mosse correlation

.gitignore

@@ -1,2 +1,3 @@
 **/*.egg-info
 /dist
+**/__pycache__/

demo/mosse_viz.py

@@ -0,0 +1,134 @@
+import numpy as np
+import scipy.signal as signal
+from scipy.fftpack import fft, fftshift, ifft
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Slider
+
+N = 256
+
+def gaussian_filter1d(size,sigma):
+    filter_range = np.linspace(-int(size/2),int(size/2),size)
+    gaussian_filter = [1 / (sigma * np.sqrt(2*np.pi)) * np.exp(-x**2/(2*sigma**2)) for x in filter_range]
+    return gaussian_filter
+
+def generate_signal(N:int) -> np.ndarray:
+    x = np.arange(1, N)
+    y = np.zeros((N))
+    for i in range(len(x)):
+        y[i] = np.random.normal(scale=1) + (y[i-1] if i > 1 else 0)
+    return np.convolve(y,gaussian_filter1d(N,1),'same')
+
+if __name__ == '__main__':
+
+    np.random.seed(42)
+    f_full = generate_signal(2048)
+    x = np.arange(-180,180,360/N)
+    f = f_full[1024:1024+N]
+    h = f_full[1024:1024+N]
+    g = signal.gaussian(N, std=10,sym=True)
+
+    F  = fft(f)
+    F_ = np.conjugate(F)
+    G  = fft(g)
+
+    K_ = (G*F_)/(F*F_)
+
+    H = fft(h)
+    r = ifft(H*K_)
+
+    # ==========================================
+
+    fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7) = plt.subplots(7, 1, gridspec_kw={'height_ratios':[4,4,4,1,1,1,1]})
+
+    ax1.set_title('Terrain')
+    plt_f, = ax1.plot(x,f)
+    plt_h, = ax1.plot(x,h)
+
+    ax2.set_title('MOSSE response signal')
+    ax2.set_ylim([0, 1.2])
+    line_r = ax2.axvline(x=-N//2+np.argmax(abs(r)), color='r')
+    plt_r, = ax2.plot(x,abs(r))
+    plt_r2, = ax2.plot(x,abs(r))
+
+    ax3.set_title('Gaussian')
+    plt_g, = ax3.plot(x,g)
+
+    ax1.set_xlim([-180,180])
+    ax2.set_xlim([-180,180])
+    ax3.set_xlim([-180,180])
+
+    slider1 = Slider(ax4, 'sigma', 0.3, 10, valinit=0.1)
+    slider2 = Slider(ax5, 'shift', -N//2  , N//2, valinit=0, valstep=1)
+    slider3 = Slider(ax6, 'seed', 0  , 50, valinit=0, valstep=1)
+    slider4 = Slider(ax7, 'N', 128  , 1024, valinit=256, valstep=8)
+
+    sigma = 0.3
+    shift = 0
+
+    def update():
+        # K_ = (G*F_)/(F*F_)
+        window = np.ones((N)) #signal.windows.hamming(N)
+        H = fft(h*window)
+        F = fft(f*window)
+        R = H*G/F
+        r = ifft(R)
+        s = np.argmax(abs(r))
+        r2 = np.copy(r)
+        r2[s-5:s+5] = 0
+
+        plt_g.set_data(x,g)
+        plt_r.set_data(x,abs(r))
+        plt_r2.set_data(x,abs(r2))
+        plt_h.set_data(x,h)
+        plt_f.set_data(x,f)
+        ax1.set_ylim([min(np.min(h),np.min(f))-1, max(np.max(h),np.max(f))+1])
+        ax2.set_ylim([0, np.max(r)+0.2])
+        line_r.set_xdata(round((np.argmax(abs(r))/N-0.5)*360))
+        fig.canvas.draw_idle()
+
+    def update_sigma(val):
+        global g, G, sigma, N
+        sigma = val
+        g = signal.gaussian(N, std=sigma,sym=True)
+        G  = fft(g)
+        update()
+
+    def update_shift(val):
+        global shift, H, h
+        shift = -val
+        h = f_full[1024+round(shift*(N/360)):1024+N+round(shift*(N/360))]
+        noise = np.random.normal(0,0.5, N)
+        h = h+noise
+        update()
+
+    def update_seed(val):
+        global f_full, f, h, F, H, F_, shift
+        np.random.seed(val)
+        f_full = generate_signal(2048)
+        f = f_full[1024:1024+N]
+        h = f_full[1024+round(shift*(N/360)):1024+N+round(shift*(N/360))]
+        noise = np.random.normal(0,0.5, N)
+        h = h+noise
+        update()
+
+    def update_n(val):
+        global g, G, N, f_full, f, h, F, H, F_, shift, x, sigma, slider2
+        N = val
+        x = np.arange(-180,180,360/N)
+        g = signal.gaussian(N, std=sigma,sym=True)
+        G  = fft(g)
+        f = f_full[1024:1024+N]
+        h = f_full[1024+shift:1024+N+shift]
+        noise = np.random.normal(0,0.5, N)
+        h = h+noise
+
+        update()
+
+
+    slider1.on_changed(update_sigma)
+    slider2.on_changed(update_shift)
+    slider3.on_changed(update_seed)
+    slider4.on_changed(update_n)
+
+    plt.subplots_adjust(hspace=0.5)
+    plt.show()

