 | CFRelease(image_provider); 67 | CFRelease(file_data_ref); 68 | 69 | *out_width = width; 70 | *out_height = height; 71 | *out_channels = channels; 72 | return result; 73 | } 74 | 75 | NSData *CompressImage(NSMutableData *image, int width, int height, int bytesPerPixel) { 76 | const int channels = 4; 77 | CGColorSpaceRef color_space = CGColorSpaceCreateDeviceRGB(); 78 | CGContextRef context = CGBitmapContextCreate([image mutableBytes], width, height, 79 | bytesPerPixel*8, width*channels*bytesPerPixel, color_space, 80 | kCGImageAlphaPremultipliedLast | (bytesPerPixel == 4 ? kCGBitmapFloatComponents : kCGBitmapByteOrder32Big)); 81 | CGColorSpaceRelease(color_space); 82 | if (context == nil) return nil; 83 | 84 | CGImageRef imgRef = CGBitmapContextCreateImage(context); 85 | CGContextRelease(context); 86 | if (imgRef == nil) return nil; 87 | 88 | UIImage* img = [UIImage imageWithCGImage:imgRef]; 89 | CGImageRelease(imgRef); 90 | if (img == nil) return nil; 91 | 92 | return UIImagePNGRepresentation(img); 93 | } 94 | -------------------------------------------------------------------------------- /ios/tflite.podspec: -------------------------------------------------------------------------------- 1 | # 2 | # To learn more about a Podspec see http://guides.cocoapods.org/syntax/podspec.html 3 | # 4 | Pod::Spec.new do |s| 5 | s.name = 'tflite' 6 | s.version = '1.1.2' 7 | s.summary = 'A Flutter plugin for accessing TensorFlow Lite.' 8 | s.description = <<-DESC 9 | A Flutter plugin for accessing TensorFlow Lite. Supports both iOS and Android. 10 | DESC 11 | s.homepage = 'https://github.com/shaqian/flutter_tflite' 12 | s.license = { :file => '../LICENSE' } 13 | s.author = { 'Qian Sha' => 'https://github.com/shaqian' } 14 | s.source = { :path => '.' } 15 | s.source_files = 'Classes/**/*' 16 | s.public_header_files = 'Classes/**/*.h' 17 | s.dependency 'Flutter' 18 | s.dependency 'TensorFlowLiteC' 19 | s.xcconfig = { 'USER_HEADER_SEARCH_PATHS' => '$(inherited) "${PODS_ROOT}/Headers/Private" "${PODS_ROOT}/Headers/Private/tflite" "${PODS_ROOT}/Headers/Public" "${PODS_ROOT}/Headers/Public/Flutter" "${PODS_ROOT}/Headers/Public/TensorFlowLite/tensorflow_lite" "${PODS_ROOT}/Headers/Public/tflite" "${PODS_ROOT}/TensorFlowLite/Frameworks/tensorflow_lite.framework/Headers" "${PODS_ROOT}/TensorFlowLiteC/Frameworks/TensorFlowLiteC.framework/Headers"' } 20 | 21 | s.ios.deployment_target = '9.0' 22 | s.static_framework = true 23 | end 24 | 25 | -------------------------------------------------------------------------------- /lib/tensorflow_lite_flutter.dart: -------------------------------------------------------------------------------- 1 | import 'dart:async'; 2 | import 'package:flutter/services.dart'; 3 | 4 | /// TensorFlow Lite plugin for Flutter 5 | /// 6 | /// This class provides methods to interact with TensorFlow Lite models for 7 | /// various machine learning tasks including image classification, object detection, 8 | /// image-to-image translation, semantic segmentation, and pose estimation. 9 | class Tflite { 10 | /// Method channel for communicating with native code 11 | static const MethodChannel _channel = MethodChannel('tflite'); 12 | 13 | /// Loads a TensorFlow Lite model into memory 14 | /// 15 | /// [model] - Path to the model file (required) 16 | /// [labels] - Path to the labels file (optional) 17 | /// [numThreads] - Number of threads to use for inference (default: 1) 18 | /// [isAsset] - Whether the model is an asset or a file path (default: true) 19 | /// [useGpuDelegate] - Whether to use GPU acceleration (default: false) 20 | /// 21 | /// Returns a message indicating success or failure 22 | static Future loadModel({ 23 | required String model, 24 | String labels = "", 25 | int numThreads = 1, 26 | bool isAsset = true, 27 | bool useGpuDelegate = false, 28 | }) async { 29 | return await _channel.invokeMethod( 30 | 'loadModel', 31 | { 32 | "model": model, 33 | "labels": labels, 34 | "numThreads": numThreads, 35 | "isAsset": isAsset, 36 | 'useGpuDelegate': useGpuDelegate 37 | }, 38 | ); 39 | } 40 | 41 | /// Runs inference on an image file for image classification 42 | /// 43 | /// [path] - Path to the image file (required) 44 | /// [imageMean] - Mean normalization value (default: 117.0) 45 | /// [imageStd] - Standard deviation normalization value (default: 1.0) 46 | /// [numResults] - Maximum number of results to return (default: 5) 47 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 48 | /// [asynch] - Whether to run inference asynchronously (default: true) 49 | /// 50 | /// Returns a list of classification results, each containing: 51 | /// - index: The class index 52 | /// - label: The class label (if a labels file was provided) 53 | /// - confidence: The confidence score (between 0-1) 54 | static Future runModelOnImage({ 55 | required String path, 56 | double imageMean = 117.0, 57 | double imageStd = 1.0, 58 | int