55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import cv2
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import numpy as np
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def extract_building_coordinates(image_path):
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# 加载图片
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img = cv2.imread(image_path)
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if img is None:
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print("无法加载图片")
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return
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# 转换到灰度空间
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 针对 Google Maps 风格的建筑颜色进行阈值处理
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# 建筑通常是特定的浅灰色 (约 230-245 之间)
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# 我们通过 inRange 提取这个颜色区间
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mask = cv2.inRange(gray, 220, 245)
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# 进行形态学操作以去除细小噪声
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kernel = np.ones((3, 3), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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# 查找轮廓
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# RETR_EXTERNAL 只查找最外层轮廓
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# CHAIN_APPROX_TC89_KCOS 使用精度较高的近似算法
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
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buildings_data = []
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for cnt in contours:
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# 过滤掉面积过小的噪点
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if cv2.contourArea(cnt) < 50:
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continue
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# 多边形拟合，epsilon 越小，顶点越密集，形状越精确
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epsilon = 0.002 * cv2.arcLength(cnt, True)
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approx = cv2.approxPolyDP(cnt, epsilon, True)
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points = []
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for point in approx:
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x, y = point[0]
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points.append((float(x), float(y)))
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buildings_data.append(points)
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# 输出结果
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for i, building in enumerate(buildings_data):
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print(f"# Building {i+1}")
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for x, y in building:
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print(f"{x}, {y}")
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print() # 每个建筑间空一行
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if __name__ == "__main__":
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# 将 'nasa.jpg' 替换为你的文件名
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extract_building_coordinates('nasa.png') |