import os, sys
import subprocess
from huggingface_hub import snapshot_download
is_local_run = False
code_dir = snapshot_download("One-2-3-45/code") if not is_local_run else "../code" # , token=os.environ['TOKEN']
sys.path.append(code_dir)
elev_est_dir = os.path.abspath(os.path.join(code_dir, "one2345_elev_est"))
sys.path.append(elev_est_dir)
if not is_local_run:
# import pip
# pip.main(['install', elev_est_dir])
# export TORCH_CUDA_ARCH_LIST="7.0;7.2;8.0;8.6"
# export IABN_FORCE_CUDA=1
os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
os.environ["IABN_FORCE_CUDA"] = "1"
os.environ["FORCE_CUDA"] = "1"
# pip.main(["install", "inplace_abn"])
subprocess.run(['pip', 'install', 'inplace_abn'])
# FORCE_CUDA=1 pip install --no-cache-dir git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
# pip.main(["install", "--no-cache-dir", "git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0"])
subprocess.run(['pip', 'install', '--no-cache-dir', 'git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0'])
import shutil
import torch
import fire
import gradio as gr
import numpy as np
import plotly.graph_objects as go
from functools import partial
import cv2
from PIL import Image
import trimesh
import tempfile
from zero123_utils import init_model, predict_stage1_gradio, zero123_infer
from sam_utils import sam_init, sam_out_nosave
from utils import image_preprocess_nosave, gen_poses
from one2345_elev_est.tools.estimate_wild_imgs import estimate_elev
from rembg import remove
_GPU_INDEX = 0
_TITLE = '''One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization'''
_DESCRIPTION = '''
We reconstruct a 3D textured mesh from a single image by initially predicting multi-view images and then lifting them to 3D.
'''
_USER_GUIDE = "Please upload an image in the block above (or choose an example above) and click **Run Generation**."
_BBOX_1 = "Predicting bounding box for the input image..."
_BBOX_2 = "Bounding box adjusted. Continue adjusting or **Run Generation**."
_BBOX_3 = "Bounding box predicted. Adjust it using sliders or **Run Generation**."
_SAM = "Preprocessing the input image... (safety check, SAM segmentation, *etc*.)"
_GEN_1 = "Predicting multi-view images... (may take \~13 seconds)
Images will be shown in the bottom right blocks."
_GEN_2 = "Predicting nearby views and generating mesh... (may take \~33 seconds)
Mesh will be shown on the right."
_DONE = "Done! Mesh is shown on the right.
If it is not satisfactory, please select **Retry view** checkboxes for inaccurate views and click **Regenerate selected view(s)** at the bottom."
_REGEN_1 = "Selected view(s) are regenerated. You can click **Regenerate nearby views and mesh**.
Alternatively, if the regenerated view(s) are still not satisfactory, you can repeat the previous step (select the view and regenerate)."
_REGEN_2 = "Regeneration done. Mesh is shown on the right."
def calc_cam_cone_pts_3d(polar_deg, azimuth_deg, radius_m, fov_deg):
'''
:param polar_deg (float).
:param azimuth_deg (float).
:param radius_m (float).
:param fov_deg (float).
:return (5, 3) array of float with (x, y, z).
'''
polar_rad = np.deg2rad(polar_deg)
azimuth_rad = np.deg2rad(azimuth_deg)
fov_rad = np.deg2rad(fov_deg)
polar_rad = -polar_rad # NOTE: Inverse of how used_x relates to x.
# Camera pose center:
cam_x = radius_m * np.cos(azimuth_rad) * np.cos(polar_rad)
cam_y = radius_m * np.sin(azimuth_rad) * np.cos(polar_rad)
cam_z = radius_m * np.sin(polar_rad)
