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import os
import random
import re
from typing import Optional, Dict, NewType, Union, List, Set
import numpy as np
import torch
from torch.utils import data
from torchvision.io import read_image
import torchvision.transforms as transforms
ClipLabels = NewType('ClipLabels', Set[str])
ClipConditions = NewType('ClipConditions', Set[str])
ClipViews = NewType('ClipViews', Set[str])
default_frame_transform = transforms.Compose([
transforms.Resize(size=(64, 32))
])
class CASIAB(data.Dataset):
"""CASIA-B multi-view gait dataset"""
def __init__(
self,
root_dir: str,
is_train: bool = True,
train_size: int = 74,
num_sampled_frames: int = 30,
selector: Optional[Dict[
str, Union[ClipLabels, ClipConditions, ClipLabels]
]] = None,
num_input_channels: int = 3,
frame_height: int = 64,
frame_width: int = 32,
device: torch.device = torch.device('cpu')
):
"""
:param root_dir: Directory to dataset root.
:param is_train: Train or test, True for train, False for test.
:param train_size: Number of subjects in train, when `is_train`
is False, test size will be inferred.
:param num_sampled_frames: Number of sampled frames for train
:param selector: Restrict data labels, conditions and views
:param num_input_channels Number of input channel, RBG image
has 3 channel, grayscale image has 1 channel
:param frame_height Frame height after transforms
:param frame_width Frame width after transforms
:param device Device be used for transforms
"""
super(CASIAB, self).__init__()
self.root_dir = root_dir
self.is_train = is_train
self.train_size = train_size
self.num_sampled_frames = num_sampled_frames
self.num_input_channels = num_input_channels
self.frame_height = frame_height
self.frame_width = frame_width
self.device = device
self.frame_transform: transforms.Compose
transform_compose_list = [
transforms.Resize(size=(self.frame_height, self.frame_width))
]
if self.num_input_channels == 1:
transform_compose_list.insert(0, transforms.Grayscale())
self.frame_transform = transforms.Compose(transform_compose_list)
# Labels, conditions and views corresponding to each video clip
self.labels: np.ndarray[np.str_]
self.conditions: np.ndarray[np.str_]
self.views: np.ndarray[np.str_]
# Video clip directory names
self._clip_names: List[str] = []
# Labels, conditions and views in dataset,
# set of three attributes above
self.metadata = Dict[str, Set[str]]
clip_names = sorted(os.listdir(self.root_dir))
if self.is_train:
clip_names = clip_names[:self.train_size * 10 * 11]
else: # is_test
clip_names = clip_names[self.train_size * 10 * 11:]
# Remove empty clips
for clip_name in clip_names.copy():
if len(os.listdir(os.path.join(self.root_dir, clip_name))) == 0:
print("Clip '{}' is empty.".format(clip_name))
clip_names.remove(clip_name)
# clip name constructed by label, condition and view
# e.g 002-bg-02-090 means clip from Subject #2
# in Bag #2 condition from 90 degree angle
labels, conditions, views = [], [], []
if selector:
selected_labels = selector.pop('labels', None)
selected_conditions = selector.pop('conditions', None)
selected_views = selector.pop('views', None)
label_regex = r'\d{3}'
condition_regex = r'(nm|bg|cl)-0[0-4]'
view_regex = r'\d{3}'
# Match required data using RegEx
if selected_labels:
label_regex = '|'.join(selected_labels)
if selected_conditions:
condition_regex = '|'.join(selected_conditions)
if selected_views:
view_regex = '|'.join(selected_views)
clip_regex = '(' + ')-('.join([
label_regex, condition_regex, view_regex
]) + ')'
for clip_name in clip_names:
match = re.fullmatch(clip_regex, clip_name)
if match:
labels.append(match.group(1))
conditions.append(match.group(2))
views.append(match.group(3))
self._clip_names.append(match.group(0))
self.metadata = {
'labels': selected_labels,
'conditions': selected_conditions,
'views': selected_views
}
else: # Add all
self._clip_names += clip_names
for clip_name in self._clip_names:
split_clip_name = clip_name.split('-')
label = split_clip_name[0]
labels.append(label)
condition = '-'.join(split_clip_name[1:2 + 1])
conditions.append(condition)
view = split_clip_name[-1]
views.append(view)
self.labels = np.asarray(labels)
self.conditions = np.asarray(conditions)
self.views = np.asarray(views)
if not selector:
self.metadata = {
'labels': set(self.labels.tolist()),
'conditions': set(self.conditions.tolist()),
'views': set(self.views.tolist())
}
def __len__(self) -> int:
return len(self.labels)
def __getitem__(self, index: int) -> Dict[str, Union[str, torch.Tensor]]:
label = self.labels[index]
condition = self.conditions[index]
view = self.views[index]
clip_name = self._clip_names[index]
clip = self._read_video(clip_name)
sample = {
'label': label,
'condition': condition,
'view': view,
'clip': clip
}
return sample
def _read_video(self, clip_name: str) -> torch.Tensor:
frames = []
clip_path = os.path.join(self.root_dir, clip_name)
sampled_frame_names = self._sample_frames(clip_path)
for frame_name in sampled_frame_names:
frame_path = os.path.join(clip_path, frame_name)
frame = read_image(frame_path)
frame = self.frame_transform(frame.to(self.device))
frames.append(frame.cpu())
clip = torch.stack(frames)
return clip
def _sample_frames(self, clip_path: str) -> List[str]:
frame_names = os.listdir(clip_path)
if self.is_train:
num_frames = len(frame_names)
if num_frames < self.num_sampled_frames:
frame_names = random.choices(frame_names,
k=self.num_sampled_frames)
else:
frame_names = random.sample(frame_names,
k=self.num_sampled_frames)
return sorted(frame_names)
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