| import argparse | |
| import cv2 | |
| import glob | |
| import matplotlib | |
| import numpy as np | |
| import os | |
| import torch | |
| import torch.nn.functional as F | |
| from depth_anything_v2.dpt import DepthAnythingV2 | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description='Depth Anything V2') | |
| 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('--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]) | |
| depth_anything.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{args.encoder}.pth', 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_r') | |
| 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) | |
| 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) | |
| if args.pred_only: | |
| cv2.imwrite(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), 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(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), combined_result) |