今年年初的Depth-Anything和去年刚出的SAM一样让人感觉震撼。

如何获得深度图？

Depth-Anything足够了。

就试试

如果只是想几张图片试着看看，可以直接去这个网址Depth Anything

如果像我一样想得到很多图片的深度图，就得本地安装了。

搭环境

参考https://github.com/LiheYoung/Depth-Anythinghttps://github.com/LiheYoung/Depth-Anything

conda create -n depthanything python=3.8

conda activate depthanything

git clone https://github.com/LiheYoung/Depth-Anything.git

cd Depth-Anything

pip install -r requirements.txt

运行

图片

在命令行输入

export HF_ENDPOINT=https://hf-mirror.com

python run.py --encoder vitb --img-path /home/xxx/train_329/0a2f2bd294/00000.jpg --outdir output/

视频

在命令行输入

 python run_video.py --encoder vitb --video-path assets/examples_video --outdir output/

批量处理脚本

在run.py的基础上修改

import argparse
import cv2
import numpy as np
import os
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose
from tqdm import tqdm

from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--img-path', type=str)
    parser.add_argument('--outdir', type=str, default='./vis_depth')
    parser.add_argument('--encoder', type=str, default='vitb', choices=['vits', 'vitb', 'vitl'])
    
    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()
    
    margin_width = 50
    caption_height = 60
    
    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = 1
    font_thickness = 2
    
    DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
    depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(args.encoder)).to(
        DEVICE).eval()

    total_params = sum(param.numel() for param in depth_anything.parameters())
    print('Total parameters: {:.2f}M'.format(total_params / 1e6))

    transform = Compose([
        Resize(
            width=518,
            height=518,
            resize_target=False,
            keep_aspect_ratio=True,
            ensure_multiple_of=14,
            resize_method='lower_bound',
            image_interpolation_method=cv2.INTER_CUBIC,
        ),
        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        PrepareForNet(),
    ])

    # ----------------------  change start -----------------------
    old_dir = "/home/xxxx/JPEGImages/"
    new_dir = "/home/xxxx/JPEGImages_dpanything/"
    pic_list = os.listdir(old_dir)
    # print(pic_list)
    for pic_folder in pic_list:
        input_dir = old_dir + pic_folder
        out_dir = new_dir + pic_folder
        if os.path.exists(out_dir):
            print("exist")
            continue
        print("processing: ", input_dir)

        if os.path.isfile(args.img_path):
            if args.img_path.endswith('txt'):
                with open(args.img_path, 'r') as f:
                    filenames = f.read().splitlines()
            else:
                filenames = [args.img_path]
        else:
            args.img_path = input_dir
            filenames = os.listdir(args.img_path)
            filenames = [os.path.join(args.img_path, filename) for filename in filenames if
                         not filename.startswith('.')]
            filenames.sort()
        args.outdir = out_dir
        os.makedirs(args.outdir, exist_ok=True)

        for filename in tqdm(filenames):
            raw_image = cv2.imread(filename)
            image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0

            h, w = image.shape[:2]

            image = transform({'image': image})['image']
            image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)

            with torch.no_grad():
                depth = depth_anything(image)

            depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0

            depth = depth.cpu().numpy().astype(np.uint8)

            if args.grayscale:
                depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
            else:
                depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)

            filename = os.path.basename(filename)

            args.pred_only = True
            if args.pred_only:
                cv2.imwrite(os.path.join(args.outdir, filename[:filename.rfind('.')] + '.jpg'), depth)
            else:
                split_region = np.ones((raw_image.shape[0], margin_width, 3), dtype=np.uint8) * 255
                combined_results = cv2.hconcat([raw_image, split_region, depth])

                caption_space = np.ones((caption_height, combined_results.shape[1], 3), dtype=np.uint8) * 255
                captions = ['Raw image', 'Depth Anything']
                segment_width = w + margin_width

                for i, caption in enumerate(captions):
                    # Calculate text size
                    text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0]

                    # Calculate x-coordinate to center the text
                    text_x = int((segment_width * i) + (w - text_size[0]) / 2)

                    # Add text caption
                    cv2.putText(caption_space, caption, (text_x, 40), font, font_scale, (0, 0, 0), font_thickness)

                final_result = cv2.vconcat([caption_space, combined_results])

                cv2.imwrite(os.path.join(args.outdir, filename[:filename.rfind('.')] + '_img_depth.png'), final_result)

    # ----------------------  change end  -----------------------