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import torch
import torch.nn as nn
import cv2 as cv
import numpy as np
import re
import os
import logging
from glob import glob
from models.rays import gen_rays_from_single_image, gen_random_rays_from_single_image
from data.scene import get_boundingbox
def load_K_Rt_from_P(filename, P=None):
if P is None:
lines = open(filename).read().splitlines()
if len(lines) == 4:
lines = lines[1:]
lines = [[x[0], x[1], x[2], x[3]] for x in (x.split(" ") for x in lines)]
P = np.asarray(lines).astype(np.float32).squeeze()
out = cv.decomposeProjectionMatrix(P)
K = out[0]
R = out[1]
t = out[2]
K = K / K[2, 2]
intrinsics = np.eye(4)
intrinsics[:3, :3] = K
pose = np.eye(4, dtype=np.float32)
pose[:3, :3] = R.transpose() # ? why need transpose here
pose[:3, 3] = (t[:3] / t[3])[:, 0]
return intrinsics, pose # ! return cam2world matrix here
class DtuFit:
def __init__(self, root_dir, split, scan_id, n_views, train_img_idx=[], test_img_idx=[],
img_wh=[800, 600], clip_wh=[0, 0], original_img_wh=[1600, 1200],
N_rays=512, h_patch_size=5, near=425, far=900):
super(DtuFit, self).__init__()
logging.info('Load data: Begin')
self.root_dir = root_dir
self.split = split
self.scan_id = scan_id
self.n_views = n_views
self.near = near
self.far = far
if self.scan_id is not None:
self.data_dir = os.path.join(self.root_dir, self.scan_id)
else:
self.data_dir = self.root_dir
self.img_wh = img_wh
self.clip_wh = clip_wh
if len(self.clip_wh) == 2:
self.clip_wh = self.clip_wh + self.clip_wh
self.original_img_wh = original_img_wh
self.N_rays = N_rays
self.h_patch_size = h_patch_size # used to extract patch for supervision
self.train_img_idx = train_img_idx
self.test_img_idx = test_img_idx
camera_dict = np.load(os.path.join(self.data_dir, 'cameras.npz'), allow_pickle=True)
self.images_list = sorted(glob(os.path.join(self.data_dir, "image/*.png")))
# world_mat: projection matrix: world to image
self.world_mats_np = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in
range(len(self.images_list))]
self.raw_near_fars = np.stack([np.array([self.near, self.far]) for i in range(len(self.images_list))])
# - reference image; transform the world system to the ref-camera system
self.ref_img_idx = self.train_img_idx[0]
ref_world_mat = self.world_mats_np[self.ref_img_idx]
self.ref_w2c = np.linalg.inv(load_K_Rt_from_P(None, ref_world_mat[:3, :4])[1])
self.all_images = []
self.all_intrinsics = []
self.all_w2cs = []
self.load_scene() # load the scene
# ! estimate scale_mat
self.scale_mat, self.scale_factor = self.cal_scale_mat(
img_hw=[self.img_wh[1], self.img_wh[0]],
intrinsics=self.all_intrinsics[self.train_img_idx],
extrinsics=self.all_w2cs[self.train_img_idx],
near_fars=self.raw_near_fars[self.train_img_idx],
factor=1.1)
# * after scaling and translation, unit bounding box
self.scaled_intrinsics, self.scaled_w2cs, self.scaled_c2ws, \
self.scaled_affine_mats, self.scaled_near_fars = self.scale_cam_info()
# import ipdb; ipdb.set_trace()
self.bbox_min = np.array([-1.0, -1.0, -1.0])
self.bbox_max = np.array([1.0, 1.0, 1.0])
self.partial_vol_origin = torch.Tensor([-1., -1., -1.])
