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""" "This module contains a video feature loader."""
import logging
import os
from typing import List, Tuple
import numpy as np
import torch
from torch import Tensor
from torch.utils import data
from feature_extractor import read_features
class FeaturesLoader:
"""Loads video features that are stored as text files."""
def __init__(
self,
features_path: str,
annotation_path: str,
bucket_size: int = 30,
iterations: int = 20000,
) -> None:
"""
Args:
features_path: Path to the directory that contains the features in text files
annotation_path: Path to the annotation file
bucket_size: Size of each bucket
iterations: How many iterations the loader should perform
"""
super().__init__()
self._features_path = features_path
self._bucket_size = bucket_size
# load video list
(
self.features_list_normal,
self.features_list_anomaly,
) = FeaturesLoader._get_features_list(
features_path=self._features_path, annotation_path=annotation_path
)
self._iterations = iterations
self._features_cache = {}
self._i = 0
def __len__(self) -> int:
return self._iterations
def __getitem__(self, index: int) -> Tuple[Tensor, Tensor]:
if self._i == len(self):
self._i = 0
raise StopIteration
succ = False
while not succ:
try:
feature, label = self.get_features()
succ = True
except Exception as e:
index = np.random.choice(range(0, self.__len__()))
logging.warning(
f"VideoIter:: ERROR!! (Force using another index:\n{index})\n{e}"
)
self._i += 1
return feature, label
def get_features(self) -> Tuple[Tensor, Tensor]:
"""Fetches a bucket sample from the dataset."""
normal_paths = np.random.choice(
self.features_list_normal, size=self._bucket_size
)
abnormal_paths = np.random.choice(
self.features_list_anomaly, size=self._bucket_size
)
all_paths = np.concatenate([normal_paths, abnormal_paths])
features = torch.stack(
[
read_features(f"{feature_subpath}.txt", self._features_cache)
for feature_subpath in all_paths
]
)
return (
features,
torch.cat([torch.zeros(self._bucket_size), torch.ones(self._bucket_size)]),
)
@staticmethod
def _get_features_list(
features_path: str, annotation_path: str
) -> Tuple[List[str], List[str]]:
"""Retrieves the paths of all feature files contained within the
annotation file.
Args:
features_path: Path to the directory that contains feature text files
annotation_path: Path to the annotation file
Returns:
Tuple[List[str], List[str]]: Two list that contain the corresponding paths of normal and abnormal
feature files.
"""
assert os.path.exists(features_path)
features_list_normal = []
features_list_anomaly = []
with open(annotation_path) as f:
lines = f.read().splitlines(keepends=False)
for line in lines:
items = line.split()
file = items[0].split(".")[0]
file = file.replace("/", os.sep)
feature_path = os.path.join(features_path, file)
if "Normal" in feature_path:
features_list_normal.append(feature_path)
else:
features_list_anomaly.append(feature_path)
return features_list_normal, features_list_anomaly
@property
def get_feature_dim(self) -> int:
return self[0][0].shape[-1]
class FeaturesLoaderVal(data.Dataset):
"""Loader for video features for validation phase."""
def __init__(self, features_path, annotation_path):
super().__init__()
self.features_path = features_path
# load video list
self.state = "Normal"
self.features_list = FeaturesLoaderVal._get_features_list(
features_path=features_path, annotation_path=annotation_path
)
def __len__(self):
return len(self.features_list)
def __getitem__(self, index: int):
succ = False
while not succ:
try:
data = self.get_feature(index)
succ = True
except Exception as e:
logging.warning(
f"VideoIter:: ERROR!! (Force using another index:\n{index})\n{e}"
)
return data
def get_feature(self, index: int):
"""Fetch feature that matches given index in the dataset.
Args:
index (int): Index of the feature to fetch.
Returns:
_type_: _description_
"""
feature_subpath, start_end_couples, length = self.features_list[index]
features = read_features(f"{feature_subpath}.txt")
return features, start_end_couples, length
@staticmethod
def _get_features_list(
features_path: str, annotation_path: str
) -> List[Tuple[str, Tensor, int]]:
"""Retrieves the paths of all feature files contained within the
annotation file.
Args:
features_path: Path to the directory that contains feature text files
annotation_path: Path to the annotation file
Returns:
List[Tuple[str, Tensor, int]]: A list of tuples that describe each video and the temporal annotations
of anomalies in the videos
"""
assert os.path.exists(features_path)
features_list = []
with open(annotation_path) as f:
lines = f.read().splitlines(keepends=False)
for line in lines:
items = line.split()
anomalies_frames = [int(x) for x in items[3:]]
start_end_couples = torch.tensor(
[anomalies_frames[:2], anomalies_frames[2:]]
)
file = items[0].split(".")[0]
file = file.replace("/", os.sep)
feature_path = os.path.join(features_path, file)
length = int(items[1])
features_list.append((feature_path, start_end_couples, length))
return features_list
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