demo/test_terrain.py

@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2 as cv
+import os
+import pyskyline
+
+import pyskyline.localization
+
+FILE = 'terrain'
+SIZE = 256
+X    = SIZE//2
+Y    = SIZE//2
+
+if __name__ == '__main__':
+    if os.path.isfile(f'{FILE}_{SIZE}.npz'):
+        print("Save detected, loading ...")
+        terrain = pyskyline.Terrain.load(f'{FILE}_{SIZE}')
+    else:
+        print("No save detected, generating new terrain ...")
+        terrain = pyskyline.Terrain.generate(size=SIZE)
+        terrain.compute_all_skylines()
+        terrain.save(f'{FILE}_{SIZE}')
+        print(f"Done, terrain saved as {FILE}_{SIZE}.npz")
+
+    # Output elevation map
+    cv.imwrite('elevation_map.jpg', terrain.dem.elevation)
+
+    # Output color map
+    cv.imwrite('color_map.jpg', terrain.dem.color)
+
+    # Ouput field of view visualization
+    cv.imwrite('fov.jpg', terrain.compute_fov(X, Y))
+
+    # Ouput skyline
+    skyline = terrain.compute_skyline(X, Y)
+    plt.figure()
+    plt.grid(False)
+    plt.plot(np.arange(-180,180,360/256),skyline)
+    plt.title('Skyline')
+    plt.savefig('skyline.svg')
+
+    # Generate heatmap
+    skyline  = np.roll(skyline, 8)           # Add heading error
+    skyline += np.random.normal(0,0.5, 256)  # Add random noise
+    mosse_correlation = pyskyline.MosseCorrelation(terrain, skyline)
+    scoremap = mosse_correlation.generate_scoremap()
+
+    plt.figure()
+    plt.grid(False)
+    fig = plt.imshow(scoremap)
+    plt.title('Score map')
+    plt.colorbar(fig)
+    plt.savefig('scoremap.svg')
+

src/pyskyline/__init__.py

@@ -1,18 +1,4 @@
-"""
-Copyright (C) 2024 University of South Brittany, Lab-STICC UMR 6285 All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-"""
-
-from .raycast import bresenham_line
+from .digital_map import DigitalMap
+from .raycast import bresenham_line, get_highest_angle
 from .terrain import Terrain
+from .localization.mosse_correlation import MosseCorrelation

