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Commit 5069f8ae authored by BobYeah's avatar BobYeah
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Gaze

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gen_image.py
View file @ 5069f8ae
import matplotlib.pyplot as plt
import numpy as np
import torch
import glm
def RandomGenSamplesInPupil(conf, n_samples):
def Fov2Length(angle):
'''
'''
return np.tan(angle * np.pi / 360) * 2
def RandomGenSamplesInPupil(pupil_size, n_samples):
'''
Random sample n_samples positions in pupil region
......@@ -18,14 +26,14 @@ def RandomGenSamplesInPupil(conf, n_samples):
samples = torch.empty(n_samples, 2)
i = 0
while i < n_samples:
s = (torch.rand(2) - 0.5) * conf.pupil_size
if np.linalg.norm(s) > conf.pupil_size / 2.:
s = (torch.rand(2) - 0.5) * pupil_size
if np.linalg.norm(s) > pupil_size / 2.:
continue
samples[i, :] = s
samples[i, :] = [ s[0], s[1], 0 ]
i += 1
return samples
def GenSamplesInPupil(conf, circles):
def GenSamplesInPupil(pupil_size, circles):
'''
Sample positions on circles in pupil region
......@@ -38,68 +46,116 @@ def GenSamplesInPupil(conf, circles):
--------
a n_samples x 2 tensor with 2D sample position in each row
'''
samples = torch.tensor([[ 0., 0. ]])
samples = torch.zeros(1, 3)
for i in range(1, circles):
r = conf.pupil_size / 2. / (circles - 1) * i
r = pupil_size / 2. / (circles - 1) * i
n = 4 * i
for j in range(0, n):
angle = 2 * np.pi / n * j
samples = torch.cat((samples, torch.tensor([[ r * np.cos(angle), r * np.sin(angle)]])),dim=0)
samples = torch.cat([ samples, torch.tensor([[ r * np.cos(angle), r * np.sin(angle), 0 ]]) ], 0)
return samples
def GenRetinal2LayerMappings(conf, df, v, u):
class RetinalGen(object):
'''
Generate the mapping matrix from retinal to layers.
Class for retinal generation process
Parameters
Properties
--------
conf - multi-layers' parameters configuration
df - focal distance
v - a 1 x 2 tensor stores half viewport
u - a M x 2 tensor stores M sample positions on pupil
u - M x 3 tensor, M sample positions in pupil
p_r - H_r x W_r x 3 tensor, retinal pixel grid, [H_r, W_r] is the retinal resolution
Phi - N x H_r x W_r x M x 2 tensor, retinal to layers mapping, N is number of layers
mask - N x H_r x W_r x M x 2 tensor, indicates invalid (out-of-range) mapping
Returns
Methods
--------
The mapping matrix
'''
H_r = conf.retinal_res[0]
W_r = conf.retinal_res[1]
D_r = conf.retinal_res.double()
N = conf.n_layers
M = u.size()[0] #41
Phi = torch.empty(H_r, W_r, N, M, 2, dtype=torch.long)
p_rx, p_ry = torch.meshgrid(torch.tensor(range(0, H_r)),
torch.tensor(range(0, W_r)))
p_r = torch.stack([p_rx, p_ry], 2).unsqueeze(2).expand(-1, -1, M, -1)
# print(p_r.shape) #torch.Size([480, 640, 41, 2])
for i in range(0, N):
dpi = conf.h_layer[i] / conf.layer_res[0] # 1 / 480
ci = conf.layer_res / 2 # [240,320]
di = conf.d_layer[i] # 深度
pi_r = di * v * (1. / D_r * (p_r + 0.5) - 0.5) / dpi # [480, 640, 41, 2]
wi = (1 - di / df) / dpi # (1 - 深度/聚焦) / dpi df = 2.625 di = 1.75
pi = torch.floor(pi_r + ci + wi * u)
torch.clamp_(pi[:, :, :, 0], 0, conf.layer_res[0] - 1)
torch.clamp_(pi[:, :, :, 1], 0, conf.layer_res[1] - 1)
Phi[:, :, i, :, :] = pi
return Phi
def __init__(self, conf, u):
'''
Initialize retinal generator instance
def GenRetinalFromLayers(layers, Phi):
# layers: 2, color, height, width
# Phi:torch.Size([480, 640, 2, 41, 2])
M = Phi.size()[3] # 41
N = Phi.size()[2] # 2
# print(layers.shape)# torch.Size([2, 3, 480, 640])
# print(Phi.shape)# torch.Size([480, 640, 2, 41, 2])
# retinal image: 3channels x retinal_size
retinal = torch.zeros(3, Phi.size()[0], Phi.size()[1])
for j in range(0, M):
retinal_view = torch.zeros(3, Phi.size()[0], Phi.size()[1])
for i in range(0, N):
retinal_view.add_(layers[i,:, Phi[:, :, i, j, 0], Phi[:, :, i, j, 1]])
retinal.add_(retinal_view)
retinal.div_(M)
return retinal
Parameters
--------
conf - multi-layers' parameters configuration
u - a M x 3 tensor stores M sample positions in pupil
'''
self.conf = conf
# self.u = u.to(cuda_dev)
self.u = u # M x 3 M sample positions
self.D_r = conf.retinal_res # retinal res 480 x 640
self.N = conf.GetNLayers() # 2
self.M = u.size()[0] # samples
p_rx, p_ry = torch.meshgrid(torch.tensor(range(0, self.D_r[0])),
torch.tensor(range(0, self.D_r[1])))
self.p_r = torch.cat([
((torch.stack([p_rx, p_ry], 2) + 0.5) / self.D_r - 0.5) * conf.GetEyeViewportSize(), # 眼球视野
torch.ones(self.D_r[0], self.D_r[1], 1)
], 2)
# self.Phi = torch.empty(N, D_r[0], D_r[1], M, 2, device=cuda_dev, dtype=torch.long)
# self.mask = torch.empty(self.N, self.D_r[0], self.D_r[1], self.M, 2, dtype=torch.float) # 2 x 480 x 640 x 41 x 2
def CalculateRetinal2LayerMappings(self, df, gaze):
'''
Calculate the mapping matrix from retinal to layers.
