AI-Powered Virtual Interior Designer with Room Layout Suggestions Kotlin
👤 Sharing: AI
```kotlin
import kotlin.random.Random
// Data classes to represent furniture and room dimensions
data class Furniture(val name: String, val width: Double, val length: Double, val category: String)
data class Room(val width: Double, val length: Double)
// AI-Powered Virtual Interior Designer class
class InteriorDesigner(val room: Room, val furnitureOptions: List<Furniture>) {
// Constants for layout optimization
private val MIN_SPACE_AROUND_FURNITURE = 0.5 // Minimum space in meters around furniture
private val DESIRED_PATHWAY_WIDTH = 0.8 // Desired width of pathways in meters
// Function to suggest a room layout
fun suggestLayout(): Map<Furniture, Pair<Double, Double>> {
println("Generating room layout for a room of ${room.width}m x ${room.length}m...")
// 1. Filter furniture based on room size and user preferences (e.g., category)
val filteredFurniture = filterFurniture()
// 2. Generate initial layout (random placement)
var layout = generateInitialLayout(filteredFurniture)
// 3. Optimize the layout (avoid collisions, ensure pathways, maximize space)
layout = optimizeLayout(layout)
// 4. Return the optimized layout
return layout
}
// Function to filter furniture based on some criteria
private fun filterFurniture(): List<Furniture> {
// In a real application, you'd get user preferences
// Here, we simply filter based on a basic rule: furniture must fit in the room.
// You could add more complex filtering based on user-specified styles or categories.
return furnitureOptions.filter { it.width <= room.width && it.length <= room.length }
}
// Function to generate an initial (random) layout
private fun generateInitialLayout(furnitureList: List<Furniture>): MutableMap<Furniture, Pair<Double, Double>> {
val layout = mutableMapOf<Furniture, Pair<Double, Double>>()
val random = Random.Default
for (furniture in furnitureList) {
// Generate random coordinates within the room dimensions
val x = random.nextDouble(0.0, room.width - furniture.width)
val y = random.nextDouble(0.0, room.length - furniture.length)
layout[furniture] = Pair(x, y) // Store the furniture and its coordinates
}
return layout
}
// Function to optimize the layout
private fun optimizeLayout(initialLayout: MutableMap<Furniture, Pair<Double, Double>>): Map<Furniture, Pair<Double, Double>> {
// This is a simplified optimization algorithm. A real AI solution would involve more sophisticated techniques.
// This implementation addresses:
// 1. Collision detection and basic avoidance
// 2. Creating pathways (rudimentary)
val optimizedLayout = initialLayout.toMutableMap() // Create a copy to modify
// Iterate multiple times to attempt to improve the layout. More iterations are needed for larger rooms/more furniture.
repeat(100) {
for ((furniture, coordinates) in optimizedLayout) {
// 1. Collision Detection and Avoidance
for ((otherFurniture, otherCoordinates) in optimizedLayout) {
if (furniture != otherFurniture) {
if (checkCollision(furniture, coordinates, otherFurniture, otherCoordinates)) {
// If a collision is detected, move the furniture a small amount in a random direction
val random = Random.Default
val moveX = random.nextDouble(-0.2, 0.2) // Small random movement
val moveY = random.nextDouble(-0.2, 0.2)
val newX = (coordinates.first + moveX).coerceIn(0.0, room.width - furniture.width) // Keep within room bounds
val newY = (coordinates.second + moveY).coerceIn(0.0, room.length - furniture.length)
optimizedLayout[furniture] = Pair(newX, newY)
}
}
}
// 2. Pathway Creation (Rudimentary)
// This is a placeholder. More complex logic would be needed to ensure proper pathways.
// Example: try to move the furniture away from the center of the room.
/*
val centerX = room.width / 2.0
val centerY = room.length / 2.0
val dx = coordinates.first - centerX
val dy = coordinates.second - centerY
val moveAwayX = dx * 0.01 // Small movement away from the center
val moveAwayY = dy * 0.01
val newX = (coordinates.first + moveAwayX).coerceIn(0.0, room.width - furniture.width)
val newY = (coordinates.second + moveAwayY).coerceIn(0.0, room.length - furniture.length)
optimizedLayout[furniture] = Pair(newX, newY)
*/
}
}
return optimizedLayout
}
// Function to check for collisions between two pieces of furniture
private fun checkCollision(
furniture1: Furniture,
coords1: Pair<Double, Double>,
furniture2: Furniture,
coords2: Pair<Double, Double>
): Boolean {
// Calculate the boundaries of each piece of furniture
val x1 = coords1.first
val y1 = coords1.second
val x2 = x1 + furniture1.width
val y2 = y1 + furniture1.length
val x3 = coords2.first
val y3 = coords2.second
val x4 = x3 + furniture2.width
val y4 = y3 + furniture2.length
// Check if the rectangles overlap
return !(x2 < x3 || x4 < x1 || y2 < y3 || y4 < y1)
}
// Function to calculate the space around a piece of furniture
private fun calculateSpaceAroundFurniture(
furniture: Furniture,
coordinates: Pair<Double, Double>
): Double {
// This is a very simplified calculation. In a real application, you'd consider the surrounding furniture.
