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data_generator.py
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data_generator.py
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'''
Data Generator for Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Author: Xin Liu
'''
import math
import h5py
import numpy as np
from tensorflow import keras
class DataGenerator(keras.utils.Sequence):
'Generates data for Keras'
def __init__(self, paths_of_videos, nframe_per_video, dim, batch_size=32, frame_depth=10,
shuffle=True, temporal=True, respiration=0):
self.dim = dim
self.batch_size = batch_size
self.paths_of_videos = paths_of_videos
self.nframe_per_video = nframe_per_video
self.shuffle = shuffle
self.temporal = temporal
self.frame_depth = frame_depth
self.respiration = respiration
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return math.ceil(len(self.paths_of_videos) / self.batch_size)
def __getitem__(self, index):
'Generate one batch of data'
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
list_IDs_temp = [self.paths_of_videos[k] for k in indexes]
X, y = self.__data_generation(list_IDs_temp)
return X, y
def on_epoch_end(self):
# 'Updates indexes after each epoch'
self.indexes = np.arange(len(self.paths_of_videos))
if self.shuffle:
np.random.shuffle(self.indexes)
def __data_generation(self, list_video_temp):
'Generates data containing batch_size samples'
if self.respiration == 1:
label_key = "drsub"
else:
label_key = 'dysub'
if self.temporal == 'CAN_3D':
num_window = self.nframe_per_video - (self.frame_depth + 1)
data = np.zeros((num_window*len(list_video_temp), self.dim[0], self.dim[1], self.frame_depth, 6),
dtype=np.float32)
label = np.zeros((num_window*len(list_video_temp), self.frame_depth), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"]))
dysub = np.array(f1[label_key])
tempX = np.array([dXsub[f:f + self.frame_depth, :, :, :] # (169, 10, 36, 36, 6)
for f in range(num_window)])
tempY = np.array([dysub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempX = np.swapaxes(tempX, 1, 3) # (169, 36, 36, 10, 6)
tempX = np.swapaxes(tempX, 1, 2) # (169, 36, 36, 10, 6)
tempY = np.reshape(tempY, (num_window, self.frame_depth)) # (169, 10)
data[index*num_window:(index+1)*num_window, :, :, :, :] = tempX
label[index*num_window:(index+1)*num_window, :] = tempY
output = (data[:, :, :, :, :3], data[:, :, :, :, -3:])
elif self.temporal == 'MT_CAN_3D':
num_window = self.nframe_per_video - (self.frame_depth + 1)
data = np.zeros((num_window*len(list_video_temp), self.dim[0], self.dim[1], self.frame_depth, 6),
dtype=np.float32)
label_y = np.zeros((num_window*len(list_video_temp), self.frame_depth), dtype=np.float32)
label_r = np.zeros((num_window * len(list_video_temp), self.frame_depth), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"]))
drsub = np.array(f1['drsub'])
dysub = np.array(f1['dysub'])
tempX = np.array([dXsub[f:f + self.frame_depth, :, :, :] # (169, 10, 36, 36, 6)
for f in range(num_window)])
tempY_y = np.array([dysub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempY_r = np.array([drsub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempX = np.swapaxes(tempX, 1, 3) # (169, 36, 36, 10, 6)
tempX = np.swapaxes(tempX, 1, 2) # (169, 36, 36, 10, 6)
tempY_y = np.reshape(tempY_y, (num_window, self.frame_depth)) # (169, 10)
tempY_r = np.reshape(tempY_r, (num_window, self.frame_depth)) # (169, 10)
data[index*num_window:(index+1)*num_window, :, :, :, :] = tempX
label_y[index*num_window:(index+1)*num_window, :] = tempY_y
label_r[index * num_window:(index + 1) * num_window, :] = tempY_r
output = (data[:, :, :, :, :3], data[:, :, :, :, -3:])
label = (label_y, label_r)
elif self.temporal == 'CAN':
data = np.zeros((self.nframe_per_video * len(list_video_temp), self.dim[0], self.dim[1], 6), dtype=np.float32)
label = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"])) #dRsub for respiration
dysub = np.array(f1[label_key])
data[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :, :, :] = dXsub
label[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :] = dysub
output = (data[:, :, :, :3], data[:, :, :, -3:])
elif self.temporal == 'MT_CAN':
data = np.zeros((self.nframe_per_video * len(list_video_temp), self.dim[0], self.dim[1], 6),
dtype=np.float32)
label_y = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
label_r = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"])) # dRsub for respiration
drsub = np.array(f1['drsub'])
dysub = np.array(f1['dysub'])
data[index * self.nframe_per_video:(index + 1) * self.nframe_per_video, :, :, :] = dXsub
label_y[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :] = dysub
label_r[index * self.nframe_per_video:(index + 1) * self.nframe_per_video, :] = drsub
output = (data[:, :, :, :3], data[:, :, :, -3:])
label = (label_y, label_r)
elif self.temporal == 'TS_CAN':
