app.py

import gradio as gr
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import open3d as o3d
from pathlib import Path

# Load model and feature extractor
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")

def process_image(image_path):
    image_path = Path(image_path) if isinstance(image_path, str) else image_path
    try:
        image_raw = Image.open(image_path).convert("RGB")
    except Exception as e:
        return f"Error loading image: {e}"

    # Resize while maintaining aspect ratio
    image = image_raw.resize(
        (800, int(800 * image_raw.size[1] / image_raw.size[0])),
        Image.Resampling.LANCZOS
    )

    encoding = feature_extractor(image, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**encoding)
        predicted_depth = outputs.predicted_depth

    # Normalize depth image
    prediction = torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1),
        size=image.size[::-1],
        mode="bicubic",
        align_corners=False,
    ).squeeze()
    output = prediction.cpu().numpy()
    
    if np.max(output) > 0:
        depth_image = (output * 255 / np.max(output)).astype('uint8')
    else:
        depth_image = np.zeros_like(output, dtype='uint8')  # Handle empty output
    
    try:
        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
    except Exception:
        gltf_path = create_3d_obj(np.array(image), depth_image, image_path, depth=8)
    
    return [Image.fromarray(depth_image), gltf_path]

def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
    depth_o3d = o3d.geometry.Image(depth_image)
    image_o3d = o3d.geometry.Image(rgb_image)
    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
        image_o3d, depth_o3d, convert_rgb_to_intensity=False)

    w, h = depth_image.shape[1], depth_image.shape[0]
    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
    camera_intrinsic.set_intrinsics(w, h, 500, 500, w / 2, h / 2)

    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
    pcd.estimate_normals(
        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
    pcd.orient_normals_towards_camera_location(camera_location=np.array([0., 0., 1000.]))

    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):
        mesh_raw, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
            pcd, depth=depth, width=0, scale=1.1, linear_fit=True)

    voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256
    mesh = mesh_raw.simplify_vertex_clustering(voxel_size=voxel_size)

    bbox = pcd.get_axis_aligned_bounding_box()
    mesh_crop = mesh.crop(bbox)
    
    gltf_path = f'./{image_path.stem}.gltf'
    o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)
    
    return gltf_path

title = "Zero-shot Depth Estimation with DPT + 3D Point Cloud"
description = "DPT model predicts depth from an image, followed by 3D Point Cloud reconstruction."

with gr.Blocks() as iface:
    gr.Markdown("# Zero-shot Depth Estimation with DPT + 3D Point Cloud")
    with gr.Row():
        image_input = gr.Image(type="filepath", label="Input Image")
        depth_output = gr.Image(label="Predicted Depth", type="pil")
    gltf_output = gr.File(label="Download 3D gLTF")

    # Embed an iframe for previewing the .gltf
    with gr.Row():
        gr.HTML('<iframe id="gltf-viewer" width="100%" height="400px"></iframe>')

    def update_gltf_viewer(image_path):
        gltf_path = process_image(image_path)[1]
        iframe_html = f'''
        <script>
            document.getElementById('gltf-viewer').src = 'https://gltf-viewer.donmccurdy.com/?url=file://{gltf_path}';
        </script>
        '''
        return process_image(image_path)[0], gltf_path, iframe_html

    image_input.change(update_gltf_viewer, inputs=[image_input], outputs=[depth_output, gltf_output, gr.HTML()])

iface.launch()