pretrained_model = False
#pretrained_model = False
test_dir = './alhpamatting'
test_outdir = './test_predict'
#validation_dir = '/data/gezheng/data-matting/new2/validation'
#pretrained_vgg_model_path
model_path = './vgg16_weights.npz'
log_dir = 'matting_log'
dataset_alpha = 'train_data/alpha'
dataset_eps = 'train_data/eps'
dataset_BG = 'train_data/bg'
paths_alpha, paths_eps, paths_BG = load_path(dataset_alpha,
dataset_eps,
dataset_BG,
hard_mode=hard_mode)
range_size = len(paths_alpha)
print('range_size is %d' % range_size)
#range_size/batch_size has to be int
batchs_per_epoch = int(range_size / train_batch_size)
index_queue = tf.train.range_input_producer(range_size,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32)
index_dequeue_op = index_queue.dequeue_many(train_batch_size, 'index_dequeue')
image_batch = tf.placeholder(tf.float32,
import os from scipy import misc image_size = 320 batch_size = 25 max_epochs = 1000000 #pretrained_vgg_model_path model_path = './vgg16_weights.npz' log_dir = './tensor_log' dataset_RGB = '/data/gezheng/data-matting/new/comp_RGB' dataset_alpha = '/data/gezheng/data-matting/new/alpha_final' dataset_FG = '/data/gezheng/data-matting/new/FG_final' dataset_BG = '/data/gezheng/data-matting/new/BG' paths_RGB,paths_alpha,paths_FG,paths_BG = load_path(dataset_RGB,dataset_alpha,dataset_FG,dataset_BG) range_size = len(paths_RGB) #range_size/batch_size has to be int batchs_per_epoch = int(range_size/batch_size) index_queue = tf.train.range_input_producer(range_size, num_epochs=None,shuffle=True, seed=None, capacity=32) index_dequeue_op = index_queue.dequeue_many(batch_size, 'index_dequeue') image_batch = tf.placeholder(tf.float32, shape=(batch_size,image_size,image_size,3)) GT_matte_batch = tf.placeholder(tf.float32, shape = (batch_size,image_size,image_size,1)) GT_trimap = tf.placeholder(tf.float32, shape = (batch_size,image_size,image_size,1)) GTBG_batch = tf.placeholder(tf.float32, shape = (batch_size,image_size,image_size,3)) GTFG_batch = tf.placeholder(tf.float32, shape = (batch_size,image_size,image_size,3)) is_train = tf.placeholder(tf.bool, name = 'is_train') en_parameters = []
