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 def identity(img): return img, img @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( # """ #

Geowizard Depth & Normal Estimation

# """ # ) # 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(): 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) if __name__ == "__main__": main()