def loadImage(self, image_path):
# load and preprocess image, save to npy
image = self.preprocessImage(image_path)
# todo: emmit lua sub process
# wait lua to generate corresponding data file, and read
data_file = self.dataPipePath + 'data_demo_*'
while True:
filename = glob.glob(data_file)
if len(filename) >= 1:
if not os.path.isfile(self.dataPipePath + 'writing_block_' +
filename[0].split('_')[-1]):
# lua writing file finished.
break
assert (len(filename) == 1)
filename = filename[0]
# json load data_$pipIndex_*
with open(filename, 'rb') as f:
reader = torchfile.T7Reader(f, utf8_decode_strings=True)
data = reader.read_obj()
box_scores = np.squeeze(data['box_scores'])
box_coords = data['boxes_pred']
box_feats = data['box_feats'] #np.expand_dims(,axis=0)
global_feat = data['glob_feat']
return image, box_scores, box_coords, box_feats, global_feat
def from_t7(t, b64=False):
if b64:
t = base64.b64decode(t)
with open('/dev/shm/t7', 'wb') as ff:
ff.write(t)
ff.close()
sf = open('/dev/shm/t7', 'rb')
return torchfile.T7Reader(sf).read_obj()
def loadOneJson(self, mode, pInd):
'''
Every time loadJson load data for ONE image. The loaded data variable contains fields below:
# data['info'] = info[1]
# data['box_scores'] = 256x1
# data['boxes_pred'] = [~128]x4
# data['boxes_gt'] = [~128]x4
# data['box_captions_gt'] = [~128]x15
# data['box_feats'] = [~128]x512x7x7
# data['glob_feat'] = 512x30x45
# data['glob_caption_gt'] = 100
'''
# list data_$pipIndex_*
# pInd = self.pipeIndex
data_file = self.dataPipePath + 'data_' + str(pInd) + '_*'
pick_confirm_file = self.dataPipePath + 'pick_confirm_' + str(pInd)
writing_block_file = self.dataPipePath + 'writing_block_' + str(pInd)
reading_block_file = self.dataPipePath + 'reading_block_' + str(pInd)
while True:
filename = glob.glob(data_file)
if len(filename) >= 1:
#if (not os.path.isfile(pick_confirm_file)) or mode=='test':
# pick_confirm file has been removed by lua loader. so this file is new.
if (not os.path.isfile(writing_block_file)) and (
not os.path.isfile(reading_block_file)):
# lua writing file finished.
if mode == 'train':
os.mknod(reading_block_file)
break
assert (len(filename) == 1)
filename = filename[0]
# read iter and numiters
tmp = filename.split('/')[-1].split('_')
numiters = tmp[-1]
itr = tmp[-2]
# json load data_$pipIndex_*
with open(filename, 'rb') as f:
reader = torchfile.T7Reader(f, utf8_decode_strings=True)
data = reader.read_obj()
if mode == 'train':
#os.remove(filename)
os.remove(reading_block_file)
# place pick_confirm_$pipIndex to notify lua program
#os.mknod(pick_confirm_file)
# update pInd
# self.updatePipeIndex()
# return data, iter, numiters
return data, itr, numiters