182 lines
6.0 KiB
Python
182 lines
6.0 KiB
Python
import World
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from PIL import Image
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from PIL import ImageFilter
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import numpy as np
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import networkx as nx
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from scipy import ndimage
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from scipy.ndimage import gaussian_gradient_magnitude
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from scipy.ndimage import label
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from Skeleton import Skeleton
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def get_data(world: World):
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heightmap, watermap, treemap = world.getData()
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heightmap.save('./data/heightmap.png')
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watermap.save('./data/watermap.png')
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treemap.save('./data/treemap.png')
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return heightmap, watermap, treemap
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def filter_inverse(image: Image) -> Image:
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"""
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Invert the colors of an image.
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Args:
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image (image): image to filter
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"""
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return Image.fromarray(np.invert(np.array(image)))
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def filter_sobel(image) -> Image:
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"""
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Edge detection algorithms from an image.
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Args:
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image (image): image to filter
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"""
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# Open the image
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if isinstance(image, str):
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image = Image.open(image).convert('RGB')
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img = np.array(image).astype(np.uint8)
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# Apply gray scale
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gray_img = np.round(
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0.299 * img[:, :, 0] + 0.587 * img[:, :, 1] + 0.114 * img[:, :, 2]
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).astype(np.uint8)
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# Sobel Operator
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h, w = gray_img.shape
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# define filters
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horizontal = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) # s2
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vertical = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) # s1
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# define images with 0s
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newhorizontalImage = np.zeros((h, w))
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newverticalImage = np.zeros((h, w))
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newgradientImage = np.zeros((h, w))
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# offset by 1
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for i in range(1, h - 1):
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for j in range(1, w - 1):
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horizontalGrad = (
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(horizontal[0, 0] * gray_img[i - 1, j - 1])
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+ (horizontal[0, 1] * gray_img[i - 1, j])
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+ (horizontal[0, 2] * gray_img[i - 1, j + 1])
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+ (horizontal[1, 0] * gray_img[i, j - 1])
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+ (horizontal[1, 1] * gray_img[i, j])
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+ (horizontal[1, 2] * gray_img[i, j + 1])
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+ (horizontal[2, 0] * gray_img[i + 1, j - 1])
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+ (horizontal[2, 1] * gray_img[i + 1, j])
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+ (horizontal[2, 2] * gray_img[i + 1, j + 1])
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)
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newhorizontalImage[i - 1, j - 1] = abs(horizontalGrad)
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verticalGrad = (
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(vertical[0, 0] * gray_img[i - 1, j - 1])
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+ (vertical[0, 1] * gray_img[i - 1, j])
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+ (vertical[0, 2] * gray_img[i - 1, j + 1])
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+ (vertical[1, 0] * gray_img[i, j - 1])
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+ (vertical[1, 1] * gray_img[i, j])
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+ (vertical[1, 2] * gray_img[i, j + 1])
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+ (vertical[2, 0] * gray_img[i + 1, j - 1])
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+ (vertical[2, 1] * gray_img[i + 1, j])
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+ (vertical[2, 2] * gray_img[i + 1, j + 1])
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)
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newverticalImage[i - 1, j - 1] = abs(verticalGrad)
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# Edge Magnitude
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mag = np.sqrt(pow(horizontalGrad, 2.0) + pow(verticalGrad, 2.0))
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newgradientImage[i - 1, j - 1] = mag
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image = Image.fromarray(newgradientImage)
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image = image.convert("L")
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return image
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def filter_smooth(image, radius: int = 3):
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"""
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:param image: white and black image representing the derivative of the terrain (sobel), where black is flat and white is very steep.
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:param radius: Radius of the Gaussian blur.
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Returns:
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image: black or white image, with black as flat areas to be skeletonized
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"""
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if isinstance(image, str):
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image = Image.open(image)
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# image = image.filter(ImageFilter.SMOOTH_MORE)
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# image = image.filter(ImageFilter.SMOOTH_MORE)
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# image = image.filter(ImageFilter.SMOOTH_MORE)
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image = image.convert('L')
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image = image.filter(ImageFilter.GaussianBlur(radius))
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array = np.array(image)
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bool_array = array > 7
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# bool_array = ndimage.binary_opening(bool_array, structure=np.ones((3,3)), iterations=1)
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# bool_array = ndimage.binary_closing(bool_array, structure=np.ones((3,3)), iterations=1)
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# bool_array = ndimage.binary_opening(bool_array, structure=np.ones((5,5)), iterations=1)
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# bool_array = ndimage.binary_closing(bool_array, structure=np.ones((5,5)), iterations=1)
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# bool_array = ndimage.binary_opening(bool_array, structure=np.ones((7,7)), iterations=1)
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# bool_array = ndimage.binary_closing(bool_array, structure=np.ones((7,7)), iterations=1)
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return Image.fromarray(bool_array)
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def remove_water_from_map(image: Image) -> Image:
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watermap = Image.open('./data/watermap.png').convert('L')
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array_heightmap = np.array(image)
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array_watermap = np.array(watermap)
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mask = array_watermap == 255
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array_heightmap[mask] = 0
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result_image = Image.fromarray(array_heightmap)
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return result_image
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def group_map(image1: Image, image2: Image) -> Image:
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array1 = np.array(image1)
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array2 = np.array(image2)
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mask = array1 == 255
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array2[mask] = 255
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result_image = Image.fromarray(array2)
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return result_image
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def highway_map() -> Image:
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smooth_sobel = filter_smooth("./data/sobelmap.png", 1)
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inverse_sobel = filter_inverse(smooth_sobel)
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sobel_no_water = remove_water_from_map(inverse_sobel)
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sobel_no_water.save("./data/test.png")
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array = np.array(sobel_no_water)
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array = ndimage.binary_erosion(array, iterations=10)
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array = ndimage.binary_dilation(array, iterations=5)
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image = Image.fromarray(array)
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smooth_image = filter_smooth(image, 5)
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array = np.array(smooth_image)
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array = ndimage.binary_erosion(array, iterations=17)
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image = Image.fromarray(array)
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smooth_image = filter_smooth(image, 6)
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array = np.array(smooth_image)
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array = ndimage.binary_dilation(array, iterations=3)
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image = Image.fromarray(array)
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image.save('./data/highwaymap.png')
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return image
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def skeletonnize_map(map: Image):
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skeleton = Skeleton()
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image_array = np.array(map)
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skeleton.setSkeleton(image_array)
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skeleton.parseGraph()
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heightmap_skeleton, roadsArea = skeleton.map()
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heightmap_skeleton.save('./data/skeleton.png')
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roadsArea.save('./data/roads.png') |