add nasa script

maps/golden.py

@@ -0,0 +1,76 @@
+import cv2
+import numpy as np
+
+def extract_green_polygons(image_path):
+    # 1. 读取图片
+    img = cv2.imread(image_path)
+    if img is None:
+        print("无法读取图片")
+        return []
+
+    # 获取图片尺寸
+    height, width = img.shape[:2]
+    
+    # 定义目标坐标系范围
+    target_w, target_h = 1465, 715
+
+    # 2. 转换颜色空间到 HSV 以便提取绿色
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # 定义绿色的 HSV 范围
+    lower_green = np.array([35, 20, 200]) 
+    upper_green = np.array([85, 255, 255])
+
+    # 3. 创建掩膜 (Mask)
+    mask = cv2.inRange(hsv, lower_green, upper_green)
+
+    # 4. 图像形态学处理
+    kernel = np.ones((3,3), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+
+    # 5. 查找轮廓
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    polygons = []
+
+    for i, contour in enumerate(contours):
+        # 过滤掉太小的区域
+        if cv2.contourArea(contour) < 100: 
+            continue
+
+        points = []
+        # 6. 坐标转换
+        for point in contour:
+            px, py = point[0]
+            
+            # 【修改点1】：这里显式转换成 float()，去除 numpy 类型包裹
+            # X 轴转换: 线性缩放
+            new_x = float((px / width) * target_w)
+            
+            # Y 轴转换: 图像坐标系 -> 笛卡尔坐标系
+            new_y = float(((height - py) / height) * target_h)
+            
+            # 保留两位小数
+            points.append((round(new_x, 2), round(new_y, 2)))
+        
+        polygons.append({
+            "id": i,
+            "vertex_count": len(points),
+            "coordinates": points
+        })
+
+    return polygons
+
+# --- 使用说明 ---
+results = extract_green_polygons("golden_gate_bridge.png")
+
+if not results:
+    print("未找到多边形或图片读取失败")
+else:
+    for poly in results:
+        print(f"--- 多边形 ID: {poly['id']} (顶点数: {poly['vertex_count']}) ---")
+        # 【修改点2】：遍历坐标列表，每行输出两个数字 (x y)
+        for x, y in poly['coordinates']:
+            print(f"{x}, {y}")
+        print("") # 每个多边形之间空一行，方便区分

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')