logging.info('Load data: End')
def load_scene(self):
scale_x = self.img_wh[0] / self.original_img_wh[0]
scale_y = self.img_wh[1] / self.original_img_wh[1]
for idx in range(len(self.images_list)):
image = cv.imread(self.images_list[idx])
image = cv.resize(image, (self.img_wh[0], self.img_wh[1])) / 255.
image = image[self.clip_wh[1]:self.img_wh[1] - self.clip_wh[3],
self.clip_wh[0]:self.img_wh[0] - self.clip_wh[2]]
self.all_images.append(np.transpose(image[:, :, ::-1], (2, 0, 1))) # append [3,]
P = self.world_mats_np[idx]
P = P[:3, :4]
intrinsics, c2w = load_K_Rt_from_P(None, P)
w2c = np.linalg.inv(c2w)
intrinsics[:1] *= scale_x
intrinsics[1:2] *= scale_y
intrinsics[0, 2] -= self.clip_wh[0]
intrinsics[1, 2] -= self.clip_wh[1]
self.all_intrinsics.append(intrinsics)
# - transform from world system to ref-camera system
self.all_w2cs.append(w2c @ np.linalg.inv(self.ref_w2c))
self.all_images = torch.from_numpy(np.stack(self.all_images)).to(torch.float32)
self.all_intrinsics = torch.from_numpy(np.stack(self.all_intrinsics)).to(torch.float32)
self.all_w2cs = torch.from_numpy(np.stack(self.all_w2cs)).to(torch.float32)
self.img_wh = [self.img_wh[0] - self.clip_wh[0] - self.clip_wh[2],
self.img_wh[1] - self.clip_wh[1] - self.clip_wh[3]]
def cal_scale_mat(self, img_hw, intrinsics, extrinsics, near_fars, factor=1.):
center, radius, _ = get_boundingbox(img_hw, intrinsics, extrinsics, near_fars)
radius = radius * factor
scale_mat = np.diag([radius, radius, radius, 1.0])
scale_mat[:3, 3] = center.cpu().numpy()
scale_mat = scale_mat.astype(np.float32)
return scale_mat, 1. / radius.cpu().numpy()
def scale_cam_info(self):
new_intrinsics = []
new_near_fars = []
new_w2cs = []
new_c2ws = []
new_affine_mats = []
for idx in range(len(self.all_images)):
intrinsics = self.all_intrinsics[idx]
P = intrinsics @ self.all_w2cs[idx] @ self.scale_mat
P = P.cpu().numpy()[:3, :4]
# - should use load_K_Rt_from_P() to obtain c2w
c2w = load_K_Rt_from_P(None, P)[1]
w2c = np.linalg.inv(c2w)
new_w2cs.append(w2c)
new_c2ws.append(c2w)
new_intrinsics.append(intrinsics)
affine_mat = np.eye(4)
affine_mat[:3, :4] = intrinsics[:3, :3] @ w2c[:3, :4]
new_affine_mats.append(affine_mat)
camera_o = c2w[:3, 3]
dist = np.sqrt(np.sum(camera_o ** 2))
near = dist - 1
far = dist + 1
new_near_fars.append([0.95 * near, 1.05 * far])
new_intrinsics, new_w2cs, new_c2ws, new_affine_mats, new_near_fars = \
np.stack(new_intrinsics), np.stack(new_w2cs), np.stack(new_c2ws), \
np.stack(new_affine_mats), np.stack(new_near_fars)
new_intrinsics = torch.from_numpy(np.float32(new_intrinsics))
new_w2cs = torch.from_numpy(np.float32(new_w2cs))
new_c2ws = torch.from_numpy(np.float32(new_c2ws))
new_affine_mats = torch.from_numpy(np.float32(new_affine_mats))
new_near_fars = torch.from_numpy(np.float32(new_near_fars))
return new_intrinsics, new_w2cs, new_c2ws, new_affine_mats, new_near_fars
def get_conditional_sample(self):
sample = {}
support_idxs = self.train_img_idx
sample['images'] = self.all_images[support_idxs] # (V, 3, H, W)
sample['w2cs'] = self.scaled_w2cs[self.train_img_idx] # (V, 4, 4)
