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DragGan / visualizer_drag_gradio.py

radames HF staff

fix initial seed

6f44efe 11 months ago

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	# https://huggingface.co/DragGan/DragGan-Models
	# https://arxiv.org/abs/2305.10973
	import os
	import os.path as osp
	from argparse import ArgumentParser
	from functools import partial
	from pathlib import Path
	import time

	import psutil

	import gradio as gr
	import numpy as np
	import torch
	from PIL import Image

	import dnnlib
	from gradio_utils import (ImageMask, draw_mask_on_image, draw_points_on_image,
	get_latest_points_pair, get_valid_mask,
	on_change_single_global_state)
	from viz.renderer import Renderer, add_watermark_np


	# download models from Hugging Face hub
	from huggingface_hub import snapshot_download

	model_dir = Path('./checkpoints')
	snapshot_download('DragGan/DragGan-Models',
	repo_type='model', local_dir=model_dir)

	parser = ArgumentParser()
	parser.add_argument('--share', action='store_true')
	parser.add_argument('--cache-dir', type=str, default='./checkpoints')
	args = parser.parse_args()

	cache_dir = args.cache_dir

	device = 'cuda'
	IS_SPACE = "DragGan/DragGan" in os.environ.get('SPACE_ID', '')
	TIMEOUT = 80


	def reverse_point_pairs(points):
	new_points = []
	for p in points:
	new_points.append([p[1], p[0]])
	return new_points


	def clear_state(global_state, target=None):
	"""Clear target history state from global_state
	If target is not defined, points and mask will be both removed.
	1. set global_state['points'] as empty dict
	2. set global_state['mask'] as full-one mask.
	"""
	if target is None:
	target = ['point', 'mask']
	if not isinstance(target, list):
	target = [target]
	if 'point' in target:
	global_state['points'] = dict()
	print('Clear Points State!')
	if 'mask' in target:
	image_raw = global_state["images"]["image_raw"]
	global_state['mask'] = np.ones((image_raw.size[1], image_raw.size[0]),
	dtype=np.uint8)
	print('Clear mask State!')

	return global_state


	def init_images(global_state):
	"""This function is called only ones with Gradio App is started.
	0. pre-process global_state, unpack value from global_state of need
	1. Re-init renderer
	2. run `renderer._render_drag_impl` with `is_drag=False` to generate
	new image
	3. Assign images to global state and re-generate mask
	"""

	if isinstance(global_state, gr.State):
	state = global_state.value
	else:
	state = global_state

	state['renderer'].init_network(
	state['generator_params'], # res
	valid_checkpoints_dict[state['pretrained_weight']], # pkl
	state['params']['seed'], # w0_seed,
	None, # w_load
	state['params']['latent_space'] == 'w+', # w_plus
	'const',
	state['params']['trunc_psi'], # trunc_psi,
	state['params']['trunc_cutoff'], # trunc_cutoff,
	None, # input_transform
	state['params']['lr'] # lr,
	)

	state['renderer']._render_drag_impl(state['generator_params'],
	is_drag=False,
	to_pil=True)

	init_image = state['generator_params'].image
	state['images']['image_orig'] = init_image
	state['images']['image_raw'] = init_image
	state['images']['image_show'] = Image.fromarray(
	add_watermark_np(np.array(init_image)))
	state['mask'] = np.ones((init_image.size[1], init_image.size[0]),
	dtype=np.uint8)
	return global_state


	def update_image_draw(image, points, mask, show_mask, global_state=None):

	image_draw = draw_points_on_image(image, points)
	if show_mask and mask is not None and not (mask == 0).all() and not (
	mask == 1).all():
	image_draw = draw_mask_on_image(image_draw, mask)

	image_draw = Image.fromarray(add_watermark_np(np.array(image_draw)))
	if global_state is not None:
	global_state['images']['image_show'] = image_draw
	return image_draw


	def preprocess_mask_info(global_state, image):
	"""Function to handle mask information.
	1. last_mask is None: Do not need to change mask, return mask
	2. last_mask is not None:
	2.1 global_state is remove_mask:
	2.2 global_state is add_mask:
	"""
	if isinstance(image, dict):
	last_mask = get_valid_mask(image['mask'])
	else:
	last_mask = None
	mask = global_state['mask']