numResults = 5, 59 | double threshold = 0.1, 60 | bool asynch = true, 61 | }) async { 62 | return await _channel.invokeMethod( 63 | 'runModelOnImage', 64 | { 65 | "path": path, 66 | "imageMean": imageMean, 67 | "imageStd": imageStd, 68 | "numResults": numResults, 69 | "threshold": threshold, 70 | "asynch": asynch, 71 | }, 72 | ); 73 | } 74 | 75 | /// Runs inference on binary image data for image classification 76 | /// 77 | /// [binary] - Binary image data (required) 78 | /// [numResults] - Maximum number of results to return (default: 5) 79 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 80 | /// [asynch] - Whether to run inference asynchronously (default: true) 81 | /// 82 | /// Returns a list of classification results, each containing: 83 | /// - index: The class index 84 | /// - label: The class label (if a labels file was provided) 85 | /// - confidence: The confidence score (between 0-1) 86 | static Future runModelOnBinary({ 87 | required Uint8List binary, 88 | int numResults = 5, 89 | double threshold = 0.1, 90 | bool asynch = true, 91 | }) async { 92 | return await _channel.invokeMethod( 93 | 'runModelOnBinary', 94 | { 95 | "binary": binary, 96 | "numResults": numResults, 97 | "threshold": threshold, 98 | "asynch": asynch, 99 | }, 100 | ); 101 | } 102 | 103 | /// Runs inference on camera frame data for image classification 104 | /// 105 | /// [bytesList] - List of byte arrays from camera planes (required) 106 | /// [imageHeight] - Height of the image (default: 1280) 107 | /// [imageWidth] - Width of the image (default: 720) 108 | /// [imageMean] - Mean normalization value (default: 127.5) 109 | /// [imageStd] - Standard deviation normalization value (default: 127.5) 110 | /// [rotation] - Rotation of the image in degrees, Android only (default: 90) 111 | /// [numResults] - Maximum number of results to return (default: 5) 112 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 113 | /// [asynch] - Whether to run inference asynchronously (default: true) 114 | /// 115 | /// Returns a list of classification results, each containing: 116 | /// - index: The class index 117 | /// - label: The class label (if a labels file was provided) 118 | /// - confidence: The confidence score (between 0-1) 119 | static Future runModelOnFrame({ 120 | required List bytesList, 121 | int imageHeight = 1280, 122 | int imageWidth = 720, 123 | double imageMean = 127.5, 124 | double imageStd = 127.5, 125 | int rotation = 90, // Android only 126 | int numResults = 5, 127 | double threshold = 0.1, 128 | bool asynch = true, 129 | }) async { 130 | return await _channel.invokeMethod( 131 | 'runModelOnFrame', 132 | { 133 | "bytesList": bytesList, 134 | "imageHeight": imageHeight, 135 | "imageWidth": imageWidth, 136 | "imageMean": imageMean, 137 | "imageStd": imageStd, 138 | "rotation": rotation, 139 | "numResults": numResults, 140 | "threshold": threshold, 141 | "asynch": asynch, 142 | }, 143 | ); 144 | } 145 | 146 | /// Default anchor values for YOLO object detection model 147 | /// 148 | /// These anchors are used for the YOLO model to define the default shapes 149 | /// of bounding boxes at different scales 150 | static const List anchors = [ 151 | 0.57273, 152 | 0.677385, 153 | 1.87446, 154 | 2.06253, 155 | 3.33843, 156 | 5.47434, 157 | 7.88282, 158 | 3.52778, 159 | 9.77052, 160 | 9.16828 161 | ]; 162 | 163 | /// Detects objects in an image file using either SSD MobileNet or YOLO models 164 | /// 165 | /// [path] - Path to the image file (required) 166 | /// [model] - Model to use: "SSDMobileNet" or "YOLO" (default: "SSDMobileNet") 167 | /// [imageMean] - Mean normalization value (default: 127.5) 168 | /// [imageStd] - Standard deviation normalization value (default: 127.5) 169 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 170 | /// [numResultsPerClass] - Maximum number of results per class (default: 5) 171 | /// [anchors] - Anchor values for YOLO model (default: predefined anchors) 172 | /// [blockSize] - Block size for YOLO model (default: 32) 173 | /// [numBoxesPerBlock] - Number of boxes per block for YOLO model (default: 5) 174 | /// [asynch] - Whether to run inference asynchronously (default: true) 175 | /// 176 | /// Returns a list of detected objects, each containing: 177 | /// - detectedClass: The class label of the detected object 178 | /// - confidenceInClass: The confidence score (between 0-1) 179 | /// - rect: Object containing x, y, w, h coordinates (normalized 0-1) 180 | static Future detectObjectOnImage({ 181 | required String path, 182 | String model = "SSDMobileNet", 183 | double imageMean = 127.5, 184 | double imageStd = 127.5, 185 | double threshold = 0.1, 186 | int numResultsPerClass = 5, 187 | // Used in YOLO only 188 | List anchors = anchors, 189 | int blockSize = 32, 190 | int numBoxesPerBlock = 5, 191 | bool asynch = true, 192 | }) async { 193 | return await _channel.invokeMethod( 194 | 'detectObjectOnImage', 