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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from .image_processor import VaeImageProcessor, is_valid_image, is_valid_image_imagelist
class VideoProcessor(VaeImageProcessor):
r"""Simple video processor."""
def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor:
r"""
Preprocesses input video(s).
Args:
video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `torch.Tensor`, `np.array`, `List[torch.Tensor]`, `List[np.array]`):
The input video. It can be one of the following:
* List of the PIL images.
* List of list of PIL images.
* 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`).
* 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).
* List of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height,
width)`).
* List of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).
* 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width,
num_channels)`.
* 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height,
width)`.
height (`int`, *optional*, defaults to `None`):
The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to
get default height.
width (`int`, *optional*`, defaults to `None`):
The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get
the default width.
"""
if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5:
warnings.warn(
"Passing `video` as a list of 5d np.ndarray is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray",
FutureWarning,
)
video = np.concatenate(video, axis=0)
if isinstance(video, list) and isinstance(video[0], torch.Tensor) and video[0].ndim == 5:
warnings.warn(
"Passing `video` as a list of 5d torch.Tensor is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 5d torch.Tensor",
FutureWarning,
)
video = torch.cat(video, axis=0)
# ensure the input is a list of videos:
# - if it is a batch of videos (5d torch.Tensor or np.ndarray), it is converted to a list of videos (a list of 4d torch.Tensor or np.ndarray)
# - if it is is a single video, it is convereted to a list of one video.
if isinstance(video, (np.ndarray, torch.Tensor)) and video.ndim == 5:
video = list(video)
elif isinstance(video, list) and is_valid_image(video[0]) or is_valid_image_imagelist(video):
video = [video]
elif isinstance(video, list) and is_valid_image_imagelist(video[0]):
video = video
else:
raise ValueError(
"Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image"
)
video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0)
# move the number of channels before the number of frames.
video = video.permute(0, 2, 1, 3, 4)
return video
def postprocess_video(
self, video: torch.Tensor, output_type: str = "np"
) -> Union[np.ndarray, torch.Tensor, List[PIL.Image.Image]]:
r"""
Converts a video tensor to a list of frames for export.
Args:
video (`torch.Tensor`): The video as a tensor.
output_type (`str`, defaults to `"np"`): Output type of the postprocessed `video` tensor.
"""
batch_size = video.shape[0]
outputs = []
for batch_idx in range(batch_size):
batch_vid = video[batch_idx].permute(1, 0, 2, 3)
batch_output = self.postprocess(batch_vid, output_type)
outputs.append(batch_output)
if output_type == "np":
outputs = np.stack(outputs)
elif output_type == "pt":
outputs = torch.stack(outputs)
elif not output_type == "pil":
raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']")
return outputs
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