import spaces 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 @spaces.GPU def process( pipe, path_input, ensemble_size, denoise_steps, processing_res, domain, path_out_16bit=None, path_out_fp32=None, path_out_vis=None, normal_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, ensemble_size=ensemble_size, denoising_steps=denoise_steps, processing_res=processing_res, domain=domain, batch_size=1 if processing_res == 0 else 0, 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], ) def run_demo_server(pipe): process_pipe = functools.partial(process, pipe) os.environ["GRADIO_ALLOW_FLAGGING"] = "never" with gr.Blocks( analytics_enabled=False, title="Marigold Depth Estimation", css=""" #download { height: 118px; } .slider .inner { width: 5px; background: #FFF; } .viewport { aspect-ratio: 4/3; } """, ) as demo: gr.Markdown( """

Marigold Depth Estimation

badge-github-stars social

Marigold is the new state-of-the-art depth estimator for images in the wild. Upload your image into the left side, or click any of the examples below. The result will be computed and appear on the right in the output comparison window. NEW: Scroll down to the new 3D printing part of the demo!

""" ) with gr.Row(): with gr.Column(): input_image = gr.Image( label="Input Image", type="filepath", ) with gr.Accordion("Advanced options", open=False): ensemble_size = gr.Slider( label="Ensemble size", minimum=1, maximum=20, step=1, value=1, ) denoise_steps = gr.Slider( label="Number of denoising steps", minimum=1, maximum=20, step=1, value=10, ) processing_res = gr.Radio( [ ("Native", 0), ("Recommended", 768), ], label="Processing resolution", value=768, ) domain = gr.Radio( [ ("indoor", "indoor"), ("outdoor", "outdoor"), ("object", "object"), ], label="scene type", value='indoor', ) 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 Depth", 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, domain, 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", 0.0, # plane_near 0.5, # plane_far 20, # embossing 3, # filter_size 0, # frame_near ], ], 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.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, submit_3d, input_image, input_output_16bit, input_output_fp32, input_output_vis, output_slider, files, viewer_3d, files_3d, ], ) demo.queue( api_open=False, ).launch( server_name="0.0.0.0", server_port=7860, ) def prefetch_hf_cache(pipe): process(pipe, "files/bee.jpg", 1, 1, 64) shutil.rmtree("files/bee_output") 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 try: import xformers pipe.enable_xformers_memory_efficient_attention() except: pass # run without xformers pipe = pipe.to('cuda') prefetch_hf_cache(pipe) run_demo_server(pipe) if __name__ == "__main__": main()