195 | { 196 | "path": path, 197 | "model": model, 198 | "imageMean": imageMean, 199 | "imageStd": imageStd, 200 | "threshold": threshold, 201 | "numResultsPerClass": numResultsPerClass, 202 | "anchors": anchors, 203 | "blockSize": blockSize, 204 | "numBoxesPerBlock": numBoxesPerBlock, 205 | "asynch": asynch, 206 | }, 207 | ); 208 | } 209 | 210 | /// Detects objects in binary image data using either SSD MobileNet or YOLO models 211 | /// 212 | /// [binary] - Binary image data (required) 213 | /// [model] - Model to use: "SSDMobileNet" or "YOLO" (default: "SSDMobileNet") 214 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 215 | /// [numResultsPerClass] - Maximum number of results per class (default: 5) 216 | /// [anchors] - Anchor values for YOLO model (default: predefined anchors) 217 | /// [blockSize] - Block size for YOLO model (default: 32) 218 | /// [numBoxesPerBlock] - Number of boxes per block for YOLO model (default: 5) 219 | /// [asynch] - Whether to run inference asynchronously (default: true) 220 | /// 221 | /// Returns a list of detected objects, each containing: 222 | /// - detectedClass: The class label of the detected object 223 | /// - confidenceInClass: The confidence score (between 0-1) 224 | /// - rect: Object containing x, y, w, h coordinates (normalized 0-1) 225 | static Future detectObjectOnBinary({ 226 | required Uint8List binary, 227 | String model = "SSDMobileNet", 228 | double threshold = 0.1, 229 | int numResultsPerClass = 5, 230 | // Used in YOLO only 231 | List anchors = anchors, 232 | int blockSize = 32, 233 | int numBoxesPerBlock = 5, 234 | bool asynch = true, 235 | }) async { 236 | return await _channel.invokeMethod( 237 | 'detectObjectOnBinary', 238 | { 239 | "binary": binary, 240 | "model": model, 241 | "threshold": threshold, 242 | "numResultsPerClass": numResultsPerClass, 243 | "anchors": anchors, 244 | "blockSize": blockSize, 245 | "numBoxesPerBlock": numBoxesPerBlock, 246 | "asynch": asynch, 247 | }, 248 | ); 249 | } 250 | 251 | /// Detects objects in camera frame data using either SSD MobileNet or YOLO models 252 | /// 253 | /// [bytesList] - List of byte arrays from camera planes (required) 254 | /// [model] - Model to use: "SSDMobileNet" or "YOLO" (default: "SSDMobileNet") 255 | /// [imageHeight] - Height of the image (default: 1280) 256 | /// [imageWidth] - Width of the image (default: 720) 257 | /// [imageMean] - Mean normalization value (default: 127.5) 258 | /// [imageStd] - Standard deviation normalization value (default: 127.5) 259 | /// [threshold] - Minimum confidence threshold for results (default: 0.1) 260 | /// [numResultsPerClass] - Maximum number of results per class (default: 5) 261 | /// [rotation] - Rotation of the image in degrees, Android only (default: 90) 262 | /// [anchors] - Anchor values for YOLO model (default: predefined anchors) 263 | /// [blockSize] - Block size for YOLO model (default: 32) 264 | /// [numBoxesPerBlock] - Number of boxes per block for YOLO model (default: 5) 265 | /// [asynch] - Whether to run inference asynchronously (default: true) 266 | /// 267 | /// Returns a list of detected objects, each containing: 268 | /// - detectedClass: The class label of the detected object 269 | /// - confidenceInClass: The confidence score (between 0-1) 270 | /// - rect: Object containing x, y, w, h coordinates (normalized 0-1) 271 | static Future detectObjectOnFrame({ 272 | required List bytesList, 273 | String model = "SSDMobileNet", 274 | int imageHeight = 1280, 275 | int imageWidth = 720, 276 | double imageMean = 127.5, 277 | double imageStd = 127.5, 278 | double threshold = 0.1, 279 | int numResultsPerClass = 5, 280 | int rotation = 90, // Android only 281 | // Used in YOLO only 282 | List anchors = anchors, 283 | int blockSize = 32, 284 | int numBoxesPerBlock = 5, 285 | bool asynch = true, 286 | }) async { 287 | return await _channel.invokeMethod( 288 | 'detectObjectOnFrame', 289 | { 290 | "bytesList": bytesList, 291 | "model": model, 292 | "imageHeight": imageHeight, 293 | "imageWidth": imageWidth, 294 | "imageMean": imageMean, 295 | "imageStd": imageStd, 296 | "rotation": rotation, 297 | "threshold": threshold, 298 | "numResultsPerClass": numResultsPerClass, 299 | "anchors": anchors, 300 | "blockSize": blockSize, 301 | "numBoxesPerBlock": numBoxesPerBlock, 302 | "asynch": asynch, 303 | }, 304 | ); 305 | } 306 | 307 | static Future close() async { 308 | return await _channel.invokeMethod('close'); 309 | } 310 | 311 | /// Runs Pix2Pix image-to-image translation on an image file 312 | /// 313 | /// Pix2Pix is a conditional GAN that transforms images from one domain to another 314 | /// (e.g., sketch to photo, day to night, etc.) 315 | /// 316 | /// [path] - Path to the image file (required) 317 | /// [imageMean] - Mean normalization value (default: 0) 318 | /// [imageStd] - Standard deviation normalization value (default: 255.0) 319 