	# mask in global state is a placeholder with all 1.
	if (mask == 1).all():
	mask = last_mask

	# last_mask = global_state['last_mask']
	editing_mode = global_state['editing_state']

	if last_mask is None:
	return global_state

	if editing_mode == 'remove_mask':
	updated_mask = np.clip(mask - last_mask, 0, 1)
	print(f'Last editing_state is {editing_mode}, do remove.')
	elif editing_mode == 'add_mask':
	updated_mask = np.clip(mask + last_mask, 0, 1)
	print(f'Last editing_state is {editing_mode}, do add.')
	else:
	updated_mask = mask
	print(f'Last editing_state is {editing_mode}, '
	'do nothing to mask.')

	global_state['mask'] = updated_mask
	# global_state['last_mask'] = None # clear buffer
	return global_state


	def print_memory_usage():
	# Print system memory usage
	print(f"System memory usage: {psutil.virtual_memory().percent}%")

	# Print GPU memory usage
	if torch.cuda.is_available():
	device = torch.device("cuda")
	print(f"GPU memory usage: {torch.cuda.memory_allocated() / 1e9} GB")
	print(
	f"Max GPU memory usage: {torch.cuda.max_memory_allocated() / 1e9} GB")
	device_properties = torch.cuda.get_device_properties(device)
	available_memory = device_properties.total_memory - \
	torch.cuda.max_memory_allocated()
	print(f"Available GPU memory: {available_memory / 1e9} GB")
	else:
	print("No GPU available")


	# filter large models running on SPACES
	allowed_checkpoints = [] # all checkpoints
	if IS_SPACE:
	allowed_checkpoints = ["stylegan_human_v2_512.pkl",
	"stylegan2_dogs_1024_pytorch.pkl"]

	valid_checkpoints_dict = {
	f.name.split('.')[0]: str(f)
	for f in Path(cache_dir).glob('*.pkl')
	if f.name in allowed_checkpoints or not IS_SPACE
	}
	print('Valid checkpoint file:')
	print(valid_checkpoints_dict)

	init_pkl = 'stylegan_human_v2_512'

	with gr.Blocks() as app:
	gr.Markdown("""
	# DragGAN - Drag Your GAN
	## Interactive Point-based Manipulation on the Generative Image Manifold
	### Unofficial Gradio Demo

	Due to high demand, only one model can be run at a time, or you can duplicate the space and run your own copy.

	<a href="https://huggingface.co/spaces/radames/DragGan?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
	<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p>

	* Official Repo: [XingangPan](https://github.com/XingangPan/DragGAN)
	* Gradio Demo by: [LeoXing1996](https://github.com/LeoXing1996) © [OpenMMLab MMagic](https://github.com/open-mmlab/mmagic)
	""")

	# renderer = Renderer()
	global_state = gr.State({
	"images": {
	# image_orig: the original image, change with seed/model is changed
	# image_raw: image with mask and points, change durning optimization
	# image_show: image showed on screen
	},
	"temporal_params": {
	# stop
	},
	'mask':
	None, # mask for visualization, 1 for editing and 0 for unchange
	'last_mask': None, # last edited mask
	'show_mask': True, # add button
	"generator_params": dnnlib.EasyDict(),
	"params": {
	"seed": int(np.random.randint(0, 2**32 - 1)),
	"motion_lambda": 20,
	"r1_in_pixels": 3,
	"r2_in_pixels": 12,
	"magnitude_direction_in_pixels": 1.0,
	"latent_space": "w+",
	"trunc_psi": 0.7,
	"trunc_cutoff": None,
	"lr": 0.001,
	},
	"device": device,
	"draw_interval": 1,
	"renderer": Renderer(disable_timing=True),
	"points": {},
	"curr_point": None,
	"curr_type_point": "start",
	'editing_state': 'add_points',
	'pretrained_weight': init_pkl
	})

	# init image
	global_state = init_images(global_state)
	with gr.Row():

	with gr.Row():

	# Left --> tools
	with gr.Column(scale=3):

	# Pickle
	with gr.Row():

	with gr.Column(scale=1, min_width=10):
	gr.Markdown(value='Pickle', show_label=False)

	with gr.Column(scale=4, min_width=10):
	form_pretrained_dropdown = gr.Dropdown(
	choices=list(valid_checkpoints_dict.keys()),
	label="Pretrained Model",
	value=init_pkl,
	)

