import os import gradio as gr from PIL import Image from pytorch3d.structures import Meshes from gradio_app.utils import clean_up from gradio_app.custom_models.mvimg_prediction import run_mvprediction from gradio_app.custom_models.normal_prediction import predict_normals from scripts.refine_lr_to_sr import run_sr_fast from scripts.utils import save_glb_and_video from scripts.multiview_inference import geo_reconstruct def generate3dv2(preview_img, input_processing, seed, render_video=True, do_refine=True, expansion_weight=0.1, init_type="std"): if preview_img is None: raise gr.Error("preview_img is none") if isinstance(preview_img, str): preview_img = Image.open(preview_img) if preview_img.size[0] <= 512: preview_img = run_sr_fast([preview_img])[0] rgb_pils, front_pil = run_mvprediction(preview_img, remove_bg=input_processing, seed=int(seed)) # 6s new_meshes = geo_reconstruct(rgb_pils, None, front_pil, do_refine=do_refine, predict_normal=True, expansion_weight=expansion_weight, init_type=init_type) vertices = new_meshes.verts_packed() vertices = vertices / 2 * 1.35 vertices[..., [0, 2]] = - vertices[..., [0, 2]] new_meshes = Meshes(verts=[vertices], faces=new_meshes.faces_list(), textures=new_meshes.textures) ret_mesh, video = save_glb_and_video("/tmp/gradio/generated", new_meshes, with_timestamp=True, dist=3.5, fov_in_degrees=2 / 1.35, cam_type="ortho", export_video=render_video) return ret_mesh, video ####################################### def create_ui(concurrency_id="wkl"): with gr.Row(): with gr.Column(scale=2): input_image = gr.Image(type='pil', image_mode='RGBA', label='Frontview') example_folder = os.path.join(os.path.dirname(__file__), "./examples") example_fns = sorted([os.path.join(example_folder, example) for example in os.listdir(example_folder)]) gr.Examples( examples=example_fns, inputs=[input_image], cache_examples=False, label='Examples (click one of the images below to start)', examples_per_page=12 ) with gr.Column(scale=3): # export mesh display output_mesh = gr.Model3D(value=None, label="Mesh Model", show_label=True, height=320) output_video = gr.Video(label="Preview", show_label=True, show_share_button=True, height=320, visible=False) input_processing = gr.Checkbox( value=True, label='Remove Background', visible=True, ) do_refine = gr.Checkbox(value=True, label="Refine Multiview Details", visible=False) expansion_weight = gr.Slider(minimum=-1., maximum=1.0, value=0.1, step=0.1, label="Expansion Weight", visible=False) init_type = gr.Dropdown(choices=["std", "thin"], label="Mesh Initialization", value="std", visible=False) setable_seed = gr.Slider(-1, 1000000000, -1, step=1, visible=True, label="Seed") render_video = gr.Checkbox(value=False, visible=False, label="generate video") fullrunv2_btn = gr.Button('Generate 3D', interactive=True) fullrunv2_btn.click( fn = generate3dv2, inputs=[input_image, input_processing, setable_seed, render_video, do_refine, expansion_weight, init_type], outputs=[output_mesh, output_video], concurrency_id=concurrency_id, api_name="generate3dv2", ).success(clean_up, api_name=False) return input_image