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 320 | /// [asynch] - Whether to run inference asynchronously (default: true) 321 | /// 322 | /// Returns a Uint8List containing: 323 | /// - If outputType is "png": PNG image data that can be displayed directly 324 | /// - If outputType is "raw": Raw pixel data in RGBA format 325 | static Future runPix2PixOnImage({ 326 | required String path, 327 | double imageMean = 0, 328 | double imageStd = 255.0, 329 | String outputType = "png", 330 | bool asynch = true, 331 | }) async { 332 | return await _channel.invokeMethod( 333 | 'runPix2PixOnImage', 334 | { 335 | "path": path, 336 | "imageMean": imageMean, 337 | "imageStd": imageStd, 338 | "outputType": outputType, 339 | "asynch": asynch, 340 | }, 341 | ); 342 | } 343 | 344 | /// Runs Pix2Pix image-to-image translation on binary image data 345 | /// 346 | /// [binary] - Binary image data (required) 347 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 348 | /// [asynch] - Whether to run inference asynchronously (default: true) 349 | /// 350 | /// Returns a Uint8List containing: 351 | /// - If outputType is "png": PNG image data that can be displayed directly 352 | /// - If outputType is "raw": Raw pixel data in RGBA format 353 | static Future runPix2PixOnBinary({ 354 | required Uint8List binary, 355 | String outputType = "png", 356 | bool asynch = true, 357 | }) async { 358 | return await _channel.invokeMethod( 359 | 'runPix2PixOnBinary', 360 | { 361 | "binary": binary, 362 | "outputType": outputType, 363 | "asynch": asynch, 364 | }, 365 | ); 366 | } 367 | 368 | /// Runs Pix2Pix image-to-image translation on camera frame data 369 | /// 370 | /// [bytesList] - List of byte arrays from camera planes (required) 371 | /// [imageHeight] - Height of the image (default: 1280) 372 | /// [imageWidth] - Width of the image (default: 720) 373 | /// [imageMean] - Mean normalization value (default: 0) 374 | /// [imageStd] - Standard deviation normalization value (default: 255.0) 375 | /// [rotation] - Rotation of the image in degrees, Android only (default: 90) 376 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 377 | /// [asynch] - Whether to run inference asynchronously (default: true) 378 | /// 379 | /// Returns a Uint8List containing: 380 | /// - If outputType is "png": PNG image data that can be displayed directly 381 | /// - If outputType is "raw": Raw pixel data in RGBA format 382 | static Future runPix2PixOnFrame({ 383 | required List bytesList, 384 | int imageHeight = 1280, 385 | int imageWidth = 720, 386 | double imageMean = 0, 387 | double imageStd = 255.0, 388 | int rotation = 90, // Android only 389 | String outputType = "png", 390 | bool asynch = true, 391 | }) async { 392 | return await _channel.invokeMethod( 393 | 'runPix2PixOnFrame', 394 | { 395 | "bytesList": bytesList, 396 | "imageHeight": imageHeight, 397 | "imageWidth": imageWidth, 398 | "imageMean": imageMean, 399 | "imageStd": imageStd, 400 | "rotation": rotation, 401 | "outputType": outputType, 402 | "asynch": asynch, 403 | }, 404 | ); 405 | } 406 | 407 | // https://github.com/meetshah1995/pytorch-semseg/blob/master/ptsemseg/loader/pascal_voc_loader.py 408 | /// Default color palette for Pascal VOC dataset semantic segmentation 409 | /// 410 | /// Each color represents a different class in the Pascal VOC dataset 411 | /// These colors are used to visualize the segmentation results 412 | static final List pascalVOCLabelColors = [ 413 | Color.fromARGB(255, 0, 0, 0).value, // background 414 | Color.fromARGB(255, 128, 0, 0).value, // aeroplane 415 | Color.fromARGB(255, 0, 128, 0).value, // bicycle 416 | Color.fromARGB(255, 128, 128, 0).value, // bird 417 | Color.fromARGB(255, 0, 0, 128).value, // boat 418 | Color.fromARGB(255, 128, 0, 128).value, // bottle 419 | Color.fromARGB(255, 0, 128, 128).value, // bus 420 | Color.fromARGB(255, 128, 128, 128).value, // car 421 | Color.fromARGB(255, 64, 0, 0).value, // cat 422 | Color.fromARGB(255, 192, 0, 0).value, // chair 423 | Color.fromARGB(255, 64, 128, 0).value, // cow 424 | Color.fromARGB(255, 192, 128, 0).value, // diningtable 425 | Color.fromARGB(255, 64, 0, 128).value, // dog 426 | Color.fromARGB(255, 192, 0, 128).value, // horse 427 | Color.fromARGB(255, 64, 128, 128).value, // motorbike 428 | Color.fromARGB(255, 192, 128, 128).value, // person 429 | Color.fromARGB(255, 0, 64, 0).value, // potted plant 430 | Color.fromARGB(255, 128, 64, 0).value, // sheep 431 | Color.fromARGB(255, 0, 192, 0).value, // sofa 432 | Color.fromARGB(255, 128, 192, 0).value, // train 433 | Color.fromARGB(255, 0, 64, 128).value, // tv-monitor 434 | ]; 435 | 436 | /// Runs semantic segmentation on an image file 437 | /// 438 | /// Semantic segmentation classifies each pixel in an image, assigning it to a specific class 439 | /// (e.g., person, car, road, etc.) 440 | /// 441 | /// [path] - Path to the image file (required) 442 | /// [imageMean] - Mean