app.py

import torch
import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
import streamlit as st
import cv2

# Load model and image processor
image_processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")

# Set the device for model (CUDA if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Use FP16 if available (half precision for speed)
if torch.cuda.is_available():
    model = model.half()

# Streamlit App
st.title("Depth Estimation from Webcam")

# Capture image from webcam
image_data = st.camera_input("Capture an image")

if image_data is not None:
    # Convert the captured image data to a PIL image
    image = Image.open(image_data)

    # Prepare the image for the model
    inputs = image_processor(images=image, return_tensors="pt").to(device)

    # Model inference (no gradients needed)
    with torch.no_grad():
        outputs = model(**inputs)
        predicted_depth = outputs.predicted_depth

    # Interpolate depth map to match the image's dimensions
    prediction = torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1),
        size=(image.height, image.width),  # Match the image's dimensions
        mode="bicubic",
        align_corners=False,
    )

    # Convert depth map to numpy for visualization
    depth_map = prediction.squeeze().cpu().numpy()

    # Normalize depth map for display (visualization purposes)
    depth_map_normalized = np.uint8(depth_map / np.max(depth_map) * 255)
    depth_map_colored = cv2.applyColorMap(depth_map_normalized, cv2.COLORMAP_JET)

    # Display the original image and the depth map in Streamlit
    st.image(image, caption="Captured Image", use_column_width=True)
    st.image(depth_map_colored, caption="Depth Map", use_column_width=True)