Merge pull request #55 from eukarya-inc/dev/py3dtiles-beta

#54 supports new features and bump 0.0.13

Author: smellman

.vscode/settings.json

@@ -1,4 +1,6 @@
 {
-    "python.formatting.provider": "black",
-    "editor.formatOnSave": true
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnSave": true
+    }
 }

dev-requirements.txt

@@ -40,7 +40,7 @@ pluggy==1.0.0
 plyfile==1.0.1
 psutil==5.9.5
 psycopg2-binary==2.9.7
-py3dtiles==6.0.0
+git+https://gitlab.com/Oslandia/py3dtiles.git@489289d2f7310fbcb8e95710e250d893999adf5d#egg=py3dtiles
 Pygments==2.15.1
 pyproj==3.6.1
 pyproject_hooks==1.0.0

doc/plateauutils.citygmlfinder.rst

@@ -2,7 +2,7 @@ plateauutils.citygmlfinder パッケージ
 ======================================
 
 plateauutils.citygmlfinder.from_reearth_cms モジュール
---------------------------------------------------
+-------------------------------------------------------
 
 .. automodule:: plateauutils.citygmlfinder.from_reearth_cms
     :members:

plateauutils/abc/numpy_tile_parser.py

@@ -17,17 +17,28 @@ def _parse_tile(self):
         tile_list = self.__make_tile_list()
         lons = []  # 経度
         lats = []  # 緯度
-        maps = []  # 標高
+        dems = []  # 標高
+        classifications = []  # 属性値
         # それぞれのタイルをパース
         for tile in tile_list:
             t = np.load(tile)
             lons.append(t["lons"])
             lats.append(t["lats"])
-            map = t["map"]
-            # 標高がnanの場合は0にする
-            map[np.isnan(map)] = 0
-            maps.append(map)
-        return np.concatenate(lons), np.concatenate(lats), np.concatenate(maps)
+            dems.append(t["dem"])
+            classifications.append(t["classification"])
+        # np.arrayに変換
+        np_lons, np_lats, np_dems, np_classifications = (
+            np.concatenate(lons),
+            np.concatenate(lats),
+            np.concatenate(dems),
+            np.concatenate(classifications),
+        )
+        # nanを除去
+        np_lons = np_lons[~np.isnan(np_dems)]
+        np_lats = np_lats[~np.isnan(np_dems)]
+        np_classifications = np_classifications[~np.isnan(np_dems)]
+        np_dems = np_dems[~np.isnan(np_dems)]
+        return np_lons, np_lats, np_dems, np_classifications
 
     def __make_tile_list(self):
         """タイルのリストを作成するメソッド"""

plateauutils/flood_converter/flood_to_3dtiles.py

@@ -27,7 +27,7 @@ def convert(
         """
         # xyzファイルを作成
         xyz = FloodToXyz(path).parse()
-        tmp = tempfile.mktemp(suffix=".xyz")
+        tmp = tempfile.mktemp(suffix=".csv")
         # 一時ファイルにxyzを書き込み
         with open(tmp, "w") as f:
             f.write(xyz)

plateauutils/flood_converter/flood_to_png.py

@@ -26,49 +26,50 @@ def parse(self, output_dir: str = ""):
         output_dir : str
             pngの出力先
         """
-        # lontitude, latitude, altitudeを取得
-        lons, lats, maps = self._parse_tile()
-        # storeを作成
-        store = Store()
-        # storeのaddメソッドをベクトル化
-        vfunc = np.vectorize(store.add)
-        # storeにlontitude, latitude, altitudeを追加
-        vfunc(lons, lats, maps)
-        # pngを書き出す
-        writer = PngWriter(output_dir, 15)
-        writer.setStore(store)
-        writer.write()
+        # lontitude, latitude, classificationsを取得
+        lons, lats, _, classifications = self._parse_tile()
+        # ズームレベル8から15までのpngを作成
+        for zoom in range(8, 16):
+            # storeを作成
+            store = Store(zoom)
+            # storeのaddメソッドをベクトル化
+            vfunc = np.vectorize(store.add)
+            # storeにlontitude, latitude, classificationsを追加
+            vfunc(lons, lats, classifications)
+            # pngを書き出す
+            writer = PngWriter(output_dir, zoom)
+            writer.setStore(store)
+            writer.write()
 
 
 class Store(object):
     """タイルの座標及び標高を保持するクラス"""
 
