import gradio as gr import os from PIL import Image import subprocess os.system('pip install -e ./simple-knn') os.system('pip install -e ./diff-gaussian-rasterization') # check if there is a picture uploaded or selected def check_img_input(control_image): if control_image is None: raise gr.Error("Please select or upload an input image") def optimize(image_block: Image.Image, preprocess_chk=True, elevation_slider=0): stage_1_output = optimize_stage_1(image_block, preprocess_chk, elevation_slider) stage_2_output = optimize_stage_2(elevation_slider) return stage_1_output, stage_2_output def optimize_stage_1(image_block: Image.Image, preprocess_chk: bool, elevation_slider: float): if not os.path.exists('tmp_data'): os.makedirs('tmp_data') if preprocess_chk: # save image to a designated path image_block.save('tmp_data/tmp.png') # preprocess image subprocess.run([f'python process.py tmp_data/tmp.png'], shell=True) else: image_block.save('tmp_data/tmp_rgba.png') # stage 1 subprocess.run([ f'python main.py --config configs/image.yaml input=tmp_data/tmp_rgba.png save_path=tmp mesh_format=glb elevation={elevation_slider} force_cuda_rast=True'], shell=True) return f'logs/tmp_mesh.glb' def optimize_stage_2(elevation_slider: float): # stage 2 subprocess.run([ f'python main2.py --config configs/image.yaml input=tmp_data/tmp_rgba.png save_path=tmp mesh_format=glb elevation={elevation_slider} force_cuda_rast=True'], shell=True) return f'logs/tmp.glb' if __name__ == "__main__": _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation''' _DESCRIPTION = '''
We present DreamGausssion, a 3D content generation framework that significantly improves the efficiency of 3D content creation. ''' _DUPLICATE =''' [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/jiawei011/dreamgaussian?duplicate=true) ''' _IMG_USER_GUIDE = "Please upload an image in the block above (or choose an example above) and click **Generate 3D**." # load images in 'data' folder as examples example_folder = os.path.join(os.path.dirname(__file__), 'data') example_fns = os.listdir(example_folder) example_fns.sort() examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')] # Compose demo layout & data flow with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo: with gr.Row(): with gr.Column(scale=1): gr.Markdown('# ' + _TITLE) with gr.Column(scale=0): gr.Markdown(_DUPLICATE) gr.Markdown(_DESCRIPTION) # Image-to-3D with gr.Row(variant='panel'): left_column = gr.Column(scale=5) with left_column: image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image', tool=None) elevation_slider = gr.Slider(-90, 90, value=0, step=1, label='Estimated elevation angle') gr.Markdown( "default to 0 (horizontal), range from [-90, 90]. If you upload a look-down image, try a value like -30") preprocess_chk = gr.Checkbox(True, label='Preprocess image automatically (remove background and recenter object)') with gr.Column(scale=5): obj3d_stage1 = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Stage 1)") obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)") with left_column: gr.Examples( examples=examples_full, # NOTE: elements must match inputs list! inputs=[image_block], outputs=[obj3d_stage1, obj3d], fn=optimize, cache_examples=True, label='Examples (click one of the images below to start)', examples_per_page=40 ) img_run_btn = gr.Button("Generate 3D") img_guide_text = gr.Markdown(_IMG_USER_GUIDE, visible=True) # if there is an input image, continue with inference # else display an error message img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(optimize_stage_1, inputs=[image_block, preprocess_chk, elevation_slider], outputs=[ obj3d_stage1]).success( optimize_stage_2, inputs=[elevation_slider], outputs=[obj3d]) # demo.launch(enable_queue=True) demo.queue(max_size=20) # <-- Sets up a queue with default parameters demo.launch()