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spatial_dataloader.py
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spatial_dataloader.py
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import pickle
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import random
from split_train_test_video import *
from skimage import io, color, exposure
class spatial_dataset(Dataset):
def __init__(self, dic, root_dir, mode, transform=None):
self.keys = dic.keys()
self.values=dic.values()
self.root_dir = root_dir
self.mode =mode
self.transform = transform
def __len__(self):
return len(self.keys)
def load_ucf_image(self,video_name, index):
if video_name.split('_')[0] == 'HandstandPushups':
n,g = video_name.split('_',1)
name = 'HandStandPushups_'+g
path = self.root_dir + 'HandstandPushups'+'/separated_images/v_'+name+'/v_'+name+'_'
else:
path = self.root_dir + video_name.split('_')[0]+'/separated_images/v_'+video_name+'/v_'+video_name+'_'
img = Image.open(path +str(index)+'.jpg')
transformed_img = self.transform(img)
img.close()
return transformed_img
def __getitem__(self, idx):
if self.mode == 'train':
video_name, nb_clips = self.keys[idx].split(' ')
nb_clips = int(nb_clips)
clips = []
clips.append(random.randint(1, nb_clips/3))
clips.append(random.randint(nb_clips/3, nb_clips*2/3))
clips.append(random.randint(nb_clips*2/3, nb_clips+1))
elif self.mode == 'val':
video_name, index = self.keys[idx].split(' ')
index =abs(int(index))
else:
raise ValueError('There are only train and val mode')
label = self.values[idx]
label = int(label)-1
if self.mode=='train':
data ={}
for i in range(len(clips)):
key = 'img'+str(i)
index = clips[i]
data[key] = self.load_ucf_image(video_name, index)
sample = (data, label)
elif self.mode=='val':
data = self.load_ucf_image(video_name,index)
sample = (video_name, data, label)
else:
raise ValueError('There are only train and val mode')
return sample
class spatial_dataloader():
def __init__(self, BATCH_SIZE, num_workers, path, ucf_list, ucf_split):
self.BATCH_SIZE=BATCH_SIZE
self.num_workers=num_workers
self.data_path=path
self.frame_count ={}
# split the training and testing videos
splitter = UCF101_splitter(path=ucf_list,split=ucf_split)
self.train_video, self.test_video = splitter.split_video()
def load_frame_count(self):
#print '==> Loading frame number of each video'
with open('dic/frame_count.pickle','rb') as file:
dic_frame = pickle.load(file)
file.close()
for line in dic_frame :
videoname = line.split('_',1)[1].split('.',1)[0]
n,g = videoname.split('_',1)
if n == 'HandStandPushups':
videoname = 'HandstandPushups_'+ g
self.frame_count[videoname]=dic_frame[line]
def run(self):
self.load_frame_count()
self.get_training_dic()
self.val_sample20()
train_loader = self.train()
val_loader = self.validate()
return train_loader, val_loader, self.test_video
def get_training_dic(self):
#print '==> Generate frame numbers of each training video'
self.dic_training={}
for video in self.train_video:
#print videoname
nb_frame = self.frame_count[video]-10+1
key = video+' '+ str(nb_frame)
self.dic_training[key] = self.train_video[video]
def val_sample20(self):
print '==> sampling testing frames'
self.dic_testing={}
for video in self.test_video:
nb_frame = self.frame_count[video]-10+1
interval = int(nb_frame/19)
for i in range(19):
frame = i*interval
key = video+ ' '+str(frame+1)
self.dic_testing[key] = self.test_video[video]
def train(self):
training_set = spatial_dataset(dic=self.dic_training, root_dir=self.data_path, mode='train', transform = transforms.Compose([
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
]))
print '==> Training data :',len(training_set),'frames'
print training_set[1][0]['img1'].size()
train_loader = DataLoader(
dataset=training_set,
batch_size=self.BATCH_SIZE,
shuffle=True,
num_workers=self.num_workers)
return train_loader
def validate(self):
validation_set = spatial_dataset(dic=self.dic_testing, root_dir=self.data_path, mode='val', transform = transforms.Compose([
transforms.Scale([224,224]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
]))
print '==> Validation data :',len(validation_set),'frames'
print validation_set[1][1].size()
val_loader = DataLoader(
dataset=validation_set,
batch_size=self.BATCH_SIZE,
shuffle=False,
num_workers=self.num_workers)
return val_loader
if __name__ == '__main__':
dataloader = spatial_dataloader(BATCH_SIZE=1, num_workers=1,
path='/home/ubuntu/data/UCF101/spatial_no_sampled/',
ucf_list='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/',
ucf_split='01')
train_loader,val_loader,test_video = dataloader.run()