-    def __init__(self):
-        self.zoom = 15
+    def __init__(self, zoom):
+        self.zoom = zoom
         self.storage = dict()
 
-    def add(self, x, y, z):
+    def add(self, x, y, classification):
         """タイルの座標及び標高を格納するメソッド"""
-        longitude, latitude, altitude = x, y, z
+        longitude, latitude = x, y
         # 座標からタイルの座標とタイル内の座標を取得
         x, y, pos_x, pos_y = self._coordinate_to_position(longitude, latitude)
         # storageに格納
-        self._insert(x, y, pos_x, pos_y, altitude)
+        self._insert(x, y, pos_x, pos_y, classification)
 
-    def _insert(self, x, y, pos_x, pos_y, altitude):
+    def _insert(self, x, y, pos_x, pos_y, classification):
         # keyがstorageに存在する場合はその値を取得
         key = (x, y)
         if key in self.storage.keys():
             array = self.storage[key]
         else:
             # 存在しない場合は256*256の配列を作成
-            array = np.zeros((256, 256))
-            array.fill(-np.inf)
+            array = np.zeros((256, 256), dtype=np.int32)
             self.storage[key] = array
         # 標高を格納
         current = array[pos_x][pos_y]
-        if current < altitude:
-            array[pos_x][pos_y] = altitude
+        if current < classification:
+            array[pos_x][pos_y] = classification
             self.storage[key] = array
 
     def _coordinate_to_position(self, longitude, latitude):
@@ -157,55 +158,50 @@ def _write(self, x, y, value):
             sys.exit(-1)
         # 標高をpngに変換
         dt = np.dtype(
-            {"names": ["r", "g", "b"], "formats": [np.uint8, np.uint8, np.uint8]}
+            {
+                "names": ["r", "g", "b", "a"],
+                "formats": [np.uint8, np.uint8, np.uint8, np.uint8],
+            }
         )
         converted1 = np.array(
-            [tuple(self._dem_to_png(v)) for v in value.reshape(value.size)], dtype=dt
+            [tuple(self._classification_to_png(v)) for v in value.reshape(value.size)],
+            dtype=dt,
         )
         converted2 = converted1.reshape(value.shape)
         filename = f"{self.directory}/{self.zoom}/{x}/{y}.png"
         width = 256
-        img = Image.new("RGB", (width, width), (128, 0, 0))
+        img = Image.new("RGBA", (width, width), (128, 0, 0, 0))
         draw = ImageDraw.Draw(img)
         for i in range(0, width):
             for j in range(0, width):
                 p = converted2[i][j]
-                draw.point([(i, j)], (int(p[0]), int(p[1]), int(p[2])))
+                draw.point([(i, j)], (int(p[0]), int(p[1]), int(p[2]), int(p[3])))
         img.save(filename)
 
