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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
@spaces.GPU
def depth_normal(img):
pipe_out = pipe(
input_image,
denoising_steps=10,
ensemble_size=1,
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():
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)
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()
if __name__ == "__main__":
main()
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