
Build an offline Flutter app that uses on-device image segmentation to remove backgrounds and replace them with custom scenes, then save or share edited images without internet.
Explore image segmentation by classifying each pixel with a mask to separate the person from the background, enabling transparent images and ML-powered apps in Flutter.
Set up the macOS Flutter environment by downloading the flutter SDK, organizing it in a development folder, updating your path via zshrc or zsh env, and verifying Flutter version.
Install Android Studio to build Flutter apps for the offline AI image editing app in Flutter with segmentation, and enable Flutter projects by installing the plugin.
Install and set up Xcode on Mac to run Flutter apps on iOS and Mac devices, including App Store download, Xcode preferences, and selecting command line tools.
Create a new Flutter project in Android Studio, choose Kotlin for Android and iOS, and install the default counter app on iOS simulator and Android emulator.
Create and launch an Android emulator, selecting Pixel 7 with Android 14, download the image, and install the application inside the emulator.
Download the flutter sdk for windows, unzip it, place the flutter folder in the c drive, then add the bin path to your environment variables and run flutter doctor.
Install Android Studio, set up flutter by adding the dart and flutter plugins, and run flutter doctor to verify the setup and accept licenses.
Learn how to create an Android virtual device in Android Studio, download Android 14, configure orientation, and launch the emulator to test applications on a Pixel 7 Pro device.
Build a Flutter image picker app with a GUI, enabling gallery selection or camera capture and displaying the chosen image for use with machine learning models.
Add the image_picker library via pub.dev, update pubspec.yaml, and run pubget to enable image selection or capture; Android requires no extra setup, and configure iOS permissions in Info.plist.
Learn to implement the image picker library in flutter for choosing images from the gallery or camera, including creating and initializing the image picker object in initState.
Long-press the button to open the camera, capture images with the image picker using the camera source, and display the captured image on screen for testing on Android emulator.
Explore building a Flutter image picker app by integrating the library, configuring iOS permissions, and handling choose or capture actions to display the selected image from xFile.
Clone the starter Flutter image picker app, configure dependencies and iOS permissions, and test gallery and camera image selection in an emulator before passing images to the segmentation model.
Integrate Google ML Kit selfie segmentation in Flutter, install the pub.dev package, and configure Android min sdk 21 and sdk 35 to enable cross-platform, real-time background separation.
Load and initialize an on-device image segmentation model in Flutter, convert images to input image, and configure the selfie segmenter with single mode and raw size mask.
Learn how to pass an input image to a selfie segmentation model in Flutter, perform async inference, and obtain a segmentation mask to remove the background.
Transform segmentation results into a transparent Flutter image by iterating pixels, applying confidence scores to keep the person, and using the image library to build the output.
Apply a threshold of 0.5 to iterate each pixel, copy confident pixels into an output image, and set the rest transparent, creating a png for the Flutter user interface.
Test and validate image segmentation results by selecting photos from the gallery, confirming transparency around subjects, and saving transparent pngs to the gallery.
Set the output image to four channels to enable transparency when saving in Flutter, after background removal, by specifying the num channels parameter in the image library.
Explore the end-to-end flutter app workflow for offline ai image editing with segmentation, converting images to model input, applying the mask, and generating a transparent output.
Interpret the model's 256x256 segmentation mask to compute per-pixel confidence on the original image. Map mask coordinates to pixels and copy or make transparent based on that confidence.
Increase the image size to 450 and hot reload to view segmentation clearly, then adjust the threshold from 0.3 to 0.8 to balance transparency.
Add a save button when the segmented image is ready, and save it to the device gallery as a transparent PNG using image_gallery_saver_plus and path_provider.
Save segmented images to the gallery in Flutter using a provided save function, path provider, and image gallery saver, writing PNG bytes to a temp file.
Learn to replace an image background in flutter by making it transparent. Apply a background from assets by declaring it in pubspec using image segmentation and the img library.
Display a horizontal list view of background options from assets, including a transparent png, and apply the chosen background to the image during segmentation.
