In the evolving world of mobile applications, the ability to harness camera functionalities can significantly enhance user experiences. With Flutter and the Flutter Camera ML Vision package, developers can seamlessly integrate camera features alongside Machine Learning capabilities. This guide walks you through the installation, configuration, and usage of the package.

Installation

To start using the Flutter Camera ML Vision package, add it as a dependency in your pubspec.yaml file:

dependencies:
  flutter:
    sdk: flutter
  flutter_camera_ml_vision: ^2.2.4

Configuring Firebase

Firebase configuration is essential for both Android and iOS platforms. Below are the detailed steps for each:

iOS Configuration

• Add the following keys to your ios/Runner/Info.plist file:
  • NSCameraUsageDescription: “Can I use the camera please?”
  • NSMicrophoneUsageDescription: “Can I use the mic please?”
• If you’re using on-device APIs, include the necessary ML Kit library models in your Podfile:

pod 'FirebaseMLVision/BarcodeModel'
pod 'FirebaseMLVision/FaceModel'
pod 'FirebaseMLVision/LabelModel'
pod 'FirebaseMLVision/TextModel'

Android Configuration

• Modify the minimum SDK version in your android/app/build.gradle file:

minSdkVersion 21

• If using on-device LabelDetector, include the latest ML Kit dependency:

dependencies {
    ...
    implementation 'com.google.firebase:firebase-ml-vision-image-label-model:19.0.0'
}

• To allow automatic downloading of the ML model from the Play Store, add the following in your AndroidManifest.xml:

Usage

To display the camera preview and detect barcodes, use the following example in your Dart file:

CameraMlVisionListBarcode(
  detector: FirebaseVision.instance.barcodeDetector().detectInImage,
  onResult: (List barcodes) {
    if (!mounted || resultSent) return;
    resultSent = true;
    Navigator.of(context).pop(barcodes.first);
  },
),

In this example, the CameraMlVision widget initiates a preview of the camera. The detector parameter uses the detectInImage method to process detected barcodes, calling the onResult callback with detected data.

Understanding the Code through Analogy

Imagine you’re setting up a smart home system that detects visitors at the door. The camera acts like your doorbell camera, capturing images constantly. The ML Vision module is like your smart assistant that recognizes whether it’s a friend, a delivery person, or a stranger based on face recognition. Similarly, this Flutter Camera ML Vision package captures images, detects barcodes (or other input), and takes action based on the results, just like a smart home assistant would do.

Exposed Functionality from CameraController

The CameraController class provides functionalities like:

• value – returns the current state of the camera.
• prepareForVideoRecording – prepares the camera for video capturing.
• startVideoRecording – starts the recording process.
• stopVideoRecording – stops the recording process.
• takePicture – captures a picture from the camera.

Getting Started

To see everything in action, check out the example directory for a complete sample app that showcases how to implement these features effectively.

Troubleshooting

If you encounter any issues, such as compilation errors or configuration hiccups, consider the following:

• Ensure all dependencies are correctly listed in your build.gradle files.
• Check your Info.plist settings for correct permissions.
• Review Firebase configurations for both platforms carefully.
• If you receive compilation errors, try using an earlier version of the ML Kit dependency.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Explore the possibilities offered by the Flutter Camera ML Vision package and elevate your mobile applications to new heights!