# 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, 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}")