src/pyskyline/digital_map.py

@@ -0,0 +1,77 @@
+import os
+import numpy as np
+import cv2 as cv
+import perlin_numpy as perlin
+
+class DigitalMap:
+    DEFAULT_MASK       = os.path.dirname(__file__) + '/../../resources/mask.jpg'
+    DEFAULT_LAYERS     = {
+        -1 : (169, 166,  97), # Water
+        18 : (175, 214, 238), # Sand
+        36 : ( 34, 139,  34), # Grass
+        82 : ( 20, 100,  20)  # Tree
+    }
+
+    def __init__(self, seed:int, size:int, height:float, scale:float) -> None:
+        self.height = height
+        self.scale  = scale
+        self.seed   = seed
+        self.size   = size
+        self.elevation : 'np.ndarray|None' = None
+        self.color     : 'np.ndarray|None' = None
+
+    def generate_elevation_map(self, mask_path:str) -> None:
+        """ Generate grayscale elevation map from 2D perlin noise
+
+        Args:
+            mask_path (str): Terrain grayscale mask path.
+        """
+        np.random.seed(self.seed)
+        perlin_noise = perlin.generate_fractal_noise_2d(
+            shape   = (512,)*2,
+            res     = (2**3,)*2,
+            octaves = 5
+        )
+
+        # Set noise in [0;1]
+        p_min = np.min(perlin_noise)
+        p_max = np.max(perlin_noise)
+        perlin_noise = (perlin_noise - p_min) / (p_max - p_min)
+
+        if self.size != 512:
+            perlin_noise = cv.resize(perlin_noise, (self.size,)*2, interpolation=cv.INTER_CUBIC)
+
+        # Open and rescale mask
+        mask = cv.imread(mask_path, cv.IMREAD_GRAYSCALE)
+        mask = cv.resize(mask, (self.size,)*2, interpolation=cv.INTER_CUBIC)
+
+        OCEAN_HEIGHT = 30
+        elevation_map = perlin_noise * mask
+        elevation_map = np.clip(elevation_map, OCEAN_HEIGHT, 255)
+        elevation_map = (elevation_map-OCEAN_HEIGHT) / (255-OCEAN_HEIGHT) * 255
+
+        self.elevation = elevation_map.astype(dtype=np.uint8)
+
+    def generate_color_map(self, layers:'dict|None'=None) -> None:
+        """ Generate RGB colored map based on elevation map.
+
+        Args:
+            layers (dict): Layer dictionary where the key is the height and value the color in BGR.
+        """
+        if layers is None:
+            layers = self.DEFAULT_LAYERS
+
+        color_map = np.full((self.size, self.size, 3), layers[-1], dtype=np.uint8)
+
+        for height, color in list(layers.items())[1:]:
+            mask            = self.elevation > height
+            color_map[mask] = color
+
+        self.color = color_map
+
+    @staticmethod
+    def generate(seed:int, size:int, height:float, scale:float, mask_path:str=DEFAULT_MASK) -> 'DigitalMap':
+        dem = DigitalMap(seed, size, height, scale)
+        dem.generate_elevation_map(mask_path)
+        dem.generate_color_map()
+        return dem

src/pyskyline/localization/localizator.py

@@ -0,0 +1,36 @@
+import numpy as np
+import cv2 as cv
+from multiprocessing import Pool, cpu_count
+
+from ..terrain import Terrain
+
+class Localizator:
+    def __init__(self, terrain:Terrain, skyline:np.ndarray) -> None:
+        self.terrain   = terrain
+        self.skyline   = skyline
+
+    def _compute_score_mp(self, func:callable, args:any, print_progress:bool=True):
+        if print_progress:
+            import rich.progress as rp
+            progress_bar = rp.Progress(
+                *rp.Progress.get_default_columns(),
+                rp.TimeElapsedColumn(),
+                rp.MofNCompleteColumn()
+            )
+            task = progress_bar.add_task('Compute heatmap', total=len(args))
+            progress_bar.start()
+
+        score_map = (self.terrain.dem.elevation != 0).astype(np.float16) * -1
+        with Pool(processes=cpu_count()) as pool:
+            results = pool.imap(func, args)
+            for x, y, score in results:
+                score_map[y, x] = np.clip(score, -65000, 65000)
+                progress_bar.advance(task, 1)
+
+        if print_progress:
+            progress_bar.stop()
+
+        return score_map
+
+    def generate_scoremap(self):
+        raise NotImplementedError()

src/pyskyline/localization/mosse_correlation.py

@@ -3,24 +3,33 @@ import math
 from scipy.fft import fft, ifft
 from scipy.signal import gaussian
 
-g = gaussian(256, std=2.25,sym=True)
-G = fft(g)
+from ..terrain import Terrain
+from .localizator import Localizator
 
