import os, sys 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"]) # 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"]) 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)) # 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. 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=1.2): 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=.8): 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=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, 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) 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, 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)