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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 | |
def depth_normal(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( | |
# """ | |
# <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() | |