def data_testsave(input_dir, output_dir):
filename = 'TestSet.tfrecord'
writer = tf.python_io.TFRecordWriter(os.path.join(output_dir + filename))
for folder in os.listdir(input_dir):
print('****************' + folder + '***********************')
img_path = input_dir + '/' + folder + '/Image/'
lbl_path = input_dir + '/' + folder + '/Label/'
for imgname in os.listdir(img_path):
lblname = imgname[:-3] + 'bmp'
print(imgname + "," + lblname)
img = cv2.imread(img_path + imgname)
lbl = cv2.imread(lbl_path + lblname)
#imgs = np.dot(img[...,:3], [0.299, 0.587, 0.114])
imgs = pr.Intensity(img)
lbls = np.dot(lbl[..., :3], [0.299, 0.587, 0.114]) // 255
lbls = lbls.astype('uint8')
img_batch = pr.down_sample(imgs).astype('float64')
lbl_batch = pr.down_sample(lbls)
img_batch_norm = pr.contrast_normalization(img_batch)
example = tf.train.Example(features=tf.train.Features(
feature={
'img_raw': _bytes_feature(img_batch_norm.tostring()),
'gt_raw': _bytes_feature(lbl_batch.tostring())
}))
writer.write(example.SerializeToString())
writer.close()
def data_trainsave(input_dir, output_dir):
if not os.path.exists(output_dir):
os.mkdir(output_dir)
filename = 'TrainSet.tfrecord'
Trans = ['False', 'Mirror', 'Rot90', 'Rot-90', 'Displa', 'Rotate']
dx = 0
dy = 0
theta = 0
writer = tf.python_io.TFRecordWriter(os.path.join(output_dir + filename))
for itr in range(2):
if itr > 6:
trans_mode = Trans[5]
elif itr > 4 and itr <= 6:
trans_mode = Trans[4]
elif itr <= 4:
trans_mode = Trans[itr]
print('____________________' + trans_mode +
'___________________________')
for folder in os.listdir(input_dir):
#img_path = folder+'/Image/'
#lbl_path = folder+'/Label/'
for sub_folder in os.listdir(input_dir + '/' + folder + '/'):
#savenum=0
#savename = filename+str(savenum)+'.tfrecord'
#writer = tf.python_io.TFRecordWriter(os.path.join(output_dir+savename))
img_folder = input_dir + '/' + folder + '/' + sub_folder + '/Image/'
lbl_folder = input_dir + '/' + folder + '/' + sub_folder + '/Label/'
print('****************' + sub_folder +
'***********************')
for imgname in os.listdir(img_folder):
lblname = imgname[:-3] + 'bmp'
print(imgname + "," + lblname)
img = cv2.imread(img_folder + imgname)
lbl = cv2.imread(lbl_folder + lblname)
#imgs = np.dot(img[...,:3], [0.299, 0.587, 0.114])
imgs = pr.Intensity(img)
lbls = np.dot(lbl[..., :3], [0.299, 0.587, 0.114]) // 255
lbls = lbls.astype('uint8')
img_batch = pr.down_sample(imgs).astype('float64')
lbl_batch = pr.down_sample(lbls)
img_batch_norm = pr.contrast_normalization(img_batch)
if trans_mode == 'Displa':
dx = random.randint(-40, 40)
dy = random.randint(-40, 40)
print('dx:' + str(dx) + '...dy:' + str(dy))
if trans_mode == 'Rotate':
theta = random.randint(-50, 50)
print('theta:' + str(dx))
img_all_batch = pr.data_argument(img_batch_norm,
trans_mode, dx, dy, theta)
lbl_all_batch = pr.data_argument(lbl_batch, trans_mode, dx,
dy, theta)
img_all = img_all_batch.reshape([1, HEIGHT, WIDTH, 1])
lbl_all = lbl_all_batch.reshape([1, HEIGHT, WIDTH, 1])
img_sp = {}
lbl_sp = {}
img_sp = pr.image_splite(img_all, COLS, ROWS)
lbl_sp = pr.image_splite(lbl_all, COLS, ROWS)
for col in range(COLS):
for row in range(ROWS):
print('~~~~~~~~' + str(col) + '~~~' + str(row) +
'~~~~~~~~')
img_save_batch = img_sp[col][row]
lbl_save_batch = lbl_sp[col][row]
img_save = img_save_batch.reshape(
[SP_HEIGHT, SP_WIDTH, 1])
lbl_save = lbl_save_batch.reshape(
[SP_HEIGHT, SP_WIDTH, 1]).astype('uint8')
example = tf.train.Example(
features=tf.train.Features(
feature={
'img_raw':
_bytes_feature(img_save.tostring()),
'gt_raw':
_bytes_feature(lbl_save.tostring())
}))
writer.write(example.SerializeToString())
writer.close()