# Obtain four corners of camera frustum, assuming it is looking at origin.
# First, obtain camera extrinsics (rotation matrix only):
camera_R = np.array([[np.cos(azimuth_rad) * np.cos(polar_rad),
-np.sin(azimuth_rad),
-np.cos(azimuth_rad) * np.sin(polar_rad)],
[np.sin(azimuth_rad) * np.cos(polar_rad),
np.cos(azimuth_rad),
-np.sin(azimuth_rad) * np.sin(polar_rad)],
[np.sin(polar_rad),
0.0,
np.cos(polar_rad)]])
# Multiply by corners in camera space to obtain go to space:
corn1 = [-1.0, np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
corn2 = [-1.0, -np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
corn3 = [-1.0, -np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
corn4 = [-1.0, np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
corn1 = np.dot(camera_R, corn1)
corn2 = np.dot(camera_R, corn2)
corn3 = np.dot(camera_R, corn3)
corn4 = np.dot(camera_R, corn4)
# Now attach as offset to actual 3D camera position:
corn1 = np.array(corn1) / np.linalg.norm(corn1, ord=2)
corn_x1 = cam_x + corn1[0]
corn_y1 = cam_y + corn1[1]
corn_z1 = cam_z + corn1[2]
corn2 = np.array(corn2) / np.linalg.norm(corn2, ord=2)
corn_x2 = cam_x + corn2[0]
corn_y2 = cam_y + corn2[1]
corn_z2 = cam_z + corn2[2]
corn3 = np.array(corn3) / np.linalg.norm(corn3, ord=2)
corn_x3 = cam_x + corn3[0]
corn_y3 = cam_y + corn3[1]
corn_z3 = cam_z + corn3[2]
corn4 = np.array(corn4) / np.linalg.norm(corn4, ord=2)
corn_x4 = cam_x + corn4[0]
corn_y4 = cam_y + corn4[1]
corn_z4 = cam_z + corn4[2]
xs = [cam_x, corn_x1, corn_x2, corn_x3, corn_x4]
ys = [cam_y, corn_y1, corn_y2, corn_y3, corn_y4]
zs = [cam_z, corn_z1, corn_z2, corn_z3, corn_z4]
return np.array([xs, ys, zs]).T
class CameraVisualizer:
def __init__(self, gradio_plot):
self._gradio_plot = gradio_plot
self._fig = None
self._polar = 0.0
self._azimuth = 0.0
self._radius = 0.0
self._raw_image = None
self._8bit_image = None
self._image_colorscale = None
def encode_image(self, raw_image, elev=90):
'''
:param raw_image (H, W, 3) array of uint8 in [0, 255].
'''
# https://stackoverflow.com/questions/60685749/python-plotly-how-to-add-an-image-to-a-3d-scatter-plot
dum_img = Image.fromarray(np.ones((3, 3, 3), dtype='uint8')).convert('P', palette='WEB')
idx_to_color = np.array(dum_img.getpalette()).reshape((-1, 3))
self._raw_image = raw_image
self._8bit_image = Image.fromarray(raw_image).convert('P', palette='WEB', dither=None)
# self._8bit_image = Image.fromarray(raw_image.clip(0, 254)).convert(
# 'P', palette='WEB', dither=None)
self._image_colorscale = [
[i / 255.0, 'rgb({}, {}, {})'.format(*rgb)] for i, rgb in enumerate(idx_to_color)]
self._elev = elev
# return self.update_figure()
def update_figure(self):
fig = go.Figure()
if self._raw_image is not None:
(H, W, C) = self._raw_image.shape
x = np.zeros((H, W))
(y, z) = np.meshgrid(np.linspace(-1.0, 1.0, W), np.linspace(1.0, -1.0, H) * H / W)
angle_deg = self._elev-90
angle = np.radians(90-self._elev)
rotation_matrix = np.array([
[np.cos(angle), 0, np.sin(angle)],
[0, 1, 0],
[-np.sin(angle), 0, np.cos(angle)]
])
# Assuming x, y, z are the original 3D coordinates of the image
coordinates = np.stack((x, y, z), axis=-1) # Combine x, y, z into a single array
# Apply the rotation matrix
rotated_coordinates = np.matmul(coordinates, rotation_matrix)
# Extract the new x, y, z coordinates from the rotated coordinates
x, y, z = rotated_coordinates[..., 0], rotated_coordinates[..., 1], rotated_coordinates[..., 2]
fig.add_trace(go.Surface(
x=x, y=y, z=z,
surfacecolor=self._8bit_image,
cmin=0,
cmax=255,
colorscale=self._image_colorscale,
showscale=False,
lighting_diffuse=1.0,
lighting_ambient=1.0,
lighting_fresnel=1.0,
lighting_roughness=1.0,
lighting_specular=0.3))
scene_bounds = 3.5
base_radius = 2.5
zoom_scale = 1.5 # Note that input radius offset is in [-0.5, 0.5].