	# Latent
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	gr.Markdown(value='Latent', show_label=False)

	with gr.Column(scale=4, min_width=10):
	form_seed_number = gr.Slider(
	mininium=0,
	maximum=2**32-1,
	step=1,
	value=global_state.value['params']['seed'],
	interactive=True,
	# randomize=True,
	label="Seed",
	)
	form_lr_number = gr.Number(
	value=global_state.value["params"]["lr"],
	interactive=True,
	label="Step Size")

	with gr.Row():
	with gr.Column(scale=2, min_width=10):
	form_reset_image = gr.Button("Reset Image")
	with gr.Column(scale=3, min_width=10):
	form_latent_space = gr.Radio(
	['w', 'w+'],
	value=global_state.value['params']
	['latent_space'],
	interactive=True,
	label='Latent space to optimize',
	show_label=False,
	)

	# Drag
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	gr.Markdown(value='Drag', show_label=False)
	with gr.Column(scale=4, min_width=10):
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	enable_add_points = gr.Button('Add Points')
	with gr.Column(scale=1, min_width=10):
	undo_points = gr.Button('Reset Points')
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	form_start_btn = gr.Button("Start")
	with gr.Column(scale=1, min_width=10):
	form_stop_btn = gr.Button("Stop")

	form_steps_number = gr.Number(value=0,
	label="Steps",
	interactive=False)

	# Mask
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	gr.Markdown(value='Mask', show_label=False)
	with gr.Column(scale=4, min_width=10):
	enable_add_mask = gr.Button('Edit Flexible Area')
	with gr.Row():
	with gr.Column(scale=1, min_width=10):
	form_reset_mask_btn = gr.Button("Reset mask")
	with gr.Column(scale=1, min_width=10):
	show_mask = gr.Checkbox(
	label='Show Mask',
	value=global_state.value['show_mask'],
	show_label=False)

	with gr.Row():
	form_lambda_number = gr.Number(
	value=global_state.value["params"]
	["motion_lambda"],
	interactive=True,
	label="Lambda",
	)

	form_draw_interval_number = gr.Number(
	value=global_state.value["draw_interval"],
	label="Draw Interval (steps)",
	interactive=True,
	visible=False)

	# Right --> Image
	with gr.Column(scale=8):
	form_image = ImageMask(
	value=global_state.value['images']['image_show'],
	brush_radius=20).style(
	width=768,
	height=768) # NOTE: hard image size code here.
	gr.Markdown("""
	## Quick Start

	1. Select desired `Pretrained Model` and adjust `Seed` to generate an
	initial image.
	2. Click on image to add control points.
	3. Click `Start` and enjoy it!

	## Advance Usage

	1. Change `Step Size` to adjust learning rate in drag optimization.
	2. Select `w` or `w+` to change latent space to optimize:
	* Optimize on `w` space may cause greater influence to the image.
	* Optimize on `w+` space may work slower than `w`, but usually achieve
	better results.
	* Note that changing the latent space will reset the image, points and
	mask (this has the same effect as `Reset Image` button).
	3. Click `Edit Flexible Area` to create a mask and constrain the
	unmasked region to remain unchanged.


	""")
	gr.HTML("""
	<style>
	.container {
	position: absolute;
	height: 50px;
	text-align: center;
	line-height: 50px;
	width: 100%;
	}
	</style>
	<div class="container">
	Gradio demo supported by
	<img src="https://avatars.githubusercontent.com/u/10245193?s=200&v=4" height="20" width="20" style="display:inline;">
	<a href="https://github.com/open-mmlab/mmagic">OpenMMLab MMagic</a>
	</div>
	""")
	# Network & latents tab listeners

	def on_change_pretrained_dropdown(pretrained_value, global_state):
	"""Function to handle model change.
	1. Set pretrained value to global_state
	2. Re-init images and clear all states
	"""

	global_state['pretrained_weight'] = pretrained_value
	init_images(global_state)
	clear_state(global_state)

	return global_state, global_state["images"]['image_show']

	form_pretrained_dropdown.change(
	on_change_pretrained_dropdown,
	inputs=[form_pretrained_dropdown, global_state],
	outputs=[global_state, form_image],
	queue=True,
	)

	def on_click_reset_image(global_state):
	"""Reset image to the original one and clear all states
	1. Re-init images
	2. Clear all states
	"""

	init_images(global_state)
	clear_state(global_state)

	return global_state, global_state['images']['image_show']

	form_reset_image.click(
	on_click_reset_image,
	inputs=[global_state],
	outputs=[global_state, form_image],
	queue=False,
	)

