# 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 PIL import Image from glob import glob from tqdm.auto import tqdm from olbedo import OlbedoIIDPipeline, OlbedoIIDOutput from olbedo.util.image_util import chw2hwc import rasterio EXTENSION_LIST = [".jpg", ".jpeg", ".png", ".tif", ".tiff"] if "__main__" == __name__: logging.basicConfig(level=logging.INFO) # -------------------- Arguments -------------------- parser = argparse.ArgumentParser( description="Olbedo: Intrinsic Image Decomposition for Large-Scale Outdoor Environments" ) parser.add_argument( "--checkpoint", type=str, default="checkpoint", help="Checkpoint path or hub name.", ) parser.add_argument( "--input_rgb_dir", type=str, required=True, help="Path to the input image folder.", ) parser.add_argument( "--output_dir", type=str, required=True, help="Output directory." ) parser.add_argument( "--denoise_steps", type=int, default=4, help="Diffusion denoising steps, more steps results in higher accuracy but slower inference speed. If set to " "`None`, default value will be read from checkpoint.", ) parser.add_argument( "--processing_res", type=int, default=2000, help="Resolution to which the input is resized before performing estimation. `0` uses the original input " "resolution; `None` resolves the best default from the model checkpoint. Default: `None`", ) parser.add_argument( "--ensemble_size", type=int, default=1, help="Number of predictions to be ensembled. Default: `1`.", ) parser.add_argument( "--half_precision", "--fp16", action="store_true", help="Run with half-precision (16-bit float), might lead to suboptimal result.", ) parser.add_argument( "--output_processing_res", action="store_true", help="Setting this flag will output the result at the effective value of `processing_res`, otherwise the " "output will be resized to the input resolution.", ) parser.add_argument( "--resample_method", choices=["bilinear", "bicubic", "nearest"], default="bilinear", help="Resampling method used to resize images and predictions. This can be one of `bilinear`, `bicubic` or " "`nearest`. Default: `bilinear`", ) parser.add_argument( "--seed", type=int, default=None, help="Reproducibility seed. Set to `None` for randomized inference. Default: `None`", ) parser.add_argument( "--batch_size", type=int, default=0, help="Inference batch size. Default: 0 (will be set automatically).", ) parser.add_argument( "--model", type=str, default="rgbx", choices=["rgbx", "others"], help="Choose model", ) args = parser.parse_args() checkpoint_path = args.checkpoint input_rgb_dir = args.input_rgb_dir output_dir = args.output_dir denoise_steps = args.denoise_steps ensemble_size = args.ensemble_size if ensemble_size > 15: logging.warning("Running with large ensemble size will be slow.") half_precision = args.half_precision processing_res = args.processing_res match_input_res = not args.output_processing_res if 0 == processing_res and match_input_res is False: logging.warning( "Processing at native resolution without resizing output might NOT lead to exactly the same resolution, " "due to the padding and pooling properties of conv layers." ) resample_method = args.resample_method seed = args.seed batch_size = args.batch_size model = args.model # -------------------- Preparation -------------------- # Output directories output_dir_vis = os.path.join(output_dir, "albedo") os.makedirs(output_dir, exist_ok=True) os.makedirs(output_dir_vis, exist_ok=True) logging.info(f"output dir = {output_dir}") # -------------------- Device -------------------- if torch.cuda.is_available(): device = torch.device("cuda") else: device = torch.device("cpu") logging.warning("CUDA is not available. Running on CPU will be slow.") logging.info(f"device = {device}") # -------------------- Data -------------------- rgb_filename_list = glob(os.path.join(input_rgb_dir, "*")) rgb_filename_list = [ f for f in rgb_filename_list if os.path.splitext(f)[1].lower() in EXTENSION_LIST ] rgb_filename_list = sorted(rgb_filename_list) n_images = len(rgb_filename_list) if n_images > 0: logging.info(f"Found {n_images} images") else: logging.error(f"No image found in '{input_rgb_dir}'") exit(1) # -------------------- Model -------------------- if half_precision: dtype = torch.float16 variant = "fp16" logging.info( f"Running with half precision ({dtype}), might lead to suboptimal result." ) else: dtype = torch.float32 variant = None pipe: OlbedoIIDPipeline = OlbedoIIDPipeline.from_pretrained( checkpoint_path, variant=variant, torch_dtype=dtype ) pipe.mode = model try: pipe.enable_xformers_memory_efficient_attention() except ImportError: pass # run without xformers pipe = pipe.to(device) logging.info("Loaded IID pipeline") # Print out config logging.info( f"Inference settings: checkpoint = `{checkpoint_path}`, " f"with predicted target names: {pipe.target_names}, " f"denoise_steps = {denoise_steps or pipe.default_denoising_steps}, " f"ensemble_size = {ensemble_size}, " f"processing resolution = {processing_res or pipe.default_processing_resolution}, " f"seed = {seed}; " ) # -------------------- Inference and saving -------------------- with torch.no_grad(): os.makedirs(output_dir, exist_ok=True) for rgb_path in tqdm(rgb_filename_list, desc="IID Inference", leave=True): # Read input image input_image = Image.open(rgb_path) # Random number generator if seed is None: generator = None else: generator = torch.Generator(device=device) generator.manual_seed(seed) # Perform inference pipe_out: OlbedoIIDOutput = pipe( input_image, denoising_steps=denoise_steps, ensemble_size=ensemble_size, processing_res=processing_res, match_input_res=match_input_res, batch_size=batch_size, show_progress_bar=False, resample_method=resample_method, generator=generator, ) rgb_name_base, rgb_ext = os.path.splitext(os.path.basename(rgb_path)) for target_name in pipe.target_names: if target_name!='albedo': continue target_entry = pipe_out[target_name] if rgb_ext.lower() in [".tif", ".tiff"]: img = np.array(target_entry.image).transpose((2, 0, 1)) with rasterio.open(rgb_path) as src: profile = src.profile.copy() with rasterio.open(os.path.join(output_dir_vis, f"{rgb_name_base}.tif"), 'w', **profile) as dst: dst.write(img.astype(profile['dtype'])) else: target_entry.image.save( os.path.join(output_dir_vis, f"{rgb_name_base}{rgb_ext}") )