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ls = np.concatenate((lx, ly, lz), axis = 0)
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ls = ls[np.newaxis, np.newaxis, :, np.newaxis, np.newaxis, :, :]
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self.ls = Variable(torch.from_numpy(ls.astype(np.float32 ) ) )
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self.SGNum = SGNum
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if isCuda:
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self.ls = self.ls.cuda()
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self.ls.requires_grad = False
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def fromSGtoIm(self, axis, lamb, weight ):
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bn = axis.size(0)
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envRow, envCol = weight.size(2), weight.size(3)
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# Turn SG parameters to environmental maps
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axis = axis.unsqueeze(-1).unsqueeze(-1)
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weight = weight.view(bn, self.SGNum, 3, envRow, envCol, 1, 1)
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lamb = lamb.view(bn, self.SGNum, 1, envRow, envCol, 1, 1)
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mi = lamb.expand([bn, self.SGNum, 1, envRow, envCol, self.envHeight, self.envWidth] )* \
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(torch.sum(axis.expand([bn, self.SGNum, 3, envRow, envCol, self.envHeight, self.envWidth]) * \
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self.ls.expand([bn, self.SGNum, 3, envRow, envCol, self.envHeight, self.envWidth] ), dim = 2).unsqueeze(2) - 1)
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envmaps = weight.expand([bn, self.SGNum, 3, envRow, envCol, self.envHeight, self.envWidth] ) * \
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torch.exp(mi).expand([bn, self.SGNum, 3, envRow, envCol, self.envHeight, self.envWidth] )
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envmaps = torch.sum(envmaps, dim=1)
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return envmaps
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def output2env(self, axisOrig, lambOrig, weightOrig ):
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bn, _, envRow, envCol = weightOrig.size()
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axis = axisOrig
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weight = 0.999 * weightOrig
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weight = torch.tan(np.pi / 2 * weight )
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lambOrig = 0.999 * lambOrig
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lamb = torch.tan(np.pi / 2 * lambOrig )
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envmaps = self.fromSGtoIm(axis, lamb, weight )
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return envmaps, axis, lamb, weight
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class renderingLayer():
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def __init__(self, imWidth = 160, imHeight = 120, fov=57, F0=0.05, cameraPos = [0, 0, 0],
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envWidth = 16, envHeight = 8, isCuda = True):
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self.imHeight = imHeight
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self.imWidth = imWidth
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self.envWidth = envWidth
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self.envHeight = envHeight
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self.fov = fov/180.0 * np.pi
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self.F0 = F0
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self.cameraPos = np.array(cameraPos, dtype=np.float32).reshape([1, 3, 1, 1])
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self.xRange = 1 * np.tan(self.fov/2)
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self.yRange = float(imHeight) / float(imWidth) * self.xRange
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self.isCuda = isCuda
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x, y = np.meshgrid(np.linspace(-self.xRange, self.xRange, imWidth),
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np.linspace(-self.yRange, self.yRange, imHeight ) )
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y = np.flip(y, axis=0)
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z = -np.ones( (imHeight, imWidth), dtype=np.float32)
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pCoord = np.stack([x, y, z]).astype(np.float32)
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self.pCoord = pCoord[np.newaxis, :, :, :]
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v = self.cameraPos - self.pCoord
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v = v / np.sqrt(np.maximum(np.sum(v*v, axis=1), 1e-12)[:, np.newaxis, :, :] )
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v = v.astype(dtype = np.float32)
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self.v = Variable(torch.from_numpy(v) )
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self.pCoord = Variable(torch.from_numpy(self.pCoord) )
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self.up = torch.Tensor([0,1,0] )
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Az = ( (np.arange(envWidth) + 0.5) / envWidth - 0.5 )* 2 * np.pi
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El = ( (np.arange(envHeight) + 0.5) / envHeight) * np.pi / 2.0
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Az, El = np.meshgrid(Az, El)
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Az = Az.reshape(-1, 1)
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El = El.reshape(-1, 1)
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lx = np.sin(El) * np.cos(Az)
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ly = np.sin(El) * np.sin(Az)
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lz = np.cos(El)
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ls = np.concatenate((lx, ly, lz), axis = 1)
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envWeight = np.sin(El ) * np.pi * np.pi / envWidth / envHeight
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self.ls = Variable(torch.from_numpy(ls.astype(np.float32 ) ) )
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self.envWeight = Variable(torch.from_numpy(envWeight.astype(np.float32 ) ) )
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self.envWeight = self.envWeight.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
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if isCuda:
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self.v = self.v.cuda()
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self.pCoord = self.pCoord.cuda()
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self.up = self.up.cuda()
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self.ls = self.ls.cuda()
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self.envWeight = self.envWeight.cuda()
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