-def compute_score(F : np.ndarray, h : np.ndarray) -> float:
-    """ Compute the correlation score of the two skyline based on a MOSSE correlation filter.
-
-    Args:
-        F (np.ndarray): Real skyline in fourier domain.
-        h (np.ndarray): Simulated skyline in time domain.
-
-    Returns:
-        float: Correlation score, higher is the score higher the correlation is.
-    """
-
-    H = fft(h)
+def compute_score(args) -> float:
+    x, y, F, H, G = args
     r = abs(ifft(H*G/F))
     shift = np.argmax(r)
     peak = r[shift]
     r[shift-5:shift+5] = 0
     score = peak/math.sqrt(np.sum(np.power(r,2)))
-    return math.log(score)
+    return x, y, score
+
+class MosseCorrelation(Localizator):
+    def __init__(self, terrain: Terrain, skyline: np.ndarray) -> None:
+        super().__init__(terrain, skyline)
+        self.F = fft(skyline)
+        self.G = fft(gaussian(skyline.shape[0], std=2.25,sym=True))
+
+    def generate_scoremap(self, ray_dist:float=1000.0):
+        ray_count = self.F.shape[0]
+        args = [(
+            x,
+            y,
+            self.F,
+            fft(self.terrain.compute_skyline(x,y,ray_count,ray_dist)),
+            self.G
+        ) for x in range(self.terrain.dem.size) for y in range(self.terrain.dem.size) if self.terrain.dem.elevation[y,x] == 0]
+        scoremap = self._compute_score_mp(compute_score, args)
+        scoremap = (scoremap-np.min(scoremap)) / (np.max(scoremap)-np.min(scoremap))
+        return scoremap

src/pyskyline/raycast.py

@@ -1,22 +1,8 @@
-#!/usr/bin/env python
-
-"""
-Copyright (C) 2024 University of South Brittany, Lab-STICC UMR 6285 All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-"""
-
 import numpy as np
+import cv2 as cv
+import math
+
+from .digital_map import DigitalMap
 
 def bresenham_line(x:int, y:int, dist:float, angle:float, limit:int) -> list:
     """Generate list of points of all grid cell where raycast hit.
@@ -42,7 +28,7 @@ def bresenham_line(x:int, y:int, dist:float, angle:float, limit:int) -> list:
     sy    = 1 if y < y1 else -1
     error = dx + dy
 
-    while (x != x1 and y != y1) and (0 <= x < limit and 0 <= y < limit):
+    while (x != x1 or y != y1) and (0 <= x < limit and 0 <= y < limit):
         points.append((x,y))
 
         e2 = 2 * error
@@ -53,5 +39,75 @@ def bresenham_line(x:int, y:int, dist:float, angle:float, limit:int) -> list:
             error += dx
             y     += sy
 
-
     return points
+
+def get_highest_angle(points:list, dem:DigitalMap) -> tuple:
+    m_height = 0
+    m_angle  = 0
+    m_pos    = points[-1]
+
+    for x, y in points[1:]:
+        height = dem.elevation[y,x]
+        if height > m_height:
+            m_height = height
+            dist  = math.sqrt((points[0][0]-x)**2+(points[0][1]-y)**2) * dem.scale
+            angle = np.arctan((height * dem.height/255)/dist)
+            if angle >= m_angle:
+                m_angle = angle
+                m_pos   = (x,y)
+
+    return np.rad2deg(m_angle), m_pos
+
+def mp_compute_skyline(args:tuple) -> tuple:
+    dem, x, y, ray_count, ray_dist = args
+    return (x,y,compute_skyline(dem,x,y,ray_count,ray_dist))
+
+def compute_skyline(dem:DigitalMap, x:int, y:int, ray_count:int, ray_dist:float) -> np.ndarray:
+    """_summary_
+
+    Args:
+        x (int): X coordinate
+        y (int): Y coordinate
+        ray_count (int, optional): Raycast count. Defaults to 256.
+        ray_dist (float, optional): Raycast distance in meter. Defaults to 1000.0.
+
+    Returns:
+        np.ndarray: skyline array.
+    """
+    ray_step  = 2 * np.pi / (ray_count-1)
+    line      = np.zeros((ray_count), dtype=np.float16)
+
+    for i in range(ray_count):
+        points   = bresenham_line(x, y, ray_dist/dem.scale, ray_step*i, dem.size)
+        angle, _ = get_highest_angle(points, dem)
+        line[i]  = angle
+
+    return line
+
+def compute_fov(dem:DigitalMap, x:int, y:int, ray_count:int=256, ray_dist:float=1000.0) -> np.ndarray:
+    """_summary_
+
+    Args:
+        x (int): X coordinate
+        y (int): Y coordinate
+        ray_count (int, optional): Raycast count. Defaults to 256.
+        ray_dist (float, optional): Raycast distance in meter. Defaults to 1000.0.
+
+    Returns:
+        np.ndarray: skyline array.
+    """
+    ray_step  = 2 * np.pi / (ray_count-1)
+    output    = np.copy(dem.color)
+
+    output = cv.circle(output, (x,y), 3, (255,125,0),-1)
+    last_pos = None
+
+    for i in range(ray_count):
+        points = bresenham_line(x, y, ray_dist/dem.scale, ray_step*i, dem.size)
+        _, pos = get_highest_angle(points, dem)
+
+        if last_pos is not None:
+            output = cv.line(output,last_pos,pos,(0,0,255),2)
+        last_pos = pos
+
+    return output

