# Depth Anything

Estimate depth from single images with Depth Anything.

{% hint style="success" %}
All examples can be run on GPU servers rented through [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Renting on CLORE.AI

1. Visit [CLORE.AI Marketplace](https://clore.ai/marketplace)
2. Filter by GPU type, VRAM, and price
3. Choose **On-Demand** (fixed rate) or **Spot** (bid price)
4. Configure your order:
   * Select Docker image
   * Set ports (TCP for SSH, HTTP for web UIs)
   * Add environment variables if needed
   * Enter startup command
5. Select payment: **CLORE**, **BTC**, or **USDT/USDC**
6. Create order and wait for deployment

### Access Your Server

* Find connection details in **My Orders**
* Web interfaces: Use the HTTP port URL
* SSH: `ssh -p <port> root@<proxy-address>`

## What is Depth Anything?

Depth Anything provides:

* State-of-the-art depth estimation
* Works on any image
* No stereo camera needed
* Fast inference

## Model Variants

| Model                | Size    | VRAM  | Speed        |
| -------------------- | ------- | ----- | ------------ |
| Depth-Anything-Small | 25M     | 2GB   | Fastest      |
| Depth-Anything-Base  | 98M     | 4GB   | Fast         |
| Depth-Anything-Large | 335M    | 8GB   | Best quality |
| Depth-Anything-V2    | Various | 4-8GB | Latest       |

## Quick Deploy

**Docker Image:**

```
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
```

**Ports:**

```
22/tcp
7860/http
```

**Command:**

```bash
pip install transformers torch gradio && \
python depth_anything_app.py
```

## Accessing Your Service

After deployment, find your `http_pub` URL in **My Orders**:

1. Go to **My Orders** page
2. Click on your order
3. Find the `http_pub` URL (e.g., `abc123.clorecloud.net`)

Use `https://YOUR_HTTP_PUB_URL` instead of `localhost` in examples below.

## Installation

```bash
pip install transformers torch
pip install opencv-python pillow
```

## Basic Usage

```python
from transformers import pipeline
from PIL import Image

# Load depth estimation pipeline
pipe = pipeline(
    task="depth-estimation",
    model="LiheYoung/depth-anything-large-hf",
    device="cuda"
)

# Estimate depth
image = Image.open("photo.jpg")
depth = pipe(image)

# Save depth map
depth["depth"].save("depth_map.png")
```

## Depth Anything V2

```python
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
import torch
from PIL import Image
import numpy as np

# Load model
processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Large-hf")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Large-hf")
model.to("cuda")

# Process image
image = Image.open("photo.jpg")
inputs = processor(images=image, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model(**inputs)
    predicted_depth = outputs.predicted_depth

# Interpolate to original size
prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
)

# Convert to numpy
depth = prediction.squeeze().cpu().numpy()
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
depth = depth.astype(np.uint8)

# Save
Image.fromarray(depth).save("depth.png")
```

## Colorized Depth Map

```python
import cv2
import numpy as np
from PIL import Image

def colorize_depth(depth_array, colormap=cv2.COLORMAP_INFERNO):
    # Normalize to 0-255
    depth_normalized = cv2.normalize(depth_array, None, 0, 255, cv2.NORM_MINMAX)
    depth_uint8 = depth_normalized.astype(np.uint8)

    # Apply colormap
    colored = cv2.applyColorMap(depth_uint8, colormap)

    return Image.fromarray(cv2.cvtColor(colored, cv2.COLOR_BGR2RGB))

# Usage
depth_colored = colorize_depth(depth)
depth_colored.save("depth_colored.png")
```

## Batch Processing

```python
from transformers import pipeline
from PIL import Image
import os

pipe = pipeline(
    task="depth-estimation",
    model="LiheYoung/depth-anything-large-hf",
    device="cuda"
)

input_dir = "./images"
output_dir = "./depth_maps"
os.makedirs(output_dir, exist_ok=True)

for filename in os.listdir(input_dir):
    if filename.endswith(('.jpg', '.png', '.jpeg')):
        image_path = os.path.join(input_dir, filename)
        image = Image.open(image_path)

        # Get depth
        depth = pipe(image)

        # Save
        output_path = os.path.join(output_dir, f"depth_{filename}")
        depth["depth"].save(output_path)
        print(f"Processed: {filename}")
```

## Gradio Interface

```python
import gradio as gr
from transformers import pipeline
import cv2
import numpy as np

pipe = pipeline(
    task="depth-estimation",
    model="LiheYoung/depth-anything-large-hf",
    device="cuda"
)

def estimate_depth(image, colormap):
    # Get depth
    result = pipe(image)
    depth = np.array(result["depth"])

    # Colorize
    depth_normalized = cv2.normalize(depth, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)

    colormaps = {
        "Inferno": cv2.COLORMAP_INFERNO,
        "Viridis": cv2.COLORMAP_VIRIDIS,
        "Plasma": cv2.COLORMAP_PLASMA,
        "Magma": cv2.COLORMAP_MAGMA,
        "Jet": cv2.COLORMAP_JET
    }

    colored = cv2.applyColorMap(depth_normalized, colormaps[colormap])
    colored = cv2.cvtColor(colored, cv2.COLOR_BGR2RGB)

    return result["depth"], colored

demo = gr.Interface(
    fn=estimate_depth,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Dropdown(
            ["Inferno", "Viridis", "Plasma", "Magma", "Jet"],
            value="Inferno",
            label="Colormap"
        )
    ],
    outputs=[
        gr.Image(label="Depth Map (Grayscale)"),
        gr.Image(label="Depth Map (Colored)")
    ],
    title="Depth Anything - Depth Estimation"
)

demo.launch(server_name="0.0.0.0", server_port=7860)
```

## API Server

```python
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import Response
from transformers import pipeline
from PIL import Image
import io
import numpy as np
import cv2

app = FastAPI()

pipe = pipeline(
    task="depth-estimation",
    model="LiheYoung/depth-anything-large-hf",
    device="cuda"
)

@app.post("/depth")
async def estimate_depth(image: UploadFile = File(...), colored: bool = True):
    # Load image
    img = Image.open(io.BytesIO(await image.read()))

    # Estimate depth
    result = pipe(img)
    depth = np.array(result["depth"])

    if colored:
        depth_normalized = cv2.normalize(depth, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
        depth_img = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_INFERNO)
        depth_img = cv2.cvtColor(depth_img, cv2.COLOR_BGR2RGB)
    else:
        depth_img = depth

    # Convert to bytes
    output = Image.fromarray(depth_img)
    buffer = io.BytesIO()
    output.save(buffer, format="PNG")

    return Response(content=buffer.getvalue(), media_type="image/png")

# Run: uvicorn server:app --host 0.0.0.0 --port 8000
```

## 3D Point Cloud Generation

```python
import numpy as np
import open3d as o3d
from PIL import Image

def depth_to_pointcloud(rgb_image, depth_map, focal_length=500):
    """Convert RGB image and depth map to 3D point cloud"""
    rgb = np.array(rgb_image)
    depth = np.array(depth_map)

    # Get image dimensions
    height, width = depth.shape

    # Create mesh grid
    u = np.arange(width)
    v = np.arange(height)
    u, v = np.meshgrid(u, v)

    # Convert to 3D coordinates
    z = depth.astype(float)
    x = (u - width / 2) * z / focal_length
    y = (v - height / 2) * z / focal_length

    # Stack coordinates
    points = np.stack([x, y, z], axis=-1).reshape(-1, 3)
    colors = rgb.reshape(-1, 3) / 255.0

    # Create point cloud
    pcd = o3d.geometry.PointCloud()
    pcd.points = o3d.utility.Vector3dVector(points)
    pcd.colors = o3d.utility.Vector3dVector(colors)

    return pcd

# Usage
rgb = Image.open("photo.jpg")
depth = pipe(rgb)["depth"]

pcd = depth_to_pointcloud(rgb, depth)
o3d.io.write_point_cloud("output.ply", pcd)
```

## Use Cases

### 3D Photo Effect

```python
def create_3d_photo(image, depth, shift=20):
    """Create parallax effect for 3D photos"""
    import cv2
    import numpy as np

    img = np.array(image)
    depth_arr = np.array(depth)

    # Normalize depth
    depth_norm = (depth_arr - depth_arr.min()) / (depth_arr.max() - depth_arr.min())

    # Create shifted version
    shifted = np.zeros_like(img)
    for y in range(img.shape[0]):
        for x in range(img.shape[1]):
            offset = int(shift * depth_norm[y, x])
            new_x = min(x + offset, img.shape[1] - 1)
            shifted[y, new_x] = img[y, x]

    return Image.fromarray(shifted)
```

### Background Blur (Portrait Mode)

```python
def portrait_mode(image, depth, blur_strength=25):
    import cv2
    import numpy as np

    img = np.array(image)
    depth_arr = np.array(depth)

    # Normalize depth
    depth_norm = (depth_arr - depth_arr.min()) / (depth_arr.max() - depth_arr.min())

    # Create blur mask (background = high depth = more blur)
    blur_mask = depth_norm

    # Apply blur
    blurred = cv2.GaussianBlur(img, (blur_strength, blur_strength), 0)

    # Blend based on depth
    mask_3d = np.stack([blur_mask] * 3, axis=-1)
    result = (img * (1 - mask_3d) + blurred * mask_3d).astype(np.uint8)

    return Image.fromarray(result)
```

## Performance

| Model    | GPU      | Time per Image |
| -------- | -------- | -------------- |
| Small    | RTX 3060 | \~50ms         |
| Base     | RTX 3060 | \~100ms        |
| Large    | RTX 3090 | \~150ms        |
| Large    | RTX 4090 | \~80ms         |
| V2-Large | RTX 4090 | \~100ms        |

## Troubleshooting

### Poor Depth Quality

* Use larger model variant
* Ensure good image quality
* Check for reflective surfaces

### Memory Issues

* Use smaller model variant
* Reduce image resolution
* Enable fp16 inference

### Slow Processing

* Use smaller model
* Batch process if possible
* Use GPU inference

## Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

| GPU       | Hourly Rate | Daily Rate | 4-Hour Session |
| --------- | ----------- | ---------- | -------------- |
| RTX 3060  | \~$0.03     | \~$0.70    | \~$0.12        |
| RTX 3090  | \~$0.06     | \~$1.50    | \~$0.25        |
| RTX 4090  | \~$0.10     | \~$2.30    | \~$0.40        |
| A100 40GB | \~$0.17     | \~$4.00    | \~$0.70        |
| A100 80GB | \~$0.25     | \~$6.00    | \~$1.00        |

*Prices vary by provider and demand. Check* [*CLORE.AI Marketplace*](https://clore.ai/marketplace) *for current rates.*

**Save money:**

* Use **Spot** market for flexible workloads (often 30-50% cheaper)
* Pay with **CLORE** tokens
* Compare prices across different providers

## Next Steps

* [ControlNet](https://docs.clore.ai/guides/image-processing/controlnet-advanced) - Use depth for control
* [Segment Anything](https://docs.clore.ai/guides/image-processing/segment-anything) - Object segmentation
* [3D Generation](https://docs.clore.ai/guides/3d-generation/triposr) - Video depth