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nerf_sample_ray_split.py
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nerf_sample_ray_split.py
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import numpy as np
from collections import OrderedDict
import torch
import cv2
import imageio
########################################################################################################################
# ray batch sampling
########################################################################################################################
def get_rays_single_image(H, W, intrinsics, c2w):
'''
:param H: image height
:param W: image width
:param intrinsics: 4 by 4 intrinsic matrix
:param c2w: 4 by 4 camera to world extrinsic matrix
:return:
'''
u, v = np.meshgrid(np.arange(W), np.arange(H))
# noise_u = np.random.rand(H*W).astype(np.float32)-0.5
# noise_v = np.random.rand(H*W).astype(np.float32)-0.5
# u = u.reshape(-1).astype(dtype=np.float32) + 0.5 + noise_u # add half pixel
# v = v.reshape(-1).astype(dtype=np.float32) + 0.5 + noise_v
u = u.reshape(-1).astype(dtype=np.float32) + 0.5 # add half pixel
v = v.reshape(-1).astype(dtype=np.float32) + 0.5
pixels = np.stack((u, v, np.ones_like(u)), axis=0) # (3, H*W)
ray_matrix = np.dot(c2w[:3, :3], np.linalg.inv(intrinsics[:3, :3]))
# rays_d = np.dot(np.linalg.inv(intrinsics[:3, :3]), pixels)
# rays_d = np.dot(c2w[:3, :3], rays_d) # (3, H*W)
rays_d = np.dot(ray_matrix, pixels) # (3, H*W)
rays_d = rays_d.transpose((1, 0)) # (H*W, 3)
rays_o = c2w[:3, 3].reshape((1, 3))
rays_o = np.tile(rays_o, (rays_d.shape[0], 1)) # (H*W, 3)
depth = np.linalg.inv(c2w)[2, 3]
depth = depth * np.ones((rays_o.shape[0],), dtype=np.float32) # (H*W,)
return rays_o, rays_d, depth, ray_matrix
class RaySamplerSingleImage(object):
def __init__(self, H, W, intrinsics, c2w,
img_path=None,
resolution_level=1,
mask_path=None,
min_depth_path=None,
max_depth=None,
use_ray_jitter=True):
super().__init__()
self.W_orig = W
self.H_orig = H
self.intrinsics_orig = intrinsics
self.c2w_mat = c2w
self.img_path = img_path
self.mask_path = mask_path
self.min_depth_path = min_depth_path
self.max_depth = max_depth
self.resolution_level = -1
self.set_resolution_level(resolution_level)
self.use_ray_jitter = use_ray_jitter
def set_resolution_level(self, resolution_level):
if resolution_level != self.resolution_level:
self.resolution_level = resolution_level
self.W = int(self.W_orig // resolution_level)
self.H = int(self.H_orig // resolution_level)
self.intrinsics = np.copy(self.intrinsics_orig)
self.intrinsics[:2, :3] /= resolution_level
# only load image at this time
if self.img_path is not None:
self.img = imageio.imread(self.img_path).astype(np.float32) / 255.
self.img = cv2.resize(self.img, (self.W, self.H), interpolation=cv2.INTER_AREA)
self.img = self.img.reshape((-1, 3))
else:
self.img = None
if self.mask_path is not None:
self.mask = imageio.imread(self.mask_path).astype(np.float32) / 255.
self.mask = cv2.resize(self.mask, (self.W, self.H), interpolation=cv2.INTER_NEAREST)
if len(self.mask.shape) == 3: # if RGB mask, take R
print('mask shape', self.mask.shape, 'taking first channel only')
self.mask = self.mask[..., 0]
self.mask = self.mask.reshape((-1,))
else:
self.mask = None
if self.min_depth_path is not None:
self.min_depth = imageio.imread(self.min_depth_path).astype(np.float32) / 255. * self.max_depth + 1e-4
self.min_depth = cv2.resize(self.min_depth, (self.W, self.H), interpolation=cv2.INTER_LINEAR)
self.min_depth = self.min_depth.reshape((-1))
else:
self.min_depth = None
self.rays_o, self.rays_d, self.depth, self.ray_matrix = get_rays_single_image(self.H, self.W,
self.intrinsics, self.c2w_mat)
def get_img(self):
if self.img is not None:
return self.img.reshape((self.H, self.W, 3))
else:
# print('bad get_img', self.img_path)
return None
def get_all(self):
if self.min_depth is not None:
min_depth = self.min_depth
else:
min_depth = 1e-4 * np.ones_like(self.rays_d[..., 0])
ret = OrderedDict([
('ray_o', self.rays_o),
('ray_d', self.rays_d),
('depth', self.depth),
('rgb', self.img),
('mask', self.mask),
('min_depth', min_depth),
])
# return torch tensors
for k in ret:
if ret[k] is not None:
ret[k] = torch.from_numpy(ret[k])
return ret
def random_sample(self, N_rand, center_crop=False):
'''
:param N_rand: number of rays to be casted
:return:
'''
if center_crop:
half_H = self.H // 2
half_W = self.W // 2
quad_H = half_H // 2
quad_W = half_W // 2
# pixel coordinates
u, v = np.meshgrid(np.arange(half_W-quad_W, half_W+quad_W),
np.arange(half_H-quad_H, half_H+quad_H))
u = u.reshape(-1)
v = v.reshape(-1)
select_inds = np.random.choice(u.shape[0], size=(N_rand,), replace=False)
# Convert back to original image
select_inds = v[select_inds] * self.W + u[select_inds]
else:
# Random from one image
select_inds = np.random.choice(self.H*self.W, size=(N_rand,), replace=False)
rays_o = self.rays_o[select_inds, :] # [N_rand, 3]
rays_d = self.rays_d[select_inds, :] # [N_rand, 3]
depth = self.depth[select_inds] # [N_rand, ]
ray_matrix = self.ray_matrix
noise = np.random.rand(2, len(select_inds)).astype(np.float32)-0.5 # [2, N_rand]
noise = np.stack((noise[0], noise[1], np.zeros(len(select_inds), dtype=np.float32)), axis=0) # [3, N_rand]
noise = np.dot(ray_matrix, noise)
noise = noise.transpose((1, 0)) # [N_rand, 3]
assert(noise.shape == rays_d.shape)
if self.use_ray_jitter:
rays_d = rays_d + noise
if self.img is not None:
rgb = self.img[select_inds, :] # [N_rand, 3]
else:
rgb = None
if self.mask is not None:
mask = self.mask[select_inds]
else:
mask = None
if self.min_depth is not None:
min_depth = self.min_depth[select_inds]
else:
min_depth = 1e-4 * np.ones_like(rays_d[..., 0])
ret = OrderedDict([
('ray_o', rays_o),
('ray_d', rays_d),
('depth', depth),
('rgb', rgb),
('mask', mask),
('min_depth', min_depth),
('img_name', self.img_path)
])
# return torch tensors
for k in ret:
if isinstance(ret[k], np.ndarray):
ret[k] = torch.from_numpy(ret[k])
return ret