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import cv2
import numpy as np
from tricks import *
from decompositioner import *
def GuidedFiltF(img, r):
eps = 0.04
I = img
I2 = cv2.pow(I, 2)
mean_I = cv2.boxFilter(I, -1, ((2 * r) + 1, (2 * r) + 1))
mean_I2 = cv2.boxFilter(I2, -1, ((2 * r) + 1, (2 * r) + 1))
cov_I = mean_I2 - cv2.pow(mean_I, 2)
var_I = cov_I
a = cv2.divide(cov_I, var_I + eps)
b = mean_I - (a * mean_I)
mean_a = cv2.boxFilter(a, -1, ((2 * r) + 1, (2 * r) + 1))
mean_b = cv2.boxFilter(b, -1, ((2 * r) + 1, (2 * r) + 1))
q = (mean_a * I) + mean_b
return q
def ComputeLightDirectionMat(Xpos, Ypos, Zpos, IndexMat3D):
out = np.copy(IndexMat3D)
Z = IndexMat3D[:, :, 0] + Zpos
Y = IndexMat3D[:, :, 1] - Ypos
X = Xpos - IndexMat3D[:, :, 2]
SUM = np.sqrt(X ** 2 + Y ** 2 + Z ** 2)
out[:, :, 0] = Z / SUM
out[:, :, 1] = Y / SUM
out[:, :, 2] = X / SUM
return out
def CreateIndexMat(height, width):
ind = np.zeros((height, width, 3))
for j in range(0, height):
for i in range(0, width):
ind[j, i, 0] = 0
ind[j, i, 1] = j
ind[j, i, 2] = i
return ind
def ComputeFresnel(dot, ior):
height, width = dot.shape
cosi = np.copy(dot)
etai = np.ones((height, width))
etat = ior
sint = etai / etat * np.sqrt(np.maximum(0.0, cosi * cosi))
sint2 = np.copy(sint)
cost = np.sqrt(np.maximum(0.0, 1 - sint * sint))
cosi = abs(cosi)
sint = (((etat * cosi) - (etai * cost)) / ((etat * cosi) + (etai * cost)) ** 2 + ((etai * cosi) - (etat * cost)) / (
(etai * cosi) + (etat * cost)) ** 2) / 2.0
sint[np.where(sint2 >= 1)] = 1
return 1 - sint
def small_render(imgN, Mask, color, s1024, r, g, b, h, left, top):
height, width, _ = color.shape
imgN = imgN.astype(np.float32) / 127.5 - 1.0
# imgN = GuidedFiltF(imgN, 7)
Xpos = 0 if left else width
Ypos = 0 if top else height
Zpos = h + 1e-5
amb = 0.55
ks = 0
alpha = 10
ind = CreateIndexMat(height, width)
Plight = 0.8
imgN2 = imgN / np.sqrt(np.sum(np.square(imgN), axis=2, keepdims=True))
LDfg = np.copy(ind)
Z = ind[:, :, 0] + Zpos
Y = ind[:, :, 1] - Ypos
X = Xpos - ind[:, :, 2]
SUM = np.sqrt(X ** 2 + Y ** 2 + Z ** 2)
LDfg[:, :, 0] = Z / SUM
LDfg[:, :, 1] = Y / SUM
LDfg[:, :, 2] = X / SUM
LDbg = LDfg.copy()
if left is False:
LDbg[:, :, 2] = -LDbg[:, :, 2]
if top is False:
LDbg[:, :, 2] = -LDbg[:, :, 2]
LD = LDbg.copy()
LD[Mask > 127] = LDfg[Mask > 127]
dot = np.sum(imgN2 * LD, axis=2)
dot[np.where(dot < 0)] = 0
dot[np.where(dot > 1.0)] = 1.0
dot = dot.astype(np.float32)
dot3 = np.stack((dot, dot, dot), axis=2)
# cv2.imwrite('da.png', (dot3 * 255.0).clip(0, 255).astype(np.uint8))
dot3 = d_resize(re_deatlize(d_resize((dot3 * 255.0).clip(0, 255).astype(np.uint8), s1024.shape), s1024), dot3.shape).astype(np.float32) / 255.0
# cv2.imwrite('db.png', (dot3 * 255.0).clip(0, 255).astype(np.uint8))
dot_ori = dot3.copy()
dot3[dot_ori > 0] = 0
dot3[dot_ori > 0.3] = 0.8
dot3[dot_ori > 0.35] = 0.9
dot3[dot_ori > 0.4] = 1.0
dot3[np.where(Mask == 0)] = dot_ori[np.where(Mask == 0)]
dot3 = cv2.GaussianBlur(dot3, (0, 0), 1.0)
dot3 = cv2.medianBlur(dot3, 5)
R = (np.multiply(2 * dot3, imgN2) - LD)[:, :, 0]
R[np.where(R < 0)] = 0
Rspec = (R ** alpha)
RspecR = (R ** (50.0 * alpha / 10.0))
RspecG = (R ** (50.0 * alpha / 10.0))
RspecB = (R ** (53.47 * alpha / 10.0))
FresnelR = RspecR + (1 - RspecR) * (1.0 - R) ** 5
FresnelG = RspecG + (1 - RspecG) * (1.0 - R) ** 5
FresnelB = RspecB + (1 - RspecB) * (1.0 - R) ** 5
dstImage = dot3[:, :, 0]
color64 = color.astype(np.dtype('float64'))
color64[:, :, 0] = np.minimum(255.0, color64[:, :, 0] * amb * b + Plight * color64[:, :, 0] * dstImage * b + Plight * b * 1.58 * ks * RspecB * FresnelB)
color64[:, :, 1] = np.minimum(255.0, color64[:, :, 1] * amb * g + Plight * color64[:, :, 1] * dstImage * g + Plight * g * 1.50 * ks * RspecG * FresnelG)
color64[:, :, 2] = np.minimum(255.0, color64[:, :, 2] * amb * r + Plight * color64[:, :, 2] * dstImage * r + Plight * r * 1.35 * ks * RspecR * FresnelR)
final = color64.astype(np.dtype('uint8'))
return final
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