import argparse import cv2 import glob import matplotlib import numpy as np import os import torch from depth_anything_v2.dpt import DepthAnythingV2 if __name__ == '__main__': parser = argparse.ArgumentParser(description='Depth Anything V2 Metric Depth Estimation') parser.add_argument('--img-path', type=str) parser.add_argument('--input-size', type=int, default=518) parser.add_argument('--outdir', type=str, default='./vis_depth') parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg']) parser.add_argument('--load-from', type=str, default='checkpoints/depth_anything_v2_metric_hypersim_vitl.pth') parser.add_argument('--max-depth', type=float, default=20) parser.add_argument('--save-numpy', dest='save_numpy', action='store_true', help='save the model raw output') parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction') parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette') args = parser.parse_args() DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' model_configs = { 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} } depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth}) depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu')) depth_anything = depth_anything.to(DEVICE).eval() if os.path.isfile(args.img_path): if args.img_path.endswith('txt'): with open(args.img_path, 'r') as f: filenames = f.read().splitlines() else: filenames = [args.img_path] else: filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True) os.makedirs(args.outdir, exist_ok=True) cmap = matplotlib.colormaps.get_cmap('Spectral') for k, filename in enumerate(filenames): print(f'Progress {k+1}/{len(filenames)}: {filename}') raw_image = cv2.imread(filename) depth = depth_anything.infer_image(raw_image, args.input_size) if args.save_numpy: output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '_raw_depth_meter.npy') np.save(output_path, depth) depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.astype(np.uint8) if args.grayscale: depth = np.repeat(depth[..., np.newaxis], 3, axis=-1) else: depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8) output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png') if args.pred_only: cv2.imwrite(output_path, depth) else: split_region = np.ones((raw_image.shape[0], 50, 3), dtype=np.uint8) * 255 combined_result = cv2.hconcat([raw_image, split_region, depth]) cv2.imwrite(output_path, combined_result)