sample['c2ws'] = self.scaled_c2ws[self.train_img_idx] # (V, 4, 4)
sample['near_fars'] = self.scaled_near_fars[self.train_img_idx] # (V, 2)
sample['intrinsics'] = self.scaled_intrinsics[self.train_img_idx][:, :3, :3] # (V, 3, 3)
sample['affine_mats'] = self.scaled_affine_mats[self.train_img_idx] # ! in world space
sample['scan'] = self.scan_id
sample['scale_factor'] = torch.tensor(self.scale_factor)
sample['scale_mat'] = torch.from_numpy(self.scale_mat)
sample['trans_mat'] = torch.from_numpy(np.linalg.inv(self.ref_w2c))
sample['img_wh'] = torch.from_numpy(np.array(self.img_wh))
sample['partial_vol_origin'] = torch.tensor(self.partial_vol_origin, dtype=torch.float32)
return sample
def __len__(self):
if self.split == 'train':
return self.n_views * 1000
else:
return len(self.test_img_idx) * 1000
def __getitem__(self, idx):
sample = {}
if self.split == 'train':
render_idx = self.train_img_idx[idx % self.n_views]
support_idxs = [idx for idx in self.train_img_idx if idx != render_idx]
else:
# render_idx = idx % self.n_test_images + self.n_train_images
render_idx = self.test_img_idx[idx % len(self.test_img_idx)]
support_idxs = [render_idx]
sample['images'] = self.all_images[support_idxs] # (V, 3, H, W)
sample['w2cs'] = self.scaled_w2cs[support_idxs] # (V, 4, 4)
sample['c2ws'] = self.scaled_c2ws[support_idxs] # (V, 4, 4)
sample['intrinsics'] = self.scaled_intrinsics[support_idxs][:, :3, :3] # (V, 3, 3)
sample['affine_mats'] = self.scaled_affine_mats[support_idxs] # ! in world space
sample['scan'] = self.scan_id
sample['scale_factor'] = torch.tensor(self.scale_factor)
sample['img_wh'] = torch.from_numpy(np.array(self.img_wh))
sample['partial_vol_origin'] = torch.tensor(self.partial_vol_origin, dtype=torch.float32)
sample['img_index'] = torch.tensor(render_idx)
# - query image
sample['query_image'] = self.all_images[render_idx]
sample['query_c2w'] = self.scaled_c2ws[render_idx]
sample['query_w2c'] = self.scaled_w2cs[render_idx]
sample['query_intrinsic'] = self.scaled_intrinsics[render_idx]
sample['query_near_far'] = self.scaled_near_fars[render_idx]
sample['meta'] = str(self.scan_id) + "_" + os.path.basename(self.images_list[render_idx])
sample['scale_mat'] = torch.from_numpy(self.scale_mat)
sample['trans_mat'] = torch.from_numpy(np.linalg.inv(self.ref_w2c))
sample['rendering_c2ws'] = self.scaled_c2ws[self.test_img_idx]
sample['rendering_imgs_idx'] = torch.Tensor(np.array(self.test_img_idx).astype(np.int32))
# - generate rays
if self.split == 'val' or self.split == 'test':
sample_rays = gen_rays_from_single_image(
self.img_wh[1], self.img_wh[0],
sample['query_image'],
sample['query_intrinsic'],
sample['query_c2w'],
depth=None,
mask=None)
else:
sample_rays = gen_random_rays_from_single_image(
self.img_wh[1], self.img_wh[0],
self.N_rays,
sample['query_image'],
sample['query_intrinsic'],
sample['query_c2w'],
depth=None,
mask=None,
dilated_mask=None,
importance_sample=False,
h_patch_size=self.h_patch_size
)
sample['rays'] = sample_rays
return sample
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