src/pyskyline/terrain.py

@@ -1,98 +1,37 @@
-#!/usr/bin/env python
-
-"""
-Copyright (C) 2024 University of South Brittany, Lab-STICC UMR 6285 All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-"""
-
-import os
-import math
+from multiprocessing import Pool, cpu_count
 import numpy as np
 import cv2 as cv
-import perlin_numpy as perlin
-from .raycast import bresenham_line
-
-print(__file__)
+from .digital_map import DigitalMap
+from . import raycast
 
 class Terrain:
-    DEFAULT_MASK       = os.path.dirname(__file__) + '/../../resources/mask.jpg'
-    DEFAULT_RAYCOUNT   = 256
-    DEFAULT_RAYDIST    = 1000.0
-    DEFAULT_LAYERS     = {
-        -1 : (169, 166,  97), # Water
-        18 : (175, 214, 238), # Sand
-        36 : ( 34, 139,  34), # Grass
-        82 : ( 20, 100,  20)  # Tree
-    }
+    def __init__(self, dem:DigitalMap) -> None:
+        self.dem            = dem
+        self.skylines       = {}
 
-    def __init__(self, seed:int, size:int, height:float, scale:float) -> None:
-        self.seed           = seed
-        self.size           = size
-        self.height         = height
-        self.scale          = scale
-        self.elevation_map  = None
-        self.color_map      = None
-        self.skylines       = None
+    def compute_all_skylines(self, ray_count:int=256, ray_dist:float=1000.0, print_progress:bool=True):
+        args = [(self.dem, x, y, ray_count, ray_dist) for x in range(self.dem.size) for y in range(self.dem.size) if self.dem.elevation[y,x] == 0]
 
-    def generate_elevation_map(self, mask_path:str) -> None:
-        """ Generate grayscale elevation map from 2D perlin noise
-
-        Args:
-            mask_path (str): Terrain grayscale mask path.
-        """
-        np.random.seed(self.seed)
-        perlin_noise = perlin.generate_fractal_noise_2d(
-            shape   = (512,)*2,
-            res     = (2**3,)*2,
-            octaves = 5
+        if print_progress:
+            import rich.progress as rp
+            progress_bar = rp.Progress(
+                *rp.Progress.get_default_columns(),
+                rp.TimeElapsedColumn(),
+                rp.MofNCompleteColumn()
             )
+            task = progress_bar.add_task('Compute skylines', total=len(args))
+            progress_bar.start()
 
-        # Set noise in [0;1]
-        p_min = np.min(perlin_noise)
-        p_max = np.max(perlin_noise)
-        perlin_noise = (perlin_noise - p_min) / (p_max - p_min)
+        with Pool(processes=cpu_count()) as pool:
+            results = pool.imap(raycast.mp_compute_skyline, args)
+            for x, y, skyline in results:
+                self.skylines[(x,y)] = skyline
+                progress_bar.advance(task, 1)
 
-        if self.size != 512:
-            perlin_noise = cv.resize(perlin_noise, (self.size,)*2, interpolation=cv.INTER_CUBIC)
+        if print_progress:
+            progress_bar.stop()
 
