try:
import spaces
except:
pass
import os
import gradio as gr
import json
import ast
import torch
from gradio_image_prompter import ImagePrompter
from sam2.sam2_image_predictor import SAM2ImagePredictor
from omegaconf import OmegaConf
from PIL import Image, ImageDraw
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, traj2cam, get_mid_params
from ZoeDepth.zoedepth.utils.misc import colorize
from cameractrl.inference import get_pipeline
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/wzhouxiff/ObjCtrl-2.5D)
---
📝 **Citation**
This project is licensed under S-Lab License 1.0,
Redistribution and use for non-commercial purposes should follow this license.
📝 **Citation**
If our work is useful for your research, please consider citing:
```bibtex
@inproceedings{objctrl2.5d,
title={ObjCtrl-2.5D: Training-free Object Control with Camera Poses},
author={Wang, Zhouxia and Lan, Yushi and Zhou, Shangchen and Loy, Chen Change},
booktitle={arXiv},
year={2024}
}
```
📧 **Contact**
If you have any questions, please feel free to reach me out at zhouzi1212@gmail.com.
"""
# pre-defined parameters
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]])
# -------------- initialization --------------
# CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
CAMERA_MODE = ["None", "ZoomIn", "ZoomOut", "PanRight", "PanLeft", "TiltUp", "TiltDown", "ClockWise", "Anti-CW", "Rotate60"]
# 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")
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=7)
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], {'image': masked_img, 'points': points}, logits / 32.0
@spaces.GPU(duration=80)
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):
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 function
@spaces.GPU(duration=7)
def process_image(raw_image, trajectory_points):
image, points = raw_image['image'], raw_image['points']
print(points)
try:
assert(len(points)) == 1, "Please draw only one bbox"
[x1, y1, _, x2, y2, _] = points[0]
image = image.crop((x1, y1, x2, y2))
image = image.resize((width, height))
except:
image = image.resize((width, height))
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(trajectory_points) > 0:
for idx, point in enumerate(trajectory_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:
line_length = np.sqrt((trajectory_points[idx][0] - trajectory_points[idx-1][0])**2 + (trajectory_points[idx][1] - trajectory_points[idx-1][1])**2)
arrow_head_length = 10
tip_length = arrow_head_length / line_length
cv2.arrowedLine(depth_img, trajectory_points[idx-1], trajectory_points[idx], (0, 255, 0), 4, tipLength=tip_length)
return image, {'image': image}, depth, depth_img, colored_depth[:, :, :3]
def draw_points_on_image(img, points):
# img = Image.fromarray(np.array(image))
draw = ImageDraw.Draw(img)
for p in points:
x1, y1, _, x2, y2, _ = p
if x2 == 0 and y2 == 0:
# Point: 青色点带黑边
point_radius = 4
draw.ellipse(
(x1 - point_radius, y1 - point_radius, x1 + point_radius, y1 + point_radius),
fill="cyan", outline="black", width=1
)
else:
# Bounding Box: 黑色矩形框
draw.rectangle([x1, y1, x2, y2], outline="black", width=3)
return img
@spaces.GPU(duration=15)
def from_examples(raw_input, raw_image_points, canvas, seg_image_points, selected_points_text, camera_option, mask_bk):
raw_image_points = ast.literal_eval(raw_image_points)
seg_image_points = ast.literal_eval(seg_image_points)
selected_points = ast.literal_eval(selected_points_text)
mask = np.array(mask_bk)
mask = mask[:,:,0] > 0
selected_points = ast.literal_eval(selected_points_text)
image, _, depth, depth_img, colored_depth = process_image({'image': raw_input['image'], 'points': raw_image_points}, selected_points)
# get camera pose
if camera_option == "None":
# traj2came
rescale = 1.0
camera_pose, camera_pose_vis, rescale, _ = traj2cam(selected_points, depth , rescale)
else:
rescale = 0.0
angle = 60
speed = 4.0
camera_pose, camera_pose_vis, rescale = get_camera_pose(CAMERA_MODE)(camera_option, depth, mask, rescale, angle, speed)
raw_image = draw_points_on_image(raw_input['image'], raw_image_points)
seg_image = draw_points_on_image(canvas['image'], seg_image_points)
return image, mask, depth, depth_img, colored_depth, camera_pose, \
camera_pose_vis, rescale, selected_points, \
gr.update(value={'image': raw_image, 'points': raw_image_points}), \
gr.update(value={'image': seg_image, 'points': seg_image_points}), \
# -------------- 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
mask_bk = gr.Image(type="pil", label="Mask", show_label=True, interactive=False, visible=False)
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)
raw_image_points = gr.Textbox(label="Raw Image Points", visible=False)
seg_image_points = gr.Textbox(label="Segment Image 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
rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True, visible=False)
angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True, visible=False)
seed = gr.Textbox(value = "42", label="Seed", interactive=True, visible=False)
scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True, visible=False)
ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.25, label="ds", interactive=True, visible=False)
dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.1, label="dt", interactive=True, visible=False)
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 and attained depth;
- Step 2: Attain the subject mask;
- Step 3: Draw trajectory on depth map or skip to use camera pose;
- Step 4: Select camera poses or skip.