	# Update parameters
	def on_change_update_image_seed(seed, global_state):
	"""Function to handle generation seed change.
	1. Set seed to global_state
	2. Re-init images and clear all states
	"""

	global_state["params"]["seed"] = int(seed)
	init_images(global_state)
	clear_state(global_state)

	return global_state, global_state['images']['image_show']

	form_seed_number.change(
	on_change_update_image_seed,
	inputs=[form_seed_number, global_state],
	outputs=[global_state, form_image],
	)

	def on_click_latent_space(latent_space, global_state):
	"""Function to reset latent space to optimize.
	NOTE: this function we reset the image and all controls
	1. Set latent-space to global_state
	2. Re-init images and clear all state
	"""

	global_state['params']['latent_space'] = latent_space
	init_images(global_state)
	clear_state(global_state)

	return global_state, global_state['images']['image_show']

	form_latent_space.change(on_click_latent_space,
	inputs=[form_latent_space, global_state],
	outputs=[global_state, form_image])

	# ==== Params
	form_lambda_number.change(
	partial(on_change_single_global_state, ["params", "motion_lambda"]),
	inputs=[form_lambda_number, global_state],
	outputs=[global_state],
	)

	def on_change_lr(lr, global_state):
	if lr == 0:
	print('lr is 0, do nothing.')
	return global_state
	else:
	global_state["params"]["lr"] = lr
	renderer = global_state['renderer']
	renderer.update_lr(lr)
	print('New optimizer: ')
	print(renderer.w_optim)
	return global_state

	form_lr_number.change(
	on_change_lr,
	inputs=[form_lr_number, global_state],
	outputs=[global_state],
	queue=False,
	)

	def on_click_start(global_state, image):
	p_in_pixels = []
	t_in_pixels = []
	valid_points = []

	# handle of start drag in mask editing mode
	global_state = preprocess_mask_info(global_state, image)

	# Prepare the points for the inference
	if len(global_state["points"]) == 0:
	# yield on_click_start_wo_points(global_state, image)
	image_raw = global_state['images']['image_raw']
	update_image_draw(
	image_raw,
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'],
	global_state,
	)

	yield (
	global_state,
	0,
	global_state['images']['image_show'],
	# gr.File.update(visible=False),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	# latent space
	gr.Radio.update(interactive=True),
	gr.Button.update(interactive=True),
	# NOTE: disable stop button
	gr.Button.update(interactive=False),

	# update other comps
	gr.Dropdown.update(interactive=True),
	gr.Number.update(interactive=True),
	gr.Number.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Checkbox.update(interactive=True),
	# gr.Number.update(interactive=True),
	gr.Number.update(interactive=True),
	)
	else:

	# Transform the points into torch tensors
	for key_point, point in global_state["points"].items():
	try:
	p_start = point.get("start_temp", point["start"])
	p_end = point["target"]

	if p_start is None or p_end is None:
	continue

	except KeyError:
	continue

	p_in_pixels.append(p_start)
	t_in_pixels.append(p_end)
	valid_points.append(key_point)

	mask = torch.tensor(global_state['mask']).float()
	drag_mask = 1 - mask

	renderer: Renderer = global_state["renderer"]
	global_state['temporal_params']['stop'] = False
	global_state['editing_state'] = 'running'

	# reverse points order
	p_to_opt = reverse_point_pairs(p_in_pixels)
	t_to_opt = reverse_point_pairs(t_in_pixels)
	print('Running with:')
	print(f' Source: {p_in_pixels}')
	print(f' Target: {t_in_pixels}')
	step_idx = 0
	last_time = time.time()
	while True:
	print_memory_usage()
	# add a TIMEOUT break
	print(f'Running time: {time.time() - last_time}')
	if IS_SPACE and time.time() - last_time > TIMEOUT:
	print('Timeout break!')
	break
	if global_state["temporal_params"]["stop"] or global_state['generator_params']["stop"]:
	break

