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# Written by Yuecong Min
# ----------------------------------------
import cv2
import pdb
import PIL
import copy
import scipy.misc
import torch
import random
import numbers
import numpy as np
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, label, file_info=None):
for t in self.transforms:
if file_info is not None and isinstance(t, WERAugment):
image, label = t(image, label, file_info)
else:
image = t(image)
return image, label
class WERAugment(object):
def __init__(self, boundary_path):
self.boundary_dict = np.load(boundary_path, allow_pickle=True).item()
self.K = 3
def __call__(self, video, label, file_info):
ind = np.arange(len(video)).tolist()
if file_info not in self.boundary_dict.keys():
return video, label
binfo = copy.deepcopy(self.boundary_dict[file_info])
binfo = [0] + binfo + [len(video)]
k = np.random.randint(min(self.K, len(label) - 1))
for i in range(k):
ind, label, binfo = self.one_operation(ind, label, binfo)
ret_video = [video[i] for i in ind]
return ret_video, label
def one_operation(self, *inputs):
prob = np.random.random()
if prob < 0.3:
return self.delete(*inputs)
elif 0.3 <= prob < 0.7:
return self.substitute(*inputs)
else:
return self.insert(*inputs)
@staticmethod
def delete(ind, label, binfo):
del_wd = np.random.randint(len(label))
ind = ind[:binfo[del_wd]] + ind[binfo[del_wd + 1]:]
duration = binfo[del_wd + 1] - binfo[del_wd]
del label[del_wd]
binfo = [i for i in binfo[:del_wd]] + [i - duration for i in binfo[del_wd + 1:]]
return ind, label, binfo
@staticmethod
def insert(ind, label, binfo):
ins_wd = np.random.randint(len(label))
ins_pos = np.random.choice(binfo)
ins_lab_pos = binfo.index(ins_pos)
ind = ind[:ins_pos] + ind[binfo[ins_wd]:binfo[ins_wd + 1]] + ind[ins_pos:]
duration = binfo[ins_wd + 1] - binfo[ins_wd]
label = label[:ins_lab_pos] + [label[ins_wd]] + label[ins_lab_pos:]
binfo = binfo[:ins_lab_pos] + [binfo[ins_lab_pos - 1] + duration] + [i + duration for i in binfo[ins_lab_pos:]]
return ind, label, binfo
@staticmethod
def substitute(ind, label, binfo):
sub_wd = np.random.randint(len(label))
tar_wd = np.random.randint(len(label))
ind = ind[:binfo[tar_wd]] + ind[binfo[sub_wd]:binfo[sub_wd + 1]] + ind[binfo[tar_wd + 1]:]
label[tar_wd] = label[sub_wd]
delta_duration = binfo[sub_wd + 1] - binfo[sub_wd] - (binfo[tar_wd + 1] - binfo[tar_wd])
binfo = binfo[:tar_wd + 1] + [i + delta_duration for i in binfo[tar_wd + 1:]]
return ind, label, binfo
class ToTensor(object):
def __call__(self, video):
if isinstance(video, list):
video = np.array(video)
video = torch.from_numpy(video.transpose((0, 3, 1, 2))).float()
if isinstance(video, np.ndarray):
video = torch.from_numpy(video.transpose((0, 3, 1, 2)))
return video
class RandomCrop(object):
"""
Extract random crop of the video.
Args:
size (sequence or int): Desired output size for the crop in format (h, w).
crop_position (str): Selected corner (or center) position from the
list ['c', 'tl', 'tr', 'bl', 'br']. If it is non, crop position is
selected randomly at each call.
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
if size < 0:
raise ValueError('If size is a single number, it must be positive')
size = (size, size)
else:
if len(size) != 2:
raise ValueError('If size is a sequence, it must be of len 2.')
self.size = size
def __call__(self, clip):
crop_h, crop_w = self.size
if isinstance(clip[0], np.ndarray):
im_h, im_w, im_c = clip[0].shape
elif isinstance(clip[0], PIL.Image.Image):
im_w, im_h = clip[0].size
else:
raise TypeError('Expected numpy.ndarray or PIL.Image' +
'but got list of {0}'.format(type(clip[0])))
if crop_w > im_w:
pad = crop_w - im_w
clip = [np.pad(img, ((0, 0), (pad // 2, pad - pad // 2), (0, 0)), 'constant', constant_values=0) for img in
clip]
w1 = 0
else:
w1 = random.randint(0, im_w - crop_w)
if crop_h > im_h:
pad = crop_h - im_h
clip = [np.pad(img, ((pad // 2, pad - pad // 2), (0, 0), (0, 0)), 'constant', constant_values=0) for img in
clip]
h1 = 0
else:
h1 = random.randint(0, im_h - crop_h)
if isinstance(clip[0], np.ndarray):
return [img[h1:h1 + crop_h, w1:w1 + crop_w, :] for img in clip]
elif isinstance(clip[0], PIL.Image.Image):
return [img.crop((w1, h1, w1 + crop_w, h1 + crop_h)) for img in clip]
class CenterCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, clip):
try:
im_h, im_w, im_c = clip[0].shape
except ValueError:
print(clip[0].shape)
new_h, new_w = self.size
new_h = im_h if new_h >= im_h else new_h
new_w = im_w if new_w >= im_w else new_w
top = int(round((im_h - new_h) / 2.))
left = int(round((im_w - new_w) / 2.))
return [img[top:top + new_h, left:left + new_w] for img in clip]
class RandomHorizontalFlip(object):
def __init__(self, prob):
self.prob = prob
def __call__(self, clip):
# B, H, W, 3
flag = random.random() < self.prob
if flag:
clip = np.flip(clip, axis=2)
clip = np.ascontiguousarray(copy.deepcopy(clip))
return np.array(clip)
class RandomRotation(object):
"""
Rotate entire clip randomly by a random angle within
given bounds
Args:
degrees (sequence or int): Range of degrees to select from
If degrees is a number instead of sequence like (min, max),
the range of degrees, will be (-degrees, +degrees).