normalization value (default: 0) 443 | /// [imageStd] - Standard deviation normalization value (default: 255.0) 444 | /// [labelColors] - List of colors to use for visualization (default: pascalVOCLabelColors) 445 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 446 | /// [asynch] - Whether to run inference asynchronously (default: true) 447 | /// 448 | /// Returns a Uint8List containing: 449 | /// - If outputType is "png": PNG image data with colored segmentation mask 450 | /// - If outputType is "raw": Raw pixel data with class indices 451 | static Future runSegmentationOnImage({ 452 | required String path, 453 | double imageMean = 0, 454 | double imageStd = 255.0, 455 | List? labelColors, 456 | String outputType = "png", 457 | bool asynch = true, 458 | }) async { 459 | return await _channel.invokeMethod( 460 | 'runSegmentationOnImage', 461 | { 462 | "path": path, 463 | "imageMean": imageMean, 464 | "imageStd": imageStd, 465 | "labelColors": labelColors ?? pascalVOCLabelColors, 466 | "outputType": outputType, 467 | "asynch": asynch, 468 | }, 469 | ); 470 | } 471 | 472 | /// Runs semantic segmentation on binary image data 473 | /// 474 | /// [binary] - Binary image data (required) 475 | /// [labelColors] - List of colors to use for visualization (default: pascalVOCLabelColors) 476 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 477 | /// [asynch] - Whether to run inference asynchronously (default: true) 478 | /// 479 | /// Returns a Uint8List containing: 480 | /// - If outputType is "png": PNG image data with colored segmentation mask 481 | /// - If outputType is "raw": Raw pixel data with class indices 482 | static Future runSegmentationOnBinary({ 483 | required Uint8List binary, 484 | List? labelColors, 485 | String outputType = "png", 486 | bool asynch = true, 487 | }) async { 488 | return await _channel.invokeMethod( 489 | 'runSegmentationOnBinary', 490 | { 491 | "binary": binary, 492 | "labelColors": labelColors ?? pascalVOCLabelColors, 493 | "outputType": outputType, 494 | "asynch": asynch, 495 | }, 496 | ); 497 | } 498 | 499 | /// Runs semantic segmentation on camera frame data 500 | /// 501 | /// [bytesList] - List of byte arrays from camera planes (required) 502 | /// [imageHeight] - Height of the image (default: 1280) 503 | /// [imageWidth] - Width of the image (default: 720) 504 | /// [imageMean] - Mean normalization value (default: 0) 505 | /// [imageStd] - Standard deviation normalization value (default: 255.0) 506 | /// [rotation] - Rotation of the image in degrees, Android only (default: 90) 507 | /// [labelColors] - List of colors to use for visualization (default: pascalVOCLabelColors) 508 | /// [outputType] - Output format, either "png" or "raw" (default: "png") 509 | /// [asynch] - Whether to run inference asynchronously (default: true) 510 | /// 511 | /// Returns a Uint8List containing: 512 | /// - If outputType is "png": PNG image data with colored segmentation mask 513 | /// - If outputType is "raw": Raw pixel data with class indices 514 | static Future runSegmentationOnFrame({ 515 | required List bytesList, 516 | int imageHeight = 1280, 517 | int imageWidth = 720, 518 | double imageMean = 0, 519 | double imageStd = 255.0, 520 | int rotation = 90, // Android only 521 | List? labelColors, 522 | String outputType = "png", 523 | bool asynch = true, 524 | }) async { 525 | return await _channel.invokeMethod( 526 | 'runSegmentationOnFrame', 527 | { 528 | "bytesList": bytesList, 529 | "imageHeight": imageHeight, 530 | "imageWidth": imageWidth, 531 | "imageMean": imageMean, 532 | "imageStd": imageStd, 533 | "rotation": rotation, 534 | "labelColors": labelColors ?? pascalVOCLabelColors, 535 | "outputType": outputType, 536 | "asynch": asynch, 537 | }, 538 | ); 539 | } 540 | 541 | /// Runs PoseNet human pose estimation on an image file 542 | /// 543 | /// PoseNet detects human figures in images and estimates the pose by finding 544 | /// body keypoints (e.g., nose, eyes, ears, shoulders, elbows, wrists, etc.) 545 | /// 546 | /// [path] - Path to the image file (required) 547 | /// [imageMean] - Mean normalization value (default: 127.5) 548 | /// [imageStd] - Standard deviation normalization value (default: 127.5) 549 | /// [numResults] - Maximum number of pose results to return (default: 5) 550 | /// [threshold] - Minimum confidence threshold for keypoints (default: 0.5) 551 | /// [nmsRadius] - Non-maximum suppression radius (default: 20) 552 | /// [asynch] - Whether to run inference asynchronously (default: true) 553 | /// 554 | /// Returns a list of detected poses, each containing: 555 | /// - score: Overall confidence score for the pose 556 | /// - keypoints: Map of keypoint positions and confidence scores 557 | /// Each keypoint contains x, y (normalized 0-1), part name, and confidence score 558 | static Future runPoseNetOnImage({ 559 | required String path, 560 | double imageMean = 127.5, 