- 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
"""
step1 = gr.Markdown(step1_dec)
raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
step1_notes = """
- Select the region using a bounding box, aiming for a ratio close to 320:576 (height:width).
- If the input is in 320 x 576, press `Process` directly.
"""
notes = gr.Markdown(step1_notes)
process_button = gr.Button("Process")
with gr.Column():
# Step 2: Get Subject Mask
step2_dec = """
Step 2: Get Subject Mask
"""
step2 = gr.Markdown(step2_dec)
canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
step2_notes = """
- Use the bounding boxes or points to select the subject.
- Press `Segment Subject` to get the mask. Can be refined iteratively by updating points.
"""
notes = gr.Markdown(step2_notes)
select_button = gr.Button("Segment Subject")
with gr.Column():
# Step 3: Get Depth and Draw Trajectory
step3_dec = """
Step 3: Draw Trajectory on Depth or SKIP
"""
step3 = gr.Markdown(step3_dec)
depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
step3_dec = """
- Selecting points on the depth image. No more than 14 points.
- Press `Undo point` to remove all points. Press `Traj2Cam` to get camera poses.
"""
notes = gr.Markdown(step3_dec)
undo_button = gr.Button("Undo point")
traj2cam_button = gr.Button("Traj2Cam")
with gr.Row():
with gr.Column():
# Step 4: Trajectory to Camera Pose or Get Camera Pose
step4_dec = """
Step 4: Get Customized Camera Poses or SKIP
"""
step4 = gr.Markdown(step4_dec)
camera_pose_vis = gr.Plot(None, label='Camera Pose')
camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=4.0, label="Speed", interactive=True, visible=True)
with gr.Column():
# Step 5: Get the final generated video
step5_dec = """
Step 5: Get the Final Generated Video
"""
step5 = gr.Markdown(step5_dec)
generated_video = gr.Video(None, label='Generated Video')
# with gr.Row():
bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True, value=False, visible=True)
generated_button = gr.Button("Generate")
get_mid_params_button = gr.Button("Get Mid Params", visible=False)
# # event definition
process_button.click(
fn = process_image,
inputs = [raw_input, selected_points],
outputs = [original_image, canvas, depth, depth_image, org_depth_image]
)
select_button.click(
segment,
[canvas, original_image, mask_logits],
[mask, canvas, mask_logits]
)
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]
)
traj2cam_button.click(
traj2cam,
[selected_points, depth, rescale],
[camera_pose, camera_pose_vis, rescale, camera_option]
)
camera_option.change(
get_camera_pose(CAMERA_MODE),
[camera_option, depth, mask, rescale, angle, speed],
[camera_pose, camera_pose_vis, rescale]
)
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],
)
get_mid_params_button.click(
get_mid_params,
[raw_input, canvas, mask, selected_points, camera_option, bg_mode, shared_wapring_latents, generated_video]
)
## Get examples
with open('./assets/examples/examples.json', 'r') as f:
examples = json.load(f)
# print(examples)
# examples = [examples]
examples = [v for k, v in examples.items()]
gr.Examples(
examples=examples,
inputs=[
raw_input,
raw_image_points,
canvas,
seg_image_points,
mask_bk,
selected_points_text, # selected_points
camera_option,
bg_mode,
shared_wapring_latents,
generated_video
],
examples_per_page=20
)
selected_points_text.change(
from_examples,
inputs=[raw_input, raw_image_points, canvas, seg_image_points, selected_points_text, camera_option, mask_bk],
outputs=[original_image, mask, depth, depth_image, org_depth_image, camera_pose, camera_pose_vis, rescale, selected_points, raw_input, canvas]
)
gr.Markdown(article)
demo.queue().launch(share=True)