fov_deg = 50.0
edges = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (3, 4), (4, 1)]
input_cone = calc_cam_cone_pts_3d(
angle_deg, 0.0, base_radius, fov_deg) # (5, 3).
output_cone = calc_cam_cone_pts_3d(
self._polar, self._azimuth, base_radius + self._radius * zoom_scale, fov_deg) # (5, 3).
output_cones = []
for i in range(1,4):
output_cones.append(calc_cam_cone_pts_3d(
angle_deg, i*90, base_radius + self._radius * zoom_scale, fov_deg))
delta_deg = 30 if angle_deg <= -15 else -30
for i in range(4):
output_cones.append(calc_cam_cone_pts_3d(
angle_deg+delta_deg, 30+i*90, base_radius + self._radius * zoom_scale, fov_deg))
cones = [(input_cone, 'rgb(174, 54, 75)', 'Input view (Predicted view 1)')]
for i in range(len(output_cones)):
cones.append((output_cones[i], 'rgb(32, 77, 125)', f'Predicted view {i+2}'))
for idx, (cone, clr, legend) in enumerate(cones):
for (i, edge) in enumerate(edges):
(x1, x2) = (cone[edge[0], 0], cone[edge[1], 0])
(y1, y2) = (cone[edge[0], 1], cone[edge[1], 1])
(z1, z2) = (cone[edge[0], 2], cone[edge[1], 2])
fig.add_trace(go.Scatter3d(
x=[x1, x2], y=[y1, y2], z=[z1, z2], mode='lines',
line=dict(color=clr, width=3),
name=legend, showlegend=(i == 1) and (idx <= 1)))
# Add label.
if cone[0, 2] <= base_radius / 2.0:
fig.add_trace(go.Scatter3d(
x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] - 0.05], showlegend=False,
mode='text', text=legend, textposition='bottom center'))
else:
fig.add_trace(go.Scatter3d(
x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] + 0.05], showlegend=False,
mode='text', text=legend, textposition='top center'))
# look at center of scene
fig.update_layout(
# width=640,
# height=480,
# height=400,
height=450,
autosize=True,
hovermode=False,
margin=go.layout.Margin(l=0, r=0, b=0, t=0),
showlegend=False,
legend=dict(
yanchor='bottom',
y=0.01,
xanchor='right',
x=0.99,
),
scene=dict(
aspectmode='manual',
aspectratio=dict(x=1, y=1, z=1.0),
camera=dict(
eye=dict(x=base_radius - 1.6, y=0.0, z=0.6),
center=dict(x=0.0, y=0.0, z=0.0),
up=dict(x=0.0, y=0.0, z=1.0)),
xaxis_title='',
yaxis_title='',
zaxis_title='',
xaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=False,
showgrid=True,
zeroline=False,
showbackground=True,
showspikes=False,
showline=False,
ticks=''),
yaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=False,
showgrid=True,
zeroline=False,
showbackground=True,
showspikes=False,
showline=False,
ticks=''),
zaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=False,
showgrid=True,
zeroline=False,
showbackground=True,
showspikes=False,
showline=False,
ticks='')))
self._fig = fig
return fig
def stage1_run(models, device, cam_vis, tmp_dir,
input_im, scale, ddim_steps, elev=None, rerun_all=[],
*btn_retrys):
is_rerun = True if cam_vis is None else False
model = models['turncam'].half()
stage1_dir = os.path.join(tmp_dir, "stage1_8")
if not is_rerun:
os.makedirs(stage1_dir, exist_ok=True)
output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4)), device=device, ddim_steps=ddim_steps, scale=scale)
stage2_steps = 50 # ddim_steps
zero123_infer(model, tmp_dir, indices=[0], device=device, ddim_steps=stage2_steps, scale=scale)
try:
elev_output = estimate_elev(tmp_dir)
except:
print("Failed to estimate polar angle")
elev_output = 90
print("Estimated polar angle:", elev_output)
gen_poses(tmp_dir, elev_output)
show_in_im1 = np.asarray(input_im, dtype=np.uint8)
