from torch.utils.data import Dataset from datasets.data_io import * import os import numpy as np import cv2 from PIL import Image from torchvision import transforms as T import random class MVSDataset(Dataset): def __init__(self, datapath, listfile, split, nviews, img_wh=(768, 576), robust_train=True): super(MVSDataset, self).__init__() self.levels = 4 self.datapath = datapath self.split = split self.listfile = listfile self.robust_train = robust_train assert self.split in ['train', 'val', 'all'], \ 'split must be either "train", "val" or "all"!' self.img_wh = img_wh if img_wh is not None: assert img_wh[0]%32==0 and img_wh[1]%32==0, \ 'img_wh must both be multiples of 32!' self.nviews = nviews self.scale_factors = {} # depth scale factors for each scan self.build_metas() self.color_augment = T.ColorJitter(brightness=0.5, contrast=0.5) def build_metas(self): self.metas = [] with open(self.listfile) as f: self.scans = [line.rstrip() for line in f.readlines()] for scan in self.scans: with open(os.path.join(self.datapath, scan, "cams/pair.txt")) as f: num_viewpoint = int(f.readline()) for _ in range(num_viewpoint): ref_view = int(f.readline().rstrip()) src_views = [int(x) for x in f.readline().rstrip().split()[1::2]] if len(src_views) >= self.nviews-1: self.metas += [(scan, ref_view, src_views)] def read_cam_file(self, scan, filename): with open(filename) as f: lines = f.readlines() lines = [line.rstrip() for line in lines] # extrinsics: line [1,5), 4x4 matrix extrinsics = np.fromstring(' '.join(lines[1:5]), dtype=np.float32, sep=' ').reshape((4, 4)) # intrinsics: line [7-10), 3x3 matrix intrinsics = np.fromstring(' '.join(lines[7:10]), dtype=np.float32, sep=' ').reshape((3, 3)) depth_min = float(lines[11].split()[0]) depth_max = float(lines[11].split()[-1]) if scan not in self.scale_factors: self.scale_factors[scan] = 100.0/depth_min depth_min *= self.scale_factors[scan] depth_max *= self.scale_factors[scan] extrinsics[:3, 3] *= self.scale_factors[scan] return intrinsics, extrinsics, depth_min, depth_max def read_depth_mask(self, scan, filename, depth_min, depth_max, scale): depth = np.array(read_pfm(filename)[0], dtype=np.float32) depth = depth * self.scale_factors[scan] * scale depth = np.squeeze(depth,2) mask = (depth>=depth_min) & (depth<=depth_max) mask = mask.astype(np.float32) if self.img_wh is not None: depth = cv2.resize(depth, self.img_wh, interpolation=cv2.INTER_NEAREST) h, w = depth.shape depth_ms = {} mask_ms = {} for i in range(4): depth_cur = cv2.resize(depth, (w//(2**i), h//(2**i)), interpolation=cv2.INTER_NEAREST) mask_cur = cv2.resize(mask, (w//(2**i), h//(2**i)), interpolation=cv2.INTER_NEAREST) depth_ms[f"level_{i}"] = depth_cur mask_ms[f"level_{i}"] = mask_cur return depth_ms, mask_ms def read_img(self, filename): img = Image.open(filename) if self.split=='train': img = self.color_augment(img) # scale 0~255 to -1~1 np_img = 2*np.array(img, dtype=np.float32) / 255. - 1 if self.img_wh is not None: np_img = cv2.resize(np_img, self.img_wh, interpolation=cv2.INTER_LINEAR) h, w, _ = np_img.shape np_img_ms = { "level_3": cv2.resize(np_img, (w//8, h//8), interpolation=cv2.INTER_LINEAR), "level_2": cv2.resize(np_img, (w//4, h//4), interpolation=cv2.INTER_LINEAR), "level_1": cv2.resize(np_img, (w//2, h//2), interpolation=cv2.INTER_LINEAR), "level_0": np_img } return np_img_ms def __len__(self): return len(self.metas) def __getitem__(self, idx): meta = self.metas[idx] scan, ref_view, src_views = meta if self.robust_train: num_src_views = len(src_views) index = random.sample(range(num_src_views), self.nviews - 1) view_ids = [ref_view] + [src_views[i] for i in index] scale = random.uniform(0.8, 1.25) else: view_ids = [ref_view] + src_views[:self.nviews - 1] scale = 1 imgs_0 = [] imgs_1 = [] imgs_2 = [] imgs_3 = [] mask = None depth = None depth_min = None depth_max = None proj_matrices_0 = [] proj_matrices_1 = [] proj_matrices_2 = [] proj_matrices_3 = [] for i, vid in enumerate(view_ids): img_filename = os.path.join(self.datapath, '{}/blended_images/{:0>8}.jpg'.format(scan, vid)) depth_filename = os.path.join(self.datapath, '{}/rendered_depth_maps/{:0>8}.pfm'.format(scan, vid)) proj_mat_filename = os.path.join(self.datapath, '{}/cams/{:0>8}_cam.txt'.format(scan, vid)) imgs = self.read_img(img_filename) imgs_0.append(imgs['level_0']) imgs_1.append(imgs['level_1']) imgs_2.append(imgs['level_2']) imgs_3.append(imgs['level_3']) # here, the intrinsics from file is already adjusted to the downsampled size of feature 1/4H0 * 1/4W0 intrinsics, extrinsics, depth_min_, depth_max_ = self.read_cam_file(scan, proj_mat_filename) extrinsics[:3, 3] *= scale proj_mat = extrinsics.copy() intrinsics[:2,:] *= 0.125 proj_mat[:3, :4] = np.matmul(intrinsics, proj_mat[:3, :4]) proj_matrices_3.append(proj_mat) proj_mat = extrinsics.copy() intrinsics[:2,:] *= 2 proj_mat[:3, :4] = np.matmul(intrinsics, proj_mat[:3, :4]) proj_matrices_2.append(proj_mat) proj_mat = extrinsics.copy() intrinsics[:2,:] *= 2 proj_mat[:3, :4] = np.matmul(intrinsics, proj_mat[:3, :4]) proj_matrices_1.append(proj_mat) proj_mat = extrinsics.copy() intrinsics[:2,:] *= 2 proj_mat[:3, :4] = np.matmul(intrinsics, proj_mat[:3, :4]) proj_matrices_0.append(proj_mat) if i == 0: # reference view depth_min = depth_min_ * scale depth_max = depth_max_ * scale depth, mask = self.read_depth_mask(scan, depth_filename, depth_min, depth_max, scale) for l in range(self.levels): mask[f'level_{l}'] = np.expand_dims(mask[f'level_{l}'],2) mask[f'level_{l}'] = mask[f'level_{l}'].transpose([2,0,1]) depth[f'level_{l}'] = np.expand_dims(depth[f'level_{l}'],2) depth[f'level_{l}'] = depth[f'level_{l}'].transpose([2,0,1]) # imgs: N*3*H0*W0, N is number of images imgs_0 = np.stack(imgs_0).transpose([0, 3, 1, 2]) imgs_1 = np.stack(imgs_1).transpose([0, 3, 1, 2]) imgs_2 = np.stack(imgs_2).transpose([0, 3, 1, 2]) imgs_3 = np.stack(imgs_3).transpose([0, 3, 1, 2]) imgs = {} imgs['level_0'] = imgs_0 imgs['level_1'] = imgs_1 imgs['level_2'] = imgs_2 imgs['level_3'] = imgs_3 # proj_matrices: N*4*4 proj_matrices_0 = np.stack(proj_matrices_0) proj_matrices_1 = np.stack(proj_matrices_1) proj_matrices_2 = np.stack(proj_matrices_2) proj_matrices_3 = np.stack(proj_matrices_3) proj={} proj['level_3']=proj_matrices_3 proj['level_2']=proj_matrices_2 proj['level_1']=proj_matrices_1 proj['level_0']=proj_matrices_0 # data is numpy array return {"imgs": imgs, # [N, 3, H, W] "proj_matrices": proj, # [N,4,4] "depth": depth, # [1, H, W] "depth_min": depth_min, # scalar "depth_max": depth_max, # scalar "mask": mask} # [1, H, W]