-    def _dem_to_png(self, dem):
-        # 標高をPNGのRGBに変換するメソッド
-        # 内容が入っていなかったら白色
-        if dem == -np.inf:
-            return (0xFF, 0xFF, 0xFF)
-        # 標高に応じて色を変更
-        if dem > 20:
-            # 185 26 248
-            return (0xB9, 0x1A, 0xF8)
-        if dem > 10:
-            # 169 8 91
-            return (0xA9, 0x08, 0x5B)
-        if dem > 5:
-            # 253 35 21
-            return (0xFD, 0x23, 0x15)
-        if dem > 4:
-            # 236 106 141
-            return (0xEC, 0x6A, 0x8D)
-        if dem > 3:
-            # 254 115 117
-            return (0xFE, 0x73, 0x75)
-        if dem > 2:
-            # 236 182 182
-            return (0xEC, 0xB6, 0xB6)
-        if dem > 1:
-            # 255 141 36
-            return (0xFF, 0x8D, 0x24)
-        if dem > 0.3:
-            # 255 225 53
-            return (0xFF, 0xE1, 0x35)
-        if dem > 0.01:
-            # 48 254 55
-            return (0x30, 0xFE, 0x37)
-        # 0.01以下は白色
+    def _classification_to_png(self, classification):
+        # 属性をPNGのRGBに変換するメソッド
+        # 内容が入っていなかったら透明色
+        if classification == 0:
+            return (0xFF, 0xFF, 0xFF, 0x00)
+        # 属性に応じて色を変更
+        if classification == 6:
+            # 220 122 220
+            return (0xDC, 0x7A, 0xDC, 0xFF)
+        if classification == 5:
+            # 242 133 201
+            return (0xF2, 0x85, 0xC9, 0xFF)
+        if classification == 4:
+            # 255 145 145
+            return (0xFF, 0x91, 0x91, 0xFF)
+        if classification == 3:
+            # 255 183 183
+            return (0xFF, 0xB7, 0xB7, 0xFF)
+        if classification == 2:
+            # 255 216 192
+            return (0xFF, 0xD8, 0xC0, 0xFF)
+        if classification == 1:
+            # 247 245 169
+            return (0xF7, 0xF5, 0xA9, 0xFF)
+        # 例外は透明色
         # 255 255 255
-        return (0xFF, 0xFF, 0xFF)
+        return (0xFF, 0xFF, 0xFF, 0x00)

plateauutils/flood_converter/flood_to_xyz.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pandas as pd
 from plateauutils.abc.numpy_tile_parser import NumpyTileParser
 
@@ -16,7 +17,23 @@ def __init__(self, path: str = ""):
 
     def parse(self):
         """洪水浸水域をxyzに変換するメソッド"""
-        lons, lats, maps = self._parse_tile()
-        # xyzファイルを作成, 並びはlatitude, longitude, altitude
-        df = pd.DataFrame({"lat": lats, "lon": lons, "map": maps})
-        return df.to_csv(index=False, header=False, sep=" ")
+        lons, lats, dems, classifications = self._parse_tile()
+        # IRGB値を作成
+        i_values = np.ones_like(dems, dtype=np.int32) * 1
+        r_values = np.ones_like(dems, dtype=np.int32) * 0
+        g_values = np.ones_like(dems, dtype=np.int32) * 191
+        b_values = np.ones_like(dems, dtype=np.int32) * 255
+        # xyzファイルを作成, 並びはlatitude, longitude, altitude, i, r, g, b, classification
+        df = pd.DataFrame(
+            {
+                "lat": lats,
+                "lon": lons,
+                "dem": dems,
+                "i": i_values,
+                "r": r_values,
+                "g": g_values,
+                "b": b_values,
+                "classification": classifications,
+            }
+        )
+        return df.to_csv(index=False, header=True, sep=" ")

plateauutils/flood_converter/tests/fixtures/15/28264/13160.npz

@@ -22,4 +22,4 @@ def test_convert(tmp_dir: str):
     assert Path(tmp_dir, "tileset.json").exists()
     assert Path(tmp_dir, "r.pnts").exists()
     tileset_path = tmp_dir / "tileset.json"
-    assert 256 * 256 == number_of_points_in_tileset(tileset_path)
+    assert 62615 == number_of_points_in_tileset(tileset_path)

plateauutils/flood_converter/tests/test_flood_to_3dtiles.py

@@ -18,3 +18,4 @@ def test_convert(tmp_dir: str):
     flood_to_png = FloodToPng(str(DATA_DIRECTORY))
     flood_to_png.parse(output_dir=str(tmp_dir))
     assert Path(tmp_dir, "15/28264/13160.png").exists()
+    assert Path(tmp_dir, "8/220/102.png").exists()