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#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# Preprocessing code for the BlendedMVS dataset
# dataset at https://github.com/YoYo000/BlendedMVS
# 1) Download BlendedMVS.zip
# 2) Download BlendedMVS+.zip
# 3) Download BlendedMVS++.zip
# 4) Unzip everything in the same /path/to/tmp/blendedMVS/ directory
# 5) python datasets_preprocess/preprocess_blendedMVS.py --blendedmvs_dir /path/to/tmp/blendedMVS/
# --------------------------------------------------------
import os
import os.path as osp
import re
from tqdm import tqdm
import numpy as np
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2
import path_to_root # noqa
from dust3r.utils.parallel import parallel_threads
from dust3r.datasets.utils import cropping # noqa
def get_parser():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--blendedmvs_dir', required=True)
parser.add_argument('--precomputed_pairs', required=True)
parser.add_argument('--output_dir', default='data/blendedmvs_processed')
return parser
def main(db_root, pairs_path, output_dir):
print('>> Listing all sequences')
sequences = [f for f in os.listdir(db_root) if len(f) == 24]
# should find 502 scenes
assert sequences, f'did not found any sequences at {db_root}'
print(f' (found {len(sequences)} sequences)')
for i, seq in enumerate(tqdm(sequences)):
out_dir = osp.join(output_dir, seq)
os.makedirs(out_dir, exist_ok=True)
# generate the crops
root = osp.join(db_root, seq)
cam_dir = osp.join(root, 'cams')
func_args = [(root, f[:-8], out_dir) for f in os.listdir(cam_dir) if not f.startswith('pair')]
parallel_threads(load_crop_and_save, func_args, star_args=True, leave=False)
# verify that all pairs are there
pairs = np.load(pairs_path)
for seqh, seql, img1, img2, score in tqdm(pairs):
for view_index in [img1, img2]:
impath = osp.join(output_dir, f"{seqh:08x}{seql:016x}", f"{view_index:08n}.jpg")
assert osp.isfile(impath), f'missing image at {impath=}'
print(f'>> Done, saved everything in {output_dir}/')
def load_crop_and_save(root, img, out_dir):
if osp.isfile(osp.join(out_dir, img + '.npz')):
return # already done
# load everything
intrinsics_in, R_camin2world, t_camin2world = _load_pose(osp.join(root, 'cams', img + '_cam.txt'))
color_image_in = cv2.cvtColor(cv2.imread(osp.join(root, 'blended_images', img +
'.jpg'), cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
depthmap_in = load_pfm_file(osp.join(root, 'rendered_depth_maps', img + '.pfm'))
# do the crop
H, W = color_image_in.shape[:2]
assert H * 4 == W * 3
image, depthmap, intrinsics_out, R_in2out = _crop_image(intrinsics_in, color_image_in, depthmap_in, (512, 384))
# write everything
image.save(osp.join(out_dir, img + '.jpg'), quality=80)
cv2.imwrite(osp.join(out_dir, img + '.exr'), depthmap)
# New camera parameters
R_camout2world = R_camin2world @ R_in2out.T
t_camout2world = t_camin2world
np.savez(osp.join(out_dir, img + '.npz'), intrinsics=intrinsics_out,
R_cam2world=R_camout2world, t_cam2world=t_camout2world)
def _crop_image(intrinsics_in, color_image_in, depthmap_in, resolution_out=(800, 800)):
image, depthmap, intrinsics_out = cropping.rescale_image_depthmap(
color_image_in, depthmap_in, intrinsics_in, resolution_out)
R_in2out = np.eye(3)
return image, depthmap, intrinsics_out, R_in2out
def _load_pose(path, ret_44=False):
f = open(path)
RT = np.loadtxt(f, skiprows=1, max_rows=4, dtype=np.float32)
assert RT.shape == (4, 4)
RT = np.linalg.inv(RT) # world2cam to cam2world
K = np.loadtxt(f, skiprows=2, max_rows=3, dtype=np.float32)
assert K.shape == (3, 3)
if ret_44:
return K, RT
return K, RT[:3, :3], RT[:3, 3] # , depth_uint8_to_f32
def load_pfm_file(file_path):
with open(file_path, 'rb') as file:
header = file.readline().decode('UTF-8').strip()
if header == 'PF':
is_color = True
elif header == 'Pf':
is_color = False
else:
raise ValueError('The provided file is not a valid PFM file.')
dimensions = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode('UTF-8'))
if dimensions:
img_width, img_height = map(int, dimensions.groups())
else:
raise ValueError('Invalid PFM header format.')
endian_scale = float(file.readline().decode('UTF-8').strip())
if endian_scale < 0:
dtype = '<f' # little-endian
else:
dtype = '>f' # big-endian
data_buffer = file.read()
img_data = np.frombuffer(data_buffer, dtype=dtype)
if is_color:
img_data = np.reshape(img_data, (img_height, img_width, 3))
else:
img_data = np.reshape(img_data, (img_height, img_width))
img_data = cv2.flip(img_data, 0)
return img_data
if __name__ == '__main__':
parser = get_parser()
args = parser.parse_args()
main(args.blendedmvs_dir, args.precomputed_pairs, args.output_dir)
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