diff --git a/Depth_ROS b/Depth_ROS
new file mode 100644
index 0000000..358afd3
--- /dev/null
+++ b/Depth_ROS
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+import rospy
+from sensor_msgs.msg import CompressedImage
+from cv_bridge import CvBridge
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torchvision.transforms import Compose
+from depth_anything.dpt import DepthAnything
+from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
+
+
+def image_callback(msg):
+    bridge = CvBridge()
+    try:
+        # Convert the compressed image to a cv2 format
+        raw_frame = bridge.compressed_imgmsg_to_cv2(msg, desired_encoding='passthrough')
+        frame_height, frame_width, _ = raw_frame.shape
+
+        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB) / 255.0
+        frame = transform({'image': frame})['image']
+        frame = torch.from_numpy(frame).unsqueeze(0).to(DEVICE)
+
+        with torch.no_grad():
+            depth = depth_anything(frame)
+
+        # Interpolating the depth to match the frame size
+        depth = F.interpolate(depth[None], (frame_height, frame_width), mode='bilinear', align_corners=False)[0, 0]
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.cpu().numpy().astype(np.uint8)
+
+        # Convert depth to color map for better visualization
+        depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
+
+        # Create split region and combine the raw frame and depth frame
+        split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
+        combined_frame = cv2.hconcat([raw_frame, split_region, depth_color])
+
+        # Display the result
+        cv2.imshow('Depth', depth_color)
+        cv2.waitKey(1)
+
+    except Exception as e:
+        rospy.logerr("Error processing image: {}".format(e))
+
+
+if __name__ == '__main__':
+    # Initialize ROS node
+    rospy.init_node('depth_estimation_from_camera', anonymous=True)
+
+    # Set up the device and model
+    DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+    depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vits14').to(DEVICE).eval()
+
+    total_params = sum(param.numel() for param in depth_anything.parameters())
+    print('Total parameters: {:.2f}M'.format(total_params / 1e6))
+
+    margin_width = 50
+
+    # Transformations for the input frame
+    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(),
+    ])
+
+    # Set up ROS subscriber to the video/compressed topic
+    rospy.Subscriber('/image/video/compressed', CompressedImage, image_callback)
+
+    # Keep the node running to continuously receive images
+    rospy.spin()