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import os, sys | |
from huggingface_hub import snapshot_download | |
is_local_run = False | |
code_dir = snapshot_download("One-2-3-45/code", token=os.environ['TOKEN']) if not is_local_run else "../code" | |
sys.path.append(code_dir) | |
elev_est_dir = os.path.join(code_dir, "one2345_elev_est/") | |
sys.path.append(elev_est_dir) | |
if not is_local_run: | |
import subprocess | |
subprocess.run(["sh", os.path.join(elev_est_dir, "install.sh")], cwd=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" | |
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 | |
subprocess.run(["pip", "install", "--no-cache-dir", "git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0"]) | |
import inspect | |
import shutil | |
import torch | |
import fire | |
import gradio as gr | |
import numpy as np | |
import plotly.graph_objects as go | |
from functools import partial | |
from lovely_numpy import lo | |
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. | |
[<a href="http://One-2-3-45.com">Project</a>] | |
[<a href="https://github.com/One-2-3-45/One-2-3-45">GitHub</a>] | |
''' | |
# _HTML = '''<p>[<a href="https://github.com/One-2-3-45/One-2-3-45">GitHub</a>] | |
# <object alt="GitHub Repo stars" src="https://img.shields.io/github/stars/One-2-3-45/One-2-3-45?style=social&link=https%3A%2F%2Fgithub.com%2FOne-2-3-45%2FOne-2-3-45"> | |
# </p>''' | |
# _HTML = '<script async defer src="https://buttons.github.io/buttons.js"></script> <a class="github-button" href="https://github.com/One-2-3-45/One-2-3-45" data-icon="octicon-star" data-show-count="true" aria-label="Star One-2-3-45/One-2-3-45 on GitHub">Star</a><p>' | |
_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) <br> Images will be shown in the bottom right blocks." | |
_GEN_2 = "Predicting nearby views and generating mesh... (may take \~35 seconds) <br> Mesh will be shown on the right." | |
_DONE = "Done! Mesh is shown on the right. <br> 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**. <br> 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] | |
print('x:', lo(x)) | |
print('y:', lo(y)) | |
print('z:', lo(z)) | |
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) | |
elev_output = estimate_elev(tmp_dir) | |
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, 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(os.path.abspath( | |
inspect.getfile(inspect.currentframe()))) | |
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 --is_continue' | |
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_path = os.path.join(tmp_dir, "mesh.obj") | |
# Read the textured mesh from .ply file | |
mesh = trimesh.load_mesh(ply_path) | |
axis = [1, 0, 0] | |
angle = np.radians(90) | |
rotation_matrix = trimesh.transformations.rotation_matrix(angle, axis) | |
mesh.apply_transform(rotation_matrix) | |
axis = [0, 0, 1] | |
angle = np.radians(180) | |
rotation_matrix = trimesh.transformations.rotation_matrix(angle, axis) | |
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 | |
mesh.export(mesh_path, file_type='obj', include_color=True) | |
torch.cuda.empty_cache() | |
if not is_rerun: | |
return (mesh_path) | |
else: | |
return (mesh_path, [], 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) | |
print('has_nsfw_concept:', has_nsfw_concept) | |
del safety_checker_input | |
if np.any(has_nsfw_concept): | |
print('NSFW content detected.') | |
# Define the image size and background color | |
image_width = image_height = 256 | |
background_color = (255, 255, 255) # White | |
# Create a blank image | |
image = Image.new("RGB", (image_width, image_height), background_color) | |
from PIL import ImageDraw | |
draw = ImageDraw.Draw(image) | |
text = "Potential NSFW content was detected." | |
text_color = (255, 0, 0) | |
text_position = (10, 123) | |
draw.text(text_position, text, fill=text_color) | |
text = "Please try again with a different image." | |
text_position = (10, 133) | |
draw.text(text_position, text, fill=text_color) | |
return image | |
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("on_coords_slider, 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)) | |
# model = models['turncam'] | |
# sampler = DDIMSampler(model) | |
# 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. | |
css = "#model-3d-out {height: 400px;} #plot-out {height: 450px;}" | |
with gr.Blocks(title=_TITLE, css=css) as demo: | |
gr.Markdown('# ' + _TITLE) | |
gr.Markdown(_DESCRIPTION) | |
# gr.HTML(_HTML) | |
with gr.Row(variant='panel'): | |
with gr.Column(scale=1.2): | |
image_block = gr.Image(type='pil', image_mode='RGBA', label='Input image', tool=None).style(height=290) | |
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') | |
run_btn = gr.Button('Run Generation', variant='primary', interactive=False) | |
guide_text = gr.Markdown(_USER_GUIDE, visible=True) | |
with gr.Column(scale=.8): | |
with gr.Row(): | |
bbox_block = gr.Image(type='pil', label="Bounding box", interactive=False).style(height=290) | |
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=0.85): | |
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=1.15): | |
gr.Markdown('Predicted multi-view images') | |
with gr.Row(): | |
view_1 = gr.Image(interactive=False, show_label=False).style(height=200) | |
view_2 = gr.Image(interactive=False, show_label=False).style(height=200) | |
view_3 = gr.Image(interactive=False, show_label=False).style(height=200) | |
view_4 = gr.Image(interactive=False, show_label=False).style(height=200) | |
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, show_label=False).style(height=200) | |
view_6 = gr.Image(interactive=False, show_label=False).style(height=200) | |
view_7 = gr.Image(interactive=False, show_label=False).style(height=200) | |
view_8 = gr.Image(interactive=False, show_label=False).style(height=200) | |
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) | |
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) | |
gr.Markdown(article) | |
# 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], | |
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, 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.launch(enable_queue=True, share=False, max_threads=80) # auth=("admin", os.environ['PASSWD']) | |
if __name__ == '__main__': | |
fire.Fire(run_demo) |