cam_vis.encode_image(show_in_im1, elev=elev_output)
new_fig = cam_vis.update_figure()
flag_lower_cam = elev_output <= 75
if flag_lower_cam:
output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4,8)), device=device, ddim_steps=ddim_steps, scale=scale)
else:
output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(8,12)), device=device, ddim_steps=ddim_steps, scale=scale)
torch.cuda.empty_cache()
return (90-elev_output, new_fig, *output_ims, *output_ims_2)
else:
rerun_idx = [i for i in range(len(btn_retrys)) if btn_retrys[i]]
if 90-int(elev["label"]) > 75:
rerun_idx_in = [i if i < 4 else i+4 for i in rerun_idx]
else:
rerun_idx_in = rerun_idx
for idx in rerun_idx_in:
if idx not in rerun_all:
rerun_all.append(idx)
print("rerun_idx", rerun_all)
output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=rerun_idx_in, device=device, ddim_steps=ddim_steps, scale=scale)
outputs = [gr.update(visible=True)] * 8
for idx, view_idx in enumerate(rerun_idx):
outputs[view_idx] = output_ims[idx]
reset = [gr.update(value=False)] * 8
torch.cuda.empty_cache()
return (rerun_all, *reset, *outputs)
def stage2_run(models, device, tmp_dir,
elev, scale, is_glb=False, rerun_all=[], stage2_steps=50):
flag_lower_cam = 90-int(elev["label"]) <= 75
is_rerun = True if rerun_all else False
model = models['turncam'].half()
if not is_rerun:
if flag_lower_cam:
zero123_infer(model, tmp_dir, indices=list(range(1,8)), device=device, ddim_steps=stage2_steps, scale=scale)
else:
zero123_infer(model, tmp_dir, indices=list(range(1,4))+list(range(8,12)), device=device, ddim_steps=stage2_steps, scale=scale)
else:
print("rerun_idx", rerun_all)
zero123_infer(model, tmp_dir, indices=rerun_all, device=device, ddim_steps=stage2_steps, scale=scale)
dataset = tmp_dir
main_dir_path = os.path.dirname(__file__)
torch.cuda.empty_cache()
os.chdir(os.path.join(code_dir, 'SparseNeuS_demo_v1/'))
bash_script = f'CUDA_VISIBLE_DEVICES={_GPU_INDEX} python exp_runner_generic_blender_val.py --specific_dataset_name {dataset} --mode export_mesh --conf confs/one2345_lod0_val_demo.conf'
print(bash_script)
os.system(bash_script)
os.chdir(main_dir_path)
ply_path = os.path.join(tmp_dir, f"meshes_val_bg/lod0/mesh_00215000_gradio_lod0.ply")
mesh_ext = ".glb" if is_glb else ".obj"
mesh_path = os.path.join(tmp_dir, f"mesh{mesh_ext}")
# Read the textured mesh from .ply file
mesh = trimesh.load_mesh(ply_path)
rotation_matrix = trimesh.transformations.rotation_matrix(np.pi/2, [1, 0, 0])
mesh.apply_transform(rotation_matrix)
rotation_matrix = trimesh.transformations.rotation_matrix(np.pi, [0, 0, 1])
mesh.apply_transform(rotation_matrix)
# flip x
mesh.vertices[:, 0] = -mesh.vertices[:, 0]
mesh.faces = np.fliplr(mesh.faces)
# Export the mesh as .obj file with colors
if not is_glb:
mesh.export(mesh_path, file_type='obj', include_color=True)
else:
mesh.export(mesh_path, file_type='glb')
torch.cuda.empty_cache()
if not is_rerun:
return (mesh_path)
else:
return (mesh_path, gr.update(value=[]), gr.update(visible=False), gr.update(visible=False))
def nsfw_check(models, raw_im, device='cuda'):
safety_checker_input = models['clip_fe'](raw_im, return_tensors='pt').to(device)
(_, has_nsfw_concept) = models['nsfw'](
images=np.ones((1, 3)), clip_input=safety_checker_input.pixel_values)
del safety_checker_input
if np.any(has_nsfw_concept):
print('NSFW content detected.')
