Move code for perlin noise to a separate file

perlin2d.py

@@ -0,0 +1,45 @@
+import numpy as np
+
+##########
+# https://stackoverflow.com/questions/42147776/producing-2d-perlin-noise-with-numpy/42154921#42154921
+def perlin(x, y, seed=0):
+    # permutation table
+    np.random.seed(seed)
+    p = np.arange(256, dtype=int)
+    np.random.shuffle(p)
+    p = np.stack([p, p]).flatten()
+    # coordinates of the top-left
+    xi, yi = x.astype(int), y.astype(int)
+    # internal coordinates
+    xf, yf = x - xi, y - yi
+    # fade factors
+    u, v = fade(xf), fade(yf)
+    # noise components
+    n00 = gradient(p[p[xi] + yi], xf, yf)
+    n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
+    n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
+    n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
+    # combine noises
+    x1 = lerp(n00, n10, u)
+    x2 = lerp(n01, n11, u)  # FIX1: I was using n10 instead of n01
+    return lerp(x1, x2, v)  # FIX2: I also had to reverse x1 and x2 here
+
+
+def lerp(a, b, x):
+    "linear interpolation"
+    return a + x * (b - a)
+
+
+def fade(t):
+    "6t^5 - 15t^4 + 10t^3"
+    return 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3
+
+
+def gradient(h, x, y):
+    "grad converts h to the right gradient vector and return the dot product with (x,y)"
+    vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
+    g = vectors[h % 4]
+    return g[:, :, 0] * x + g[:, :, 1] * y
+
+
+##########

utils.py

@@ -7,6 +7,7 @@ import scipy.signal
 from scipy.spatial import cKDTree
 
 import os
+from perlin2d import *
 
 patch_match_compiled = True
 if os.name != "nt":
@@ -24,50 +25,8 @@ except NameError:
     print("patch_match compiling failed")
     patch_match_compiled = False
 
-##########
-# https://stackoverflow.com/questions/42147776/producing-2d-perlin-noise-with-numpy
-def perlin(x, y, seed=0):
-    # permutation table
-    np.random.seed(seed)
-    p = np.arange(256, dtype=int)
-    np.random.shuffle(p)
-    p = np.stack([p, p]).flatten()
-    # coordinates of the top-left
-    xi, yi = x.astype(int), y.astype(int)
-    # internal coordinates
-    xf, yf = x - xi, y - yi
-    # fade factors
-    u, v = fade(xf), fade(yf)
-    # noise components
-    n00 = gradient(p[p[xi] + yi], xf, yf)
-    n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
-    n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
-    n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
-    # combine noises
-    x1 = lerp(n00, n10, u)
-    x2 = lerp(n01, n11, u)  # FIX1: I was using n10 instead of n01
-    return lerp(x1, x2, v)  # FIX2: I also had to reverse x1 and x2 here
 
 
-def lerp(a, b, x):
-    "linear interpolation"
-    return a + x * (b - a)
-
-
-def fade(t):
-    "6t^5 - 15t^4 + 10t^3"
-    return 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3
-
-
-def gradient(h, x, y):
-    "grad converts h to the right gradient vector and return the dot product with (x,y)"
-    vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
-    g = vectors[h % 4]
-    return g[:, :, 0] * x + g[:, :, 1] * y
-
-
-##########
-
 
 def edge_pad(img, mask, mode=1):
     if mode == 0: