diff --git a/webcam.py b/webcam.py
new file mode 100644
index 00000000..38aabe49
--- /dev/null
+++ b/webcam.py
@@ -0,0 +1,82 @@
+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('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl'])
+    parser.add_argument('--frame_width', type=int, default=640)
+    parser.add_argument('--frame_height', type=int, default=480)
+    parser.add_argument('--fps', type=int, default=30)
+    
+    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)
+    
+    total_params = sum(param.numel() for param in depth_anything.parameters())
+    print('Total parameters: {:.2f}M'.format(total_params / 1e6))
+    
+    depth_anything.eval()
+    
+    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(),
+    ])
+
+    cap = cv2.VideoCapture(0)
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.frame_width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.frame_height)
+    cap.set(cv2.CAP_PROP_FPS, args.fps)
+    
+    while True:
+        ret, raw_image = cap.read()
+        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)
+        depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
+
+        cv2.namedWindow('rgb', cv2.WINDOW_NORMAL)
+        cv2.imshow('rgb', raw_image)
+
+        cv2.namedWindow('depth', cv2.WINDOW_NORMAL)
+        cv2.imshow('depth', depth_color)
+
+        cv2.waitKey(1)