Marigold/script/depth/eval.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# --------------------------------------------------------------------------
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#   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
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#   https://huggingface.co/prs-eth
# Related projects:
#   https://rollingdepth.github.io/
#   https://marigolddepthcompletion.github.io/
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#   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,
    get_pred_name,
)
from src.util import metric
from src.util.alignment import (
    align_depth_least_square,
    depth2disparity,
    disparity2depth,
)
from src.util.metric import MetricTracker

eval_metrics = [
    "abs_relative_difference",
    "squared_relative_difference",
    "rmse_linear",
    "rmse_log",
    "log10",
    "delta1_acc",
    "delta2_acc",
    "delta3_acc",
    "i_rmse",
    "silog_rmse",
]

if "__main__" == __name__:
    logging.basicConfig(level=logging.INFO)

    # -------------------- Arguments --------------------
    parser = argparse.ArgumentParser(
        description="Marigold : Monocular Depth 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(
        "--alignment",
        choices=[None, "least_square", "least_square_disparity"],
        default=None,
        help="Method to estimate scale and shift between predictions and ground truth.",
    )
    parser.add_argument(
        "--alignment_max_res",
        type=int,
        default=None,
        help="Max operating resolution used for LS alignment",
    )
    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()

    # -------------------- 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
        depth_raw_ts = data["depth_raw_linear"].squeeze()
        valid_mask_ts = data["valid_mask_raw"].squeeze()
        rgb_name = data["rgb_relative_path"][0]

        depth_raw = depth_raw_ts.numpy()
        valid_mask = valid_mask_ts.numpy()

        depth_raw_ts = depth_raw_ts.to(device)
        valid_mask_ts = valid_mask_ts.to(device)

        # Load predictions
        rgb_basename = os.path.basename(rgb_name)
        pred_basename = get_pred_name(rgb_basename, dataset.name_mode, suffix=".npy")
        pred_name = os.path.join(os.path.dirname(rgb_name), pred_basename)
        pred_path = os.path.join(args.prediction_dir, pred_name)
        depth_pred = np.load(pred_path).astype(np.float32)

        if not os.path.exists(pred_path):
            logging.warning(f"Can't find prediction: {pred_path}")
            continue

        # Align with GT using least square
        if "least_square" == args.alignment:
            depth_pred, scale, shift = align_depth_least_square(
                gt_arr=depth_raw,
                pred_arr=depth_pred,
                valid_mask_arr=valid_mask,
                return_scale_shift=True,
                max_resolution=args.alignment_max_res,
            )
        elif "least_square_disparity" == args.alignment:
            # convert GT depth -> GT disparity
            gt_disparity, gt_non_neg_mask = depth2disparity(
                depth=depth_raw, return_mask=True
            )
            # LS alignment in disparity space
            pred_non_neg_mask = depth_pred > 0
            valid_nonnegative_mask = valid_mask & gt_non_neg_mask & pred_non_neg_mask

            disparity_pred, scale, shift = align_depth_least_square(
                gt_arr=gt_disparity,
                pred_arr=depth_pred,
                valid_mask_arr=valid_nonnegative_mask,
                return_scale_shift=True,
                max_resolution=args.alignment_max_res,
            )
            # convert to depth
            disparity_pred = np.clip(
                disparity_pred, a_min=1e-3, a_max=None
            )  # avoid 0 disparity
            depth_pred = disparity2depth(disparity_pred)

        # Clip to dataset min max
        depth_pred = np.clip(
            depth_pred, a_min=dataset.min_depth, a_max=dataset.max_depth
        )

        # clip to d > 0 for evaluation
        depth_pred = np.clip(depth_pred, a_min=1e-6, a_max=None)

        # Evaluate (using CUDA if available)
        sample_metric = []
        depth_pred_ts = torch.from_numpy(depth_pred).to(device)

        for met_func in metric_funcs:
            _metric_name = met_func.__name__
            _metric = met_func(depth_pred_ts, depth_raw_ts, valid_mask_ts).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(pred_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 += f"min_depth = {dataset.min_depth}\n"
    eval_text += f"max_depth = {dataset.max_depth}\n"
    eval_text += tabulate(
        [metric_tracker.result().keys(), metric_tracker.result().values()]
    )

    metrics_filename = "eval_metrics"
    if args.alignment:
        metrics_filename += f"-{args.alignment}"
    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}")