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https://github.com/unanmed/ginka-generator.git
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76 lines
3.1 KiB
Python
76 lines
3.1 KiB
Python
from typing import List, Dict
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import math
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import numpy as np
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# 视觉相似度，由 ChatGPT-4o 从 ts 转译而来
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class VisualSimilarityConfig:
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def __init__(self):
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self.type_weights: Dict[int, float] = {
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0: 0.2, 1: 0.3, 2: 0.6, 3: 0.7, 4: 0.7, 5: 0.5,
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6: 0.4, 7: 0.5, 8: 0.6, 9: 0.6, 10: 0.4, 11: 0.4, 12: 0.7
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}
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self.enable_visual_focus: bool = True
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self.enable_density_awareness: bool = True
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def generate_focus_weights(rows: int, cols: int) -> List[List[float]]:
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weights = []
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center_x = cols / 2
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center_y = rows / 2
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for i in range(rows):
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row_weights = []
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for j in range(cols):
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dx = (j - center_x) / cols
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dy = (i - center_y) / rows
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distance = math.sqrt(dx ** 2 + dy ** 2)
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gaussian = math.exp(-(distance ** 2) / (2 * 0.3 ** 2))
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row_weights.append(1.0 + 0.6 * gaussian)
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weights.append(row_weights)
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return weights
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def calculate_density_impact(map1: List[List[int]], map2: List[List[int]], type_weights: Dict[int, float]) -> List[List[float]]:
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rows, cols = len(map1), len(map1[0])
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density_map = [[0.0 for _ in range(cols)] for _ in range(rows)]
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window_size = 3
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half_window = window_size // 2
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for i in range(rows):
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for j in range(cols):
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density = 0
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for di in range(-half_window, half_window + 1):
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for dj in range(-half_window, half_window + 1):
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ni, nj = i + di, j + dj
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if 0 <= ni < rows and 0 <= nj < cols:
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weight1 = type_weights.get(map1[ni][nj], 0.5)
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weight2 = type_weights.get(map2[ni][nj], 0.5)
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density += (weight1 + weight2) / 2
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density_map[i][j] = 1.0 + 0.4 * (density / (window_size ** 2))
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return density_map
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def calculate_visual_similarity(map1: List[List[int]], map2: List[List[int]], config: VisualSimilarityConfig = None) -> float:
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if config is None:
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config = VisualSimilarityConfig()
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if len(map1) != len(map2) or len(map1[0]) != len(map2[0]):
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return 0.0
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rows, cols = len(map1), len(map1[0])
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total_score = 0.0
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max_possible_score = 0.0
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focus_weights = generate_focus_weights(rows, cols) if config.enable_visual_focus else [[1.0 for _ in range(cols)] for _ in range(rows)]
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density_map = calculate_density_impact(map1, map2, config.type_weights) if config.enable_density_awareness else [[1.0 for _ in range(cols)] for _ in range(rows)]
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for i in range(rows):
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for j in range(cols):
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type1 = map1[i][j]
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type2 = map2[i][j]
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base_weight = max(config.type_weights.get(type1, 0.5), config.type_weights.get(type2, 0.5))
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spatial_weight = focus_weights[i][j] * density_map[i][j]
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type_score = 1.0 if type1 == type2 else 0.0
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total_score += type_score * base_weight * spatial_weight
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max_possible_score += base_weight * spatial_weight
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return total_score / max_possible_score if max_possible_score > 0 else 0.0
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