115 lines
4.9 KiB
Python
115 lines
4.9 KiB
Python
"""
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Born out of Depth Anything V1 Issue 36
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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 set of images to generate depth maps and corresponding point clouds.
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The resulting point clouds are saved in the specified output directory.
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Usage:
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python script.py --encoder vitl --load-from path_to_model --max-depth 20 --img-path path_to_images --outdir output_directory --focal-length-x 470.4 --focal-length-y 470.4
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Arguments:
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--encoder: Model encoder to use. Choices are ['vits', 'vitb', 'vitl', 'vitg'].
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--load-from: Path to the pre-trained model weights.
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--max-depth: Maximum depth value for the depth map.
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--img-path: Path to the input image or directory containing images.
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--outdir: Directory to save the output point clouds.
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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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"""
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import argparse
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import cv2
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import glob
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import numpy as np
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import open3d as o3d
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import os
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from PIL import Image
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import torch
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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='Generate depth maps and point clouds from images.')
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parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg'],
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help='Model encoder to use.')
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parser.add_argument('--load-from', default='', type=str, required=True,
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help='Path to the pre-trained model weights.')
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parser.add_argument('--max-depth', default=20, type=float,
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help='Maximum depth value for the depth map.')
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parser.add_argument('--img-path', type=str, required=True,
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help='Path to the input image or directory containing images.')
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parser.add_argument('--outdir', type=str, default='./vis_pointcloud',
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help='Directory to save the output point clouds.')
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parser.add_argument('--focal-length-x', default=470.4, type=float,
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help='Focal length along the x-axis.')
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parser.add_argument('--focal-length-y', default=470.4, type=float,
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help='Focal length along the y-axis.')
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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], 'max_depth': args.max_depth})
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depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu'))
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depth_anything = depth_anything.to(DEVICE).eval()
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# Get the list of image files to process
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if os.path.isfile(args.img_path):
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if args.img_path.endswith('txt'):
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with open(args.img_path, 'r') as f:
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filenames = f.read().splitlines()
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else:
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filenames = [args.img_path]
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else:
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filenames = glob.glob(os.path.join(args.img_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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# Process each image file
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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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# Load the image
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color_image = Image.open(filename).convert('RGB')
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width, height = color_image.size
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# Read the image using OpenCV
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image = cv2.imread(filename)
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pred = depth_anything.infer_image(image, height)
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# Resize depth prediction to match the original image size
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resized_pred = Image.fromarray(pred).resize((width, height), Image.NEAREST)
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# Generate mesh grid and calculate point cloud coordinates
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x, y = np.meshgrid(np.arange(width), np.arange(height))
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x = (x - width / 2) / args.focal_length_x
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y = (y - height / 2) / args.focal_length_y
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z = np.array(resized_pred)
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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 = np.array(color_image).reshape(-1, 3) / 255.0
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# Create the point cloud and save it to the output directory
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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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o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd)
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if __name__ == '__main__':
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main()
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