import spaces
import os
import gradio as gr
import torch
from gradio_image_prompter import ImagePrompter
from sam2.sam2_image_predictor import SAM2ImagePredictor
from omegaconf import OmegaConf
from PIL import Image
import numpy as np
from copy import deepcopy
import cv2
import torch.nn.functional as F
import torchvision
from einops import rearrange
import tempfile
from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, get_subject_points, get_points, undo_points, mask_image
from ZoeDepth.zoedepth.utils.misc import colorize
from cameractrl.inference import get_pipeline
from objctrl_2_5d.utils.examples import examples, sync_points
from objctrl_2_5d.utils.objmask_util import RT2Plucker, Unprojected, roll_with_ignore_multidim, dilate_mask_pytorch
from objctrl_2_5d.utils.filter_utils import get_freq_filter, freq_mix_3d
### Title and Description ###
#### Description ####
title = r"""ObjCtrl-2.5D: Training-free Object Control with Camera Poses
"""
# subtitle = r"""Deployed on SVD Generation
"""
important_link = r"""
"""
authors = r"""
"""
affiliation = r"""
"""
description = r"""
Official Gradio demo for ObjCtrl-2.5D: Training-free Object Control with Camera Poses.
🔥 ObjCtrl2.5D enables object motion control in a I2V generated video via transforming 2D trajectories to 3D using depth, subsequently converting them into camera poses,
thereby leveraging the exisitng camera motion control module for object motion control without requiring additional training.
"""
article = r"""
If ObjCtrl2.5D is helpful, please help to ⭐ the Github Repo. Thanks!
[](https://github.com/TencentARC/MotionCtrl)
---
📝 **Citation**
If our work is useful for your research, please consider citing:
```bibtex
@inproceedings{wang2024motionctrl,
title={Motionctrl: A unified and flexible motion controller for video generation},
author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Li, Yaowei and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Ying},
booktitle={ACM SIGGRAPH 2024 Conference Papers},
pages={1--11},
year={2024}
}
```
📧 **Contact**
If you have any questions, please feel free to reach me out at zhouzi1212@gmail.com.
"""
# -------------- initialization --------------
CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
# select the device for computation
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
device = torch.device("cuda")
print(f"Force device to {device} due to ZeroGPU")
print(f"using device: {device}")
# segmentation model
segmentor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", cache_dir="ckpt", device=device)
# depth model
d_model_NK = torch.hub.load('./ZoeDepth', 'ZoeD_NK', source='local', pretrained=True).to(device)
# cameractrl model
config = "configs/svd_320_576_cameractrl.yaml"
model_id = "stabilityai/stable-video-diffusion-img2vid"
ckpt = "checkpoints/CameraCtrl_svd.ckpt"
if not os.path.exists(ckpt):
os.makedirs("checkpoints", exist_ok=True)
os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svd.ckpt?download=true")
os.system("mv CameraCtrl_svd.ckpt?download=true checkpoints/CameraCtrl_svd.ckpt")
model_config = OmegaConf.load(config)
pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], model_config['up_block_types'],
model_config['pose_encoder_kwargs'], model_config['attention_processor_kwargs'],
ckpt, True, device)
# segmentor = None
# d_model_NK = None
# pipeline = None
### run the demo ##
@spaces.GPU(duration=50)
def segment(canvas, image, logits):
if logits is not None:
logits *= 32.0
_, points = get_subject_points(canvas)
image = np.array(image)
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
segmentor.set_image(image)
input_points = []
input_boxes = []
for p in points:
[x1, y1, _, x2, y2, _] = p
if x2==0 and y2==0:
input_points.append([x1, y1])
else:
input_boxes.append([x1, y1, x2, y2])
if len(input_points) == 0:
input_points = None
input_labels = None
else:
input_points = np.array(input_points)
input_labels = np.ones(len(input_points))
if len(input_boxes) == 0:
input_boxes = None
else:
input_boxes = np.array(input_boxes)
masks, _, logits = segmentor.predict(
point_coords=input_points,
point_labels=input_labels,
box=input_boxes,
multimask_output=False,
return_logits=True,
mask_input=logits,
)
mask = masks > 0
masked_img = mask_image(image, mask[0], color=[252, 140, 90], alpha=0.9)
masked_img = Image.fromarray(masked_img)
return mask[0], masked_img, masked_img, logits / 32.0
@spaces.GPU(duration=50)
def get_depth(image, points):
depth = d_model_NK.infer_pil(image)
colored_depth = colorize(depth, cmap='gray_r') # [h, w, 4] 0-255
depth_img = deepcopy(colored_depth[:, :, :3])
if len(points) > 0:
for idx, point in enumerate(points):
if idx % 2 == 0:
cv2.circle(depth_img, tuple(point), 10, (255, 0, 0), -1)
else:
cv2.circle(depth_img, tuple(point), 10, (0, 0, 255), -1)
if idx > 0:
cv2.arrowedLine(depth_img, points[idx-1], points[idx], (255, 255, 255), 4, tipLength=0.5)
return depth, depth_img, colored_depth[:, :, :3]
@spaces.GPU(duration=50)
def run_objctrl_2_5d(condition_image,
mask,
depth,
RTs,
bg_mode,
shared_wapring_latents,
scale_wise_masks,
rescale,
seed,
ds, dt,
num_inference_steps=25):
DEBUG = False
if DEBUG:
cur_OUTPUT_PATH = 'outputs/tmp'
os.makedirs(cur_OUTPUT_PATH, exist_ok=True)
# num_inference_steps=25
min_guidance_scale = 1.0
max_guidance_scale = 3.0
area_ratio = 0.3
depth_scale_ = 5.2
center_margin = 10
height, width = 320, 576
num_frames = 14
intrinsics = np.array([[float(width), float(width), float(width) / 2, float(height) / 2]])
intrinsics = np.repeat(intrinsics, num_frames, axis=0) # [n_frame, 4]
fx = intrinsics[0, 0] / width
fy = intrinsics[0, 1] / height
cx = intrinsics[0, 2] / width
cy = intrinsics[0, 3] / height
down_scale = 8
H, W = height // down_scale, width // down_scale
K = np.array([[width / down_scale, 0, W / 2], [0, width / down_scale, H / 2], [0, 0, 1]])
seed = int(seed)
center_h_margin, center_w_margin = center_margin, center_margin
depth_center = np.mean(depth[height//2-center_h_margin:height//2+center_h_margin, width//2-center_w_margin:width//2+center_w_margin])
if rescale > 0:
depth_rescale = round(depth_scale_ * rescale / depth_center, 2)
else:
depth_rescale = 1.0
depth = depth * depth_rescale
depth_down = F.interpolate(torch.tensor(depth).unsqueeze(0).unsqueeze(0),
(H, W), mode='bilinear', align_corners=False).squeeze().numpy() # [H, W]
## latent
generator = torch.Generator()
generator.manual_seed(seed)
latents_org = pipeline.prepare_latents(
1,
14,
8,
height,
width,
pipeline.dtype,
device,
generator,
None,
)
latents_org = latents_org / pipeline.scheduler.init_noise_sigma
cur_plucker_embedding, _, _ = RT2Plucker(RTs, RTs.shape[0], (height, width), fx, fy, cx, cy) # 6, V, H, W
cur_plucker_embedding = cur_plucker_embedding.to(device)
cur_plucker_embedding = cur_plucker_embedding[None, ...] # b 6 f h w
cur_plucker_embedding = cur_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
cur_plucker_embedding = cur_plucker_embedding[:, :num_frames, ...]
cur_pose_features = pipeline.pose_encoder(cur_plucker_embedding)
# bg_mode = ["Fixed", "Reverse", "Free"]
if bg_mode == "Fixed":
fix_RTs = np.repeat(RTs[0][None, ...], num_frames, axis=0) # [n_frame, 4, 3]
fix_plucker_embedding, _, _ = RT2Plucker(fix_RTs, num_frames, (height, width), fx, fy, cx, cy) # 6, V, H, W
fix_plucker_embedding = fix_plucker_embedding.to(device)
fix_plucker_embedding = fix_plucker_embedding[None, ...] # b 6 f h w
fix_plucker_embedding = fix_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
fix_plucker_embedding = fix_plucker_embedding[:, :num_frames, ...]
