Delete app.py

app.py

@@ -1,327 +0,0 @@
-import functools
-import os
-import shutil
-import sys
-import git
-
-import gradio as gr
-import numpy as np
-import torch as torch
-from PIL import Image
-
-from gradio_imageslider import ImageSlider
-
-import spaces
-
-REPO_URL = "https://github.com/lemonaddie/geowizard.git"
-CHECKPOINT = "lemonaddie/Geowizard"
-REPO_DIR = "geowizard"
-    
-if os.path.isdir(REPO_DIR):
-    shutil.rmtree(REPO_DIR)
-    
-repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
-sys.path.append(os.path.join(os.getcwd(), REPO_DIR))
-
-from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
-pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
-    
-try:
-    import xformers
-    pipe.enable_xformers_memory_efficient_attention()
-except:
-    pass  # run without xformers
-
-pipe = pipe.to(device)
-#run_demo_server(pipe)
-
-@spaces.GPU
-def depth_normal(img):
-
-    pipe_out = pipe(
-        img,
-        denoising_steps=10,
-        ensemble_size=2,
-        processing_res=768,
-        batch_size=0,
-        guidance_scale=3,
-        domain="indoor",
-        show_progress_bar=True,
-    )
-
-    depth_colored = pipe_out.depth_colored
-    normal_colored = pipe_out.normal_colored
-    
-    return depth_colored, normal_colored
-
-# @spaces.GPU
-# def run_demo_server(pipe):
-#     title = "Geowizard"
-#     description = "Gradio demo for Geowizard."
-
-#     examples = ["files/bee.jpg"]
-    
-#     # gr.Interface(
-#     #     depth_normal, 
-#     #     inputs=[gr.Image(type='pil', label="Original Image")], 
-#     #     outputs=[gr.Image(type="pil",label="Output Depth"), gr.Image(type="pil",label="Output Normal")], 
-#     #     title=title, description=description, article='1', examples=examples, analytics_enabled=False).launch()
-
-    
-# def process(
-#     pipe,
-#     path_input,
-#     ensemble_size,
-#     denoise_steps,
-#     processing_res,
-#     path_out_16bit=None,
-#     path_out_fp32=None,
-#     path_out_vis=None,
-# ):
-
-#     if path_out_vis is not None:
-#         return (
-#             [path_out_16bit, path_out_vis],
-#             [path_out_16bit, path_out_fp32, path_out_vis],
-#         )
-
-#     input_image = Image.open(path_input)
-
-#     pipe_out = pipe(
-#         input_image,
-#         denoising_steps=denoise_steps,
-#         ensemble_size=ensemble_size,
-#         processing_res=processing_res,
-#         batch_size=1 if processing_res == 0 else 0,
-#         guidance_scale=3,
-#         domain="indoor",
-#         show_progress_bar=True,
-#     )
-
-#     depth_pred = pipe_out.depth_np
-#     depth_colored = pipe_out.depth_colored
-#     depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
-
-#     path_output_dir = os.path.splitext(path_input)[0] + "_output"
-#     os.makedirs(path_output_dir, exist_ok=True)
-
-#     name_base = os.path.splitext(os.path.basename(path_input))[0]
-#     path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
-#     path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
-#     path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")
-
-#     np.save(path_out_fp32, depth_pred)
-#     Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
-#     depth_colored.save(path_out_vis)
-
-#     return (
-#         [path_out_16bit, path_out_vis],
-#         [path_out_16bit, path_out_fp32, path_out_vis],
-#     )
-
-
-# @spaces.GPU
-# def run_demo_server(pipe):
-#     process_pipe = functools.partial(process, pipe)
-#     os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
-
-#     with gr.Blocks(
-#         analytics_enabled=False,
-#         title="GeoWizard Depth and Normal Estimation",
-#         css="""
-#             #download {
-#                 height: 118px;
-#             }
-#             .slider .inner {
-#                 width: 5px;
-#                 background: #FFF;
-#             }
-#             .viewport {
-#                 aspect-ratio: 4/3;
-#             }
-#         """,
-#     ) as demo:
-#         gr.Markdown(
-#         """
-#             <h1 align="center">Geowizard Depth & Normal Estimation</h1>
-#         """
-#         )
-
-#         with gr.Row():
-#             with gr.Column():
-#                 input_image = gr.Image(
-#                     label="Input Image",
-#                     type="filepath",
-#                 )
-#                 with gr.Accordion("Advanced options", open=False):
-#                     domain = gr.Radio(
-#                         [
-#                             ("Outdoor", "outdoor"),
-#                             ("Indoor", "indoor"),
-#                             ("Object", "object"),
-#                         ],
-#                         label="Data Domain",
-#                         value="indoor",
-#                     )
-#                     cfg_scale = gr.Slider(
-#                         label="Classifier Free Guidance Scale",
-#                         minimum=1,
-#                         maximum=5,
-#                         step=1,
-#                         value=3,
-#                     )
-#                     denoise_steps = gr.Slider(
-#                         label="Number of denoising steps",