-        # Open and rescale mask
-        mask = cv.imread(mask_path, cv.IMREAD_GRAYSCALE)
-        mask = cv.resize(mask, (self.size,)*2, interpolation=cv.INTER_CUBIC)
-
-        elevation_map = perlin_noise * mask
-
-        self.elevation_map = elevation_map.astype(dtype=np.uint8)
-
-    def generate_colored_map(self, layers:'dict|None'=None) -> None:
-        """ Generate RGB colored map based on elevation map.
-
-        Args:
-            layers (dict): Layer dictionary where the key is the height and value the color in BGR.
-        """
-        if layers is None:
-            layers = self.DEFAULT_LAYERS
-
-        h,w       = self.elevation_map.shape
-        color_map = np.full((h,w,3), layers[-1], dtype=np.uint8)
-
-        for height, color in list(layers.items())[1:]:
-            mask            = self.elevation_map > height
-            color_map[mask] = color
-
-        self.color_map = cv.cvtColor(color_map, cv.COLOR_BGR2RGB)
-
-    def compute_skyline(self, x:int, y:int, ray_count:int=256, ray_dist:float=1000.0) -> np.ndarray:
+    def compute_skyline(self, x:int, y:int, ray_count:int=256, ray_dist:float=1000.0):
         """_summary_
 
         Args:
@@ -104,30 +43,55 @@ class Terrain:
         Returns:
             np.ndarray: skyline array.
         """
-        ray_step  = 2 * np.pi / (ray_count-1)
-        line      = np.zeros((ray_count), dtype=np.float16)
+        if (x,y) not in self.skylines:
+            self.skylines[(x,y)] = raycast.compute_skyline(self.dem, x, y, ray_count, ray_dist)
+        return self.skylines[(x,y)]
 
-        for i in range(ray_count):
-            max_height  = -10
-            max_v_angle = 0
-            points      = bresenham_line(x, y, ray_dist/self.scale, ray_step*i, self.size)
-            for x0, y0 in points:
-                height = self.elevation_map[y0,x0]
-                if height > max_height:
-                    max_height = height
-                    dist = math.sqrt((x0-x)**2+(y0-y)**2) * self.scale
-                    if dist != 0.0:
-                        v_angle    = np.arctan((height * self.height/255)/dist)
-                        if v_angle > max_v_angle:
-                            max_v_angle = v_angle
+    def compute_fov(self, x:int, y:int, ray_count:int=256, ray_dist:float=1000.0):
+        """_summary_
 
-            line[i] = np.rad2deg(max_v_angle)
+        Args:
+            x (int): X coordinate
+            y (int): Y coordinate
+            ray_count (int, optional): Raycast count. Defaults to 256.
+            ray_dist (float, optional): Raycast distance in meter. Defaults to 1000.0.
 
-        return line
+        Returns:
+            np.ndarray: skyline array.
+        """
+        return raycast.compute_fov(self.dem, x, y, ray_count, ray_dist)
+
+    def save(self, file:str):
+        np.savez_compressed(f'{file}.npz',
+            size          = self.dem.size,
+            seed          = self.dem.seed,
+            height        = self.dem.height,
+            scale         = self.dem.scale,
+            elevation_map = self.dem.elevation,
+            skylines      = self.skylines
+        )
+
+    @staticmethod
+    def load(file:str) -> 'Terrain':
+        if not file.endswith('.npz'):
+            file += ".npz"
+        save = np.load(file,allow_pickle=True)
+        dem = DigitalMap(
+            seed   = save['seed'],
+            size   = save['size'],
+            height = save['height'],
+            scale  = save['scale']
+        )
+        dem.elevation = save.get('elevation_map')
+        dem.generate_color_map()
+        terrain = Terrain(dem)
+        terrain.skylines = save['skylines'].item()
+
+        return terrain
 
     @staticmethod
     def generate(seed:int=0x7B16, size:int=512, height:float=25.0, scale:float=1.0,
-                 mask_path:str=DEFAULT_MASK) -> 'Terrain':
+                 mask_path:str=DigitalMap.DEFAULT_MASK) -> 'Terrain':
         """ Generate a procedural terrain using seed and mask, and precompute all horizon lines
 
         Args:
@@ -140,8 +104,7 @@ class Terrain:
         Returns:
             Terrain: _description_
         """
-        terrain = Terrain(seed,size,height,scale)
-        terrain.generate_elevation_map(mask_path)
-        terrain.generate_colored_map()
+        dem = DigitalMap.generate(seed, size, height, scale, mask_path)
+        terrain = Terrain(dem)
 
         return terrain

test/test_terrain.py

@@ -1,13 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-
-import pyskyline
-
-terrain = pyskyline.Terrain.generate()
-h = terrain.compute_skyline(330,330,360)
-
-plt.figure()
-plt.imshow(terrain.elevation_map)
-plt.figure()
-plt.plot(np.arange(0,360,360/h.shape[0]), h)
-plt.show()