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