-#                         minimum=1,
-#                         maximum=20,
-#                         step=1,
-#                         value=2,
-#                     )
-#                     ensemble_size = gr.Slider(
-#                         label="Ensemble size",
-#                         minimum=1,
-#                         maximum=15,
-#                         step=1,
-#                         value=1,
-#                     )
-#                     processing_res = gr.Radio(
-#                         [
-#                             ("Native", 0),
-#                             ("Recommended", 768),
-#                         ],
-#                         label="Processing resolution",
-#                         value=768,
-#                     )
-#                 input_output_16bit = gr.File(
-#                     label="Predicted depth (16-bit)",
-#                     visible=False,
-#                 )
-#                 input_output_fp32 = gr.File(
-#                     label="Predicted depth (32-bit)",
-#                     visible=False,
-#                 )
-#                 input_output_vis = gr.File(
-#                     label="Predicted depth (red-near, blue-far)",
-#                     visible=False,
-#                 )
-#                 with gr.Row():
-#                     submit_btn = gr.Button(value="Compute", variant="primary")
-#                     clear_btn = gr.Button(value="Clear")
-#             with gr.Column():
-#                 output_slider = ImageSlider(
-#                     label="Predicted depth (red-near, blue-far)",
-#                     type="filepath",
-#                     show_download_button=True,
-#                     show_share_button=True,
-#                     interactive=False,
-#                     elem_classes="slider",
-#                     position=0.25,
-#                 )
-#                 files = gr.Files(
-#                     label="Depth outputs",
-#                     elem_id="download",
-#                     interactive=False,
-#                 )
-
-#         blocks_settings_depth = [ensemble_size, denoise_steps, processing_res]
-#         blocks_settings = blocks_settings_depth
-#         map_id_to_default = {b._id: b.value for b in blocks_settings}
-
-#         inputs = [
-#             input_image,
-#             ensemble_size,
-#             denoise_steps,
-#             processing_res,
-#             input_output_16bit,
-#             input_output_fp32,
-#             input_output_vis,
-#         ]
-#         outputs = [
-#             submit_btn,
-#             input_image,
-#             output_slider,
-#             files,
-#         ]
-
-#         def submit_depth_fn(*args):
-#             out = list(process_pipe(*args))
-#             out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
-#             return out
-
-#         submit_btn.click(
-#             fn=submit_depth_fn,
-#             inputs=inputs,
-#             outputs=outputs,
-#             concurrency_limit=1,
-#         )
-
-#         gr.Examples(
-#             fn=submit_depth_fn,
-#             examples=[
-#                 [
-#                     "files/bee.jpg",
-#                     10,  # ensemble_size
-#                     10,  # denoise_steps
-#                     768,  # processing_res
-#                     "files/bee_depth_16bit.png",
-#                     "files/bee_depth_fp32.npy",
-#                     "files/bee_depth_colored.png",
-#                 ],
-#             ],
-#             inputs=inputs,
-#             outputs=outputs,
-#             cache_examples=True,
-#         )
-
-#         def clear_fn():
-#             out = []
-#             for b in blocks_settings:
-#                 out.append(map_id_to_default[b._id])
-#             out += [
-#                 gr.Button(interactive=True),
-#                 gr.Image(value=None, interactive=True),
-#                 None, None, None, None, None, None, None,
-#             ]
-#             return out
-
-#         clear_btn.click(
-#             fn=clear_fn,
-#             inputs=[],
-#             outputs=blocks_settings + [
-#                 submit_btn,
-#                 input_image,
-#                 input_output_16bit,
-#                 input_output_fp32,
-#                 input_output_vis,
-#                 output_slider,
-#                 files,
-#             ],
-#         )
-
-#         demo.queue(
-#             api_open=False,
-#         ).launch(
-#             server_name="0.0.0.0",
-#             server_port=7860,
-#         )
-
-
-def main():
-
-   
-
-    title = "Geowizard"
-    description = "GeoWizard is a Wizard who spells 3D geometry from a single image. Upload your image into the left side."
-    examples = ["files/indoor.jpg"]
-
-    gr.Interface(
-        depth_normal, 
-        inputs=[gr.Image(type='pil', label="Original Image")], 
-        outputs=[gr.Image(type="pil",label="Output Depth"), gr.Image(type="pil",label="Output Normal")], 
-        title=title, description=description, article='', examples=examples, analytics_enabled=False).launch()
-
-
-if __name__ == "__main__":
-    main()