Fix : Detect Mountain

world_maker/City.py

@@ -170,14 +170,15 @@ class City:
     def generate_district(self):
         image = handle_import_image('./world_maker/data/smooth_sobel_watermap.png').convert('L')
         array = np.array(image)
-        mountain_coo = detect_mountain()
+        mountain = detect_mountain()
-        self.add_district(Position(mountain_coo[0], mountain_coo[1]), "mountain")
+        for mountain_coo in mountain:
-        print("[City] District added.")
+            self.add_district(mountain_coo, "mountain")
-        remove_circle_data(array, mountain_coo)
+            print("[City] Mountain district added.")
+            remove_circle_data(array, (mountain_coo.x, mountain_coo.y))
         area = get_area_array(array)
-        sizeX, sizeY = len(array[0]), len(array)
+        size_x, size_y = len(array[0]), len(array)
-        while area > sizeX * sizeY * 0.1:
+        while area > size_x * size_y * 0.1:
-            x, y = randint(0, sizeX - 1), randint(0, sizeY - 1)
+            x, y = randint(0, size_x - 1), randint(0, size_y - 1)
             if array[y][x]:
                 self.add_district(Position(x, y))
                 remove_circle_data(array, (x, y))

world_maker/data_analysis.py

@@ -3,7 +3,7 @@ from PIL import Image, ImageFilter
 import numpy as np
 from scipy import ndimage
 from world_maker.Skeleton import Skeleton
-from typing import Union
+from world_maker.Position import Position
 from random import randint
 import cv2
 
@@ -18,13 +18,13 @@ def get_data(world: World):
     return heightmap, watermap, treemap
 
 
-def handle_import_image(image: Union[str, Image]) -> Image:
+def handle_import_image(image: str | Image.Image) -> Image.Image:
     if isinstance(image, str):
         return Image.open(image)
     return image
 
 
-def filter_negative(image: Union[str, Image]) -> Image:
+def filter_negative(image: str | Image.Image) -> Image.Image:
     """
     Invert the colors of an image.
 
@@ -35,7 +35,7 @@ def filter_negative(image: Union[str, Image]) -> Image:
     return Image.fromarray(np.invert(np.array(image)))
 
 
-def filter_sobel(image: Union[str, Image]) -> Image:
+def filter_sobel(image: str | Image.Image) -> Image.Image:
     """
     Edge detection algorithms from an image.
 
@@ -105,7 +105,7 @@ def filter_sobel(image: Union[str, Image]) -> Image:
     return image
 
 
-def filter_smooth_theshold(image: Union[str, Image], radius: int = 3):
+def filter_smooth_theshold(image: str | Image.Image, radius: int = 3):
     """
     :param image: white and black image representing the derivative of the terrain (sobel), where black is flat and white is very steep.
     :param radius: Radius of the Gaussian blur.
@@ -135,15 +135,14 @@ def filter_smooth_theshold(image: Union[str, Image], radius: int = 3):
     return Image.fromarray(bool_array)
 
 
-def filter_smooth(image: Union[str, Image], radius: int = 3):
+def filter_smooth(image: str | Image.Image, radius: int = 3):
-
     image = handle_import_image(image)
     image = image.convert('L')
     image = image.filter(ImageFilter.GaussianBlur(radius))
     return image
 
 
-def subtract_map(image: Union[str, Image], substractImage: Union[str, Image]) -> Image:
+def subtract_map(image: str | Image.Image, substractImage: str | Image.Image) -> Image.Image:
     image = handle_import_image(image)
     substractImage = handle_import_image(substractImage).convert('L')
 
@@ -156,7 +155,7 @@ def subtract_map(image: Union[str, Image], substractImage: Union[str, Image]) ->
     return Image.fromarray(array_heightmap)
 
 
-def overide_map(base: Image, top: Image) -> Image:
+def overide_map(base: Image, top: Image) -> Image.Image:
     base = handle_import_image(base).convert('L')
     top = handle_import_image(top).convert('L')
 
@@ -180,7 +179,7 @@ def overide_map(base: Image, top: Image) -> Image:
     return result_image
 
 
-def group_map(image1: Union[str, Image], image2: Union[str, Image]) -> Image:
+def group_map(image1: str | Image.Image, image2: str | Image.Image) -> Image.Image:
     image1 = handle_import_image(image1).convert('L')
     image2 = handle_import_image(image2).convert('L')
 