return Image.open("unsafe.png")
else:
print('Safety check passed.')
return False
def preprocess_run(predictor, models, raw_im, preprocess, *bbox_sliders):
raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
check_results = nsfw_check(models, raw_im, device=predictor.device)
if check_results:
return check_results
image_sam = sam_out_nosave(predictor, raw_im.convert("RGB"), *bbox_sliders)
input_256 = image_preprocess_nosave(image_sam, lower_contrast=preprocess, rescale=True)
torch.cuda.empty_cache()
return input_256
def on_coords_slider(image, x_min, y_min, x_max, y_max, color=(88, 191, 131, 255)):
"""Draw a bounding box annotation for an image."""
print("Slider adjusted, drawing bbox...")
image.thumbnail([512, 512], Image.Resampling.LANCZOS)
image_size = image.size
if max(image_size) > 224:
image.thumbnail([224, 224], Image.Resampling.LANCZOS)
shrink_ratio = max(image.size) / max(image_size)
x_min = int(x_min * shrink_ratio)
y_min = int(y_min * shrink_ratio)
x_max = int(x_max * shrink_ratio)
y_max = int(y_max * shrink_ratio)
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2BGRA)
image = cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, int(max(max(image.shape) / 400*2, 2)))
return cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA) # image[:, :, ::-1]
def init_bbox(image):
image.thumbnail([512, 512], Image.Resampling.LANCZOS)
width, height = image.size
image_rem = image.convert('RGBA')
image_nobg = remove(image_rem, alpha_matting=True)
arr = np.asarray(image_nobg)[:,:,-1]
x_nonzero = np.nonzero(arr.sum(axis=0))
y_nonzero = np.nonzero(arr.sum(axis=1))
x_min = int(x_nonzero[0].min())
y_min = int(y_nonzero[0].min())
x_max = int(x_nonzero[0].max())
y_max = int(y_nonzero[0].max())
image_mini = image.copy()
image_mini.thumbnail([224, 224], Image.Resampling.LANCZOS)
shrink_ratio = max(image_mini.size) / max(width, height)
x_min_shrink = int(x_min * shrink_ratio)
y_min_shrink = int(y_min * shrink_ratio)
x_max_shrink = int(x_max * shrink_ratio)
y_max_shrink = int(y_max * shrink_ratio)
return [on_coords_slider(image_mini, x_min_shrink, y_min_shrink, x_max_shrink, y_max_shrink),
gr.update(value=x_min, maximum=width),
gr.update(value=y_min, maximum=height),
gr.update(value=x_max, maximum=width),
gr.update(value=y_max, maximum=height)]
def run_demo(
device_idx=_GPU_INDEX,
ckpt='zero123-xl.ckpt'):
device = f"cuda:{device_idx}" if torch.cuda.is_available() else "cpu"
models = init_model(device, os.path.join(code_dir, ckpt))
# init sam model
predictor = sam_init(device_idx)
with open('instructions_12345.md', 'r') as f:
article = f.read()
# NOTE: Examples must match inputs
example_folder = os.path.join(os.path.dirname(__file__), 'demo_examples')
example_fns = os.listdir(example_folder)
example_fns.sort()
examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')]
# Compose demo layout & data flow.
with gr.Blocks(title=_TITLE, css="style.css") as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown('# ' + _TITLE)
with gr.Column(scale=0):
gr.DuplicateButton(value='Duplicate Space for private use',
elem_id='duplicate-button')
gr.Markdown(_DESCRIPTION)
with gr.Row(variant='panel'):
with gr.Column(scale=6):
image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image', tool=None)
gr.Examples(
examples=examples_full, # NOTE: elements must match inputs list!
