169 lines
7.2 KiB
Python
169 lines
7.2 KiB
Python
"""
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Born out of Depth Anything V2
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Make sure you have the necessary libraries installed.
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Code by @1ssb
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This script processes a video to generate depth maps and corresponding point clouds for each frame.
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The resulting depth maps are saved in a video format, and the point clouds can be interactively generated for selected frames.
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Usage:
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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
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Arguments:
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--video-path: Path to the input video.
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--input-size: Size to which the input frame is resized for depth prediction.
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--outdir: Directory to save the output video and point clouds.
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--encoder: Model encoder to use. Choices are ['vits', 'vitb', 'vitl', 'vitg'].
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--focal-length-x: Focal length along the x-axis.
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--focal-length-y: Focal length along the y-axis.
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--pred-only: Only display the prediction without the original frame.
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--grayscale: Do not apply colorful palette to the depth map.
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"""
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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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import open3d as o3d
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from depth_anything_v2.dpt import DepthAnythingV2
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def main():
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# Parse command-line arguments
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parser = argparse.ArgumentParser(description='Depth Anything V2 with Point Cloud Generation')
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parser.add_argument('--video-path', type=str, required=True, help='Path to the input video.')
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parser.add_argument('--input-size', type=int, default=518, help='Size to which the input frame is resized for depth prediction.')
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parser.add_argument('--outdir', type=str, default='./vis_video_depth', help='Directory to save the output video and point clouds.')
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parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'], help='Model encoder to use.')
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parser.add_argument('--focal-length-x', default=470.4, type=float, help='Focal length along the x-axis.')
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parser.add_argument('--focal-length-y', default=470.4, type=float, help='Focal length along the y-axis.')
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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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# Determine the device to use (CUDA, MPS, or CPU)
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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 configuration based on the chosen encoder
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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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# Initialize the DepthAnythingV2 model with the specified configuration
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depth_anything = DepthAnythingV2(**model_configs[args.encoder])
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depth_anything.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{args.encoder}.pth', map_location='cpu'))
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depth_anything = depth_anything.to(DEVICE).eval()
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# Get the list of video files to process
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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 = glob.glob(os.path.join(args.video_path, '**/*'), recursive=True)
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# Create the output directory if it doesn't exist
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os.makedirs(args.outdir, exist_ok=True)
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margin_width = 50
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cmap = matplotlib.colormaps.get_cmap('Spectral_r')
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for k, filename in enumerate(filenames):
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print(f'Processing {k+1}/{len(filenames)}: {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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if args.pred_only:
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output_width = frame_width
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else:
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output_width = frame_width * 2 + margin_width
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output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.mp4')
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out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height))
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frame_index = 0
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frame_data = []
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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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depth = depth_anything.infer_image(raw_frame, args.input_size)
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depth_normalized = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth_normalized = depth_normalized.astype(np.uint8)
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if args.grayscale:
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depth_colored = np.repeat(depth_normalized[..., np.newaxis], 3, axis=-1)
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else:
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depth_colored = (cmap(depth_normalized)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
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if args.pred_only:
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out.write(depth_colored)
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else:
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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_colored])
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out.write(combined_frame)
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frame_data.append((raw_frame, depth, depth_colored))
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frame_index += 1
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raw_video.release()
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out.release()
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# Function to create point cloud from depth map
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def create_point_cloud(raw_frame, depth_map, frame_index):
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height, width = raw_frame.shape[:2]
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focal_length_x = args.focal_length_x
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focal_length_y = args.focal_length_y
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x, y = np.meshgrid(np.arange(width), np.arange(height))
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x = (x - width / 2) / focal_length_x
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y = (y - height / 2) / focal_length_y
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z = np.array(depth_map)
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points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3)
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colors = raw_frame.reshape(-1, 3) / 255.0
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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pcd.colors = o3d.utility.Vector3dVector(colors)
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pcd_path = os.path.join(args.outdir, f'frame_{frame_index}_point_cloud.ply')
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o3d.io.write_point_cloud(pcd_path, pcd)
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print(f'Point cloud saved to {pcd_path}')
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# Interactive window to select a frame and generate its point cloud
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def on_trackbar(val):
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frame_index = val
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raw_frame, depth_map, _ = frame_data[frame_index]
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create_point_cloud(raw_frame, depth_map, frame_index)
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if frame_data:
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cv2.namedWindow('Select Frame for Point Cloud')
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cv2.createTrackbar('Frame', 'Select Frame for Point Cloud', 0, frame_index - 1, on_trackbar)
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while True:
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key = cv2.waitKey(1) & 0xFF
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if key == 27: # Esc key to exit
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break
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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