@@ -200,7 +199,7 @@ def filter_smooth_array(array: np.ndarray, radius: int = 3) -> np.ndarray:
     return array
 
 
-def filter_remove_details(image: Union[str, Image], n: int = 20) -> Image:
+def filter_remove_details(image: str | Image.Image, n: int = 20) -> Image.Image:
     image = handle_import_image(image)
     array = np.array(image)
     for _ in range(n):
@@ -212,7 +211,7 @@ def filter_remove_details(image: Union[str, Image], n: int = 20) -> Image:
     return image
 
 
-def highway_map() -> Image:
+def highway_map() -> Image.Image:
     print("[Data Analysis] Generating highway map...")
     smooth_sobel = filter_smooth_theshold("./world_maker/data/sobelmap.png", 1)
     negative_smooth_sobel = filter_negative(smooth_sobel)
@@ -245,7 +244,7 @@ def create_volume(surface: np.ndarray, heightmap: np.ndarray, make_it_flat: bool
     return volume
 
 
-def convert_2D_to_3D(image: Union[str, Image], make_it_flat: bool = False) -> np.ndarray:
+def convert_2D_to_3D(image: str | Image.Image, make_it_flat: bool = False) -> np.ndarray:
     image = handle_import_image(image)
     heightmap = Image.open(
         './world_maker/data/heightmap_smooth.png').convert('L')
@@ -255,7 +254,7 @@ def convert_2D_to_3D(image: Union[str, Image], make_it_flat: bool = False) -> np
     return volume
 
 
-def skeleton_highway_map(image: Union[str, Image] = './world_maker/data/highwaymap.png') -> Skeleton:
+def skeleton_highway_map(image: str | Image.Image = './world_maker/data/highwaymap.png') -> Skeleton:
     image_array = convert_2D_to_3D(image, True)
     skeleton = Skeleton(image_array)
     skeleton.parse_graph(True)
@@ -265,7 +264,7 @@ def skeleton_highway_map(image: Union[str, Image] = './world_maker/data/highwaym
     return skeleton
 
 
-def skeleton_mountain_map(image: Union[str, Image] = './world_maker/data/mountain_map.png') -> Skeleton:
+def skeleton_mountain_map(image: str | Image.Image = './world_maker/data/mountain_map.png') -> Skeleton:
     image_array = convert_2D_to_3D(image, True)
     skeleton = Skeleton(image_array)
     skeleton.parse_graph()
@@ -275,7 +274,7 @@ def skeleton_mountain_map(image: Union[str, Image] = './world_maker/data/mountai
     return skeleton
 
 
-def smooth_sobel_water() -> Image:
+def smooth_sobel_water() -> Image.Image:
     watermap = handle_import_image("./world_maker/data/watermap.png")
     watermap = filter_negative(
         filter_remove_details(filter_negative(watermap), 5))
@@ -287,32 +286,94 @@ def smooth_sobel_water() -> Image:
     return group
 
 
-def detect_mountain(image: Union[str, Image] = './world_maker/data/sobelmap.png') -> Image:
+def mountain_map_expend(mountain_map: list[list[int]], starting_point: tuple[int, int], value: int):
-    image = handle_import_image(image)
+    explore_points = [starting_point]
-    sobel = np.array(image)
+    while len(explore_points) > 0:
-    pixels = sobel.reshape((-1, 1))
+        x, y = explore_points.pop(0)
-    pixels = np.float32(pixels)
+        mountain_map[y][x] = value
+        for i in range(-1, 2):
+            for j in range(-1, 2):
+                if (0 <= x + i < len(mountain_map[0]) and 0 <= y + j < len(mountain_map) and
+                        mountain_map[y + j][x + i] == 0):
+                    if (x + i, y + j) not in explore_points:
+                        explore_points.append((x + i, y + j))
 
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
-    k = 3
-    _, labels, centers = cv2.kmeans(
-        pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
 
-    centers = np.uint8(centers)
+def set_values_of_building_mountain(mountain_map: list[list[int]], area_mountain: list[int],
-    segmented_image = centers[labels.flatten()]
+                                    building_map: str | Image.Image = "./world_maker/data/smooth_sobel_watermap.png"):
-    segmented_image = segmented_image.reshape(sobel.shape)
+    building_map = handle_import_image(building_map).convert('L')
-    mountain = segmented_image == segmented_image.max()
+    for y in range(building_map.size[1]):
+        for x in range(building_map.size[0]):
+            if building_map.getpixel((x, y)) > 144:
+                if mountain_map[y][x] != -1:
+                    area_mountain[mountain_map[y][x] - 1] += 1
 
