Delete gradio_app.py

gradio_app.py

@@ -1,188 +0,0 @@
-import logging
-import os
-import tempfile
-import time
-
-import gradio as gr
-import numpy as np
-import rembg
-import torch
-from PIL import Image
-from functools import partial
-
-from tsr.system import TSR
-from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
-
-import argparse
-
-
-if torch.cuda.is_available():
-    device = "cuda:0"
-else:
-    device = "cpu"
-
-model = TSR.from_pretrained(
-    "stabilityai/TripoSR",
-    config_name="config.yaml",
-    weight_name="model.ckpt",
-)
-
-# adjust the chunk size to balance between speed and memory usage
-model.renderer.set_chunk_size(8192)
-model.to(device)
-
-rembg_session = rembg.new_session()
-
-
-def check_input_image(input_image):
-    if input_image is None:
-        raise gr.Error("No image uploaded!")
-
-
-def preprocess(input_image, do_remove_background, foreground_ratio):
-    def fill_background(image):
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
-        image = Image.fromarray((image * 255.0).astype(np.uint8))
-        return image
-
-    if do_remove_background:
-        image = input_image.convert("RGB")
-        image = remove_background(image, rembg_session)
-        image = resize_foreground(image, foreground_ratio)
-        image = fill_background(image)
-    else:
-        image = input_image
-        if image.mode == "RGBA":
-            image = fill_background(image)
-    return image
-
-
-def generate(image, mc_resolution, formats=["obj", "glb"]):
-    print(image.shape, image.min(), image.max())
-    scene_codes = model(image, device=device)
-    mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
-    mesh = to_gradio_3d_orientation(mesh)
-    rv = []
-    for format in formats:
-        mesh_path = tempfile.NamedTemporaryFile(suffix=f".{format}", delete=False)
-        mesh.export(mesh_path.name)
-        rv.append(mesh_path.name)
-    return rv
-
-
-def run_example(image_pil):
-    preprocessed = preprocess(image_pil, False, 0.9)
-    mesh_name_obj, mesh_name_glb = generate(preprocessed, 256, ["obj", "glb"])
-    return preprocessed, mesh_name_obj, mesh_name_glb
-
-
-with gr.Blocks(title="TripoSR") as interface:
-    gr.Markdown(
-        """
-    # TripoSR Demo
-    [TripoSR](https://github.com/VAST-AI-Research/TripoSR) is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, collaboratively developed by [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/).
-    
-    **Tips:**
-    1. If you find the result is unsatisfied, please try to change the foreground ratio. It might improve the results.
-    2. You can disable "Remove Background" for the provided examples since they have been already preprocessed.
-    3. Otherwise, please disable "Remove Background" option only if your input image is RGBA with transparent background, image contents are centered and occupy more than 70% of image width or height.
-    """
-    )
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                input_image = gr.Image(
-                    label="Input Image",
-                    image_mode="RGBA",
-                    sources="upload",
-                    type="pil",
-                    elem_id="content_image",
-                )
-                processed_image = gr.Image(label="Processed Image", interactive=False)
-            with gr.Row():
-                with gr.Group():
-                    do_remove_background = gr.Checkbox(
-                        label="Remove Background", value=True
-                    )
-                    foreground_ratio = gr.Slider(
-                        label="Foreground Ratio",
-                        minimum=0.5,
-                        maximum=1.0,
-                        value=0.85,
-                        step=0.05,
-                    )
-                    mc_resolution = gr.Slider(
-                        label="Marching Cubes Resolution",
-                        minimum=32,
-                        maximum=320,
-                        value=256,
-                        step=32
-                    )
-            with gr.Row():
-                submit = gr.Button("Generate", elem_id="generate", variant="primary")
-        with gr.Column():
-            with gr.Tab("OBJ"):
-                output_model_obj = gr.Model3D(
-                    label="Output Model (OBJ Format)",
-                    interactive=False,
-                )
-                gr.Markdown("Note: The model shown here is flipped. Download to get correct results.")
-            with gr.Tab("GLB"):
-                output_model_glb = gr.Model3D(
-                    label="Output Model (GLB Format)",
-                    interactive=False,
-                )
-                gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
-    with gr.Row(variant="panel"):
-        gr.Examples(
-            examples=[
-                "examples/hamburger.png",
-                "examples/poly_fox.png",
-                "examples/robot.png",
-                "examples/teapot.png",
-                "examples/tiger_girl.png",
-                "examples/horse.png",
-                "examples/flamingo.png",
-                "examples/unicorn.png",
-                "examples/chair.png",
-                "examples/iso_house.png",
-                "examples/marble.png",
-                "examples/police_woman.png",
-                "examples/captured_p.png",
-            ],
-            inputs=[input_image],
-            outputs=[processed_image, output_model_obj, output_model_glb],
-            cache_examples=False,
-            fn=partial(run_example),
-            label="Examples",
-            examples_per_page=20,
-        )
-    submit.click(fn=check_input_image, inputs=[input_image]).success(
-        fn=preprocess,
-        inputs=[input_image, do_remove_background, foreground_ratio],
-        outputs=[processed_image],
-    ).success(
-        fn=generate,
-        inputs=[processed_image, mc_resolution],
-        outputs=[output_model_obj, output_model_glb],
-    )
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--username', type=str, default=None, help='Username for authentication')
-    parser.add_argument('--password', type=str, default=None, help='Password for authentication')
-    parser.add_argument('--port', type=int, default=7860, help='Port to run the server listener on')
-    parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-    parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-    parser.add_argument("--queuesize", type=int, default=1, help="launch gradio queue max_size")
-    args = parser.parse_args()
-    interface.queue(max_size=args.queuesize)
-    interface.launch(
-        auth=(args.username, args.password) if (args.username and args.password) else None,
-        share=args.share,
-        server_name="0.0.0.0" if args.listen else None, 
-        server_port=args.port
-    )