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https://github.com/unanmed/ginka-generator.git
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67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
import cv2
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import numpy as np
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import torch
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def blend_alpha(bg, fg, alpha):
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""" 使用 alpha 通道混合前景图块和背景图 """
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for c in range(3): # 只混合 RGB 三个通道
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bg[:, :, c] = (1 - alpha) * bg[:, :, c] + alpha * fg[:, :, c]
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return bg
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def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
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"""
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使用OpenCV加速的版本（适合大尺寸地图）
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:param map_matrix: [H, W] 的numpy数组
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:param tile_set: 字典 {tile_id: cv2图像（BGR格式）}
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:param tile_size: 图块边长（像素）
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"""
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H, W = map_matrix.shape # 获取地图尺寸
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canvas = np.zeros((H * tile_size, W * tile_size, 3), dtype=np.uint8) # 画布（黑色背景）
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# 遍历地图矩阵
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for row in range(H):
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for col in range(W):
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tile_index = str(map_matrix[row, col]) # 获取当前坐标的图块类型
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x, y = col * tile_size, row * tile_size # 计算像素位置
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# 先绘制地面（0）
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if '0' in tile_set:
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canvas[y:y+tile_size, x:x+tile_size] = tile_set['0'][:, :, :3] # 仅填充 RGB
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# 叠加其他透明图块
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if tile_index in tile_set and tile_index != 0:
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tile_rgba = tile_set[tile_index]
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tile_rgb = tile_rgba[:, :, :3] # 提取 RGB
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alpha = tile_rgba[:, :, 3] / 255.0 # 归一化 alpha
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# 混合当前图块到背景
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canvas[y:y+tile_size, x:x+tile_size] = blend_alpha(
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canvas[y:y+tile_size, x:x+tile_size], tile_rgb, alpha
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)
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return canvas
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def annotate(img: np.ndarray, text: str, y: int = 14) -> np.ndarray:
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# 在图片左上角叠加文字标注（黑色描边 + 白色填充，确保任意背景下可读）
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img = img.copy()
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cv2.putText(img, text, (2, y), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 0), 2)
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cv2.putText(img, text, (2, y), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
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return img
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def annotate_labels(
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img: np.ndarray,
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struct: torch.Tensor,
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target_density: torch.Tensor
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) -> np.ndarray:
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# 三行标注：第一行结构标签，后两行显示五维目标密度
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s = struct.tolist()
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d = target_density.tolist()
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line1 = f"sym:{s[0]} outer:{s[1]}"
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line2 = f"wall:{d[0]:.2f} door:{d[1]:.2f}"
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line3 = f"enemy:{d[2]:.2f} ent:{d[3]:.2f} res:{d[4]:.2f}"
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img = img.copy()
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for text, y in [(line1, 12), (line2, 24), (line3, 36)]:
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cv2.putText(img, text, (2, y), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 0), 2)
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cv2.putText(img, text, (2, y), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)
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return img
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