File size: 4,796 Bytes
7f921f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# More information about Marigold:
# https://marigoldmonodepth.github.io
# https://marigoldcomputervision.github.io
# Efficient inference pipelines are now part of diffusers:
# https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage
# https://huggingface.co/docs/diffusers/api/pipelines/marigold
# Examples of trained models and live demos:
# https://huggingface.co/prs-eth
# Related projects:
# https://rollingdepth.github.io/
# https://marigolddepthcompletion.github.io/
# Citation (BibTeX):
# https://github.com/prs-eth/Marigold#-citation
# If you find Marigold useful, we kindly ask you to cite our papers.
# --------------------------------------------------------------------------
import argparse
import cv2
import numpy as np
import os
from PIL import Image
from tqdm import tqdm
if "__main__" == __name__:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_dir", required=True)
parser.add_argument("--output_dir", required=True)
args = parser.parse_args()
dataset_dir = args.dataset_dir
output_dir = args.output_dir
# we only use scenes_85
scenes85_input_dir = os.path.join(dataset_dir, "scenes_85")
scenes85_output_dir = os.path.join(output_dir, "scenes_85")
if not os.path.exists(scenes85_output_dir):
os.makedirs(scenes85_output_dir, exist_ok=True)
with open(os.path.join(output_dir, "interiorverse_filtered_all.txt"), "w+") as f:
for scene in tqdm(os.listdir(scenes85_input_dir)):
for file in os.listdir(os.path.join(scenes85_input_dir, scene)):
# skip if nor RGB or normals
if "im.exr" not in file and "normal.exr" not in file:
continue
img_path = os.path.join(scenes85_input_dir, scene, file)
im = cv2.imread(
img_path, -1
) # im will be an numpy.float32 array of shape (H, W, 3)
im = cv2.cvtColor(
im, cv2.COLOR_BGR2RGB
) # cv2 reads image in BGR shape, convert into RGB
# skip if image/normal map contains nan values
if np.any(np.isnan(im)):
continue
# if RGB image
if "im" in file:
im = im.clip(0, 1) ** (
1 / 2.2
) # Convert from HDR to LDR with clipping and gamma correction
img = (im * 255).astype(np.uint8)
img = Image.fromarray(img)
rgb_name = file.replace("im.exr", "img.png")
if not os.path.exists(os.path.join(scenes85_output_dir, scene)):
os.makedirs(
os.path.join(scenes85_output_dir, scene), exist_ok=True
)
rgb_path = os.path.join(scenes85_output_dir, scene, rgb_name)
img.save(rgb_path)
elif "normal" in file:
# invalid pixels are 0
# skip if normal map contains invalid values
invalid_mask = np.linalg.norm(im, axis=2) < 0.1
if invalid_mask.sum() > 0:
continue
# normalize to unit length
im /= np.linalg.norm(im, axis=2, keepdims=True)
# save as .npy
normal_name = file.replace("normal.exr", "normal.npy")
if not os.path.exists(os.path.join(scenes85_output_dir, scene)):
os.makedirs(
os.path.join(scenes85_output_dir, scene), exist_ok=True
)
normal_path = os.path.join(scenes85_output_dir, scene, normal_name)
np.save(normal_path, im)
rgb_name = file.replace("normal.exr", "img.png")
f.write(
f"{os.path.join(scene, rgb_name)} {os.path.join(scene, normal_name)}\n"
)
print("Preprocess finished")
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