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
import os

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)
    image_raw = Image.open(image_path)
    image = image_raw.resize(
        (800, int(800 * image_raw.size[1] / image_raw.size[0])),
        Image.Resampling.LANCZOS)

    # prepare image for the model
    encoding = feature_extractor(image, return_tensors="pt")

    # forward pass
    with torch.no_grad():
        outputs = model(**encoding)
        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,
    ).squeeze()
    output = prediction.cpu().numpy()
    depth_image = (output * 255 / np.max(output)).astype('uint8')
    try:
        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
        img = Image.fromarray(depth_image)
        return [img, gltf_path, gltf_path]
    except Exception as e:
        gltf_path = create_3d_obj(
            np.array(image), depth_image, image_path, depth=8)
        img = Image.fromarray(depth_image)
        return [img, gltf_path, gltf_path]
    except:
        print("Error reconstructing 3D model")
        raise Exception("Error reconstructing 3D model")


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 = int(depth_image.shape[1])
    h = int(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)

    print('normals')
    pcd.normals = o3d.utility.Vector3dVector(
        np.zeros((1, 3)))  # invalidate existing normals
    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.]))
    pcd.transform([[1, 0, 0, 0],
                   [0, -1, 0, 0],
                   [0, 0, -1, 0],
                   [0, 0, 0, 1]])
    pcd.transform([[-1, 0, 0, 0],
                   [0, 1, 0, 0],
                   [0, 0, 1, 0],
                   [0, 0, 0, 1]])

    print('run Poisson surface reconstruction')
    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
        mesh_raw, densities = 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
    print(f'voxel_size = {voxel_size:e}')
    mesh = mesh_raw.simplify_vertex_clustering(
        voxel_size=voxel_size,
        contraction=o3d.geometry.SimplificationContraction.Average)

    # vertices_to_remove = densities < np.quantile(densities, 0.001)
    # mesh.remove_vertices_by_mask(vertices_to_remove)
    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 = "LIVE: Towards Layer-wise Image Vectorization (CVPR 2022 Oral)"
description = "This demo shows the effectiveness of LIVE <a href='' target='_blank'>Paper</a>. Given the input image, LIVE is able to progressively build the SVG output with a layer-wise representation." \
              "<br>NOTE: for efficiency, we resize input images to 240x240 for Huggingface Space. "
examples = [["examples/" + img] for img in os.listdir("examples/")]

iface = gr.Interface(fn=process_image,
                     inputs=[gr.inputs.Image(type="filepath", label="Input Image")
                     ],
                     outputs=[gr.outputs.Image(label="predicted depth", type="pil"),
                              gr.outputs.Image3D(label="3d mesh reconstruction", clear_color=[
                                                 1.0, 1.0, 1.0, 1.0]),
                              gr.outputs.File(label="3d gLTF")],
                     title=title,
                     description=description,
                     examples=examples,
                     allow_flagging="never")
iface.launch(debug=True, enable_queue=False, cache_examples=True)