fix_pose_features = pipeline.pose_encoder(fix_plucker_embedding)
elif bg_mode == "Reverse":
bg_plucker_embedding, _, _ = RT2Plucker(RTs[::-1], RTs.shape[0], (height, width), fx, fy, cx, cy) # 6, V, H, W
bg_plucker_embedding = bg_plucker_embedding.to(device)
bg_plucker_embedding = bg_plucker_embedding[None, ...] # b 6 f h w
bg_plucker_embedding = bg_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
bg_plucker_embedding = bg_plucker_embedding[:, :num_frames, ...]
fix_pose_features = pipeline.pose_encoder(bg_plucker_embedding)
else:
fix_pose_features = None
#### preparing mask
mask = Image.fromarray(mask)
mask = mask.resize((W, H))
mask = np.array(mask).astype(np.float32)
mask = np.expand_dims(mask, axis=-1)
# visulize mask
if DEBUG:
mask_sum_vis = mask[..., 0]
mask_sum_vis = (mask_sum_vis * 255.0).astype(np.uint8)
mask_sum_vis = Image.fromarray(mask_sum_vis)
mask_sum_vis.save(f'{cur_OUTPUT_PATH}/org_mask.png')
try:
warped_masks = Unprojected(mask, depth_down, RTs, H=H, W=W, K=K)
warped_masks.insert(0, mask)
except:
# mask to bbox
print(f'!!! Mask is too small to warp; mask to bbox')
mask = mask[:, :, 0]
coords = cv2.findNonZero(mask)
x, y, w, h = cv2.boundingRect(coords)
# mask[y:y+h, x:x+w] = 1.0
center_x, center_y = x + w // 2, y + h // 2
center_z = depth_down[center_y, center_x]
# RTs [n_frame, 3, 4] to [n_frame, 4, 4] , add [0, 0, 0, 1]
RTs = np.concatenate([RTs, np.array([[[0, 0, 0, 1]]] * num_frames)], axis=1)
# RTs: world to camera
P0 = np.array([center_x, center_y, 1])
Pc0 = np.linalg.inv(K) @ P0 * center_z
pw = np.linalg.inv(RTs[0]) @ np.array([Pc0[0], Pc0[1], center_z, 1]) # [4]
P = [np.array([center_x, center_y])]
for i in range(1, num_frames):
Pci = RTs[i] @ pw
Pi = K @ Pci[:3] / Pci[2]
P.append(Pi[:2])
warped_masks = [mask]
for i in range(1, num_frames):
shift_x = int(round(P[i][0] - P[0][0]))
shift_y = int(round(P[i][1] - P[0][1]))
cur_mask = roll_with_ignore_multidim(mask, [shift_y, shift_x])
warped_masks.append(cur_mask)
warped_masks = [v[..., None] for v in warped_masks]
warped_masks = np.stack(warped_masks, axis=0) # [f, h, w]
warped_masks = np.repeat(warped_masks, 3, axis=-1) # [f, h, w, 3]
mask_sum = np.sum(warped_masks, axis=0, keepdims=True) # [1, H, W, 3]
mask_sum[mask_sum > 1.0] = 1.0
mask_sum = mask_sum[0,:,:, 0]
if DEBUG:
## visulize warp mask
warp_masks_vis = torch.tensor(warped_masks)
warp_masks_vis = (warp_masks_vis * 255.0).to(torch.uint8)
torchvision.io.write_video(f'{cur_OUTPUT_PATH}/warped_masks.mp4', warp_masks_vis, fps=10, video_codec='h264', options={'crf': '10'})
# visulize mask
mask_sum_vis = mask_sum
mask_sum_vis = (mask_sum_vis * 255.0).astype(np.uint8)
mask_sum_vis = Image.fromarray(mask_sum_vis)
mask_sum_vis.save(f'{cur_OUTPUT_PATH}/merged_mask.png')
if scale_wise_masks:
min_area = H * W * area_ratio # cal in downscale
non_zero_len = mask_sum.sum()
print(f'non_zero_len: {non_zero_len}, min_area: {min_area}')
if non_zero_len > min_area:
kernel_sizes = [1, 1, 1, 3]
elif non_zero_len > min_area * 0.5:
kernel_sizes = [3, 1, 1, 5]
else:
kernel_sizes = [5, 3, 3, 7]
else:
kernel_sizes = [1, 1, 1, 1]
mask = torch.from_numpy(mask_sum) # [h, w]
mask = mask[None, None, ...] # [1, 1, h, w]
