mirror of
https://github.com/MackinationsAi/UDAV2-ControlNet.git
synced 2026-01-26 15:29:47 +00:00
83 lines
3.7 KiB
Python
83 lines
3.7 KiB
Python
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 warnings
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from tqdm import tqdm
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from safetensors.torch import load_file
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from depth_anything_v2.dpt import DepthAnythingV2
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# Code upgraded by: MackinationsAi
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warnings.filterwarnings("ignore", message=".*cudnnStatus.*")
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def process_image(img_path, output_path, input_size, encoder, pred_only, grayscale):
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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_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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depth_anything = DepthAnythingV2(**model_configs[encoder])
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state_dict = load_file(f'checkpoints/depth_anything_v2_{encoder}.safetensors')
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depth_anything.load_state_dict(state_dict)
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depth_anything = depth_anything.to(DEVICE).eval()
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if os.path.isfile(img_path) and (img_path.endswith('.png') or img_path.endswith('.jpg') or img_path.endswith('.jpeg')):
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filenames = [img_path]
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else:
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filenames = glob.glob(os.path.join(img_path, '**/*.*'), recursive=True)
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filenames = [f for f in filenames if f.endswith(('.png', '.jpg', '.jpeg'))]
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os.makedirs(output_path, exist_ok=True)
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cmap = matplotlib.colormaps.get_cmap('gray')
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for k, filename in enumerate(tqdm(filenames, desc="Processing images", unit="image")):
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print(f'Progress {k+1}/{len(filenames)}: {filename}')
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raw_image = cv2.imread(filename)
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depth = depth_anything.infer_image(raw_image, input_size)
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 65025.0
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depth = depth.astype(np.uint16)
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depth_gray = np.repeat(depth[..., np.newaxis], 3, axis=-1)
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cv2.imwrite(os.path.join(output_path, os.path.splitext(os.path.basename(filename))[0] + '_depth_grayscale.png'), depth_gray)
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def main():
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while True:
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parser = argparse.ArgumentParser(description='Depth Anything V2')
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parser.add_argument('--img-path', type=str, help='Path to the image file or directory containing images')
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parser.add_argument('--input-size', type=int, default=2018)
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parser.add_argument('--outdir', type=str, default='vis_img_depth', help='Output directory')
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parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
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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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if not args.img_path:
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args.img_path = input("Path to image file/directory, can right click a file and Copy as Path, remove quotation marks if any: ").strip()
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if not args.outdir:
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args.outdir = input("Please enter the output directory (default is 'vis_img_depth'): ").strip() or 'vis_depth'
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process_image(args.img_path, args.outdir, args.input_size, args.encoder, args.pred_only, args.grayscale)
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again = input("Convert another image? Y/N: ").strip().lower()
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if again not in ['y', 'yes']:
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break
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if __name__ == '__main__':
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main()
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