def loadSampledData(self):
path = str(self.path)
Num_train = self.Num_train
Num_test = self.Num_test
sampledata(path + '/train.txt', Num_train, 0,
path + '/trainSample.txt')
sampledata(path + '/test.txt', Num_test, 0, path + '/testSample.txt')
train_data = []
train_labels = []
max_l = -1
fp = open(os.path.join(path, "trainSample.txt"), 'r')
line = fp.readline() # 调用文件的 readline()
while len(line):
# print '*'*10,line
Level = int(line[0])
if Level > max_l:
max_l = Level
imgpath = line[2:-1]
mat = sio.loadmat(imgpath)
# print mat.keys()
train_data.append(mat['P'])
train_labels.append(Level)
line = fp.readline()
fp.close()
train_labels = np_utils.to_categorical(train_labels, max_l + 1)
train_data = np.asarray(train_data, dtype="float32")
print ' Train: ', train_data.shape
test_data = []
test_labels = []
max_l = -1
fp = open(os.path.join(path, "testSample.txt"), 'r')
line = fp.readline() # 调用文件的 readline()
while len(line):
# print '*'*10,line
Level = int(line[0])
if Level > max_l:
max_l = Level
imgpath = line[2:-1]
mat = sio.loadmat(imgpath)
# print mat.keys()
test_data.append(mat['P'])
test_labels.append(Level)
line = fp.readline()
fp.close()
test_labels = np_utils.to_categorical(test_labels, max_l + 1)
test_data = np.asarray(test_data, dtype="float32")
print ' Test: ', test_data.shape
(X_train, y_train), (X_test, y_test) = (train_data,
train_labels), (test_data,
test_labels)
return (X_train, y_train), (X_test, y_test)
def loadData(self, train, isSample):
path = str(self.path)
if train:
if isSample:
Num_train = self.Num_train
sampledata(path + '/train.txt', Num_train, 0,
path + '/trainSample.txt')
fp = open(os.path.join(path, "trainSample.txt"), 'r')
else:
fp = open(os.path.join(path, "train.txt"), 'r')
else:
if isSample:
Num_test = self.Num_test
sampledata(path + '/test.txt', Num_test, 0,
path + '/testSample.txt')
fp = open(os.path.join(path, "testSample.txt"), 'r')
else:
fp = open(os.path.join(path, "test.txt"), 'r')
data = []
labels = []
line = fp.readline()
while len(line):
Level = int(line[0])
imgpath = line[2:-1]
# 是否进行数据扩充
if (not self.isAug) and self.lineSearch(imgpath):
line = fp.readline()
continue
if 'npy' in imgpath:
mat = np.load(imgpath)
else:
mat = Image.open(imgpath)
data.append(mat)
labels.append(Level)
line = fp.readline()
fp.close()
return (data, np.array(labels, dtype='float32'))
def loadSampledData(self, train):
path = str(self.path)
if train:
Num_train = self.Num_train
sampledata(path + '/train.txt', Num_train, 0,
path + '/trainSample.txt')
fp = open(os.path.join(path, "trainSample.txt"), 'r')
else:
Num_test = self.Num_test
sampledata(path + '/test.txt', Num_test, 0,
path + '/testSample.txt')
fp = open(os.path.join(path, "testSample.txt"), 'r')
data = []
labels = []
line = fp.readline()
while len(line):
if self.isAug:
Level = int(line[0])
imgpath = line[2:-1]
mat = sio.loadmat(imgpath)
data.append(mat['P'])
labels.append(Level)
else:
if self.lineSearch(
line,
['_90', '_270', '_180', '_lr', 'ud', 'tr', 'tr2']):
Level = int(line[0])
imgpath = line[2:-1]
mat = sio.loadmat(imgpath)
data.append(mat['P'])
labels.append(Level)
line = fp.readline()
fp.close()
labels = np.asarray(labels, dtype="float32")
data = np.asarray(data)
return (data, labels)