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04c78c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import math
import torch
def encode_as_unit_interval(tensor):
"""
Maps range [-1, 1] to [0, 1]
"""
return (tensor + 1) / 2
def decode_from_unit_interval(tensor):
"""
Maps range [0, 1] to [-1, 1]
"""
return tensor * 2 - 1
def gamma_decode(images):
return torch.pow(images, 2.2)
def gamma_encode(images):
return torch.pow(images, 1.0/2.2)
def dot_product(a, b):
return torch.sum(torch.mul(a, b), dim=-3, keepdim=True)
def normalize(a):
return torch.div(a, torch.sqrt(dot_product(a, a)))
def generate_normalized_random_direction(count, min_eps = 0.001, max_eps = 0.05):
r1 = torch.Tensor(count, 1).uniform_(0.0 + min_eps, 1.0 - max_eps)
r2 = torch.Tensor(count, 1).uniform_(0.0, 1.0)
r = torch.sqrt(r1)
phi = 2 * math.pi * r2
x = r * torch.cos(phi)
y = r * torch.sin(phi)
z = torch.sqrt(1.0 - r**2)
return torch.cat([x, y, z], axis=-1)
def generate_random_scenes(count):
# Randomly distribute both, view and light positions
view_positions = generate_normalized_random_direction(count, 0.001, 0.1) # shape = [count, 3]
light_positions = generate_normalized_random_direction(count, 0.001, 0.1)
scenes = []
for i in range(count):
c = Camera(view_positions[i])
# Light has lower power as the distance to the material plane is not as large
l = Light(light_positions[i], [20.]*3)
scenes.append(Scene(c, l))
return scenes
def generate_specular_scenes(count):
# Only randomly distribute view positions and place lights in a perfect mirror configuration
view_positions = generate_normalized_random_direction(count, 0.001, 0.1) # shape = [count, 3]
light_positions = view_positions * torch.Tensor([-1.0, -1.0, 1.0]).unsqueeze(0)
# Reference: "parameters chosen empirically to have a nice distance from a -1;1 surface.""
distance_view = torch.exp(torch.Tensor(count, 1).normal_(mean=0.5, std=0.75))
distance_light = torch.exp(torch.Tensor(count, 1).normal_(mean=0.5, std=0.75))
# Reference: "Shift position to have highlight elsewhere than in the center."
# NOTE: This code only creates guaranteed specular highlights in the orthographic rendering, not in the perspective one.
# This is because the camera is -looking- at the center of the patch.
shift = torch.cat([torch.Tensor(count, 2).uniform_(-1.0, 1.0), torch.zeros((count, 1)) + 0.0001], dim=-1)
view_positions = view_positions * distance_view + shift
light_positions = light_positions * distance_light + shift
scenes = []
for i in range(count):
c = Camera(view_positions[i])
l = Light(light_positions[i], [20, 20.0, 20.0])
scenes.append(Scene(c, l))
return scenes
class Camera:
def __init__(self, pos):
self.pos = pos
def __str__(self):
return f'Camera({self.pos.tolist()})'
class Light:
def __init__(self, pos, color):
self.pos = pos
self.color = color
def __str__(self):
return f'Light({self.pos.tolist()}, {self.color})'
class Scene:
def __init__(self, camera, light):
self.camera = camera
self.light = light
def __str__(self):
return f'Scene({self.camera}, {self.light})'
@classmethod
def load(cls, o):
cam, light, color = o
return Scene(Camera(cam), Light(light, color))
def export(self):
return [self.camera.pos, self.light.pos, self.light.color]
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