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Camera Preview:
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Tflite the package provides us CameraPreview a widget to preview the camera on the app screen, it takes cameraController . To display the boxes and camera preview together we need Stack . displayBoxesAroundRecognizedObjects return a list of boxes, we need to add this list into the Stack so we have created a list variable. Add the boxes in the list .
Model
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Load Model: Tflite provides us loadModel method to load our model. It takes two values model file path and labels file path. Future loadModel() async { Tflite. close (); await Tflite. loadModel ( model: "assets/ssd_mobilenet.tflite", labels: "assets/ssd_mobilenet.txt"); } Run Model: In this method, we will run the model using Tflite . Here we are using the live stream of the image so we will have to use the detectObjectOnFrame method to run our model. runModel() async { recognitionsList = await Tflite. detectObjectOnFrame ( bytesList: cameraImage.planes.map((plane) { return plane.bytes; }).toList(), imageHeight: cameraImage.height, imageWidth: cameraImage.width, imageMean: 127.5, imageStd: 127.5, numResultsPerClass: 1, threshold: 0.4, ); setState(() { cameraImage; }); }
Initialize camera
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Initializing Camera: Inside the main method initialize the available cameras using availableCameras . List<CameraDescription> cameras; Future<void> main() async { WidgetsFlutterBinding. ensureInitialized (); cameras = await availableCameras (); runApp(MyApp()); } camera the package provides us support for live image streaming. Firstly create an object of the CameraController . CameraController takes two arguments CameraDescription and ResolutionPreset . initialize the cameraController and then we can start our image streaming using the startImageStream method. startImageStream the method provides us the images, we will give these images to cameraImage , and then we will run our model. CameraImage cameraImage; CameraController cameraController; initCamera() { cameraController = CameraController(cameras[0], ResolutionPreset.medium); cameraControl...
Android Configuration
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Android Configuration: Change the minimum Android SDK version to 21 (or higher) in your android/app/build.gradle file. minSdkVersion 21 In android/app/build.gradle , add the following setting in android block. aaptOptions { noCompress 'tflite' noCompress 'lite' } Add model and label files in the assets folder, also add them in pubspec.yaml
Object Detection App With Flutter and TensorFlow Lite
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Let’s learn how to build a flutter app that detects objects on a live camera. In this blog, we shall learn how to build an app that can detect Objects, and using AI and Deep Learning it can determine what the object is. Tflite provides us access to TensorFlow Lite . TensorFlow Lite is an open-source deep learning framework for on-device inference. To integrate tflite into our flutter app, we need to install tflite package and we need two files model.tflite and labels.txt . model.tflite is the trained model and labels.txt the file is a text file containing all the labels. Many websites provide us facility to train our model with our dataset and deploy them on TensorFlow Lite and we can directly get these two files from there. You can read my blog on Object Detection App with Flutter and TensorFlow Lite to trains your model with your own dataset.