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downsampling_dataset_test.py
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downsampling_dataset_test.py
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import torch
import torch.utils.data as data
from PIL import Image
import os
import csv
def pil_loader(path):
"""
open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
:param path: image path
:return: image data
"""
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('L')
def accimage_loader(path):
"""
compared with PIL, accimage loader eliminates useless function within class, so that it is faster than PIL
:param path: image path
:return: image data
"""
try:
import accimage
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def get_default_image_loader():
"""
choose accimage as image loader if it is available, PIL otherwise
"""
# from torchvision import get_image_backend
# if get_image_backend() == 'accimage':
# return accimage_loader
# else:
return pil_loader
def get_video(video_path, frame_indices):
"""
generate a video clip which is a list of selected frames
:param video_path: path of video folder which contains video frames
:param frame_indices: list of selected indices of frames. e.g. if index is 1, then selected frame's name is "img_1.png"
:return: a list of selected frames which are PIL.Image or accimage form
"""
image_reader = get_default_image_loader()
video = []
for image_index in frame_indices:
image_name = 'img_' + str(image_index) + '.png'
image_path = os.path.join(video_path, image_name)
img = image_reader(image_path)
video.append(img)
return video
def get_clips(video_path, video_begin, video_end, label, view, sample_duration):
"""
be used when validation set is generated. be used to divide a video interval into video clips
:param video_path: validation data path
:param video_begin: begin index of frames
:param video_end: end index of frames
:param label: 1(normal) / 0(anormal)
:param view: front_depth / front_IR / top_depth / top_IR
:param sample_duration: how many frames should one sample contain
:return: a list which contains validation video clips
"""
clips = []
sample = {
'video': video_path,
'label': label,
'subset': 'validation',
'view': view,
}
# step = 1
step = sample_duration
seven = int(sample_duration / 2) - step
nine = int(sample_duration / 2) + step
eight = int(sample_duration / 2)
if video_begin == 0:
for i in range(seven):
if len([0] * (seven - i) + list(range(0, i + nine))) == sample_duration:
sample_ = sample.copy()
sample_['frame_indices'] = [0] * (seven - i) + list(range(0, i + nine))
clips.append(sample_)
for i in range(seven, video_end + 1, step):
if len(list(range(i - seven, i + nine))) == sample_duration:
sample_ = sample.copy()
sample_['frame_indices'] = list(range(i - seven, i + nine))
clips.append(sample_)
elif video_end == 9999:
for i in range(video_begin, 9999 - seven, step):
if i + nine < 9999 and len(list(range(i - seven, i + nine))) == sample_duration:
sample_ = sample.copy()
sample_['frame_indices'] = list(range(i - seven, i + nine))
clips.append(sample_)
for i in range(eight):
if len(list(range(9999 - seven * 2 + i, 10000)) + [9999] * (i + 1)) == sample_duration:
sample_ = sample.copy()
sample_['frame_indices'] = list(range(9999 - seven * 2 + i, 10000)) + [9999] * (i + 1)
clips.append(sample_)
else:
for i in range(video_begin, video_end + 1, step):
if i + nine < 9999 and len(list(range(i - seven, i + nine))) == sample_duration:
sample_ = sample.copy()
sample_['frame_indices'] = list(range(i - seven, i + nine))
clips.append(sample_)
return clips
def listdir(path):
"""
show every files or folders under the path folder
"""
for f in os.listdir(path):
yield f
def make_dataset(root_path, subset, view, sample_duration, type=None):
"""
Only be used at test time
:param root_path: root path, e.g. "/usr/home/sut/datasets/DAD/DAD/"
:param subset: validation
:param view: front_depth / front_IR / top_depth / top_IR
:param sample_duration: how many frames should one sample contain
:param type: during training process: type = None
:return: list of data samples, each sample is in form {'video':video_path, 'label': 0/1, 'subset': 'train'/'validation', 'view': 'front_depth' / 'front_IR' / 'top_depth' / 'top_IR', 'action': 'normal' / other anormal actions}
"""
dataset = []
if subset == 'validation' and type == None:
# load valiation data as well as thier labels
csv_path = root_path + 'LABEL.csv'
# csv_file = open(csv_path)
with open(csv_path) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
if row[-1] == '':
continue
if row[0] != '':
which_val_path = os.path.join(root_path, row[0].strip())
if row[1] != '':
video_path = os.path.join(which_val_path, row[1], view)
video_begin = int(row[2])
video_end = int(row[3])
# print(video_begin, video_end)
if row[4] == 'N':
label = 1
elif row[4] == 'A':
label = 0
clips = get_clips(video_path, video_begin, video_end, label, view, sample_duration)
dataset = dataset + clips
else:
print('!!!DATA LOADING FAILURE!!!THIS DATASET IS ONLY USED IN TESTING MODE!!!PLEASE CHECK INPUT!!!')
return dataset
class DAD_Test(data.Dataset):
"""
This dataset is only used at test time to genrate consecutive video samples.
"""
def __init__(self,
root_path,
subset,
view,
sample_duration=16,
type=None,
get_loader=get_video,
spatial_transform=None,
temporal_transform=None,
):
self.data = make_dataset(root_path, subset, view, sample_duration, type)
self.sample_duration = sample_duration
self.subset = subset
self.loader = get_loader
self.spatial_transform = spatial_transform
self.temporal_transform = temporal_transform
def __getitem__(self, index):
if self.subset == 'validation':
video_path = self.data[index]['video']
ground_truth = self.data[index]['label']
frame_indices = self.data[index]['frame_indices']
if self.temporal_transform is not None:
frame_indices = self.temporal_transform(frame_indices)
clip = self.loader(video_path, frame_indices)
self.spatial_transform.randomize_parameters()
clip = [self.spatial_transform(img) for img in clip]
clip = torch.stack(clip, 0).permute(1, 0, 2, 3)
return clip, ground_truth
else:
print('!!!DATA LOADING FAILURE!!!THIS DATASET IS ONLY USED IN TESTING MODE!!!PLEASE CHECK INPUT!!!')
def __len__(self):
return len(self.data)