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import spaces |
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import os |
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import gradio as gr |
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from PIL import Image |
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from pytorch3d.structures import Meshes |
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from gradio_app.utils import clean_up |
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from gradio_app.custom_models.mvimg_prediction import run_mvprediction |
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from gradio_app.custom_models.normal_prediction import predict_normals |
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from scripts.refine_lr_to_sr import run_sr_fast |
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from scripts.utils import save_glb_and_video |
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from scripts.multiview_inference import geo_reconstruct_part1, geo_reconstruct_part2, geo_reconstruct_part3 |
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@spaces.GPU(duration=100) |
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def run_mv(preview_img, input_processing, seed): |
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if preview_img.size[0] <= 512: |
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preview_img = run_sr_fast([preview_img])[0] |
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rgb_pils, front_pil = run_mvprediction(preview_img, remove_bg=input_processing, seed=int(seed)) |
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return rgb_pils, front_pil |
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@spaces.GPU(duration=100) |
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def generate3dv2(preview_img, input_processing, seed, render_video=True, do_refine=True, expansion_weight=0.1, init_type="std"): |
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if preview_img is None: |
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raise gr.Error("The input image is none!") |
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if isinstance(preview_img, str): |
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preview_img = Image.open(preview_img) |
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rgb_pils, front_pil = run_mv(preview_img, input_processing, seed) |
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vertices, faces, img_list = geo_reconstruct_part1(rgb_pils, None, front_pil, do_refine=do_refine, predict_normal=True, expansion_weight=expansion_weight, init_type=init_type) |
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meshes = geo_reconstruct_part2(vertices, faces) |
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new_meshes = geo_reconstruct_part3(meshes, img_list) |
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vertices = new_meshes.verts_packed() |
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vertices = vertices / 2 * 1.35 |
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vertices[..., [0, 2]] = - vertices[..., [0, 2]] |
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new_meshes = Meshes(verts=[vertices], faces=new_meshes.faces_list(), textures=new_meshes.textures) |
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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) |
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return ret_mesh, video |
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def create_ui(concurrency_id="wkl"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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input_image = gr.Image(type='pil', image_mode='RGBA', label='Frontview') |
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example_folder = os.path.join(os.path.dirname(__file__), "./examples") |
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example_fns = sorted([os.path.join(example_folder, example) for example in os.listdir(example_folder)]) |
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gr.Examples( |
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examples=example_fns, |
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inputs=[input_image], |
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cache_examples=False, |
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label='Examples', |
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examples_per_page=12 |
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) |
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with gr.Column(scale=1): |
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output_mesh = gr.Model3D(value=None, label="Mesh Model", show_label=True, height=320, camera_position=(90, 90, 2)) |
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output_video = gr.Video(label="Preview", show_label=True, show_share_button=True, height=320, visible=False) |
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input_processing = gr.Checkbox( |
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value=True, |
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label='Remove Background', |
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visible=True, |
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) |
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do_refine = gr.Checkbox(value=True, label="Refine Multiview Details", visible=False) |
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expansion_weight = gr.Slider(minimum=-1., maximum=1.0, value=0.1, step=0.1, label="Expansion Weight", visible=False) |
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init_type = gr.Dropdown(choices=["std", "thin"], label="Mesh Initialization", value="std", visible=False) |
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setable_seed = gr.Slider(-1, 1000000000, -1, step=1, visible=True, label="Seed") |
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render_video = gr.Checkbox(value=False, visible=False, label="generate video") |
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fullrunv2_btn = gr.Button('Generate 3D', variant = "primary", interactive=True) |
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fullrunv2_btn.click( |
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fn = generate3dv2, |
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inputs=[input_image, input_processing, setable_seed, render_video, do_refine, expansion_weight, init_type], |
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outputs=[output_mesh, output_video], |
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concurrency_id=concurrency_id, |
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api_name="generate3dv2", |
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).success(clean_up, api_name=False) |
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return input_image |
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