\ No newline at end of file
Parameters
--------
df - focus distance
gaze - 2 x 1 tensor, eye rotation angle (degs) in horizontal and vertical direction
'''
Phi = torch.empty(self.N, self.D_r[0], self.D_r[1], self.M, 2, dtype=torch.long) # 2 x 480 x 640 x 41 x 2
mask = torch.empty(self.N, self.D_r[0], self.D_r[1], self.M, 2, dtype=torch.float)
D_r = self.conf.retinal_res # D_r: Resolution of retinal 480 640
V = self.conf.GetEyeViewportSize() # V: Viewport size of eye
c = (self.conf.layer_res / 2) # c: Center of layers (pixel)
p_f = self.p_r * df # p_f: H x W x 3, focus positions of retinal pixels on focus plane
rot_forward = glm.dvec3(glm.tan(glm.radians(glm.dvec2(gaze[1], -gaze[0]))), 1)
rot_mat = torch.from_numpy(np.array(
glm.dmat3(glm.lookAtLH(glm.dvec3(), rot_forward, glm.dvec3(0, 1, 0)))))
rot_mat = rot_mat.float()
u_rot = torch.mm(self.u, rot_mat)
v_rot = torch.matmul(p_f, rot_mat).unsqueeze(2).expand(
-1, -1, self.u.size()[0], -1) - u_rot # v_rot: H x W x M x 3, rotated rays' direction vector
v_rot.div_(v_rot[:, :, :, 2].unsqueeze(3)) # make z = 1 for each direction vector in v_rot
for i in range(0, self.conf.GetNLayers()):
dp_i = self.conf.GetLayerSize(i)[0] / self.conf.layer_res[0] # dp_i: Pixel size of layer i
d_i = self.conf.d_layer[i] # d_i: Distance of layer i
k = (d_i - u_rot[:, 2]).unsqueeze(1)
pi_r = (u_rot[:, 0:2] + v_rot[:, :, :, 0:2] * k) / dp_i # pi_r: H x W x M x 2, rays' pixel coord on layer i
Phi[i, :, :, :, :] = torch.floor(pi_r + c)
mask[:, :, :, :, 0] = ((Phi[:, :, :, :, 0] >= 0) & (Phi[:, :, :, :, 0] < self.conf.layer_res[0])).float()
mask[:, :, :, :, 1] = ((Phi[:, :, :, :, 1] >= 0) & (Phi[:, :, :, :, 1] < self.conf.layer_res[1])).float()
Phi[:, :, :, :, 0].clamp_(0, self.conf.layer_res[0] - 1)
Phi[:, :, :, :, 1].clamp_(0, self.conf.layer_res[1] - 1)
retinal_mask = mask.prod(0).prod(2).prod(2)
return [ Phi, retinal_mask ]
def GenRetinalFromLayers(self, layers, Phi):
'''
Generate retinal image from layers, using precalculated mapping matrix
Parameters
--------
layers - 3N x H_l x W_l tensor, stacked layer images, with 3 channels in each layer
Returns
--------
3 x H_r x W_r tensor, 3 channels retinal image
H_r x W_r tensor, retinal image mask, indicates pixels valid or not
'''
# FOR GRAYSCALE 1 FOR RGB 3
mapped_layers = torch.empty(self.N, 3, self.D_r[0], self.D_r[1], self.M) # 2 x 3 x 480 x 640 x 41
# print("mapped_layers:",mapped_layers.shape)
for i in range(0, Phi.size()[0]):
# print("gather layers:",layers[(i * 3) : (i * 3 + 3),Phi[i, :, :, :, 0],Phi[i, :, :, :, 1]].shape)
mapped_layers[i, :, :, :, :] = layers[(i * 3) : (i * 3 + 3),
Phi[i, :, :, :, 0],
Phi[i, :, :, :, 1]]
# print("mapped_layers:",mapped_layers.shape)
retinal = mapped_layers.prod(0).sum(3).div(Phi.size()[3])
# print("retinal:",retinal.shape)
return retinal
\ No newline at end of file
main.py
View file @ 5069f8ae
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