Image Stitcher API by Benscode (Ibnu Fadhil)

Introduction

Image Stitcher API is the solution for the Stitch-a-ton Contest by Mas Adin. The implementation emphasizes correctness, performance optimization, and code clarity.

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1. System Setup and Deployment

Prerequisites

• .NET 8 SDK or newer.

Development Environment Setup

1. Clone the Repository

   git clone https://null.formulatrix.dev/benscode/benscode-StitcherApi.git
   cd benscode-StitcherApi
   

2. Configure Image Assets

   • Create an assets folder in the project root.
   • Place your image tiles in this folder.
   • Optionally set the environment variable ASSET_PATH_RO to override the asset path.

3. Run the Application

   dotnet run
   

   The API will be hosted locally at:

   http://localhost:5229
   

Production Environment Deployment

1. Publish the Application

   dotnet publish -c Release -r linux-arm64 --self-contained true
   

2. Transfer the publish directory to the server.

3. Configure Asset Path

   export ASSET_PATH_RO="/path/to/your/image-tiles"
   

4. Execute the Application

   chmod +x ./StitcherApi
   ./StitcherApi
   

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2. API Specification

Endpoint:

POST /api/image/generate

Request Body (application/json):

{
  "canvas_rect": "A1:H12",
  "crop_offset": [0.25, 0.25],
  "crop_size": [0.5, 0.5],
  "output_scale": 1.0
}

Parameters

• canvas_rect: Defines the bounding box of tiles forming the canvas.
• crop_offset: [x, y] normalized offset from top-left.
• crop_size: [width, height] normalized size relative to the canvas.
• output_scale: Scale factor for the final cropped image.

Responses

• 200 OK – Returns the generated PNG image.
• 400 Bad Request – Malformed request or invalid parameters.
• 404 Not found - Image not found
• 500 Internal Server Error – Unexpected processing error.

Response Example using Postman

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3. System Architecture and Logic

The system uses a layered architecture:

• Program.cs – Application entry point.
• ImageController – Handles HTTP traffic.
• ImageService – Orchestrates business logic.
• Utility classes – Image parsing and processing.

Core Processing Logic

1. Dimension Calculation: Compute canvas size without allocating memory.
2. Absolute Crop Determination: Convert normalized crop to pixel coordinates.
3. Minimal Tile Selection: Load only tiles intersecting with the crop.
4. Integrated Stitch and Crop: Copy relevant pixels directly to final canvas.
5. Final Scaling and Encoding: Scale and encode to PNG for the response.

Optimization:

• Avoids creating large intermediate images.
• Processes one tile at a time for low memory usage.

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4. Performance Analysis

• Time Complexity: O(W_crop * H_crop) Dependent on cropped image size, not full canvas size.

• Space Complexity: O(W_crop * H_crop + W_tile * H_tile)

  • Memory for final image + one 720x720 tile at a time.
  • Predictable and low memory footprint.

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