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| """ | |
| Born out of Depth Anything V2 | |
| Make sure you have the necessary libraries installed. | |
| Code by @1ssb | |
| This script processes a video to generate depth maps and corresponding point clouds for each frame. | |
| The resulting depth maps are saved in a video format, and the point clouds can be interactively generated for selected frames. | |
| Usage: | |
| python script.py --video-path path_to_video --input-size 518 --outdir output_directory --encoder vitl --focal-length-x 470.4 --focal-length-y 470.4 --pred-only --grayscale | |
| Arguments: | |
| --video-path: Path to the input video. | |
| --input-size: Size to which the input frame is resized for depth prediction. | |
| --outdir: Directory to save the output video and point clouds. | |
| --encoder: Model encoder to use. Choices are ['vits', 'vitb', 'vitl', 'vitg']. | |
| --focal-length-x: Focal length along the x-axis. | |
| --focal-length-y: Focal length along the y-axis. | |
| --pred-only: Only display the prediction without the original frame. | |
| --grayscale: Do not apply colorful palette to the depth map. | |
| """ | |
| import argparse | |
| import cv2 | |
| import glob | |
| import matplotlib | |
| import numpy as np | |
| import os | |
| import torch | |
| import open3d as o3d | |
| from depth_anything_v2.dpt import DepthAnythingV2 | |
| def main(): | |
| # Parse command-line arguments | |
| parser = argparse.ArgumentParser(description='Depth Anything V2 with Point Cloud Generation') | |
| parser.add_argument('--video-path', type=str, required=True, help='Path to the input video.') | |
| parser.add_argument('--input-size', type=int, default=518, help='Size to which the input frame is resized for depth prediction.') | |
| parser.add_argument('--outdir', type=str, default='./vis_video_depth', help='Directory to save the output video and point clouds.') | |
| parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'], help='Model encoder to use.') | |
| parser.add_argument('--focal-length-x', default=470.4, type=float, help='Focal length along the x-axis.') | |
| parser.add_argument('--focal-length-y', default=470.4, type=float, help='Focal length along the y-axis.') | |
| 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() | |
| # Determine the device to use (CUDA, MPS, or CPU) | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' | |
| # Model configuration based on the chosen encoder | |
| 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]} | |
| } | |
| # Initialize the DepthAnythingV2 model with the specified configuration | |
| 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() | |
| # Get the list of video files to process | |
| 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) | |
| # Create the output directory if it doesn't exist | |
| 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'Processing {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)) | |
| if args.pred_only: | |
| output_width = frame_width | |
| else: | |
| 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)) | |
| frame_index = 0 | |
| frame_data = [] | |
| 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_normalized = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 | |
| depth_normalized = depth_normalized.astype(np.uint8) | |
| if args.grayscale: | |
| depth_colored = np.repeat(depth_normalized[..., np.newaxis], 3, axis=-1) | |
| else: | |
| depth_colored = (cmap(depth_normalized)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8) | |
| if args.pred_only: | |
| out.write(depth_colored) | |
| else: | |
| split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255 | |
| combined_frame = cv2.hconcat([raw_frame, split_region, depth_colored]) | |
| out.write(combined_frame) | |
| frame_data.append((raw_frame, depth, depth_colored)) | |
| frame_index += 1 | |
| raw_video.release() | |
| out.release() | |
| # Function to create point cloud from depth map | |
| def create_point_cloud(raw_frame, depth_map, frame_index): | |
| height, width = raw_frame.shape[:2] | |
| focal_length_x = args.focal_length_x | |
| focal_length_y = args.focal_length_y | |
| x, y = np.meshgrid(np.arange(width), np.arange(height)) | |
| x = (x - width / 2) / focal_length_x | |
| y = (y - height / 2) / focal_length_y | |
| z = np.array(depth_map) | |
| points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3) | |
| colors = raw_frame.reshape(-1, 3) / 255.0 | |
| pcd = o3d.geometry.PointCloud() | |
| pcd.points = o3d.utility.Vector3dVector(points) | |
| pcd.colors = o3d.utility.Vector3dVector(colors) | |
| pcd_path = os.path.join(args.outdir, f'frame_{frame_index}_point_cloud.ply') | |
| o3d.io.write_point_cloud(pcd_path, pcd) | |
| print(f'Point cloud saved to {pcd_path}') | |
| # Interactive window to select a frame and generate its point cloud | |
| def on_trackbar(val): | |
| frame_index = val | |
| raw_frame, depth_map, _ = frame_data[frame_index] | |
| create_point_cloud(raw_frame, depth_map, frame_index) | |
| if frame_data: | |
| cv2.namedWindow('Select Frame for Point Cloud') | |
| cv2.createTrackbar('Frame', 'Select Frame for Point Cloud', 0, frame_index - 1, on_trackbar) | |
| while True: | |
| key = cv2.waitKey(1) & 0xFF | |
| if key == 27: # Esc key to exit | |
| break | |
| cv2.destroyAllWindows() | |
| if __name__ == '__main__': | |
| main() | |