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import cv2
import numpy as np
import torch
import torchvision
class Equirectangular():
"""
Random sample a panorama image into a perspective view
take https://github.com/fuenwang/Equirec2Perspec/blob/master/Equirec2Perspec.py as a reference
"""
def __init__(self, width = 256, height = 256, FovX = 100, theta = [0, 0]):
"""
width: output image's width
height: output image's height
FovX: perspective camera FOV on x-axis (degree)
theta: theta field where img's theta degree from
"""
self.theta = theta
self.width = width
self.height = height
self.type = type
#create x-axis coordinates and corresponding y-axis coordinates
x = np.arange(width)
y = np.arange(height)
x, y = np.meshgrid(x, y)
#create homogenerous coordinates
z = np.ones_like(x)
xyz = np.concatenate([x[..., None], y[..., None], z[..., None]], axis=-1)
#translation matrix
f = 0.5 * width * 1 / np.tan(np.radians(FovX/2))
# cx = (width - 1) / 2.0
# cy = (height - 1) / 2.0
cx = (width) / 2.0
cy = (height) / 2.0
K = np.array([
[f, 0, cx],
[0, f, cy],
[0, 0, 1],
], np.float32)
K_inv = np.linalg.inv(K)
xyz = xyz @ K_inv.T
self.xyz = xyz ### self.xyz is the direction of the each ray in the camera space when camera is fixed
def __call__(self, img1):
batch = img1.shape[0]
PHI, THETA = self.getRandomRotation(batch)
y_axis = np.array([0.0, 1.0, 0.0], np.float32)
x_axis = np.array([1.0, 0.0, 0.0], np.float32)
#rotation matrix
xy_grid = []
for i in range(batch):
R1, _ = cv2.Rodrigues(y_axis * np.radians(PHI[i]))
R2, _ = cv2.Rodrigues(np.dot(R1, x_axis) * np.radians(THETA[i]))
R = R2 @ R1
#rotate
xyz = self.xyz @ R.T ### ### xyz is the direction of the each ray in the camera space when camera is rotate
norm = np.linalg.norm(xyz, axis=-1, keepdims=True)
xyz_norm = xyz / norm
#transfer to image coordinates
xy = self.xyz2xy(xyz_norm)
device = img1.device
xy = torch.from_numpy(xy).to(device).unsqueeze(0)
xy_grid.append(xy)
xy = torch.cat(xy_grid,dim=0)
#resample
return xy
def xyz2xy(self, xyz_norm):
#normlize
x = xyz_norm[..., 0]
y = xyz_norm[..., 1]
z = xyz_norm[..., 2]
lon = np.arctan2(x, z)
lat = np.arcsin(y)
### transfer to the lon and lat
X = lon / (np.pi)
Y = lat / (np.pi) * 2
xy = np.stack([X, Y], axis=-1)
xy = xy.astype(np.float32)
return xy
def getRandomRotation(self,batch_size):
# phi = np.random.rand(batch_size) * 360 -180
phi = np.random.randint(-180,180,batch_size)
assert(self.theta[0]<self.theta[1])
theta = np.random.randint(self.theta[0],self.theta[1],batch_size)
# theta = np.random.rand(batch_size)*(self.theta[1]-self.theta[0])-self.theta[0]
return phi, theta
if __name__=='__main__':
# test demo
e = Equirectangular(theta=[0., 40.],width = 64, height = 64,FovX = 100)
img = cv2.imread('dataset/CVACT/streetview/__-DFIFxvZBCn1873qkqXA_grdView.png')[:,:,::-1]/255.0
img = img.transpose(2, 0, 1).astype(np.float32)
img = torch.from_numpy(img).unsqueeze(0).repeat(10, 1, 1, 1)
equ= e(img)
# print(PHI, THETA)
torchvision.utils.save_image(torch.nn.functional.grid_sample(img, equ.float(), align_corners = True)*0.99, 'test_30.png') |