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import gradio as gr |
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from PIL import Image |
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from gradio_app.custom_models.mvimg_prediction import run_mvprediction |
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from gradio_app.utils import make_image_grid, split_image |
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from scripts.utils import save_glb_and_video |
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def concept_to_multiview(preview_img, input_processing, seed, guidance=1.): |
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seed = int(seed) |
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if preview_img is None: |
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raise gr.Error("preview_img 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_mvprediction(preview_img, remove_bg=input_processing, seed=seed, guidance_scale=guidance) |
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rgb_pil = make_image_grid(rgb_pils, rows=2) |
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return rgb_pil, front_pil |
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def concept_to_multiview_ui(concurrency_id="wkl"): |
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with gr.Row(): |
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with gr.Column(scale=2): |
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preview_img = gr.Image(type='pil', image_mode='RGBA', label='Frontview') |
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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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) |
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seed = gr.Slider(minimum=-1, maximum=1000000000, value=-1, step=1.0, label="seed") |
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guidance = gr.Slider(minimum=1.0, maximum=5.0, value=1.0, label="Guidance Scale", step=0.5) |
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run_btn = gr.Button('Generate Multiview', interactive=True) |
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with gr.Column(scale=3): |
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output_rgb = gr.Image(type='pil', label="RGB", show_label=True) |
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output_front = gr.Image(type='pil', image_mode='RGBA', label="Frontview", show_label=True) |
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run_btn.click( |
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fn = concept_to_multiview, |
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inputs=[preview_img, input_processing, seed, guidance], |
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outputs=[output_rgb, output_front], |
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concurrency_id=concurrency_id, |
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api_name=False, |
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) |
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return output_rgb, output_front |
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from gradio_app.custom_models.normal_prediction import predict_normals |
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from scripts.multiview_inference import geo_reconstruct |
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def multiview_to_mesh_v2(rgb_pil, normal_pil, front_pil, do_refine=False, expansion_weight=0.1, init_type="std"): |
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rgb_pils = split_image(rgb_pil, rows=2) |
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if normal_pil is not None: |
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normal_pil = split_image(normal_pil, rows=2) |
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if front_pil is None: |
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front_pil = rgb_pils[0] |
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new_meshes = geo_reconstruct(rgb_pils, normal_pil, front_pil, do_refine=do_refine, predict_normal=normal_pil is None, expansion_weight=expansion_weight, init_type=init_type) |
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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=False) |
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return ret_mesh |
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def new_multiview_to_mesh_ui(concurrency_id="wkl"): |
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with gr.Row(): |
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with gr.Column(scale=2): |
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rgb_pil = gr.Image(type='pil', image_mode='RGB', label='RGB') |
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front_pil = gr.Image(type='pil', image_mode='RGBA', label='Frontview(Optinal)') |
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normal_pil = gr.Image(type='pil', image_mode='RGBA', label='Normal(Optinal)') |
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do_refine = gr.Checkbox( |
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value=False, |
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label='Refine rgb', |
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visible=False, |
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) |
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expansion_weight = gr.Slider(minimum=-1.0, 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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run_btn = gr.Button('Generate 3D', interactive=True) |
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with gr.Column(scale=3): |
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output_mesh = gr.Model3D(value=None, label="mesh model", show_label=True) |
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run_btn.click( |
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fn = multiview_to_mesh_v2, |
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inputs=[rgb_pil, normal_pil, front_pil, do_refine, expansion_weight, init_type], |
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outputs=[output_mesh], |
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concurrency_id=concurrency_id, |
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api_name="multiview_to_mesh", |
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) |
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return rgb_pil, front_pil, output_mesh |
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def create_step_ui(concurrency_id="wkl"): |
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with gr.Tab(label="3D:concept_to_multiview"): |
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concept_to_multiview_ui(concurrency_id) |
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with gr.Tab(label="3D:new_multiview_to_mesh"): |
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new_multiview_to_mesh_ui(concurrency_id) |
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