DeepMosaics / util /dataloader.py
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import os
import random
import numpy as np
from multiprocessing import Process, Queue
from . import image_processing as impro
from . import mosaic,data
class VideoLoader(object):
"""docstring for VideoLoader
Load a single video(Converted to images)
How to use:
1.Init VideoLoader as loader
2.Get data by loader.ori_stream
3.loader.next() to get next stream
"""
def __init__(self, opt, video_dir, test_flag=False):
super(VideoLoader, self).__init__()
self.opt = opt
self.test_flag = test_flag
self.video_dir = video_dir
self.t = 0
self.n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
self.transform_params = data.get_transform_params()
self.ori_load_pool = []
self.mosaic_load_pool = []
self.previous_pred = None
feg_ori = impro.imread(os.path.join(video_dir,'origin_image','00001.jpg'),loadsize=self.opt.loadsize,rgb=True)
feg_mask = impro.imread(os.path.join(video_dir,'mask','00001.png'),mod='gray',loadsize=self.opt.loadsize)
self.mosaic_size,self.mod,self.rect_rat,self.feather = mosaic.get_random_parameter(feg_ori,feg_mask)
self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
self.loadsize = self.opt.loadsize
#Init load pool
for i in range(self.opt.S*self.opt.T):
_ori_img = impro.imread(os.path.join(video_dir,'origin_image','%05d' % (i+1)+'.jpg'),loadsize=self.loadsize,rgb=True)
_mask = impro.imread(os.path.join(video_dir,'mask','%05d' % (i+1)+'.png' ),mod='gray',loadsize=self.loadsize)
_mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
_ori_img = data.random_transform_single_image(_ori_img,opt.finesize,self.transform_params)
_mosaic_img = data.random_transform_single_image(_mosaic_img,opt.finesize,self.transform_params)
self.ori_load_pool.append(self.normalize(_ori_img))
self.mosaic_load_pool.append(self.normalize(_mosaic_img))
self.ori_load_pool = np.array(self.ori_load_pool)
self.mosaic_load_pool = np.array(self.mosaic_load_pool)
#Init frist stream
self.ori_stream = self.ori_load_pool [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
# stream B,T,H,W,C -> B,C,T,H,W
self.ori_stream = self.ori_stream.reshape (1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
#Init frist previous frame
self.previous_pred = self.ori_load_pool[self.opt.S*self.opt.N-1].copy()
# previous B,C,H,W
self.previous_pred = self.previous_pred.reshape(1,opt.finesize,opt.finesize,3).transpose((0,3,1,2))
def normalize(self,data):
'''
normalize to -1 ~ 1
'''
return (data.astype(np.float32)/255.0-0.5)/0.5
def anti_normalize(self,data):
return np.clip((data*0.5+0.5)*255,0,255).astype(np.uint8)
def next(self):
# random
if np.random.random()<0.05:
self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
if np.random.random()<0.02:
self.transform_params['rate']['crop'] = [np.random.random(),np.random.random()]
if np.random.random()<0.02:
self.loadsize = np.random.randint(self.opt.finesize,self.opt.loadsize)
if self.t != 0:
self.previous_pred = None
self.ori_load_pool [:self.opt.S*self.opt.T-1] = self.ori_load_pool [1:self.opt.S*self.opt.T]
self.mosaic_load_pool[:self.opt.S*self.opt.T-1] = self.mosaic_load_pool[1:self.opt.S*self.opt.T]
#print(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'))
_ori_img = impro.imread(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'),loadsize=self.loadsize,rgb=True)
_mask = impro.imread(os.path.join(self.video_dir,'mask','%05d' % (self.opt.S*self.opt.T+self.t)+'.png' ),mod='gray',loadsize=self.loadsize)
_mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
_ori_img = data.random_transform_single_image(_ori_img,self.opt.finesize,self.transform_params)
_mosaic_img = data.random_transform_single_image(_mosaic_img,self.opt.finesize,self.transform_params)
_ori_img,_mosaic_img = self.normalize(_ori_img),self.normalize(_mosaic_img)
self.ori_load_pool [self.opt.S*self.opt.T-1] = _ori_img
self.mosaic_load_pool[self.opt.S*self.opt.T-1] = _mosaic_img
self.ori_stream = self.ori_load_pool [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
# stream B,T,H,W,C -> B,C,T,H,W
self.ori_stream = self.ori_stream.reshape (1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
self.t += 1
class VideoDataLoader(object):
"""VideoDataLoader"""
def __init__(self, opt, videolist, test_flag=False):
super(VideoDataLoader, self).__init__()
self.videolist = []
self.opt = opt
self.test_flag = test_flag
for i in range(self.opt.n_epoch):
self.videolist += videolist.copy()
random.shuffle(self.videolist)
self.each_video_n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
self.n_iter = len(self.videolist)//self.opt.load_thread//self.opt.batchsize*self.each_video_n_iter*self.opt.load_thread
self.queue = Queue(self.opt.load_thread)
self.ori_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
self.mosaic_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
self.previous_pred = np.zeros((self.opt.batchsize,3,self.opt.finesize,self.opt.finesize),dtype=np.float32)
self.load_init()
def load(self,videolist):
for load_video_iter in range(len(videolist)//self.opt.batchsize):
iter_videolist = videolist[load_video_iter*self.opt.batchsize:(load_video_iter+1)*self.opt.batchsize]
videoloaders = [VideoLoader(self.opt,os.path.join(self.opt.dataset,iter_videolist[i]),self.test_flag) for i in range(self.opt.batchsize)]
for each_video_iter in range(self.each_video_n_iter):
for i in range(self.opt.batchsize):
self.ori_stream[i] = videoloaders[i].ori_stream
self.mosaic_stream[i] = videoloaders[i].mosaic_stream
if each_video_iter == 0:
self.previous_pred[i] = videoloaders[i].previous_pred
videoloaders[i].next()
if each_video_iter == 0:
self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),self.previous_pred])
else:
self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),None])
def load_init(self):
ptvn = len(self.videolist)//self.opt.load_thread #pre_thread_video_num
for i in range(self.opt.load_thread):
p = Process(target=self.load,args=(self.videolist[i*ptvn:(i+1)*ptvn],))
p.daemon = True
p.start()
def get_data(self):
return self.queue.get()