app.txt

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

# 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("Real-time Depth Estimation from Webcam")

# Initialize the webcam capture (OpenCV)
cap = cv2.VideoCapture(0)

# Streamlit button to capture a screenshot
if st.button("Capture Screenshot"):
    ret, frame = cap.read()
    if ret:
        # Process the frame for depth estimation
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame_rgb)

        # Prepare 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 frame's dimensions
        prediction = torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1),
            size=(frame.shape[0], frame.shape[1]),  # Match the frame'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 frame and the depth map in Streamlit
        st.image(frame, caption="Original Webcam Image", channels="BGR", use_column_width=True)
        st.image(depth_map_colored, caption="Depth Map", channels="BGR", use_column_width=True)

# Release the capture object when done
cap.release()