561 | double imageStd = 127.5, 562 | int numResults = 5, 563 | double threshold = 0.5, 564 | int nmsRadius = 20, 565 | bool asynch = true, 566 | }) async { 567 | return await _channel.invokeMethod( 568 | 'runPoseNetOnImage', 569 | { 570 | "path": path, 571 | "imageMean": imageMean, 572 | "imageStd": imageStd, 573 | "numResults": numResults, 574 | "threshold": threshold, 575 | "nmsRadius": nmsRadius, 576 | "asynch": asynch, 577 | }, 578 | ); 579 | } 580 | 581 | /// Runs PoseNet human pose estimation on binary image data 582 | /// 583 | /// [binary] - Binary image data (required) 584 | /// [numResults] - Maximum number of pose results to return (default: 5) 585 | /// [threshold] - Minimum confidence threshold for keypoints (default: 0.5) 586 | /// [nmsRadius] - Non-maximum suppression radius (default: 20) 587 | /// [asynch] - Whether to run inference asynchronously (default: true) 588 | /// 589 | /// Returns a list of detected poses, each containing: 590 | /// - score: Overall confidence score for the pose 591 | /// - keypoints: Map of keypoint positions and confidence scores 592 | /// Each keypoint contains x, y (normalized 0-1), part name, and confidence score 593 | static Future runPoseNetOnBinary({ 594 | required Uint8List binary, 595 | int numResults = 5, 596 | double threshold = 0.5, 597 | int nmsRadius = 20, 598 | bool asynch = true, 599 | }) async { 600 | return await _channel.invokeMethod( 601 | 'runPoseNetOnBinary', 602 | { 603 | "binary": binary, 604 | "numResults": numResults, 605 | "threshold": threshold, 606 | "nmsRadius": nmsRadius, 607 | "asynch": asynch, 608 | }, 609 | ); 610 | } 611 | 612 | /// Runs PoseNet human pose estimation on camera frame data 613 | /// 614 | /// [bytesList] - List of byte arrays from camera planes (required) 615 | /// [imageHeight] - Height of the image (default: 1280) 616 | /// [imageWidth] - Width of the image (default: 720) 617 | /// [imageMean] - Mean normalization value (default: 127.5) 618 | /// [imageStd] - Standard deviation normalization value (default: 127.5) 619 | /// [rotation] - Rotation of the image in degrees, Android only (default: 90) 620 | /// [numResults] - Maximum number of pose results to return (default: 5) 621 | /// [threshold] - Minimum confidence threshold for keypoints (default: 0.5) 622 | /// [nmsRadius] - Non-maximum suppression radius (default: 20) 623 | /// [asynch] - Whether to run inference asynchronously (default: true) 624 | /// 625 | /// Returns a list of detected poses, each containing: 626 | /// - score: Overall confidence score for the pose 627 | /// - keypoints: Map of keypoint positions and confidence scores 628 | /// Each keypoint contains x, y (normalized 0-1), part name, and confidence score 629 | static Future runPoseNetOnFrame({ 630 | required List bytesList, 631 | int imageHeight = 1280, 632 | int imageWidth = 720, 633 | double imageMean = 127.5, 634 | double imageStd = 127.5, 635 | int rotation = 90, // Android only 636 | int numResults = 5, 637 | double threshold = 0.5, 638 | int nmsRadius = 20, 639 | bool asynch = true, 640 | }) async { 641 | return await _channel.invokeMethod( 642 | 'runPoseNetOnFrame', 643 | { 644 | "bytesList": bytesList, 645 | "imageHeight": imageHeight, 646 | "imageWidth": imageWidth, 647 | "imageMean": imageMean, 648 | "imageStd": imageStd, 649 | "rotation": rotation, 650 | "numResults": numResults, 651 | "threshold": threshold, 652 | "nmsRadius": nmsRadius, 653 | "asynch": asynch, 654 | }, 655 | ); 656 | } 657 | } 658 | -------------------------------------------------------------------------------- /pubspec.yaml: -------------------------------------------------------------------------------- 1 | name: tensorflow_lite_flutter 2 | description: A Flutter plugin for accessing TensorFlow Lite. Supports both iOS and Android. 3 | version: 3.0.0 4 | homepage: https://github.com/draz26648/tflite_edited 5 | repository: https://github.com/draz26648/tflite_edited 6 | issue_tracker: https://github.com/draz26648/tflite_edited/issues 7 | 8 | environment: 9 | sdk: '>=3.2.0 <4.0.0' 10 | flutter: ">=3.16.0" 11 | 12 | dependencies: 13 | flutter: 14 | sdk: flutter 15 | meta: ^1.10.0 16 | 17 | dev_dependencies: 18 | flutter_test: 19 | sdk: flutter 20 | flutter_lints: ^3.0.1 21 | test: ^1.24.9 22 | 23 | # The following section is specific to Flutter. 