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ecd43ed | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # 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 sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
import argparse
import logging
import numpy as np
import os
import torch
from omegaconf import OmegaConf
from tabulate import tabulate
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from src.dataset import DatasetMode, get_dataset
from src.util import metric
from src.util.metric import MetricTracker, compute_cosine_error
eval_metrics = [
"mean_angular_error",
"median_angular_error",
"sub5_error",
"sub7_5_error",
"sub11_25_error",
"sub22_5_error",
"sub30_error",
]
if "__main__" == __name__:
logging.basicConfig(level=logging.INFO)
# -------------------- Arguments --------------------
parser = argparse.ArgumentParser(
description="Marigold : Surface Normals Estimation : Metrics Evaluation"
)
parser.add_argument(
"--prediction_dir",
type=str,
required=True,
help="Directory with predictions obtained from inference.",
)
parser.add_argument(
"--dataset_config",
type=str,
required=True,
help="Path to the config file of the evaluation dataset.",
)
parser.add_argument(
"--base_data_dir",
type=str,
required=True,
help="Base path to the datasets.",
)
parser.add_argument(
"--output_dir", type=str, required=True, help="Output directory."
)
parser.add_argument(
"--use_mask", action="store_true", help="Evaluate only in the masked region."
)
parser.add_argument("--no_cuda", action="store_true", help="Run without cuda.")
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
# -------------------- Device --------------------
cuda_avail = torch.cuda.is_available() and not args.no_cuda
device = torch.device("cuda" if cuda_avail else "cpu")
logging.info(f"Device: {device}")
# -------------------- Data --------------------
cfg_data = OmegaConf.load(args.dataset_config)
dataset = get_dataset(
cfg_data, base_data_dir=args.base_data_dir, mode=DatasetMode.EVAL
)
dataloader = DataLoader(dataset, batch_size=1, num_workers=0)
# -------------------- Eval metrics --------------------
metric_funcs = [getattr(metric, _met) for _met in eval_metrics]
metric_tracker = MetricTracker(*[m.__name__ for m in metric_funcs])
metric_tracker.reset()
# -------------------- Results Dictionary --------------------
results = {}
# -------------------- Per-sample metrics file --------------------
per_sample_filename = os.path.join(args.output_dir, "per_sample_metrics.csv")
# write title
with open(per_sample_filename, "w+") as f:
f.write("filename,")
f.write(",".join([m.__name__ for m in metric_funcs]))
f.write("\n")
# -------------------- Evaluate --------------------
for data in tqdm(dataloader, desc="Evaluating"):
# GT data
rgb_name = data["rgb_relative_path"][0]
normals_gt = data["normals"].to(device) # [1,3,H,W]
# Load predictions
rgb_basename = os.path.basename(rgb_name)
scene_dir = os.path.join(args.prediction_dir, os.path.dirname(rgb_name))
rgb_basename_without_extension = os.path.splitext(rgb_basename)[0]
pred_basename = rgb_basename_without_extension + ".npy"
pred_path = os.path.join(scene_dir, pred_basename)
if not os.path.exists(pred_path):
logging.warning(f"Can't find prediction: {pred_path}")
continue
normals_pred = (
torch.from_numpy(np.load((pred_path)).astype(np.float32))
.unsqueeze(0)
.to(device)
) # [1,3,H,W]
cosine_error = compute_cosine_error(normals_pred, normals_gt, masked=True)
sample_metric = []
for met_func in metric_funcs:
_metric_name = met_func.__name__
_metric = met_func(cosine_error).item()
sample_metric.append(_metric.__str__())
metric_tracker.update(_metric_name, _metric)
# Save per-sample metric
with open(per_sample_filename, "a+") as f:
f.write(rgb_name + ",")
f.write(",".join(sample_metric))
f.write("\n")
# -------------------- Save metrics to file --------------------
eval_text = f"Evaluation metrics:\n\
of predictions: {args.prediction_dir}\n\
on dataset: {dataset.disp_name}\n\
with samples in: {dataset.filename_ls_path}\n"
eval_text += tabulate(
[metric_tracker.result().keys(), metric_tracker.result().values()]
)
metrics_filename = "eval_metrics"
metrics_filename += ".txt"
_save_to = os.path.join(args.output_dir, metrics_filename)
with open(_save_to, "w+") as f:
f.write(eval_text)
logging.info(f"Evaluation metrics saved to {_save_to}")
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