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L40S
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import torch
import numpy as np
import json
import imageio
from PIL import Image
from torchvision.transforms import v2
from einops import rearrange
import torchvision
import logging
from config import TEST_DATA_DIR
from camera_utils import Camera, parse_matrix, get_relative_pose
logger = logging.getLogger(__name__)
class VideoProcessor:
def __init__(self, pipe):
self.pipe = pipe
self.default_height = 480
self.default_width = 832
def crop_and_resize(self, image, height, width):
"""Crop and resize image to match target dimensions"""
width_img, height_img = image.size
scale = max(width / width_img, height / height_img)
image = torchvision.transforms.functional.resize(
image,
(round(height_img*scale), round(width_img*scale)),
interpolation=torchvision.transforms.InterpolationMode.BILINEAR
)
return image
def load_video_frames(self, video_path, num_frames=81, height=480, width=832):
"""Load and process video frames"""
reader = imageio.get_reader(video_path)
frames = []
# Create frame processor with specified dimensions
frame_process = v2.Compose([
v2.CenterCrop(size=(height, width)),
v2.Resize(size=(height, width), antialias=True),
v2.ToTensor(),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
for i in range(num_frames):
try:
frame = reader.get_data(i)
frame = Image.fromarray(frame)
frame = self.crop_and_resize(frame, height, width)
frame = frame_process(frame)
frames.append(frame)
except:
# If we run out of frames, repeat the last one
if frames:
frames.append(frames[-1])
else:
raise ValueError("Video is too short!")
reader.close()
frames = torch.stack(frames, dim=0)
frames = rearrange(frames, "T C H W -> C T H W")
video_tensor = frames.unsqueeze(0) # Add batch dimension
return video_tensor
def load_camera_trajectory(self, cam_type, num_frames=81):
"""Load camera trajectory for the selected type"""
tgt_camera_path = "./camera_trajectories/camera_extrinsics.json"
with open(tgt_camera_path, 'r') as file:
cam_data = json.load(file)
# Get camera trajectory for selected type
cam_idx = list(range(num_frames))[::4] # Sample every 4 frames
traj = [parse_matrix(cam_data[f"frame{idx}"][f"cam{int(cam_type):02d}"]) for idx in cam_idx]
traj = np.stack(traj).transpose(0, 2, 1)
c2ws = []
for c2w in traj:
c2w = c2w[:, [1, 2, 0, 3]]
c2w[:3, 1] *= -1.
c2w[:3, 3] /= 100
c2ws.append(c2w)
tgt_cam_params = [Camera(cam_param) for cam_param in c2ws]
relative_poses = []
for i in range(len(tgt_cam_params)):
relative_pose = get_relative_pose([tgt_cam_params[0], tgt_cam_params[i]])
relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:][1])
pose_embedding = torch.stack(relative_poses, dim=0) # 21x3x4
pose_embedding = rearrange(pose_embedding, 'b c d -> b (c d)')
camera_tensor = pose_embedding.to(torch.bfloat16).unsqueeze(0) # Add batch dimension
return camera_tensor
def process_video(self, video_path, text_prompt, cam_type, num_frames=81, height=480, width=832, seed=0, num_inference_steps=50, cfg_scale=5.0):
"""Process video through ReCamMaster model"""
# Load video frames
video_tensor = self.load_video_frames(video_path, num_frames, height, width)
# Load camera trajectory
camera_tensor = self.load_camera_trajectory(cam_type, num_frames)
# Generate video with ReCamMaster
video = self.pipe(
prompt=[text_prompt],
negative_prompt=["worst quality, low quality, blurry, jittery, distorted"],
source_video=video_tensor,
target_camera=camera_tensor,
height=height,
width=width,
num_frames=num_frames,
cfg_scale=cfg_scale,
num_inference_steps=num_inference_steps,
seed=seed,
tiled=True
)
return video |