inputs=[image_block],
outputs=[image_block],
cache_examples=False,
label='Examples (click one of the images below to start)',
examples_per_page=40
)
preprocess_chk = gr.Checkbox(
False, label='Reduce image contrast (mitigate shadows on the backside)')
with gr.Accordion('Advanced options', open=False):
scale_slider = gr.Slider(0, 30, value=3, step=1,
label='Diffusion guidance scale')
steps_slider = gr.Slider(5, 200, value=75, step=5,
label='Number of diffusion inference steps')
glb_chk = gr.Checkbox(
False, label='Export the mesh in .glb format')
run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
guide_text = gr.Markdown(_USER_GUIDE, visible=True)
with gr.Column(scale=4):
with gr.Row():
bbox_block = gr.Image(type='pil', label="Bounding box", height=290, interactive=False)
sam_block = gr.Image(type='pil', label="SAM output", interactive=False)
max_width = max_height = 256
with gr.Row():
x_min_slider = gr.Slider(label="X min", interactive=True, value=0, minimum=0, maximum=max_width, step=1)
y_min_slider = gr.Slider(label="Y min", interactive=True, value=0, minimum=0, maximum=max_height, step=1)
with gr.Row():
x_max_slider = gr.Slider(label="X max", interactive=True, value=max_width, minimum=0, maximum=max_width, step=1)
y_max_slider = gr.Slider(label="Y max", interactive=True, value=max_height, minimum=0, maximum=max_height, step=1)
bbox_sliders = [x_min_slider, y_min_slider, x_max_slider, y_max_slider]
mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="One-2-3-45's Textured Mesh", elem_id="model-3d-out")
with gr.Row(variant='panel'):
with gr.Column(scale=85):
elev_output = gr.Label(label='Estimated elevation (degree, w.r.t. the horizontal plane)')
vis_output = gr.Plot(label='Camera poses of the input view (red) and predicted views (blue)', elem_id="plot-out")
with gr.Column(scale=115):
gr.Markdown('Predicted multi-view images')
with gr.Row():
view_1 = gr.Image(interactive=False, height=200, show_label=False)
view_2 = gr.Image(interactive=False, height=200, show_label=False)
view_3 = gr.Image(interactive=False, height=200, show_label=False)
view_4 = gr.Image(interactive=False, height=200, show_label=False)
with gr.Row():
btn_retry_1 = gr.Checkbox(label='Retry view 1')
btn_retry_2 = gr.Checkbox(label='Retry view 2')
btn_retry_3 = gr.Checkbox(label='Retry view 3')
btn_retry_4 = gr.Checkbox(label='Retry view 4')
with gr.Row():
view_5 = gr.Image(interactive=False, height=200, show_label=False)
view_6 = gr.Image(interactive=False, height=200, show_label=False)
view_7 = gr.Image(interactive=False, height=200, show_label=False)
view_8 = gr.Image(interactive=False, height=200, show_label=False)
with gr.Row():
btn_retry_5 = gr.Checkbox(label='Retry view 5')
btn_retry_6 = gr.Checkbox(label='Retry view 6')
btn_retry_7 = gr.Checkbox(label='Retry view 7')
btn_retry_8 = gr.Checkbox(label='Retry view 8')
with gr.Row():
regen_view_btn = gr.Button('1. Regenerate selected view(s)', variant='secondary', visible=False)
regen_mesh_btn = gr.Button('2. Regenerate nearby views and mesh', variant='secondary', visible=False)
gr.Markdown(article)
gr.HTML("""
""")
update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT)
views = [view_1, view_2, view_3, view_4, view_5, view_6, view_7, view_8]
btn_retrys = [btn_retry_1, btn_retry_2, btn_retry_3, btn_retry_4, btn_retry_5, btn_retry_6, btn_retry_7, btn_retry_8]
rerun_idx = gr.State([])
tmp_dir = gr.State('./demo_tmp/tmp_dir')
def refresh(tmp_dir):
if os.path.exists(tmp_dir):
shutil.rmtree(tmp_dir)