"""
def __init__(self, degrees):
if isinstance(degrees, numbers.Number):
if degrees < 0:
raise ValueError('If degrees is a single number,'
'must be positive')
degrees = (-degrees, degrees)
else:
if len(degrees) != 2:
raise ValueError('If degrees is a sequence,'
'it must be of len 2.')
self.degrees = degrees
def __call__(self, clip):
"""
Args:
img (PIL.Image or numpy.ndarray): List of images to be cropped
in format (h, w, c) in numpy.ndarray
Returns:
PIL.Image or numpy.ndarray: Cropped list of images
"""
angle = random.uniform(self.degrees[0], self.degrees[1])
if isinstance(clip[0], np.ndarray):
rotated = [scipy.misc.imrotate(img, angle) for img in clip]
elif isinstance(clip[0], PIL.Image.Image):
rotated = [img.rotate(angle) for img in clip]
else:
raise TypeError('Expected numpy.ndarray or PIL.Image' +
'but got list of {0}'.format(type(clip[0])))
return rotated
class TemporalRescale(object):
def __init__(self, temp_scaling=0.2):
self.min_len = 32
self.max_len = 230
self.L = 1.0 - temp_scaling
self.U = 1.0 + temp_scaling
def __call__(self, clip):
vid_len = len(clip)
new_len = int(vid_len * (self.L + (self.U - self.L) * np.random.random()))
if new_len < self.min_len:
new_len = self.min_len
if new_len > self.max_len:
new_len = self.max_len
if (new_len - 4) % 4 != 0:
new_len += 4 - (new_len - 4) % 4
if new_len <= vid_len:
index = sorted(random.sample(range(vid_len), new_len))
else:
index = sorted(random.choices(range(vid_len), k=new_len))
return clip[index]
class RandomResize(object):
"""
Resize video bysoomingin and out.
Args:
rate (float): Video is scaled uniformly between
[1 - rate, 1 + rate].
interp (string): Interpolation to use for re-sizing
('nearest', 'lanczos', 'bilinear', 'bicubic' or 'cubic').
"""
def __init__(self, rate=0.0, interp='bilinear'):
self.rate = rate
self.interpolation = interp
def __call__(self, clip):
scaling_factor = random.uniform(1 - self.rate, 1 + self.rate)
if isinstance(clip[0], np.ndarray):
im_h, im_w, im_c = clip[0].shape
elif isinstance(clip[0], PIL.Image.Image):
im_w, im_h = clip[0].size
new_w = int(im_w * scaling_factor)
new_h = int(im_h * scaling_factor)
new_size = (new_h, new_w)
if isinstance(clip[0], np.ndarray):
return [scipy.misc.imresize(img, size=(new_h, new_w), interp=self.interpolation) for img in clip]
elif isinstance(clip[0], PIL.Image.Image):
return [img.resize(size=(new_w, new_h), resample=self._get_PIL_interp(self.interpolation)) for img in clip]
else:
raise TypeError('Expected numpy.ndarray or PIL.Image' +
'but got list of {0}'.format(type(clip[0])))
def _get_PIL_interp(self, interp):
if interp == 'nearest':
return PIL.Image.NEAREST
elif interp == 'lanczos':
return PIL.Image.LANCZOS
elif interp == 'bilinear':
return PIL.Image.BILINEAR
elif interp == 'bicubic':
return PIL.Image.BICUBIC
elif interp == 'cubic':
return PIL.Image.CUBIC
class Resize(object):
"""
Resize video bysoomingin and out.
Args:
rate (float): Video is scaled uniformly between
[1 - rate, 1 + rate].
interp (string): Interpolation to use for re-sizing
('nearest', 'lanczos', 'bilinear', 'bicubic' or 'cubic').
"""
def __init__(self, rate=0.0, interp='bilinear'):
self.rate = rate
self.interpolation = interp
def __call__(self, clip):
scaling_factor = self.rate
if isinstance(clip[0], np.ndarray):
im_h, im_w, im_c = clip[0].shape
elif isinstance(clip[0], PIL.Image.Image):
im_w, im_h = clip[0].size
new_w = int(im_w * scaling_factor)
new_h = int(im_h * scaling_factor)
new_size = (new_w, new_h)
if isinstance(clip[0], np.ndarray):
return [np.array(PIL.Image.fromarray(img).resize(new_size)) for img in clip]
elif isinstance(clip[0], PIL.Image.Image):
return [img.resize(size=(new_w, new_h), resample=self._get_PIL_interp(self.interpolation)) for img in clip]
else:
raise TypeError('Expected numpy.ndarray or PIL.Image' +
'but got list of {0}'.format(type(clip[0])))
def _get_PIL_interp(self, interp):
if interp == 'nearest':
return PIL.Image.NEAREST
elif interp == 'lanczos':
return PIL.Image.LANCZOS
elif interp == 'bilinear':
return PIL.Image.BILINEAR
elif interp == 'bicubic':
return PIL.Image.BICUBIC
elif interp == 'cubic':
return PIL.Image.CUBIC
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