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Zero
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import os
import json
import torch
import decord
import torchvision
import numpy as np
from PIL import Image
from einops import rearrange
from typing import Dict, List, Tuple
class_labels_map = None
cls_sample_cnt = None
def temporal_sampling(frames, start_idx, end_idx, num_samples):
"""
Given the start and end frame index, sample num_samples frames between
the start and end with equal interval.
Args:
frames (tensor): a tensor of video frames, dimension is
`num video frames` x `channel` x `height` x `width`.
start_idx (int): the index of the start frame.
end_idx (int): the index of the end frame.
num_samples (int): number of frames to sample.
Returns:
frames (tersor): a tensor of temporal sampled video frames, dimension is
`num clip frames` x `channel` x `height` x `width`.
"""
index = torch.linspace(start_idx, end_idx, num_samples)
index = torch.clamp(index, 0, frames.shape[0] - 1).long()
frames = torch.index_select(frames, 0, index)
return frames
def numpy2tensor(x):
return torch.from_numpy(x)
def get_filelist(file_path):
Filelist = []
for home, dirs, files in os.walk(file_path):
for filename in files:
Filelist.append(os.path.join(home, filename))
# Filelist.append( filename)
return Filelist
def load_annotation_data(data_file_path):
with open(data_file_path, 'r') as data_file:
return json.load(data_file)
def get_class_labels(num_class, anno_pth='./k400_classmap.json'):
global class_labels_map, cls_sample_cnt
if class_labels_map is not None:
return class_labels_map, cls_sample_cnt
else:
cls_sample_cnt = {}
class_labels_map = load_annotation_data(anno_pth)
for cls in class_labels_map:
cls_sample_cnt[cls] = 0
return class_labels_map, cls_sample_cnt
def load_annotations(ann_file, num_class, num_samples_per_cls):
dataset = []
class_to_idx, cls_sample_cnt = get_class_labels(num_class)
with open(ann_file, 'r') as fin:
for line in fin:
line_split = line.strip().split('\t')
sample = {}
idx = 0
# idx for frame_dir
frame_dir = line_split[idx]
sample['video'] = frame_dir
idx += 1
# idx for label[s]
label = [x for x in line_split[idx:]]
assert label, f'missing label in line: {line}'
assert len(label) == 1
class_name = label[0]
class_index = int(class_to_idx[class_name])
# choose a class subset of whole dataset
if class_index < num_class:
sample['label'] = class_index
if cls_sample_cnt[class_name] < num_samples_per_cls:
dataset.append(sample)
cls_sample_cnt[class_name]+=1
return dataset
class DecordInit(object):
"""Using Decord(https://github.com/dmlc/decord) to initialize the video_reader."""
def __init__(self, num_threads=1, **kwargs):
self.num_threads = num_threads
self.ctx = decord.cpu(0)
self.kwargs = kwargs
def __call__(self, filename):
"""Perform the Decord initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
reader = decord.VideoReader(filename,
ctx=self.ctx,
num_threads=self.num_threads)
return reader
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'sr={self.sr},'
f'num_threads={self.num_threads})')
return repr_str
class FaceForensics(torch.utils.data.Dataset):
"""Load the FaceForensics video files
Args:
target_video_len (int): the number of video frames will be load.
align_transform (callable): Align different videos in a specified size.
temporal_sample (callable): Sample the target length of a video.
"""
def __init__(self,
configs,
transform=None,
temporal_sample=None):
self.configs = configs
self.data_path = configs.data_path
self.video_lists = get_filelist(configs.data_path)
self.transform = transform
self.temporal_sample = temporal_sample
self.target_video_len = self.configs.num_frames
self.v_decoder = DecordInit()
def __getitem__(self, index):
path = self.video_lists[index]
vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW')
total_frames = len(vframes)
# Sampling video frames
start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
assert end_frame_ind - start_frame_ind >= self.target_video_len
frame_indice = np.linspace(start_frame_ind, end_frame_ind-1, self.target_video_len, dtype=int)
video = vframes[frame_indice]
# videotransformer data proprecess
video = self.transform(video) # T C H W
return {'video': video, 'video_name': 1}
def __len__(self):
return len(self.video_lists)
if __name__ == '__main__':
pass |