	# do drage here!
	renderer._render_drag_impl(
	global_state['generator_params'],
	p_to_opt, # point
	t_to_opt, # target
	drag_mask, # mask,
	global_state['params']['motion_lambda'], # lambda_mask
	reg=0,
	feature_idx=5, # NOTE: do not support change for now
	r1=global_state['params']['r1_in_pixels'], # r1
	r2=global_state['params']['r2_in_pixels'], # r2
	# random_seed = 0,
	# noise_mode = 'const',
	trunc_psi=global_state['params']['trunc_psi'],
	# force_fp32 = False,
	# layer_name = None,
	# sel_channels = 3,
	# base_channel = 0,
	# img_scale_db = 0,
	# img_normalize = False,
	# untransform = False,
	is_drag=True,
	to_pil=True)

	if step_idx % global_state['draw_interval'] == 0:
	print('Current Source:')
	for key_point, p_i, t_i in zip(valid_points, p_to_opt,
	t_to_opt):
	global_state["points"][key_point]["start_temp"] = [
	p_i[1],
	p_i[0],
	]
	global_state["points"][key_point]["target"] = [
	t_i[1],
	t_i[0],
	]
	start_temp = global_state["points"][key_point][
	"start_temp"]
	print(f' {start_temp}')

	image_result = global_state['generator_params']['image']
	image_draw = update_image_draw(
	image_result,
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'],
	global_state,
	)
	global_state['images']['image_raw'] = image_result

	yield (
	global_state,
	step_idx,
	global_state['images']['image_show'],
	# gr.File.update(visible=False),
	gr.Button.update(interactive=False),
	gr.Button.update(interactive=False),
	gr.Button.update(interactive=False),
	gr.Button.update(interactive=False),
	gr.Button.update(interactive=False),
	# latent space
	gr.Radio.update(interactive=False),
	gr.Button.update(interactive=False),
	# enable stop button in loop
	gr.Button.update(interactive=True),

	# update other comps
	gr.Dropdown.update(interactive=False),
	gr.Number.update(interactive=False),
	gr.Number.update(interactive=False),
	gr.Button.update(interactive=False),
	gr.Button.update(interactive=False),
	gr.Checkbox.update(interactive=False),
	# gr.Number.update(interactive=False),
	gr.Number.update(interactive=False),
	)

	# increate step
	step_idx += 1

	image_result = global_state['generator_params']['image']
	global_state['images']['image_raw'] = image_result
	image_draw = update_image_draw(image_result,
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'],
	global_state)

	# fp = NamedTemporaryFile(suffix=".png", delete=False)
	# image_result.save(fp, "PNG")

	global_state['editing_state'] = 'add_points'

	yield (
	global_state,
	0, # reset step to 0 after stop.
	global_state['images']['image_show'],
	# gr.File.update(visible=True, value=fp.name),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	gr.Button.update(interactive=True),
	# latent space
	gr.Radio.update(interactive=True),
	gr.Button.update(interactive=True),
	# NOTE: disable stop button with loop finish
	gr.Button.update(interactive=False),

	# update other comps
	gr.Dropdown.update(interactive=True),
	gr.Number.update(interactive=True),
	gr.Number.update(interactive=True),
	gr.Checkbox.update(interactive=True),
	gr.Number.update(interactive=True),
	)

	form_start_btn.click(
	on_click_start,
	inputs=[global_state, form_image],
	outputs=[
	global_state,
	form_steps_number,
	form_image,
	# form_download_result_file,
	# >>> buttons
	form_reset_image,
	enable_add_points,
	enable_add_mask,
	undo_points,
	form_reset_mask_btn,
	form_latent_space,
	form_start_btn,
	form_stop_btn,
	# <<< buttonm
	# >>> inputs comps
	form_pretrained_dropdown,
	form_seed_number,
	form_lr_number,
	show_mask,
	form_lambda_number,
	],
	)

	def on_click_stop(global_state):
	"""Function to handle stop button is clicked.
	1. send a stop signal by set global_state["temporal_params"]["stop"] as True
	2. Disable Stop button
	"""
	global_state["temporal_params"]["stop"] = True

	return global_state, gr.Button.update(interactive=False)

	form_stop_btn.click(on_click_stop,
	inputs=[global_state],
	outputs=[global_state, form_stop_btn],
	queue=False)

	form_draw_interval_number.change(
	partial(
	on_change_single_global_state,
	"draw_interval",
	map_transform=lambda x: int(x),
	),
	inputs=[form_draw_interval_number, global_state],
	outputs=[global_state],
	queue=False,
	)

	def on_click_remove_point(global_state):
	choice = global_state["curr_point"]
	del global_state["points"][choice]

	choices = list(global_state["points"].keys())

	if len(choices) > 0:
	global_state["curr_point"] = choices[0]

	return (
	gr.Dropdown.update(choices=choices, value=choices[0]),
	global_state,
	)