24 | flutter: 25 | plugin: 26 | platforms: 27 | android: 28 | package: sq.flutter.tflite 29 | pluginClass: TflitePlugin 30 | ios: 31 | pluginClass: TflitePlugin 32 | -------------------------------------------------------------------------------- /test/tflite_test.dart: -------------------------------------------------------------------------------- 1 | // ignore_for_file: deprecated_member_use 2 | 3 | import 'package:flutter/services.dart'; 4 | import 'package:flutter_test/flutter_test.dart'; 5 | import 'package:tensorflow_lite_flutter/tensorflow_lite_flutter.dart'; 6 | 7 | void main() { 8 | TestWidgetsFlutterBinding.ensureInitialized(); 9 | const MethodChannel channel = MethodChannel( 10 | 'tflite', 11 | ); 12 | 13 | final List log = []; 14 | 15 | setUp(() async { 16 | channel.setMockMethodCallHandler((MethodCall methodCall) { 17 | log.add(methodCall); 18 | return null; 19 | }); 20 | log.clear(); 21 | }); 22 | test('loadModel', () async { 23 | await Tflite.loadModel( 24 | model: 'assets/mobilenet_v1_1.0_224.tflite', 25 | labels: 'assets/mobilenet_v1_1.0_224.txt', 26 | numThreads: 2, 27 | isAsset: false, 28 | useGpuDelegate: true, 29 | ); 30 | expect( 31 | log, 32 | [ 33 | isMethodCall( 34 | 'loadModel', 35 | arguments: { 36 | 'model': 'assets/mobilenet_v1_1.0_224.tflite', 37 | 'labels': 'assets/mobilenet_v1_1.0_224.txt', 38 | 'numThreads': 2, 39 | 'isAsset': false, 40 | 'useGpuDelegate': true, 41 | }, 42 | ), 43 | ], 44 | ); 45 | }); 46 | 47 | test('runModelOnImage', () async { 48 | await Tflite.runModelOnImage( 49 | path: '/image/path', 50 | imageMean: 127.5, 51 | imageStd: 0.5, 52 | numResults: 6, 53 | threshold: 0.1, 54 | asynch: false, 55 | ); 56 | expect( 57 | log, 58 | [ 59 | isMethodCall( 60 | 'runModelOnImage', 61 | arguments: { 62 | 'path': '/image/path', 63 | 'imageMean': 127.5, 64 | 'imageStd': 0.5, 65 | 'numResults': 6, 66 | 'threshold': 0.1, 67 | 'asynch': false, 68 | }, 69 | ), 70 | ], 71 | ); 72 | }); 73 | 74 | test('runModelOnBinary', () async { 75 | await Tflite.runModelOnBinary( 76 | binary: Uint8List.fromList([ 77 | 0, 78 | 1, 79 | 2, 80 | ]), 81 | numResults: 15, 82 | threshold: 0.8, 83 | asynch: false, 84 | ); 85 | expect( 86 | log, 87 | [ 88 | isMethodCall( 89 | 'runModelOnBinary', 90 | arguments: { 91 | 'binary': Uint8List.fromList([ 92 | 0, 93 | 1, 94 | 2, 95 | ]), 96 | 'numResults': 15, 97 | 'threshold': 0.8, 98 | 'asynch': false, 99 | }, 100 | ), 101 | ], 102 | ); 103 | }); 104 | 105 | test('runModelOnFrame', () async { 106 | await Tflite.runModelOnFrame( 107 | bytesList: [ 108 | Uint8List.fromList([ 109 | 0, 110 | 1, 111 | 2, 112 | ]), 113 | Uint8List.fromList([ 114 | 0, 115 | 1, 116 | 2, 117 | ]), 118 | ], 119 | imageHeight: 100, 120 | imageWidth: 200, 121 | imageMean: 127.5, 122 | imageStd: 0.5, 123 | rotation: 30, 124 | numResults: 10, 125 | threshold: 0.2, 126 | asynch: false, 127 | ); 128 | expect( 129 | log, 130 | [ 131 | isMethodCall( 132 | 'runModelOnFrame', 133 | arguments: { 134 | 'bytesList': [ 135 | Uint8List.fromList([ 136 | 0, 137 | 1, 138 | 2, 139 | ]), 140 | Uint8List.fromList([ 141 | 0, 142 | 1, 143 | 2, 144 | ]), 145 | ], 146 | 'imageHeight': 100, 147 | 'imageWidth': 200, 148 | 'imageMean': 127.5, 149 | 'imageStd': 0.5, 150 | 'rotation': 30, 151 | 'numResults': 10, 152 | 'threshold': 0.2, 153 | 'asynch': false, 154 | }, 155 | ), 156 | ], 157 | ); 158 | }); 159 | 160 | test('detectObjectOnImage', () async { 161 | await Tflite.detectObjectOnImage( 162 | path: '/image/path', 163 | model: 'YOLO', 164 | imageMean: 127.5, 165 | imageStd: 0.5, 166 | threshold: 0.1, 167 | numResultsPerClass: 5, 168 | anchors: [ 169 | 1, 170 | 2, 171 | 3, 172 | 4, 173 | ], 174 | blockSize: 32, 175 | numBoxesPerBlock: 5, 176 | asynch: false, 177 | ); 178 | expect( 179 | log, 180 | [ 181 | isMethodCall( 182 | 'detectObjectOnImage', 183 | arguments: { 184 | 'path': '/image/path', 185 | 'model': 'YOLO', 186 | 'imageMean': 127.5, 187 | 'imageStd': 0.5, 188 | 'threshold': 0.1, 189 | 'numResultsPerClass': 5, 190 | 'anchors': [ 191 | 1, 192 | 2, 193 | 3, 194 | 4, 195 | ], 196 | 'blockSize': 32, 197 | 'numBoxesPerBlock': 5, 198 | 'asynch': false, 199 | }, 200 | ), 201 | ], 202 | ); 203 | }); 204 | 205 | test('detectObjectOnBinary', () async { 206 | await Tflite.detectObjectOnBinary( 207 | binary: Uint8List.fromList([ 208 | 0, 209 | 1, 210 | 2, 211 | ]), 212 | model: "YOLO", 213 | threshold: 0.2, 214 | numResultsPerClass: 10, 215 | anchors: [ 216 | 1, 217 | 2, 218 | 3, 219 | 4, 220 | ], 221 | blockSize: 32, 222 | numBoxesPerBlock: 5, 223 | asynch: false, 224 | ); 225 | expect( 226 | log, 227 | [ 228 | isMethodCall( 229 | 'detectObjectOnBinary', 230 | arguments: { 231 | 'binary': Uint8List.fromList([ 232 | 0, 233 | 1, 234 | 2, 235 | ]), 236 | 'model': "YOLO", 237 | 'threshold': 0.2, 238 | 'numResultsPerClass': 10, 239 | 'anchors': [ 240 | 1, 241 | 2, 242 | 3, 243 | 4, 244 | ], 245 | 'blockSize': 32, 246 | 'numBoxesPerBlock': 5, 247 | 'asynch': false, 248 | }, 249 | ), 250 | ], 251 | ); 252 | }); 253 | 254 | test('detectObjectOnFrame', () async { 255 | await Tflite.detectObjectOnFrame( 256 | bytesList: [ 257 | Uint8List.fromList([ 258 | 0, 259 | 1, 260 | 2, 261 | ]), 262 | Uint8List.fromList([ 263 | 0, 264 | 1, 265 | 2, 266 | ]), 267 | ], 268 | model: "YOLO", 269 | imageHeight: 100, 270 | imageWidth: 200, 271 | imageMean: 127.5, 272 | imageStd: 0.5, 273 | rotation: 30, 274 | threshold: 0.2, 275 | numResultsPerClass: 10, 276 | anchors: [ 277 | 1, 278 | 2, 279 | 3, 280 | 4, 281 | ], 282 | blockSize: 