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') parser.add_argument('--video-path', type=str) parser.add_argument('--input-size', type=int, default=518) parser.add_argument('--outdir', type=str, default='./vis_video_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' # 'we are undergoing company review procedures to release Depth-Anything-Giant checkpoint 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.video_path): if args.video_path.endswith('txt'): with open(args.video_path, 'r') as f: lines = f.read().splitlines() else: filenames = [args.video_path] else: filenames = glob.glob(os.path.join(args.video_path, '**/*'), recursive=True) os.makedirs(args.outdir, exist_ok=True) margin_width = 50 cmap = matplotlib.colormaps.get_cmap('Spectral_r') for k, filename in enumerate(filenames): print(f'Progress {k+1}/{len(filenames)}: {filename}') raw_video = cv2.VideoCapture(filename) frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS)) output_width = frame_width * 2 + margin_width output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.mp4') out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height)) while raw_video.isOpened(): ret, raw_frame = raw_video.read() if not ret: break depth = depth_anything.infer_image(raw_frame, 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: out.write(depth) else: split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255 combined_frame = cv2.hconcat([raw_frame, split_region, depth]) out.write(combined_frame) raw_video.release() out.release()