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import os
import glob
import hydra
import cv2
import numpy as np
import torch
from lib.utils import utils
from .pretrained_encoders import EncodingBackbone
class Dataset(torch.utils.data.Dataset):
def __init__(self, metainfo, split):
root = os.path.abspath(os.path.join(hydra.utils.get_original_cwd(), 'data', metainfo.data_dir))
self.start_frame = metainfo.start_frame
self.end_frame = metainfo.end_frame
self.skip_step = 1
self.images, self.img_sizes = [], []
self.training_indices = list(range(metainfo.start_frame, metainfo.end_frame, self.skip_step))
self.frame_encoding_backbone = EncodingBackbone()
# images
img_dir = os.path.join(root, "image")
self.img_paths = sorted(glob.glob(f"{img_dir}/*.png"))
# only store the image paths to avoid OOM
self.img_paths = [self.img_paths[i] for i in self.training_indices]
for img_path in self.img_paths:
if not os.path.exists(img_path):
print(f'!!! The path {img_path} does not exist. !!!')
self.img_size = cv2.imread(self.img_paths[0]).shape[:2]
self.n_images = len(self.img_paths)
# coarse projected SMPL masks, only for sampling
mask_dir = os.path.join(root, "mask")
self.mask_paths = sorted(glob.glob(f"{mask_dir}/*.png"))
self.mask_paths = [self.mask_paths[i] for i in self.training_indices]
self.shape = np.load(os.path.join(root, "mean_shape.npy"))
self.poses = np.load(os.path.join(root, 'poses.npy'))[self.training_indices]
self.trans = np.load(os.path.join(root, 'normalize_trans.npy'))[self.training_indices]
# cameras
camera_dict = np.load(os.path.join(root, "cameras_normalize.npz"))
scale_mats = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in self.training_indices]
world_mats = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in self.training_indices]
self.scale = 1 / scale_mats[0][0, 0]
self.intrinsics_all = []
self.pose_all = []
for scale_mat, world_mat in zip(scale_mats, world_mats):
P = world_mat @ scale_mat
P = P[:3, :4]
intrinsics, pose = utils.load_K_Rt_from_P(None, P)
self.intrinsics_all.append(torch.from_numpy(intrinsics).float())
self.pose_all.append(torch.from_numpy(pose).float())
assert len(self.intrinsics_all) == len(self.pose_all)
# other properties
self.num_sample = split.num_sample
self.sampling_strategy = "weighted"
def __len__(self):
return self.n_images
def __getitem__(self, idx):
# normalize RGB
img = cv2.imread(self.img_paths[idx])
# preprocess: BGR -> RGB -> Normalize
img = img[:, :, ::-1] / 255
mask = cv2.imread(self.mask_paths[idx])
# preprocess: BGR -> Gray -> Mask
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY) > 0
img_size = self.img_size
uv = np.mgrid[:img_size[0], :img_size[1]].astype(np.int32)
uv = np.flip(uv, axis=0).copy().transpose(1, 2, 0).astype(np.float32)
smpl_params = torch.zeros([86]).float()
smpl_params[0] = torch.from_numpy(np.asarray(self.scale)).float()
smpl_params[1:4] = torch.from_numpy(self.trans[idx]).float()
smpl_params[4:76] = torch.from_numpy(self.poses[idx]).float()
smpl_params[76:] = torch.from_numpy(self.shape).float()
# Pretrained encoding vector
self.frame_encoding_backbone.eval()
frame_encoding_vector = self.frame_encoding_backbone(torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).float())
if self.num_sample > 0:
data = {
"rgb": img,
"uv": uv,
"object_mask": mask,
}
samples, index_outside = utils.weighted_sampling(data, img_size, self.num_sample)
inputs = {
"uv": samples["uv"].astype(np.float32),
"uv_0": np.copy(samples["uv"].astype(np.float32)),
"intrinsics": self.intrinsics_all[idx],
"pose": self.pose_all[idx],
"smpl_params": smpl_params,
'index_outside': index_outside,
"frame_encoding_vector": frame_encoding_vector[0],
"idx": idx
}
images = {"rgb": samples["rgb"].astype(np.float32)}
return inputs, images
else:
inputs = {
"uv": uv.reshape(-1, 2).astype(np.float32),
"intrinsics": self.intrinsics_all[idx],
"pose": self.pose_all[idx],
"smpl_params": smpl_params,
"frame_encoding_vector": frame_encoding_vector[0],
"idx": idx
}
images = {
"rgb": img.reshape(-1, 3).astype(np.float32),
"img_size": self.img_size
}
return inputs, images
# TODO: At the moment the validset creates an instance of the validation set but using the training set as basis. We need to change to use the videos in the validset
class ValDataset(torch.utils.data.Dataset):
def __init__(self, metainfo, split):
self.dataset = Dataset(metainfo, split)
self.img_size = self.dataset.img_size
self.total_pixels = np.prod(self.img_size)
self.pixel_per_batch = split.pixel_per_batch
def __len__(self):
return 1
def __getitem__(self, idx):
image_id = int(np.random.choice(len(self.dataset), 1))
self.data = self.dataset[image_id]
inputs, images = self.data
inputs = {
"uv": inputs["uv"],
"intrinsics": inputs['intrinsics'],
"pose": inputs['pose'],
"smpl_params": inputs["smpl_params"],
'image_id': image_id,
"frame_encoding_vector": inputs["frame_encoding_vector"],
"idx": inputs['idx']
}
images = {
"rgb": images["rgb"],
"img_size": images["img_size"],
'pixel_per_batch': self.pixel_per_batch,
'total_pixels': self.total_pixels
}
return inputs, images
class TestDataset(torch.utils.data.Dataset):
def __init__(self, metainfo, split):
self.dataset = Dataset(metainfo, split)
self.img_size = self.dataset.img_size
self.total_pixels = np.prod(self.img_size)
self.pixel_per_batch = split.pixel_per_batch
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
data = self.dataset[idx]
inputs, images = data
inputs = {
"uv": inputs["uv"],
"intrinsics": inputs['intrinsics'],
"pose": inputs['pose'],
"smpl_params": inputs["smpl_params"],
"frame_encoding_vector": inputs["frame_encoding_vector"],
"idx": inputs['idx']
}
images = {
"rgb": images["rgb"],
"img_size": images["img_size"]
}
return inputs, images, self.pixel_per_batch, self.total_pixels, idx
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