32, 283 | numBoxesPerBlock: 5, 284 | asynch: false, 285 | ); 286 | expect( 287 | log, 288 | [ 289 | isMethodCall( 290 | 'detectObjectOnFrame', 291 | arguments: { 292 | 'bytesList': [ 293 | Uint8List.fromList([ 294 | 0, 295 | 1, 296 | 2, 297 | ]), 298 | Uint8List.fromList([ 299 | 0, 300 | 1, 301 | 2, 302 | ]), 303 | ], 304 | 'model': "YOLO", 305 | 'imageHeight': 100, 306 | 'imageWidth': 200, 307 | 'imageMean': 127.5, 308 | 'imageStd': 0.5, 309 | 'rotation': 30, 310 | 'threshold': 0.2, 311 | 'numResultsPerClass': 10, 312 | 'anchors': [ 313 | 1, 314 | 2, 315 | 3, 316 | 4, 317 | ], 318 | 'blockSize': 32, 319 | 'numBoxesPerBlock': 5, 320 | 'asynch': false, 321 | }, 322 | ), 323 | ], 324 | ); 325 | }); 326 | 327 | test('runPix2PixOnImage', () async { 328 | await Tflite.runPix2PixOnImage( 329 | path: '/image/path', 330 | imageMean: 127.5, 331 | imageStd: 0.5, 332 | outputType: 'png', 333 | asynch: false, 334 | ); 335 | expect( 336 | log, 337 | [ 338 | isMethodCall( 339 | 'runPix2PixOnImage', 340 | arguments: { 341 | 'path': '/image/path', 342 | 'imageMean': 127.5, 343 | 'imageStd': 0.5, 344 | 'outputType': 'png', 345 | 'asynch': false, 346 | }, 347 | ), 348 | ], 349 | ); 350 | }); 351 | 352 | test('runPix2PixOnBinary', () async { 353 | await Tflite.runPix2PixOnBinary( 354 | binary: Uint8List.fromList([ 355 | 0, 356 | 1, 357 | 2, 358 | ]), 359 | outputType: 'png', 360 | asynch: false, 361 | ); 362 | expect( 363 | log, 364 | [ 365 | isMethodCall( 366 | 'runPix2PixOnBinary', 367 | arguments: { 368 | 'binary': Uint8List.fromList([ 369 | 0, 370 | 1, 371 | 2, 372 | ]), 373 | 'outputType': 'png', 374 | 'asynch': false, 375 | }, 376 | ), 377 | ], 378 | ); 379 | }); 380 | 381 | test('runPix2PixOnFrame', () async { 382 | await Tflite.runPix2PixOnFrame( 383 | bytesList: [ 384 | Uint8List.fromList([ 385 | 0, 386 | 1, 387 | 2, 388 | ]), 389 | Uint8List.fromList([ 390 | 0, 391 | 1, 392 | 2, 393 | ]), 394 | ], 395 | imageHeight: 100, 396 | imageWidth: 200, 397 | imageMean: 127.5, 398 | imageStd: 0.5, 399 | rotation: 30, 400 | outputType: 'png', 401 | asynch: false, 402 | ); 403 | expect( 404 | log, 405 | [ 406 | isMethodCall( 407 | 'runPix2PixOnFrame', 408 | arguments: { 409 | 'bytesList': [ 410 | Uint8List.fromList([ 411 | 0, 412 | 1, 413 | 2, 414 | ]), 415 | Uint8List.fromList([ 416 | 0, 417 | 1, 418 | 2, 419 | ]), 420 | ], 421 | 'imageHeight': 100, 422 | 'imageWidth': 200, 423 | 'imageMean': 127.5, 424 | 'imageStd': 0.5, 425 | 'rotation': 30, 426 | 'outputType': 'png', 427 | 'asynch': false, 428 | }, 429 | ), 430 | ], 431 | ); 432 | }); 433 | 434 | test('runSegmentationOnImage', () async { 435 | await Tflite.runSegmentationOnImage( 436 | path: '/image/path', 437 | imageMean: 127.5, 438 | imageStd: 0.5, 439 | labelColors: [ 440 | 1, 441 | 2, 442 | 3, 443 | ], 444 | outputType: 'png', 445 | asynch: false, 446 | ); 447 | expect( 448 | log, 449 | [ 450 | isMethodCall( 451 | 'runSegmentationOnImage', 452 | arguments: { 453 | 'path': '/image/path', 454 | 'imageMean': 127.5, 455 | 'imageStd': 0.5, 456 | 'labelColors': [ 457 | 1, 458 | 2, 459 | 3, 460 | ], 461 | 'outputType': 'png', 462 | 'asynch': false, 463 | }, 464 | ), 465 | ], 466 | ); 467 | }); 468 | 469 | test('runSegmentationOnBinary', () async { 470 | await Tflite.runSegmentationOnBinary( 471 | binary: Uint8List.fromList([ 472 | 0, 473 | 1, 474 | 2, 475 | ]), 476 | labelColors: [ 477 | 1, 478 | 2, 479 | 3, 480 | ], 481 | outputType: 'png', 482 | asynch: false, 483 | ); 484 | expect( 485 | log, 486 | [ 487 | isMethodCall( 488 | 'runSegmentationOnBinary', 489 | arguments: { 490 | 'binary': Uint8List.fromList([ 491 | 0, 492 | 1, 493 | 2, 494 | ]), 495 | 'labelColors': [ 496 | 1, 497 | 2, 498 | 3, 499 | ], 500 | 'outputType': 'png', 501 | 'asynch': false, 502 | }, 503 | ), 504 | ], 505 | ); 506 | }); 507 | 508 | test('runSegmentationOnFrame', () async { 509 | await Tflite.runSegmentationOnFrame( 510 | bytesList: [ 511 | Uint8List.fromList([ 512 | 0, 513 | 1, 514 | 2, 515 | ]), 516 | Uint8List.fromList([ 517 | 0, 518 | 1, 519 | 2, 520 | ]), 521 | ], 522 | imageHeight: 100, 523 | imageWidth: 200, 524 | imageMean: 127.5, 525 | imageStd: 0.5, 526 | rotation: 30, 527 | labelColors: [ 528 | 1, 529 | 2, 530 | 3, 531 | ], 532 | outputType: 'png', 533 | asynch: false, 534 | ); 535 | expect( 536 | log, 537 | [ 538 | isMethodCall( 539 | 'runSegmentationOnFrame', 540 | arguments: { 541 | 'bytesList': [ 542 | Uint8List.fromList([ 543 | 0, 544 | 1, 545 | 2, 546 | ]), 547 | Uint8List.fromList([ 548 | 0, 549 | 1, 550 | 2, 551 | ]), 552 | ], 553 | 'imageHeight': 100, 554 | 'imageWidth': 200, 555 | 'imageMean': 127.5, 556 | 'imageStd': 0.5, 557 | 'rotation': 30, 558 | 'labelColors': [ 559 | 1, 560 | 2, 561 | 3, 562 | ], 563 | 'outputType': 'png', 564 | 'asynch': false, 565 | }, 566 | ), 567 | ], 568 | ); 569 | }); 570 | } 571 | --------------------------------------------------------------------------------