data = np.zeros((self.nframe_per_video * len(list_video_temp), self.dim[0], self.dim[1], 6), dtype=np.float32)
label = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
num_window = int(self.nframe_per_video / self.frame_depth) * len(list_video_temp)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"])) #dRsub for respiration
dysub = np.array(f1[label_key])
data[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :, :, :] = dXsub
label[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :] = dysub
motion_data = data[:, :, :, :3]
apperance_data = data[:, :, :, -3:]
apperance_data = np.reshape(apperance_data, (num_window, self.frame_depth, self.dim[0], self.dim[1], 3))
apperance_data = np.average(apperance_data, axis=1)
apperance_data = np.repeat(apperance_data[:, np.newaxis, :, :, :], self.frame_depth, axis=1)
apperance_data = np.reshape(apperance_data, (apperance_data.shape[0] * apperance_data.shape[1],
apperance_data.shape[2], apperance_data.shape[3],
apperance_data.shape[4]))
output = (motion_data, apperance_data)
elif self.temporal == 'MTTS_CAN':
data = np.zeros((self.nframe_per_video * len(list_video_temp), self.dim[0], self.dim[1], 6), dtype=np.float32)
label_y = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
label_r = np.zeros((self.nframe_per_video * len(list_video_temp), 1), dtype=np.float32)
num_window = int(self.nframe_per_video / self.frame_depth) * len(list_video_temp)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"])) #dRsub for respiration
drsub = np.array(f1['drsub'])
dysub = np.array(f1['dysub'])
data[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :, :, :] = dXsub
label_y[index*self.nframe_per_video:(index+1)*self.nframe_per_video, :] = dysub
label_r[index * self.nframe_per_video:(index + 1) * self.nframe_per_video, :] = drsub
motion_data = data[:, :, :, :3]
apperance_data = data[:, :, :, -3:]
apperance_data = np.reshape(apperance_data, (num_window, self.frame_depth, self.dim[0], self.dim[1], 3))
apperance_data = np.average(apperance_data, axis=1)
apperance_data = np.repeat(apperance_data[:, np.newaxis, :, :, :], self.frame_depth, axis=1)
apperance_data = np.reshape(apperance_data, (apperance_data.shape[0] * apperance_data.shape[1],
apperance_data.shape[2], apperance_data.shape[3],
apperance_data.shape[4]))
output = (motion_data, apperance_data)
label = (label_y, label_r)
elif self.temporal == 'MT_Hybrid_CAN':
num_window = self.nframe_per_video - (self.frame_depth + 1)
data = np.zeros((num_window*len(list_video_temp), self.dim[0], self.dim[1], self.frame_depth, 6),
dtype=np.float32)
label_y = np.zeros((num_window*len(list_video_temp), self.frame_depth), dtype=np.float32)
label_r = np.zeros((num_window * len(list_video_temp), self.frame_depth), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"]))
drsub = np.array(f1['drsub'])
dysub = np.array(f1['dysub'])
tempX = np.array([dXsub[f:f + self.frame_depth, :, :, :] # (169, 10, 36, 36, 6)
for f in range(num_window)])
tempY_y = np.array([dysub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempY_r = np.array([drsub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempX = np.swapaxes(tempX, 1, 3) # (169, 36, 36, 10, 6)
tempX = np.swapaxes(tempX, 1, 2) # (169, 36, 36, 10, 6)
tempY_y = np.reshape(tempY_y, (num_window, self.frame_depth)) # (169, 10)
tempY_r = np.reshape(tempY_r, (num_window, self.frame_depth)) # (169, 10)
data[index*num_window:(index+1)*num_window, :, :, :, :] = tempX
label_y[index*num_window:(index+1)*num_window, :] = tempY_y
label_r[index * num_window:(index + 1) * num_window, :] = tempY_r
motion_data = data[:, :, :, :, :3]
apperance_data = np.average(data[:, :, :, :, -3:], axis=-2)
output = (motion_data, apperance_data)
label = (label_y, label_r)
elif self.temporal == 'Hybrid_CAN':
num_window = self.nframe_per_video - (self.frame_depth + 1)
data = np.zeros((num_window*len(list_video_temp), self.dim[0], self.dim[1], self.frame_depth, 6),
dtype=np.float32)
label = np.zeros((num_window*len(list_video_temp), self.frame_depth), dtype=np.float32)
for index, temp_path in enumerate(list_video_temp):
f1 = h5py.File(temp_path, 'r')
dXsub = np.transpose(np.array(f1["dXsub"]))
dysub = np.array(f1[label_key])
tempX = np.array([dXsub[f:f + self.frame_depth, :, :, :] # (169, 10, 36, 36, 6)
for f in range(num_window)])
tempY = np.array([dysub[f:f + self.frame_depth] # (169, 10, 1)
for f in range(num_window)])
tempX = np.swapaxes(tempX, 1, 3) # (169, 36, 36, 10, 6)
tempX = np.swapaxes(tempX, 1, 2) # (169, 36, 36, 10, 6)
tempY = np.reshape(tempY, (num_window, self.frame_depth)) # (169, 10)
data[index*num_window:(index+1)*num_window, :, :, :, :] = tempX
label[index*num_window:(index+1)*num_window, :] = tempY
motion_data = data[:, :, :, :, :3]
apperance_data = np.average(data[:, :, :, :, -3:], axis=-2)
output = (motion_data, apperance_data)
else:
raise ValueError('Unsupported Model!')
return output, label