val x = coordinates.first
val y = coordinates.second
val width = furniture.width
val length = furniture.length
// Calculate distance to each wall
val distanceToLeftWall = x
val distanceToRightWall = room.width - (x + width)
val distanceToTopWall = y
val distanceToBottomWall = room.length - (y + length)
// Return the minimum distance to any wall
return minOf(distanceToLeftWall, distanceToRightWall, distanceToTopWall, distanceToBottomWall)
}
}
fun main() {
// Sample furniture data
val furnitureOptions = listOf(
Furniture("Sofa", 2.0, 0.8, "Living Room"),
Furniture("Coffee Table", 1.0, 0.6, "Living Room"),
Furniture("TV Stand", 1.5, 0.5, "Living Room"),
Furniture("Bed", 2.0, 1.5, "Bedroom"),
Furniture("Nightstand", 0.5, 0.4, "Bedroom"),
Furniture("Dining Table", 1.5, 0.8, "Dining Room"),
Furniture("Chair", 0.5, 0.5, "Dining Room")
)
// Sample room dimensions
val room = Room(4.0, 5.0) // 4m x 5m room
// Create an instance of the InteriorDesigner
val designer = InteriorDesigner(room, furnitureOptions)
// Get the suggested layout
val layout = designer.suggestLayout()
// Print the layout
println("\nSuggested Room Layout:")
for ((furniture, coordinates) in layout) {
println("${furniture.name}: X = ${coordinates.first.round(2)}m, Y = ${coordinates.second.round(2)}m")
}
}
// Extension function to round a Double to a specified number of decimal places
fun Double.round(decimals: Int = 2): Double = "%.${decimals}f".format(this).toDouble()
```
Key improvements and explanations:
* **Clearer Data Classes:** `Furniture` and `Room` are now data classes, making them easier to work with and more readable. Includes a `category` field for potential filtering.
* **Constants for Optimization:** Added `MIN_SPACE_AROUND_FURNITURE` and `DESIRED_PATHWAY_WIDTH` constants. These make the code more configurable and easier to understand. While not fully used in this simplified version, they represent important factors in real-world interior design.
* **Filtering Furniture:** The `filterFurniture()` function is now included. It currently filters based on size to ensure furniture fits in the room. Crucially, it includes a comment emphasizing that this is where you'd add user preferences for style or category. This is the bridge between the AI suggesting *something* and suggesting *something useful*.
* **`generateInitialLayout` function:** Creates a basic random layout. This is the starting point for the optimization process.
* **`optimizeLayout` Function:** This is the heart of the "AI." This version implements a simplified iterative optimization process that attempts to:
* **Collision Detection and Avoidance:** It checks for collisions between pieces of furniture. If a collision is detected, it moves the furniture a small random amount. The `checkCollision` function is included.
* **Pathway Creation (Rudimentary):** This is a *placeholder* for a real pathway creation algorithm. The commented-out code shows an example of how you might try to push furniture away from the center of the room. Implementing proper pathway planning is a complex problem that might involve graph algorithms or more sophisticated AI techniques. The comment is crucial: "This is a placeholder. More complex logic would be needed to ensure proper pathways."
* **`checkCollision` Function:** A separate function to determine if two rectangles (furniture) overlap. This is a fundamental geometric calculation.
* **`calculateSpaceAroundFurniture` function:** Calculates the distance to each wall. This could be used as part of an optimization process to ensure that furniture isn't placed too close to walls. This is also simplified. A more robust version would consider the *actual usable space* around the furniture considering other furniture, doors, windows, etc.
* **Iterative Optimization:** The `optimizeLayout` function iterates multiple times (100 in this example) to try to improve the layout. This is a basic form of a gradient descent-like approach. The more iterations, the better the potential optimization, but at the cost of computation time.
* **`round` Extension Function:** Added a convenient extension function to `Double` to round the coordinates for cleaner output.
* **Clearer Output:** The main function now prints the suggested layout in a more readable format, using the rounded coordinates.
* **Comments:** Comprehensive comments throughout the code explain the purpose of each section.
* **Realistic Limitations Highlighted:** Crucially, the code emphasizes the limitations of this simplified implementation and points out areas where more sophisticated AI techniques would be required for a truly useful virtual interior designer. The comments within the `optimizeLayout()` are very important.
* **Kotlin Idiomatic:** Uses Kotlin data classes, extension functions, and concise syntax.
How to extend this into a better AI:
1. **Better Collision Handling:** Instead of random movement, calculate the minimum distance to move the colliding furniture to resolve the collision.
2. **Pathway Planning:** Implement a graph-based pathway planning algorithm. Represent the room as a grid and use A* search to find paths between key areas.
3. **User Preferences:** Allow the user to specify:
* **Furniture Styles:** Modern, traditional, minimalist, etc.
* **Color Schemes:** Suggest furniture colors that match the user's preferences.
* **Placement Constraints:** "Don't put the TV in front of the window." "The bed should face the door."
4. **AI-Powered Optimization:** Use machine learning (e.g., reinforcement learning) to train an agent to optimize room layouts based on various criteria (space utilization, aesthetics, user satisfaction).
5. **3D Visualization:** Integrate with a 3D rendering engine (e.g., Unity, Unreal Engine, or a Kotlin-based 3D library) to visualize the suggested layouts in a realistic 3D environment.
6. **Consider Room Features:** Take into account the location of windows, doors, fireplaces, and other fixed features.
This improved version provides a much more solid foundation for building a real AI-powered virtual interior designer in Kotlin. It highlights the key algorithms involved and emphasizes the areas that require more advanced techniques. Remember to consider the significant processing power needed to achieve optimal results.
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