	# Mask
	def on_click_reset_mask(global_state):
	global_state['mask'] = np.ones(
	(
	global_state["images"]["image_raw"].size[1],
	global_state["images"]["image_raw"].size[0],
	),
	dtype=np.uint8,
	)
	image_draw = update_image_draw(global_state['images']['image_raw'],
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'], global_state)
	return global_state, image_draw

	form_reset_mask_btn.click(
	on_click_reset_mask,
	inputs=[global_state],
	outputs=[global_state, form_image],
	)

	# Image
	def on_click_enable_draw(global_state, image):
	"""Function to start add mask mode.
	1. Preprocess mask info from last state
	2. Change editing state to add_mask
	3. Set curr image with points and mask
	"""
	global_state = preprocess_mask_info(global_state, image)
	global_state['editing_state'] = 'add_mask'
	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(image_raw, global_state['points'],
	global_state['mask'], True,
	global_state)
	return (global_state,
	gr.Image.update(value=image_draw, interactive=True))

	def on_click_remove_draw(global_state, image):
	"""Function to start remove mask mode.
	1. Preprocess mask info from last state
	2. Change editing state to remove_mask
	3. Set curr image with points and mask
	"""
	global_state = preprocess_mask_info(global_state, image)
	global_state['edinting_state'] = 'remove_mask'
	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(image_raw, global_state['points'],
	global_state['mask'], True,
	global_state)
	return (global_state,
	gr.Image.update(value=image_draw, interactive=True))

	enable_add_mask.click(on_click_enable_draw,
	inputs=[global_state, form_image],
	outputs=[
	global_state,
	form_image,
	],
	queue=False)

	def on_click_add_point(global_state, image: dict):
	"""Function switch from add mask mode to add points mode.
	1. Updaste mask buffer if need
	2. Change global_state['editing_state'] to 'add_points'
	3. Set current image with mask
	"""

	global_state = preprocess_mask_info(global_state, image)
	global_state['editing_state'] = 'add_points'
	mask = global_state['mask']
	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(image_raw, global_state['points'], mask,
	global_state['show_mask'], global_state)

	return (global_state,
	gr.Image.update(value=image_draw, interactive=False))

	enable_add_points.click(on_click_add_point,
	inputs=[global_state, form_image],
	outputs=[global_state, form_image],
	queue=False)

	def on_click_image(global_state, evt: gr.SelectData):
	"""This function only support click for point selection
	"""
	xy = evt.index
	if global_state['editing_state'] != 'add_points':
	print(f'In {global_state["editing_state"]} state. '
	'Do not add points.')

	return global_state, global_state['images']['image_show']

	points = global_state["points"]

	point_idx = get_latest_points_pair(points)
	if point_idx is None:
	points[0] = {'start': xy, 'target': None}
	print(f'Click Image - Start - {xy}')
	elif points[point_idx].get('target', None) is None:
	points[point_idx]['target'] = xy
	print(f'Click Image - Target - {xy}')
	else:
	points[point_idx + 1] = {'start': xy, 'target': None}
	print(f'Click Image - Start - {xy}')

	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(
	image_raw,
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'],
	global_state,
	)

	return global_state, image_draw

	form_image.select(
	on_click_image,
	inputs=[global_state],
	outputs=[global_state, form_image],
	queue=False,
	)

	def on_click_clear_points(global_state):
	"""Function to handle clear all control points
	1. clear global_state['points'] (clear_state)
	2. re-init network
	2. re-draw image
	"""
	clear_state(global_state, target='point')

	renderer: Renderer = global_state["renderer"]
	renderer.feat_refs = None

	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(image_raw, {}, global_state['mask'],
	global_state['show_mask'], global_state)
	return global_state, image_draw

	undo_points.click(on_click_clear_points,
	inputs=[global_state],
	outputs=[global_state, form_image],
	queue=False)

	def on_click_show_mask(global_state, show_mask):
	"""Function to control whether show mask on image."""
	global_state['show_mask'] = show_mask

	image_raw = global_state['images']['image_raw']
	image_draw = update_image_draw(
	image_raw,
	global_state['points'],
	global_state['mask'],
	global_state['show_mask'],
	global_state,
	)
	return global_state, image_draw

	show_mask.change(
	on_click_show_mask,
	inputs=[global_state, show_mask],
	outputs=[global_state, form_image],
	queue=False,
	)

	print("SHAReD: Start app", parser.parse_args())
	gr.close_all()
	app.queue(concurrency_count=1, max_size=200, api_open=False)
	app.launch(share=args.share, show_api=False)