File size: 2,540 Bytes
80c0c44 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import torch
import random
from typing import List, Tuple
def generate_noise_map(width: int, height: int) -> torch.Tensor:
# A simple single-layer 'generator' that creates a correlated noise map
# representing roads vs buildings.
noise = torch.rand((1, 1, height, width))
# simple smoothing using average pooling to create clumps
if height >= 3 and width >= 3:
pool = torch.nn.AvgPool2d(kernel_size=3, stride=1, padding=1)
noise = pool(noise)
return noise[0, 0]
def generate_grid(difficulty: str) -> Tuple[List[List[str]], tuple, List[tuple], List[tuple]]:
width, height = 12, 12
noise_map = generate_noise_map(width, height)
# Threshold for roads (lower values will be roads, higher building/tree)
# Target approx 40% roads
threshold = torch.quantile(noise_map, 0.4).item()
grid = [["" for _ in range(width)] for _ in range(height)]
road_coords = []
for y in range(height):
for x in range(width):
val = noise_map[y, x].item()
if val < threshold:
grid[y][x] = "🛣️"
road_coords.append((x, y))
else:
# randomly assign building or tree
if random.random() < 0.6:
grid[y][x] = "🏢"
else:
grid[y][x] = "🌳"
# Guarantee at least one road cell
if not road_coords:
grid[0][0] = "🛣️"
road_coords.append((0, 0))
# Get configuration based on difficulty
if difficulty == "easy":
num_targets = 2
num_obstacles = 2
elif difficulty == "medium":
num_targets = 4
num_obstacles = 4
else:
num_targets = 7
num_obstacles = 6
# We must restrict to available road coords minus 1 for drone
available_coords = random.sample(road_coords, min(len(road_coords), num_targets + num_obstacles + 1))
drone_start = available_coords.pop(0)
grid[drone_start[1]][drone_start[0]] = "🚁"
targets = []
for _ in range(min(num_targets, len(available_coords))):
t_coord = available_coords.pop(0)
grid[t_coord[1]][t_coord[0]] = "🎯"
targets.append(t_coord)
obstacles = []
for _ in range(min(num_obstacles, len(available_coords))):
o_coord = available_coords.pop(0)
grid[o_coord[1]][o_coord[0]] = "🚧"
obstacles.append(o_coord)
return grid, drone_start, targets, obstacles
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