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9d0b4d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | 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() |