-    contours, _ = cv2.findContours(mountain.astype(
-        np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
-    max_contour = max(contours, key=cv2.contourArea)
+def get_index_of_biggest_area_mountain(area_mountain: list[int], exception: list[int]) -> int:
-    M = cv2.moments(max_contour)
+    max_value = -1
-    cX = int(M["m10"] / M["m00"])
+    index = -1
-    cY = int(M["m01"] / M["m00"])
+    for i in range(len(area_mountain)):
+        if i not in exception and area_mountain[i] > max_value:
+            max_value = area_mountain[i]
+            index = i
+    return index
 
-    print(f"[Data Analysis] The center of the mountain is at ({cX}, {cY})")
+
-    return (cX, cY)
+def get_center_of_area_mountain(mountain_map: list[list[int]], index: int) -> Position:
+    sum_x = 0
+    sum_y = 0
+    count = 0
+    for y in range(len(mountain_map)):
+        for x in range(len(mountain_map[0])):
+            if mountain_map[y][x] == index + 1:
+                sum_x += x
+                sum_y += y
+                count += 1
+    return Position(sum_x // count, sum_y // count)
+
+
+def detect_mountain(number_of_mountain: int = 2, height_threshold: int = 10,
+                    image_heightmap: str | Image.Image = './world_maker/data/heightmap.png') -> list[Position]:
+    print("[Data Analysis] Detecting mountains...")
+    image_heightmap = handle_import_image(image_heightmap).convert('L')
+
+    avg_height = 0
+    for y in range(image_heightmap.size[1]):
+        for x in range(image_heightmap.size[0]):
+            avg_height += image_heightmap.getpixel((x, y))
+    avg_height = int(avg_height / (image_heightmap.size[0] * image_heightmap.size[1]))
+    print("[Data Analysis] Average height:", avg_height)
+
+    mountain_map = [[-1 if image_heightmap.getpixel((x, y)) < (avg_height + height_threshold) else 0 for x in
+                     range(image_heightmap.size[0])] for y in
+                    range(image_heightmap.size[1])]
+
+    area_mountain = []
+    for y in range(image_heightmap.size[1]):
+        print(y)
+        for x in range(image_heightmap.size[0]):
+            if mountain_map[y][x] == 0:
+                area_mountain.append(0)
+                print("Enter the mountain")
+                mountain_map_expend(mountain_map, (x, y), len(area_mountain))
+
+    if not area_mountain:
+        print("[Data Analysis] No mountain detected.")
+        return []
+
+    set_values_of_building_mountain(mountain_map, area_mountain)
+    if number_of_mountain < len(area_mountain):
+        index_mountain = []
+        for n in range(number_of_mountain):
+            index_mountain.append(get_index_of_biggest_area_mountain(area_mountain, index_mountain))
+    else:
+        index_mountain = [i for i in range(len(area_mountain))]
+
+    position_mountain = []
+    for i in range(len(index_mountain)):
+        position_mountain.append(get_center_of_area_mountain(mountain_map, index_mountain[i]))
+
+    return position_mountain
 
 
 def rectangle_2D_to_3D(rectangle: list[tuple[tuple[int, int], tuple[int, int]]],
@@ -334,7 +395,8 @@ def rectangle_2D_to_3D(rectangle: list[tuple[tuple[int, int], tuple[int, int]]],
     return new_rectangle
 
 
-def transpose_form_heightmap(heightmap: Union[str, Image], coordinates, origin: tuple[int, int]) -> tuple[int, int, int]:
+def transpose_form_heightmap(heightmap: str | Image.Image, coordinates, origin: tuple[int, int]) -> tuple[
+    int, int, int]:
     heightmap = handle_import_image(heightmap).convert('L')
 
     xMin, zMin = origin

world_maker/world_maker.py

@@ -17,10 +17,12 @@ def world_maker():
 
     smooth_sobel_water_map = smooth_sobel_water()
     skeleton_highway = skeleton_highway_map(highway_map())
+
     city = City()
     city.generate_district()
     city.loop_expend_district()
     city.district_draw_map()
+
     road_grid = city.district_generate_road()
     image_mountain_map = city.get_district_mountain_map()
     road = city.draw_roads(4)