import functools import os import shutil import sys import gradio as gr import numpy as np import torch as torch from PIL import Image from gradio_imageslider import ImageSlider def process( pipe, path_input, ensemble_size, denoise_steps, processing_res, path_out_16bit=None, path_out_fp32=None, path_out_vis=None, _input_3d_plane_near=None, _input_3d_plane_far=None, _input_3d_embossing=None, _input_3d_filter_size=None, _input_3d_frame_near=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, 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 process_3d( input_image, files, size_longest_px, size_longest_cm, filter_size, plane_near, plane_far, embossing, frame_thickness, frame_near, frame_far, ): if input_image is None or len(files) < 1: raise gr.Error("Please upload an image (or use examples) and compute depth first") if plane_near >= plane_far: raise gr.Error("NEAR plane must have a value smaller than the FAR plane") # sanitize 3d viewer glb path to keep babylon.js happy path_viewer_glb_sanitized = os.path.join(os.path.dirname(path_viewer_glb), "preview.glb") if path_viewer_glb_sanitized != path_viewer_glb: os.rename(path_viewer_glb, path_viewer_glb_sanitized) path_viewer_glb = path_viewer_glb_sanitized return path_viewer_glb, [path_files_glb, path_files_stl] 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( """ """ ) 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=10, ) 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, ) 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, ) demo_3d_header = gr.Markdown( """

Depth Maps

TBD result (see Pro Tips below).

""", render=False, ) demo_3d = gr.Row(render=False) with demo_3d: with gr.Column(): with gr.Accordion("3D printing demo: Main options", open=True): plane_near = gr.Slider( label="Relative position of the near plane (between 0 and 1)", minimum=0.0, maximum=1.0, step=0.001, value=0.0, ) plane_far = gr.Slider( label="Relative position of the far plane (between near and 1)", minimum=0.0, maximum=1.0, step=0.001, value=1.0, ) embossing = gr.Slider( label="Embossing level", minimum=0, maximum=100, step=1, value=20, ) with gr.Accordion("3D printing demo: Advanced options", open=False): size_longest_px = gr.Slider( label="Size (px) of the longest side", minimum=256, maximum=1024, step=256, value=512, ) size_longest_cm = gr.Slider( label="Size (cm) of the longest side", minimum=1, maximum=100, step=1, value=10, ) filter_size = gr.Slider( label="Size (px) of the smoothing filter", minimum=1, maximum=5, step=2, value=3, ) frame_thickness = gr.Slider( label="Frame thickness", minimum=0, maximum=100, step=1, value=5, ) frame_near = gr.Slider( label="Frame's near plane offset", minimum=-100, maximum=100, step=1, value=1, ) frame_far = gr.Slider( label="Frame's far plane offset", minimum=1, maximum=10, step=1, value=1, ) with gr.Row(): submit_3d = gr.Button(value="Create 3D", variant="primary") clear_3d = gr.Button(value="Clear 3D") gr.Markdown( """
Pro Tips
  1. Re-render with new parameters: Click "Clear 3D" and then "Create 3D".
  2. Adjust 3D scale and cut-off focus: Set the frame's near plane offset to the minimum and use 3D preview to evaluate depth scaling. Repeat until the scale is correct and everything important is in the focus. Set the optimal value for frame's near plane offset as a last step.
  3. Increase details: Decrease size of the smoothing filter (also increases noise).
""" ) with gr.Column(): viewer_3d = gr.Model3D( camera_position=(75.0, 90.0, 1.25), elem_classes="viewport", label="3D preview (low-res, relief highlight)", interactive=False, ) files_3d = gr.Files( label="3D model outputs (high-res)", elem_id="download", interactive=False, ) blocks_settings_depth = [ensemble_size, denoise_steps, processing_res] blocks_settings_3d = [plane_near, plane_far, embossing, size_longest_px, size_longest_cm, filter_size, frame_thickness, frame_near, frame_far] blocks_settings = blocks_settings_depth + blocks_settings_3d 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, plane_near, plane_far, embossing, filter_size, frame_near, ] 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, ) demo_3d_header.render() demo_3d.render() 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, ], ) def submit_3d_fn(*args): out = list(process_3d(*args)) out = [gr.Button(interactive=False)] + out return out submit_3d.click( fn=submit_3d_fn, inputs=[ input_image, files, size_longest_px, size_longest_cm, filter_size, plane_near, plane_far, embossing, frame_thickness, frame_near, frame_far, ], outputs=[submit_3d, viewer_3d, files_3d], concurrency_limit=1, ) def clear_3d_fn(): return [gr.Button(interactive=True), None, None] clear_3d.click( fn=clear_3d_fn, inputs=[], outputs=[submit_3d, viewer_3d, files_3d], ) 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 = git.Repo.clone_from(REPO_URL) sys.path.append(os.path.join(os.getcwd())) from models.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline pipeline = DepthNormalEstimationPipeline.from_pretrained("lemonaddie/Geowizard") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(1) pipe = pipeline.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()