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dataset.py
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dataset.py
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import torch.utils.data as data
import random
from PIL import Image
import os
import os.path
import numpy as np
from numpy.random import randint
import pandas as pd
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def path(self):
return self._data[0]
@property
def num_frames(self):
return int(self._data[1])
@property
def label(self):
return int(self._data[2])
class TSNDataSet(data.Dataset):
def __init__(self, root_path, list_file,
num_segments=3, new_length=1, modality='RGB',
image_tmpl='img_{:05d}.jpg', transform=None,
force_grayscale=False, random_shift=True,
test_mode=False, dataset='jester'):
self.root_path = root_path
self.list_file = list_file
self.num_segments = num_segments
self.new_length = new_length
self.modality = modality
self.image_tmpl = image_tmpl
self.transform = transform
self.random_shift = random_shift
self.test_mode = test_mode
self.dataset = dataset
if self.modality == 'RGBDiff' or self.modality == 'RGBFlow':
self.new_length += 1 # Diff needs one more image to calculate diff
self._parse_list()
def _load_image(self, directory, idx, isLast=False):
if self.modality == 'RGB' or self.modality == 'RGBDiff':
try:
return [Image.open(os.path.join(self.root_path, "rgb", directory, self.image_tmpl.format(idx))).convert(
'RGB')]
except Exception:
print('error loading image:',
os.path.join(self.root_path, "rgb", directory, self.image_tmpl.format(idx)))
return [Image.open(os.path.join(self.root_path, "rgb", directory, self.image_tmpl.format(1))).convert(
'RGB')]
elif self.modality == 'Flow':
try:
x_img = Image.open(
os.path.join(self.root_path, "flow/u", directory, self.image_tmpl.format(idx))).convert('L')
y_img = Image.open(
os.path.join(self.root_path, "flow/v", directory, self.image_tmpl.format(idx))).convert('L')
except Exception:
print('error loading flow file:',
os.path.join(self.root_path, "flow/u", directory, self.image_tmpl.format(idx_skip)))
return [x_img, y_img]
elif self.modality == 'RGBFlow':
if isLast:
return [Image.open(os.path.join(self.root_path, "rgb", directory, self.image_tmpl.format(idx))).convert(
'RGB')]
else:
x_img = Image.open(
os.path.join(self.root_path, "flow/u", directory, self.image_tmpl.format(idx))).convert('L')
y_img = Image.open(
os.path.join(self.root_path, "flow/v", directory, self.image_tmpl.format(idx))).convert('L')
return [x_img, y_img]
def _parse_list(self):
# check the frame number is large >3:
# usualy it is [video_id, num_frames, class_idx]
tmp = [x.strip().split(' ') for x in open(self.list_file)]
tmp = [item for item in tmp if int(item[1]) >= 3]
self.video_list = [VideoRecord(item) for item in tmp]
print('video number:%d' % (len(self.video_list)))
def _sample_indices(self, record):
"""
:param record: VideoRecord
:return: list
"""
average_duration = (record.num_frames - self.new_length + 1) // self.num_segments
if average_duration > 0:
offsets = np.multiply(list(range(self.num_segments)), average_duration) + randint(average_duration,
size=self.num_segments)
elif record.num_frames > self.num_segments:
offsets = np.sort(randint(record.num_frames - self.new_length + 1, size=self.num_segments))
else:
offsets = np.zeros((self.num_segments,))
return offsets + 1
def _get_val_indices(self, record):
if record.num_frames > self.num_segments + self.new_length - 1:
tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
else:
offsets = np.zeros((self.num_segments,))
return offsets + 1
def _get_test_indices(self, record):
tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
return offsets + 1
def __getitem__(self, index):
record = self.video_list[index]
# check this is a legit video folder
if self.modality == 'RGBFlow':
while not os.path.exists(os.path.join(self.root_path, "rgb", record.path, self.image_tmpl.format(1))):
print('image does not exist:' + os.path.join(self.root_path, "rgb", record.path,
self.image_tmpl.format(1)))
index = np.random.randint(len(self.video_list))
record = self.video_list[index]
else:
while not os.path.exists(os.path.join(self.root_path, "rgb", record.path, self.image_tmpl.format(1))):
print('image does not exist:' + os.path.join(self.root_path, "rgb", record.path,
self.image_tmpl.format(1)))
index = np.random.randint(len(self.video_list))
record = self.video_list[index]
if not self.test_mode:
segment_indices = self._sample_indices(record) if self.random_shift else self._get_val_indices(record)
else:
segment_indices = self._get_test_indices(record)
return self.get(record, segment_indices)
def get(self, record, indices):
images = list()
for seg_ind in indices:
p = int(seg_ind)
for i in range(self.new_length):
if self.modality == 'RGBFlow':
if i == self.new_length - 1:
seg_imgs = self._load_image(record.path, p, True)
else:
if p == record.num_frames:
seg_imgs = self._load_image(record.path, p - 1)
else:
seg_imgs = self._load_image(record.path, p)
else:
seg_imgs = self._load_image(record.path, p)
images.extend(seg_imgs)
if p < record.num_frames:
p += 1
process_data = self.transform(images)
return process_data, record.label
def __len__(self):
return len(self.video_list)