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Loadtemporal_data.py
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Loadtemporal_data.py
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from __future__ import print_function, division
import os
import torch
import pandas as pd
import cv2
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import pdb
import math
clip_frames = 160
class Normaliztion (object):
"""
same as mxnet, normalize into [-1, 1]
image = (image - 127.5)/128
"""
def __call__(self, sample):
video_x, clip_average_HR, ecg_label, frame_rate = sample['video_x'],sample['clip_average_HR'], sample['ecg'],sample['frame_rate']
new_video_x = (video_x - 127.5)/128
return {'video_x': new_video_x, 'clip_average_HR':clip_average_HR, 'ecg':ecg_label, 'frame_rate':frame_rate}
class RandomHorizontalFlip (object):
"""Horizontally flip the given Image randomly with a probability of 0.5."""
def __call__(self, sample):
video_x, clip_average_HR, ecg_label, frame_rate = sample['video_x'],sample['clip_average_HR'], sample['ecg'],sample['frame_rate']
h, w = video_x.shape[1], video_x.shape[2]
new_video_x = np.zeros((clip_frames, h, w, 3))
p = random.random()
if p < 0.5:
#print('Flip')
for i in range(clip_frames):
# video
image = video_x[i, :, :, :]
image = cv2.flip(image, 1)
new_video_x[i, :, :, :] = image
return {'video_x': new_video_x, 'clip_average_HR':clip_average_HR, 'ecg':ecg_label, 'frame_rate':frame_rate}
else:
#print('no Flip')
return {'video_x': video_x, 'clip_average_HR':clip_average_HR, 'ecg':ecg_label, 'frame_rate':frame_rate}
class ToTensor (object):
"""
Convert ndarrays in sample to Tensors.
process only one batch every time
"""
def __call__(self, sample):
video_x, clip_average_HR, ecg_label, frame_rate = sample['video_x'],sample['clip_average_HR'], sample['ecg'],sample['frame_rate']
# swap color axis because
# numpy image: (batch_size) x depth x H x W x C
# torch image: (batch_size) x C x depth X H X W
video_x = video_x.transpose((3, 0, 1, 2))
video_x = np.array(video_x)
clip_average_HR = np.array(clip_average_HR)
frame_rate = np.array(frame_rate)
return {'video_x': torch.from_numpy(video_x.astype(np.float)).float(), 'clip_average_HR': torch.from_numpy(clip_average_HR.astype(np.float)).float(), 'ecg': torch.from_numpy(ecg_label.astype(np.float)).float(), 'frame_rate': torch.from_numpy(frame_rate.astype(np.float)).float()}
# train
class VIPL_train (Dataset):
def __init__(self, info_list, root_dir, transform=None):
self.landmarks_frame = pd.read_csv(info_list, delimiter=' ', header=None)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.landmarks_frame)
def __getitem__(self, idx):
#print(self.landmarks_frame.iloc[idx, 0])
video_path = os.path.join(self.root_dir, str(self.landmarks_frame.iloc[idx, 0]))
start_frame = self.landmarks_frame.iloc[idx, 1]
frame_rate = self.landmarks_frame.iloc[idx, 2]
clip_average_HR = self.landmarks_frame.iloc[idx, 3]
p = random.random()
if p < 0.5 and start_frame==61: # sampling aug p < 0.5
ecg_label = self.landmarks_frame.iloc[idx, 5:5+160].values
video_x, clip_average_HR, ecg_label_new = self.get_single_video_x_aug(video_path, start_frame, clip_average_HR, ecg_label)
else:
video_x = self.get_single_video_x(video_path, start_frame)
ecg_label_new = self.landmarks_frame.iloc[idx, 5:5+160].values
sample = {'video_x': video_x, 'frame_rate':frame_rate, 'ecg':ecg_label_new, 'clip_average_HR':clip_average_HR}