mask = F.interpolate(mask, (height, width), mode='bilinear', align_corners=False) # [1, 1, H, W]
# mask = mask.repeat(1, num_frames, 1, 1) # [1, f, H, W]
mask = mask.to(pipeline.dtype).to(device)
##### Mask End ######
### Got blending pose features Start ###
pose_features = []
for i in range(0, len(cur_pose_features)):
kernel_size = kernel_sizes[i]
h, w = cur_pose_features[i].shape[-2:]
if fix_pose_features is None:
pose_features.append(torch.zeros_like(cur_pose_features[i]))
else:
pose_features.append(fix_pose_features[i])
cur_mask = F.interpolate(mask, (h, w), mode='bilinear', align_corners=False)
cur_mask = dilate_mask_pytorch(cur_mask, kernel_size=kernel_size) # [1, 1, H, W]
cur_mask = cur_mask.repeat(1, num_frames, 1, 1) # [1, f, H, W]
if DEBUG:
# visulize mask
mask_vis = cur_mask[0, 0].cpu().numpy() * 255.0
mask_vis = Image.fromarray(mask_vis.astype(np.uint8))
mask_vis.save(f'{cur_OUTPUT_PATH}/mask_k{kernel_size}_scale{i}.png')
cur_mask = cur_mask[None, ...] # [1, 1, f, H, W]
pose_features[-1] = cur_pose_features[i] * cur_mask + pose_features[-1] * (1 - cur_mask)
### Got blending pose features End ###
##### Warp Noise Start ######
if shared_wapring_latents:
noise = latents_org[0, 0].data.cpu().numpy().copy() #[14, 4, 40, 72]
noise = np.transpose(noise, (1, 2, 0)) # [40, 72, 4]
try:
warp_noise = Unprojected(noise, depth_down, RTs, H=H, W=W, K=K)
warp_noise.insert(0, noise)
except:
print(f'!!! Noise is too small to warp; mask to bbox')
warp_noise = [noise]
for i in range(1, num_frames):
shift_x = int(round(P[i][0] - P[0][0]))
shift_y = int(round(P[i][1] - P[0][1]))
cur_noise= roll_with_ignore_multidim(noise, [shift_y, shift_x])
warp_noise.append(cur_noise)
warp_noise = np.stack(warp_noise, axis=0) # [f, h, w, 4]
if DEBUG:
## visulize warp noise
warp_noise_vis = torch.tensor(warp_noise)[..., :3] * torch.tensor(warped_masks)
warp_noise_vis = (warp_noise_vis - warp_noise_vis.min()) / (warp_noise_vis.max() - warp_noise_vis.min())
warp_noise_vis = (warp_noise_vis * 255.0).to(torch.uint8)
torchvision.io.write_video(f'{cur_OUTPUT_PATH}/warp_noise.mp4', warp_noise_vis, fps=10, video_codec='h264', options={'crf': '10'})
warp_latents = torch.tensor(warp_noise).permute(0, 3, 1, 2).to(latents_org.device).to(latents_org.dtype) # [frame, 4, H, W]
warp_latents = warp_latents.unsqueeze(0) # [1, frame, 4, H, W]
warped_masks = torch.tensor(warped_masks).permute(0, 3, 1, 2).unsqueeze(0) # [1, frame, 3, H, W]
mask_extend = torch.concat([warped_masks, warped_masks[:,:,0:1]], dim=2) # [1, frame, 4, H, W]
mask_extend = mask_extend.to(latents_org.device).to(latents_org.dtype)
warp_latents = warp_latents * mask_extend + latents_org * (1 - mask_extend)
warp_latents = warp_latents.permute(0, 2, 1, 3, 4)
random_noise = latents_org.clone().permute(0, 2, 1, 3, 4)
filter_shape = warp_latents.shape
freq_filter = get_freq_filter(
filter_shape,
device = device,
filter_type='butterworth',
n=4,
d_s=ds,
d_t=dt
)
warp_latents = freq_mix_3d(warp_latents, random_noise, freq_filter)
warp_latents = warp_latents.permute(0, 2, 1, 3, 4)
else:
warp_latents = latents_org.clone()
generator.manual_seed(42)
with torch.no_grad():
result = pipeline(
image=condition_image,
pose_embedding=cur_plucker_embedding,
