#APK link: https://drive.google.com/file/d/1Zm0X-yP8oDmcPf9aibUZxIxlfe0nlkZf/view?usp=sharing

🧠 Real-Time Edge Detection Viewer

Android + OpenCV (C++) + OpenGL ES + TypeScript Web Viewer

A real-time edge detection system that captures live camera frames, processes them using OpenCV in C++ via JNI, and renders the processed frames using OpenGL ES 2.0. Additionally, a lightweight TypeScript web viewer displays a sample processed frame, showcasing integration between native image processing and web visualization.

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🚀 Features Implemented

📱 Android Application

• Live Camera Feed using TextureView (Camera2 API).
• JNI-based Frame Processing — each frame is sent to native C++ code for processing.
• Canny Edge Detection & Grayscale Filters implemented with OpenCV C++.
• OpenGL ES Renderer — displays the processed frames as textures in real time.
• Achieved smooth 10–20 FPS performance during processing and rendering.
• Optional toggle between raw feed and edge-detected view.
• FPS Counter overlay for performance monitoring.

🌐 TypeScript Web Viewer

• Minimal TypeScript + HTML web app that displays a sample processed frame (static/base64).
• Shows basic frame metadata — FPS, resolution, processing time.
• Demonstrates ability to connect native output to a web visualization layer.

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🧩 Project Structure

📦 RealTime-EdgeDetection ├── app/ # Java/Kotlin Android code (UI + Camera) │ ├── MainActivity.kt │ ├── CameraHandler.kt │ └── JNIInterface.kt ├── jni/ # Native C++ OpenCV processing the project in Android Studio (Hedgehog or newer):

Launch Android Studio.

Select File → Open and choose the project directory.

Wait for Gradle to sync.

Install NDK and CMake via SDK Manager:

Go to Tools → SDK Manager → SDK Tools tab.

Check and install:

✅ NDK (Side by side) ✅ CMake ✅ LLDB (optional for debugging native code)

Click Apply and wait for installation to complete.

Download and Link OpenCV SDK:

Go to the official OpenCV releases page: 👉 https://opencv.org/releases/

Download the latest OpenCV Android SDK (ZIP file).

Extract it anywhere (for example: C:/OpenCV-android-sdk/).

In Android Studio, update your CMakeLists.txt to include OpenCV paths:

Add OpenCV include directories

    set(OpenCV_DIR "C:/OpenCV-android-sdk/sdk/native/jni")
    find_package(OpenCV REQUIRED)
    include_directories(${OpenCV_INCLUDE_DIRS})
    target_link_libraries(native-lib ${OpenCV_LIBS} log)

Also make sure build.gradle includes:

externalNativeBuild { cmake { path "src/main/cpp/CMakeLists.txt" } }

Run the Project:

Connect your Android device or launch an emulator.

Click ▶️ Run in Android Studio.

You should see a real-time camera feed with edges highlighted.

💻 Web Viewer Setup

Navigate to the web folder:

    cd web
    npm install typescript
    tsc

Open index.html in your browser to view the sample processed frame.

The viewer displays:

Static base64 image (representing processed frame)

Frame metadata, such as FPS and resolution

🧠 Architecture Overview

Camera Feed (Java/Kotlin) → Captured frames passed to native via JNI.

Native Processing (C++) → OpenCV applies Canny Edge Detection or Grayscale filter.

Rendering (OpenGL ES) → Processed frames displayed as textures in real-time.

Web Layer (TypeScript) → Displays a sample processed frame, simulating result transmission to web.

Data Flow:

Camera Frame → JNI Bridge → OpenCV (C++) → Processed Output → OpenGL Texture → Display

🧩 Technologies Used

Android SDK (Kotlin/Java) NDK + JNI (C++) OpenCV (C++ for image processing) OpenGL ES 2.0 (for rendering)

TypeScript (for web visualization)│ ├── CMakeLists.txt │ ├── edge_processor.cpp │ └── native-lib.cpp ├── gl/ # OpenGL renderer classes │ ├── GLRenderer.cpp │ ├── GLRenderer.h │ └── shaders/ │ └── basic_fragment.glsl ├── web/ # TypeScript-based web viewer │ ├── index.html │ ├── main.ts │ └── tsconfig.json └── README.md

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⚙️ Setup Instructions

🧱 Android Build Setup

1. Clone the repository:

   git clone https://github.com/RealCifer/edgeviewer.git
   cd edgeviewer
   

📸 Working Model Screenshots

Raw Image	Processed Image

|Web Site SS|