if self.transform:
sample = self.transform(sample)
return sample
def get_single_video_x(self, video_path, start_frame):
video_jpgs_path = video_path
video_x = np.zeros((clip_frames, 128, 128, 3))
size_crop = np.random.randint(16)
log_file = open('temp.txt', 'w')
image_id = start_frame
for i in range(clip_frames):
s = "%05d" % image_id
image_name = 'image_' + s + '.png'
# face video
image_path = os.path.join(video_jpgs_path, image_name)
log_file.write(image_path)
log_file.write("\n")
log_file.flush()
tmp_image = cv2.imread(image_path)
#cv2.imwrite('test111.jpg', tmp_image)
if tmp_image is None: # It seems some frames missing
tmp_image = cv2.imread(self.root_dir+'p30/v1/source2/image_00737.png')
tmp_image = cv2.resize(tmp_image, (128+size_crop, 128+size_crop), interpolation=cv2.INTER_CUBIC)[(size_crop//2):(128+size_crop//2), (size_crop//2):(128+size_crop//2), :]
video_x[i, :, :, :] = tmp_image
image_id += 1
return video_x
def get_single_video_x_aug(self, video_path, start_frame, clip_average_HR, ecg_label):
video_jpgs_path = video_path
video_x = np.zeros((clip_frames, 128, 128, 3))
ecg_label_new = np.zeros(clip_frames)
size_crop = np.random.randint(16)
if clip_average_HR>88: # halve
clip_average_HR = clip_average_HR/2
for tt in range(clip_frames):
if tt%2 == 0:
image_id = start_frame + tt//2
s = "%05d" % image_id
image_name = 'image_' + s + '.png'
image_path = os.path.join(video_jpgs_path, image_name)
tmp_image = cv2.imread(image_path)
if tmp_image is None: # It seems some frames missing
tmp_image = cv2.imread(self.root_dir+'p30/v1/source2/image_00737.png')
ecg_label_new[tt] = ecg_label[tt//2]
else:
image_id1 = start_frame + tt//2
image_id2 = image_id1+1
s = "%05d" % image_id1
image_name = 'image_' + s + '.png'
image_path = os.path.join(video_jpgs_path, image_name)
tmp_image1 = cv2.imread(image_path)
s = "%05d" % image_id2
image_name = 'image_' + s + '.png'
image_path = os.path.join(video_jpgs_path, image_name)
tmp_image2 = cv2.imread(image_path)
if tmp_image1 is None: # It seems some frames missing
tmp_image1 = cv2.imread(self.root_dir+'p30/v1/source2/image_00737.png')
if tmp_image2 is None: # It seems some frames missing
tmp_image2 = cv2.imread(self.root_dir+'p30/v1/source2/image_00737.png')
tmp_image = tmp_image1//2+tmp_image2//2 # mean linear interpolation
ecg_label_new[tt] = ecg_label[tt//2]/2+ecg_label[tt//2+1]/2
tmp_image = cv2.resize(tmp_image, (128+size_crop, 128+size_crop), interpolation=cv2.INTER_CUBIC)[(size_crop//2):(128+size_crop//2), (size_crop//2):(128+size_crop//2), :]
image_x_aug = tmp_image
video_x[tt, :, :, :] = image_x_aug
else: # double
clip_average_HR = clip_average_HR*2
for tt in range(clip_frames):
image_id = start_frame + tt*2
s = "%05d" % image_id
image_name = 'image_' + s + '.png'
image_path = os.path.join(video_jpgs_path, image_name)
tmp_image = cv2.imread(image_path)
if tmp_image is None: # It seems some frames missing
tmp_image = cv2.imread(self.root_dir+'p30/v1/source2/image_00737.png')
tmp_image = cv2.resize(tmp_image, (128+size_crop, 128+size_crop), interpolation=cv2.INTER_CUBIC)[(size_crop//2):(128+size_crop//2), (size_crop//2):(128+size_crop//2), :]
image_x_aug = tmp_image
video_x[tt, :, :, :] = image_x_aug
# approximation
if tt<80:
ecg_label_new[tt] = ecg_label[tt*2]
else:
ecg_label_new[tt] = ecg_label_new[tt-80]
return video_x, clip_average_HR, ecg_label_new