mirror of
https://github.com/huchenlei/Depth-Anything.git
synced 2026-01-26 15:29:46 +00:00
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
import argparse
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import cv2
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import numpy as np
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import os
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import torch
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import torch.nn.functional as F
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from torchvision.transforms import Compose
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from depth_anything.dpt import DepthAnything
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from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--video-path', type=str)
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parser.add_argument('--outdir', type=str, default='./vis_video_depth')
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parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl'])
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args = parser.parse_args()
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margin_width = 50
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(args.encoder)).to(DEVICE).eval()
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total_params = sum(param.numel() for param in depth_anything.parameters())
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print('Total parameters: {:.2f}M'.format(total_params / 1e6))
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transform = Compose([
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Resize(
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width=518,
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height=518,
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resize_target=False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method='lower_bound',
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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])
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if os.path.isfile(args.video_path):
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if args.video_path.endswith('txt'):
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with open(args.video_path, 'r') as f:
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lines = f.read().splitlines()
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else:
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filenames = [args.video_path]
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else:
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filenames = os.listdir(args.video_path)
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filenames = [os.path.join(args.video_path, filename) for filename in filenames if not filename.startswith('.')]
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filenames.sort()
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os.makedirs(args.outdir, exist_ok=True)
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for k, filename in enumerate(filenames):
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print('Progress {:}/{:},'.format(k+1, len(filenames)), 'Processing', filename)
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raw_video = cv2.VideoCapture(filename)
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frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
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frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS))
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output_width = frame_width * 2 + margin_width
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filename = os.path.basename(filename)
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output_path = os.path.join(args.outdir, filename[:filename.rfind('.')] + '_video_depth.mp4')
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out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height))
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while raw_video.isOpened():
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ret, raw_frame = raw_video.read()
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if not ret:
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break
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB) / 255.0
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frame = transform({'image': frame})['image']
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frame = torch.from_numpy(frame).unsqueeze(0).to(DEVICE)
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with torch.no_grad():
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depth = depth_anything(frame)
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depth = F.interpolate(depth[None], (frame_height, frame_width), mode='bilinear', align_corners=False)[0, 0]
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth = depth.cpu().numpy().astype(np.uint8)
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depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
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split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
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combined_frame = cv2.hconcat([raw_frame, split_region, depth_color])
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out.write(combined_frame)
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raw_video.release()
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out.release()
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