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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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