IGEV/IGEV-MVS/datasets/dtu_yao_eval.py

from torch.utils.data import Dataset
import numpy as np
import os
from PIL import Image
from datasets.data_io import *
import cv2


class MVSDataset(Dataset):
    def __init__(self, datapath, listfile, nviews=5, img_wh=(1600, 1152)):
        super(MVSDataset, self).__init__()
        self.levels = 4
        self.datapath = datapath
        self.listfile = listfile
        self.nviews = nviews
        self.img_wh = img_wh
        self.metas = self.build_list()

    def build_list(self):
        metas = []
        with open(self.listfile) as f:
            scans = f.readlines()
            scans = [line.rstrip() for line in scans]

        for scan in scans:
            pair_file = "{}/pair.txt".format(scan)
            # read the pair file
            with open(os.path.join(self.datapath, pair_file)) as f:
                num_viewpoint = int(f.readline())
                # viewpoints (49)
                for view_idx in range(num_viewpoint):
                    ref_view = int(f.readline().rstrip())
                    src_views = [int(x) for x in f.readline().rstrip().split()[1::2]]
                    metas.append((scan, ref_view, src_views))
        print("dataset", "metas:", len(metas))
        return metas

    def __len__(self):
        return len(self.metas)

    def read_cam_file(self, 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])
        return intrinsics, extrinsics, depth_min, depth_max

    
    def read_mask(self, filename):
        img = Image.open(filename)
        np_img = np.array(img, dtype=np.float32)
        np_img = (np_img > 10).astype(np.float32)
        return np_img

    def read_img(self, filename):
        img = Image.open(filename)
        # scale 0~255 to -1~1
        np_img = 2*np.array(img, dtype=np.float32) / 255. - 1
        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 __getitem__(self, idx):
        scan, ref_view, src_views = self.metas[idx]
        # use only the reference view and first nviews-1 source views
        view_ids = [ref_view] + src_views[:self.nviews - 1]
        img_w = 1600
        img_h = 1200
        imgs_0 = []
        imgs_1 = []
        imgs_2 = []
        imgs_3 = []

        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, '{}/images/{:0>8}.jpg'.format(scan, vid))
            proj_mat_filename = os.path.join(self.datapath, '{}/cams_1/{: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'])

            intrinsics, extrinsics, depth_min_, depth_max_ = self.read_cam_file(proj_mat_filename)
            intrinsics[0] *= self.img_wh[0]/img_w
            intrinsics[1] *= self.img_wh[1]/img_h
            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_
                depth_max = depth_max_

        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


        return {"imgs": imgs,                   # N*3*H0*W0
                "proj_matrices": proj,          # N*4*4
                "depth_min": depth_min,         # scalar
                "depth_max": depth_max,         # scalar
                "filename": scan + '/{}/' + '{:0>8}'.format(view_ids[0]) + "{}"}