Wraps list view images in a gesture detector, updates the selected background on tap, runs image segmentation, and applies the chosen background to the image.
Demonstrates enhancing background selection in a Flutter list by highlighting the selected item with a deep purple border and rounded corners, using container, box decoration, and clip radius.
Optimize the Flutter UI by refining the app bar, centering the title, and enhancing the image display with a circular bordered container, shadows, and background color.
Display a placeholder image in flutter by replacing the gray box with an image icon, sized 200 and gray, then adjust height and add a bottom margin of 30.
Design and style the gallery and camera buttons in Flutter, using a bottom app bar with inkwell taps to pick images from gallery or capture with camera.
Designs a save and share button in Flutter UI, styling an elevated icon button with a download icon, white text, deep purple background, and rounded corners.
Enable users to share edited images in flutter using the share plus package. The lecture covers adding the dependency, saving a temp file, and sharing via apps like WhatsApp.
Demonstrates how to display a checkerboard transparency pattern in a Flutter list view by conditionally swapping between a transparent png and t2.png to indicate image transparency.
Learn to display a transparent background behind segmented images in Flutter by using a stack, showing a transparent asset conditionally, and managing button states based on mask data.
Create a left-to-right animated transparent background using an animation controller, a tween from 0 to 1, and a one-second duration, applied with an animated builder and align widthFactor.
Have you ever used a background remover app and thought — I want to build something like this?
This course shows you exactly how.
You'll build a complete, production-ready AI photo editing app in Flutter — with one-tap background removal, smart editing tools, and a Canva-style layered UI. Everything runs directly on the device. No internet. No cloud. No API bills.
Under the hood, you'll use TensorFlow Lite image segmentation — one of the most impressive on-device AI techniques available for mobile developers today.
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What You Will Build
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A fully featured AI photo editing app including:
✦ One-Tap AI Background Remover
Remove image backgrounds instantly using on-device AI — no upload, no waiting, no API key required.
✦ Smart Background Replacement
Swap removed backgrounds with solid colors, gradients, custom photos, and creative presets.
✦ Canva-Style Layered Editing
Pan, zoom, rotate, and layer elements with smooth gesture controls and a polished modern UI.
✦ Camera & Gallery Integration
Import photos from your gallery or capture new ones directly from the camera, then export high-quality results.
✦ 100% Offline AI Processing
All AI inference runs on the user's device. Works in airplane mode. No internet, no third-party dependency.
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What You Will Learn
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• How to integrate TFLite image segmentation models into Flutter apps
• How to process, mask, and manipulate images at the pixel level in Dart
• Building advanced Flutter UIs — gestures, transformations, layered editors
• Camera and gallery handling for real-world Flutter apps
• Clean, scalable Flutter architecture for AI-powered apps
• How to optimize on-device AI models for smooth mobile performance
• Preparing your app for App Store and Google Play submission
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Who This Course Is For
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• Flutter developers who want to add impressive AI features to their apps
• Developers who want a strong, real-world portfolio project
• Anyone building photo, design, or creative tools in Flutter
• Developers who want to learn on-device AI without cloud complexity
• Beginners to AI — no machine learning experience required, just basic Flutter & Dart
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Why This Course Is Different
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Most Flutter AI courses plug into cloud APIs. When those APIs change pricing, your app breaks overnight.
This course teaches you to build AI that lives inside the app — on the device, in the user's hands. It's faster, it's private, and it never sends a request to a server.
You'll build the same core technology used inside apps like Canva, PicsArt, Pixelcut, and Background Eraser — without depending on any external service.
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What's Included
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• Full source code for every feature built in the course
• High-quality design assets used in the app
• App Store & Google Play deployment guide
• TFLite model optimization tips for performance
• Lifetime access with updates as AI technology evolves
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By the end of this course, you'll have a real, polished AI photo editing app — built entirely by you, running entirely on-device, and ready to publish.
Enroll now and start building.