tmp_dir = tempfile.TemporaryDirectory(dir=os.path.join(os.path.dirname(__file__), 'demo_tmp'))
print("create tmp_dir", tmp_dir.name)
clear = [gr.update(value=[])] + [None] * 5 + [gr.update(visible=False)] * 2 + [None] * 8 + [gr.update(value=False)] * 8
return (tmp_dir.name, *clear)
placeholder = gr.Image(visible=False)
tmp_func = lambda x: False if not x else gr.update(visible=False)
disable_func = lambda x: gr.update(interactive=False)
enable_func = lambda x: gr.update(interactive=True)
image_block.change(disable_func, inputs=run_btn, outputs=run_btn, queue=False
).success(fn=refresh,
inputs=[tmp_dir],
outputs=[tmp_dir, rerun_idx, bbox_block, sam_block, elev_output, vis_output, mesh_output, regen_view_btn, regen_mesh_btn, *views, *btn_retrys],
queue=False
).success(fn=tmp_func, inputs=[image_block], outputs=[placeholder], queue=False
).success(fn=partial(update_guide, _BBOX_1), outputs=[guide_text], queue=False
).success(fn=init_bbox,
inputs=[image_block],
outputs=[bbox_block, *bbox_sliders], queue=False
).success(fn=partial(update_guide, _BBOX_3), outputs=[guide_text], queue=False
).success(enable_func, inputs=run_btn, outputs=run_btn, queue=False)
for bbox_slider in bbox_sliders:
bbox_slider.release(fn=on_coords_slider,
inputs=[image_block, *bbox_sliders],
outputs=[bbox_block],
queue=False
).success(fn=partial(update_guide, _BBOX_2), outputs=[guide_text], queue=False)
cam_vis = CameraVisualizer(vis_output)
# Define the function to be called when any of the btn_retry buttons are clicked
def on_retry_button_click(*btn_retrys):
any_checked = any([btn_retry for btn_retry in btn_retrys])
print('any_checked:', any_checked, [btn_retry for btn_retry in btn_retrys])
if any_checked:
return (gr.update(visible=True), gr.update(visible=True))
else:
return (gr.update(), gr.update())
# make regen_btn visible when any of the btn_retry is checked
for btn_retry in btn_retrys:
# Add the event handlers to the btn_retry buttons
btn_retry.change(fn=on_retry_button_click, inputs=[*btn_retrys], outputs=[regen_view_btn, regen_mesh_btn], queue=False)
run_btn.click(fn=partial(update_guide, _SAM), outputs=[guide_text], queue=False
).success(fn=partial(preprocess_run, predictor, models),
inputs=[image_block, preprocess_chk, *bbox_sliders],
outputs=[sam_block]
).success(fn=partial(update_guide, _GEN_1), outputs=[guide_text], queue=False
).success(fn=partial(stage1_run, models, device, cam_vis),
inputs=[tmp_dir, sam_block, scale_slider, steps_slider],
outputs=[elev_output, vis_output, *views]
).success(fn=partial(update_guide, _GEN_2), outputs=[guide_text], queue=False
).success(fn=partial(stage2_run, models, device),
inputs=[tmp_dir, elev_output, scale_slider, glb_chk],
outputs=[mesh_output]
).success(fn=partial(update_guide, _DONE), outputs=[guide_text], queue=False)
regen_view_btn.click(fn=partial(stage1_run, models, device, None),
inputs=[tmp_dir, sam_block, scale_slider, steps_slider, elev_output, rerun_idx, *btn_retrys],
outputs=[rerun_idx, *btn_retrys, *views]
).success(fn=partial(update_guide, _REGEN_1), outputs=[guide_text], queue=False)
regen_mesh_btn.click(fn=partial(stage2_run, models, device),
inputs=[tmp_dir, elev_output, scale_slider, glb_chk, rerun_idx],
outputs=[mesh_output, rerun_idx, regen_view_btn, regen_mesh_btn]
).success(fn=partial(update_guide, _REGEN_2), outputs=[guide_text], queue=False)
demo.queue().launch(share=False, max_threads=80) # auth=("admin", os.environ['PASSWD'])
if __name__ == '__main__':
fire.Fire(run_demo)
