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import os
import numpy as np
from tqdm import tqdm
from PIL import Image
from einops import rearrange
import torch
import torchvision
from torch import Tensor
from torchvision.utils import make_grid
from torchvision.transforms.functional import to_tensor
def frames_to_mp4(frame_dir,output_path,fps):
def read_first_n_frames(d: os.PathLike, num_frames: int):
if num_frames:
images = [Image.open(os.path.join(d, f)) for f in sorted(os.listdir(d))[:num_frames]]
else:
images = [Image.open(os.path.join(d, f)) for f in sorted(os.listdir(d))]
images = [to_tensor(x) for x in images]
return torch.stack(images)
videos = read_first_n_frames(frame_dir, num_frames=None)
videos = videos.mul(255).to(torch.uint8).permute(0, 2, 3, 1)
torchvision.io.write_video(output_path, videos, fps=fps, video_codec='h264', options={'crf': '10'})
def tensor_to_mp4(video, savepath, fps, rescale=True, nrow=None):
"""
video: torch.Tensor, b,c,t,h,w, 0-1
if -1~1, enable rescale=True
"""
n = video.shape[0]
video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
nrow = int(np.sqrt(n)) if nrow is None else nrow
frame_grids = [torchvision.utils.make_grid(framesheet, nrow=nrow, padding=0) for framesheet in video] # [3, grid_h, grid_w]
grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [T, 3, grid_h, grid_w]
grid = torch.clamp(grid.float(), -1., 1.)
if rescale:
grid = (grid + 1.0) / 2.0
grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, 3, grid_h, grid_w] -> [T, grid_h, grid_w, 3]
torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'})
def tensor2videogrids(video, root, filename, fps, rescale=True, clamp=True):
assert(video.dim() == 5) # b,c,t,h,w
assert(isinstance(video, torch.Tensor))
video = video.detach().cpu()
if clamp:
video = torch.clamp(video, -1., 1.)
n = video.shape[0]
video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(np.sqrt(n))) for framesheet in video] # [3, grid_h, grid_w]
grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [T, 3, grid_h, grid_w]
if rescale:
grid = (grid + 1.0) / 2.0
grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, 3, grid_h, grid_w] -> [T, grid_h, grid_w, 3]
path = os.path.join(root, filename)
torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'})
def log_local(batch_logs, save_dir, filename, save_fps=10, rescale=True):
if batch_logs is None:
return None
""" save images and videos from images dict """
def save_img_grid(grid, path, rescale):
if rescale:
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
for key in batch_logs:
value = batch_logs[key]
if isinstance(value, list) and isinstance(value[0], str):
## a batch of captions
path = os.path.join(save_dir, "%s-%s.txt"%(key, filename))
with open(path, 'w') as f:
for i, txt in enumerate(value):
f.write(f'idx={i}, txt={txt}\n')
f.close()
elif isinstance(value, torch.Tensor) and value.dim() == 5:
## save video grids
video = value # b,c,t,h,w
## only save grayscale or rgb mode
if video.shape[1] != 1 and video.shape[1] != 3:
continue
n = video.shape[0]
video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(1), padding=0) for framesheet in video] #[3, n*h, 1*w]
grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
if rescale:
grid = (grid + 1.0) / 2.0
grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
path = os.path.join(save_dir, "%s-%s.mp4"%(key, filename))
torchvision.io.write_video(path, grid, fps=save_fps, video_codec='h264', options={'crf': '10'})
## save frame sheet
img = value
video_frames = rearrange(img, 'b c t h w -> (b t) c h w')
t = img.shape[2]
grid = torchvision.utils.make_grid(video_frames, nrow=t, padding=0)
path = os.path.join(save_dir, "%s-%s.jpg"%(key, filename))
#save_img_grid(grid, path, rescale)
elif isinstance(value, torch.Tensor) and value.dim() == 4:
## save image grids
img = value
## only save grayscale or rgb mode
if img.shape[1] != 1 and img.shape[1] != 3:
continue
n = img.shape[0]
grid = torchvision.utils.make_grid(img, nrow=1, padding=0)
path = os.path.join(save_dir, "%s-%s.jpg"%(key, filename))
save_img_grid(grid, path, rescale)
else:
pass
def prepare_to_log(batch_logs, max_images=100000, clamp=True):
if batch_logs is None:
return None
# process
for key in batch_logs:
N = batch_logs[key].shape[0] if hasattr(batch_logs[key], 'shape') else len(batch_logs[key])
N = min(N, max_images)
batch_logs[key] = batch_logs[key][:N]
## in batch_logs: images <batched tensor> & caption <text list>
if isinstance(batch_logs[key], torch.Tensor):
batch_logs[key] = batch_logs[key].detach().cpu()
if clamp:
try:
batch_logs[key] = torch.clamp(batch_logs[key].float(), -1., 1.)
except RuntimeError:
print("clamp_scalar_cpu not implemented for Half")
return batch_logs
# ----------------------------------------------------------------------------------------------
def fill_with_black_squares(video, desired_len: int) -> Tensor:
if len(video) >= desired_len:
return video
return torch.cat([
video,
torch.zeros_like(video[0]).unsqueeze(0).repeat(desired_len - len(video), 1, 1, 1),
], dim=0)
# ----------------------------------------------------------------------------------------------
def load_num_videos(data_path, num_videos):
# first argument can be either data_path of np array
if isinstance(data_path, str):
videos = np.load(data_path)['arr_0'] # NTHWC
elif isinstance(data_path, np.ndarray):
videos = data_path
else:
raise Exception
if num_videos is not None:
videos = videos[:num_videos, :, :, :, :]
return videos
def npz_to_video_grid(data_path, out_path, num_frames, fps, num_videos=None, nrow=None, verbose=True):
# videos = torch.tensor(np.load(data_path)['arr_0']).permute(0,1,4,2,3).div_(255).mul_(2) - 1.0 # NTHWC->NTCHW, np int -> torch tensor 0-1
if isinstance(data_path, str):
videos = load_num_videos(data_path, num_videos)
elif isinstance(data_path, np.ndarray):
videos = data_path
else:
raise Exception
n,t,h,w,c = videos.shape
videos_th = []
for i in range(n):
video = videos[i, :,:,:,:]
images = [video[j, :,:,:] for j in range(t)]
images = [to_tensor(img) for img in images]
video = torch.stack(images)
videos_th.append(video)
if verbose:
videos = [fill_with_black_squares(v, num_frames) for v in tqdm(videos_th, desc='Adding empty frames')] # NTCHW
else:
videos = [fill_with_black_squares(v, num_frames) for v in videos_th] # NTCHW
frame_grids = torch.stack(videos).permute(1, 0, 2, 3, 4) # [T, N, C, H, W]
if nrow is None:
nrow = int(np.ceil(np.sqrt(n)))
if verbose:
frame_grids = [make_grid(fs, nrow=nrow) for fs in tqdm(frame_grids, desc='Making grids')]
else:
frame_grids = [make_grid(fs, nrow=nrow) for fs in frame_grids]
if os.path.dirname(out_path) != "":
os.makedirs(os.path.dirname(out_path), exist_ok=True)
frame_grids = (torch.stack(frame_grids) * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, H, W, C]
torchvision.io.write_video(out_path, frame_grids, fps=fps, video_codec='h264', options={'crf': '10'})
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