| 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) | |
| 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() | |