add nasa script

maps/nasa.py

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