# Path: bssrdf_estimate/tools/vector3d.py
# class Vector3D(object):
#     def __init__(self, x=0.0, y=0.0, z=0.0):
#         self._x = x
#         self._y = y
#         self._z = z
# 
#     @property
#     def x(self):
#         return self._x
# 
#     @property
#     def y(self):
#         return self._y
# 
#     @property
#     def z(self):
#         return self._z
# 
#     def dot(self, v):
#         return self._x * v.x + self._y * v.y + self._z * v.z
# 
#     def norm(self):
#         return math.sqrt(self.dot(self))
# 
#     def normalized(self):
#         return self / self.norm()
# 
#     def __neg__(self):
#         return Vector3D(-self._x, -self._y, -self._z)
# 
#     def __add__(self, v):
#         return Vector3D(self._x + v.x, self._y + v.y, self._z + v.z)
# 
#     def __sub__(self, v):
#         return Vector3D(self._x - v.x, self._y - v.y, self._z - v.z)
# 
#     def __mul__(self, s):
#         return Vector3D(self._x * s, self._y * s, self._z * s)
# 
#     def __rmul__(self, s):
#         return self.__mul__(s)
# 
#     def __truediv__(self, s):
#         return self * (1.0 / s)
# 
# Path: bssrdf_estimate/tools/diffuse_bssrdf.py
# class DiffuseBSSRDF(object):
#     def __init__(self):
#         self.distances = None
#         self.colors    = None
# 
#     @staticmethod
#     def load(filename):
#         ret = DiffuseBSSRDF()
#         with open(filename, 'rb') as fp:
#             ret.colors = [None] * 3
#             sz = struct.unpack('i', fp.read(4))[0]
#             ret.distances = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors = np.zeros((sz,3), dtype='float32')
#             ret.colors[:,0] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors[:,1] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors[:,2] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#         return ret
# 
#     def save(self, filename):
#         with open(filename, 'wb') as fp:
#             sz = self.distances.size
#             fp.write(struct.pack('i', sz))
#             fp.write(struct.pack('f' * sz, *self.distances))
#             fp.write(struct.pack('f' * sz, *self.colors[:,0]))
#             fp.write(struct.pack('f' * sz, *self.colors[:,1]))
#             fp.write(struct.pack('f' * sz, *self.colors[:,2]))
# 
#     def scaled(self, sc):
#         ret = DiffuseBSSRDF()
#         ret.distances = self.distances / sc
#         ret.colors    = self.colors / (sc * sc)
#         return ret


import math
import struct
import numpy as np
from itertools import product
from random import *
from .vector3d import Vector3D
from .diffuse_bssrdf import DiffuseBSSRDF
from .solvers import *
                dx = (depth[y,x+1] - depth[y,x-1]) * 0.5
                dy = (depth[y+1,x] - depth[y+1,x]) * 0.5
                normal = Vector3D(-dx, -dy, 1.0).normalized()
                ret.append(PixelConstraint(col, pos, normal))

        max_const = 1000
        num_const = len(ret)

        if num_const > max_const:
            print('[INFO] Masked pixels are too many. Sample to reduce the number!')
            for i in range(max_const):
                r = randint(i, num_const - 1)
                ret[i], ret[r] = ret[r], ret[i]

        return ret[:max_const]

    def translucent_shadowmap(self, lights):
        num_pixels = len(self.pixels)
        num_lights = len(lights)

        for i in range(num_pixels):
            f_normal = self.pixels[i].normal
            f_E = 0.0
            for j in range(num_lights):
                light_dir = lights[j].direction()
                angl = - light_dir.dot(f_normal)
                if angl > 0.0:
                    f_E += Ft(eta, angl) * angl * light_E
            self.pixels[i].front_E = f_E


# Path: bssrdf_estimate/tools/vector3d.py
# class Vector3D(object):
#     def __init__(self, x=0.0, y=0.0, z=0.0):
#         self._x = x
#         self._y = y
#         self._z = z
# 
#     @property
#     def x(self):
#         return self._x
# 
#     @property
#     def y(self):
#         return self._y
# 
#     @property
#     def z(self):
#         return self._z
# 
#     def dot(self, v):
#         return self._x * v.x + self._y * v.y + self._z * v.z
# 
#     def norm(self):
#         return math.sqrt(self.dot(self))
# 
#     def normalized(self):
#         return self / self.norm()
# 
#     def __neg__(self):
#         return Vector3D(-self._x, -self._y, -self._z)
# 
#     def __add__(self, v):
#         return Vector3D(self._x + v.x, self._y + v.y, self._z + v.z)
# 
#     def __sub__(self, v):
#         return Vector3D(self._x - v.x, self._y - v.y, self._z - v.z)
# 
#     def __mul__(self, s):
#         return Vector3D(self._x * s, self._y * s, self._z * s)
# 
#     def __rmul__(self, s):
#         return self.__mul__(s)
# 
#     def __truediv__(self, s):
#         return self * (1.0 / s)
# 
# Path: bssrdf_estimate/tools/diffuse_bssrdf.py
# class DiffuseBSSRDF(object):
#     def __init__(self):
#         self.distances = None
#         self.colors    = None
# 
#     @staticmethod
#     def load(filename):
#         ret = DiffuseBSSRDF()
#         with open(filename, 'rb') as fp:
#             ret.colors = [None] * 3
#             sz = struct.unpack('i', fp.read(4))[0]
#             ret.distances = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors = np.zeros((sz,3), dtype='float32')
#             ret.colors[:,0] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors[:,1] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#             ret.colors[:,2] = np.array(struct.unpack('f' * sz, fp.read(4 * sz)), dtype='float32')
#         return ret
# 
#     def save(self, filename):
#         with open(filename, 'wb') as fp:
#             sz = self.distances.size
#             fp.write(struct.pack('i', sz))
#             fp.write(struct.pack('f' * sz, *self.distances))
#             fp.write(struct.pack('f' * sz, *self.colors[:,0]))
#             fp.write(struct.pack('f' * sz, *self.colors[:,1]))
#             fp.write(struct.pack('f' * sz, *self.colors[:,2]))
# 
#     def scaled(self, sc):
#         ret = DiffuseBSSRDF()
#         ret.distances = self.distances / sc
#         ret.colors    = self.colors / (sc * sc)
#         return ret


# Path: bssrdf_estimate/tools/bssrdf_estimator.py
import math
import struct
import numpy as np
from itertools import product
from random import *
from .vector3d import Vector3D
from .diffuse_bssrdf import DiffuseBSSRDF
from .solvers import *

                dx = (depth[y,x+1] - depth[y,x-1]) * 0.5
                dy = (depth[y+1,x] - depth[y+1,x]) * 0.5
                normal = Vector3D(-dx, -dy, 1.0).normalized()
                ret.append(PixelConstraint(col, pos, normal))

        max_const = 1000
        num_const = len(ret)

        if num_const > max_const:
            print('[INFO] Masked pixels are too many. Sample to reduce the number!')
            for i in range(max_const):
                r = randint(i, num_const - 1)
                ret[i], ret[r] = ret[r], ret[i]

        return ret[:max_const]

    def translucent_shadowmap(self, lights):
        num_pixels = len(self.pixels)
        num_lights = len(lights)

        for i in range(num_pixels):
            f_normal = self.pixels[i].normal
            f_E = 0.0
            for j in range(num_lights):
                light_dir = lights[j].direction()
                angl = - light_dir.dot(f_normal)
                if angl > 0.0:
                    f_E += Ft(eta, angl) * angl * light_E
            self.pixels[i].front_E = f_E


            b_normal = Vector3D(0.0, 0.0, -1.0)