height=height,
width=width,
num_frames=num_frames,
num_inference_steps=num_inference_steps,
min_guidance_scale=min_guidance_scale,
max_guidance_scale=max_guidance_scale,
do_image_process=True,
generator=generator,
output_type='pt',
pose_features= pose_features,
latents = warp_latents
).frames[0].cpu() #[f, c, h, w]
result = rearrange(result, 'f c h w -> f h w c')
result = (result * 255.0).to(torch.uint8)
video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name
torchvision.io.write_video(video_path, result, fps=10, video_codec='h264', options={'crf': '8'})
return video_path
# -------------- UI definition --------------
with gr.Blocks() as demo:
# layout definition
gr.Markdown(title)
gr.Markdown(authors)
gr.Markdown(affiliation)
gr.Markdown(important_link)
gr.Markdown(description)
# with gr.Row():
# gr.Markdown("""# Repositioning the Subject within Image """)
mask = gr.State(value=None) # store mask
removal_mask = gr.State(value=None) # store removal mask
selected_points = gr.State([]) # store points
selected_points_text = gr.Textbox(label="Selected Points", visible=False)
original_image = gr.State(value=None) # store original input image
masked_original_image = gr.State(value=None) # store masked input image
mask_logits = gr.State(value=None) # store mask logits
depth = gr.State(value=None) # store depth
org_depth_image = gr.State(value=None) # store original depth image
camera_pose = gr.State(value=None) # store camera pose
with gr.Column():
outlines = """
There are total 5 steps to complete the task.
- Step 1: Input an image and Crop it to a suitable size;
- Step 2: Attain the subject mask;
- Step 3: Get depth and Draw Trajectory;
- Step 4: Get camera pose from trajectory or customize it;
- Step 5: Generate the final video.
"""
gr.Markdown(outlines)
with gr.Row():
with gr.Column():
# Step 1: Input Image
step1_dec = """
Step 1: Input Image
- Select the region using a bounding box, aiming for a ratio close to 320:576 (height:width).
- All provided images in `Examples` are in 320 x 576 resolution. Simply press `Process` to proceed.
"""
step1 = gr.Markdown(step1_dec)
raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
# left_up_point = gr.Textbox(value = "-1 -1", label="Left Up Point", interactive=True)
process_button = gr.Button("Process")
with gr.Column():
# Step 2: Get Subject Mask
step2_dec = """
Step 2: Get Subject Mask
- Use the bounding boxes or paints to select the subject.
- Press `Segment Subject` to get the mask. Can be refined iteratively by updating points.
"""
step2 = gr.Markdown(step2_dec)
canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
select_button = gr.Button("Segment Subject")
with gr.Row():
with gr.Column():
mask_dec = """
Mask Result
- Just for visualization purpose. No need to interact.
"""
mask_vis = gr.Markdown(mask_dec)
mask_output = gr.Image(type="pil", label="Mask", show_label=True, interactive=False)
with gr.Column():
# Step 3: Get Depth and Draw Trajectory
step3_dec = """
Step 3: Get Depth and Draw Trajectory
- Press `Get Depth` to get the depth image.
- Draw the trajectory by selecting points on the depth image. No more than 14 points.
- Press `Undo point` to remove all points.
"""
step3 = gr.Markdown(step3_dec)
depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
with gr.Row():
depth_button = gr.Button("Get Depth")
undo_button = gr.Button("Undo point")
with gr.Row():
with gr.Column():
# Step 4: Trajectory to Camera Pose or Get Camera Pose
step4_dec = """
Step 4: Get camera pose from trajectory or customize it
- Option 1: Transform the 2D trajectory to camera poses with depth. `Rescale` is used for depth alignment. Larger value can speed up the object motion.
- Option 2: Rotate the camera with a specific `Angle`.
- Option 3: Rotate the camera clockwise or counterclockwise with a specific `Angle`.
- Option 4: Translate the camera with `Tx` (Pan Left/Right), `Ty` (Pan Up/Down), `Tz` (Zoom In/Out) and `Speed`.
"""
step4 = gr.Markdown(step4_dec)
camera_pose_vis = gr.Plot(None, label='Camera Pose')
with gr.Row():
with gr.Column():
speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.0, label="Speed", interactive=True)
rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True)
# traj2pose_button = gr.Button("Option1: Trajectory to Camera Pose")
angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True)
# rotation_button = gr.Button("Option2: Rotate")
# clockwise_button = gr.Button("Option3: Clockwise")
with gr.Column():
Tx = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tx", interactive=True)
Ty = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Ty", interactive=True)
Tz = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tz", interactive=True)
# translation_button = gr.Button("Option4: Translate")
with gr.Row():
camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
with gr.Row():
get_camera_pose_button = gr.Button("Get Camera Pose")
with gr.Column():
# Step 5: Get the final generated video
step5_dec = """
Step 5: Get the final generated video
- 3 modes for background: Fixed, Reverse, Free.
- Enable Scale-wise Masks for better object control.
- Option to enable Shared Warping Latents and set stop frequency for spatial (`ds`) and temporal (`dt`) dimensions. Larger stop frequency will lead to artifacts.
"""
step5 = gr.Markdown(step5_dec)
generated_video = gr.Video(None, label='Generated Video')
with gr.Row():
seed = gr.Textbox(value = "42", label="Seed", interactive=True)
# num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=25, label="Number of Inference Steps", interactive=True)
bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
# swl_mode = gr.Radio(choices = ["Enable SWL", "Disable SWL"], label="Shared Warping Latent", value="Disable SWL", interactive=True)
scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True)
with gr.Row():
with gr.Column():
shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True)
with gr.Column():
ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="ds", interactive=True)
dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="dt", interactive=True)
generated_button = gr.Button("Generate")
# # event definition
process_button.click(
fn = process_image,
inputs = [raw_input],
outputs = [original_image, canvas]
)
select_button.click(
segment,
[canvas, original_image, mask_logits],
[mask, mask_output, masked_original_image, mask_logits]
)
depth_button.click(
get_depth,
[original_image, selected_points],
[depth, depth_image, org_depth_image]
)
depth_image.select(
get_points,
[depth_image, selected_points],
[depth_image, selected_points],
)
undo_button.click(
undo_points,
[org_depth_image],
[depth_image, selected_points]
)
get_camera_pose_button.click(
get_camera_pose(CAMERA_MODE),
[camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed],
[camera_pose, camera_pose_vis]
)
generated_button.click(
run_objctrl_2_5d,
[
original_image,
mask,
depth,
camera_pose,
bg_mode,
shared_wapring_latents,
scale_wise_masks,
rescale,
seed,
ds,
dt,
# num_inference_steps
],
[generated_video],
)
gr.Examples(
examples=examples,
inputs=[
raw_input,
rescale,
speed,
angle,
Tx,
Ty,
Tz,
camera_option,
bg_mode,
shared_wapring_latents,
scale_wise_masks,
ds,
dt,
seed,
selected_points_text # selected_points
],
outputs=[generated_video],
examples_per_page=10
)
selected_points_text.change(
sync_points,
inputs=[selected_points_text],
outputs=[selected_points]
)
gr.Markdown(article)
demo.queue().launch(share=True)