test.py

import tensorflow as tf
import numpy as np
import os
from scipy import misc
from matting import generate_trimap
import argparse
import sys

g_mean = np.array(([126.88,120.24,112.19])).reshape([1,1,3])

def main(args):
	
	gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = args.gpu_fraction)
	with tf.Session(config=tf.ConfigProto(gpu_options = gpu_options)) as sess:
		saver = tf.train.import_meta_graph('./meta_graph/my-model.meta')
		saver.restore(sess,tf.train.latest_checkpoint('./model'))
		image_batch = tf.get_collection('image_batch')[0]
		GT_trimap = tf.get_collection('GT_trimap')[0]
		pred_mattes = tf.get_collection('pred_mattes')[0]

		rgb = misc.imread(args.rgb)
		alpha = misc.imread(args.alpha,'L')
		trimap = generate_trimap(np.expand_dims(np.copy(alpha),2),np.expand_dims(alpha,2))[:,:,0]
		origin_shape = alpha.shape
		rgb = np.expand_dims(misc.imresize(rgb.astype(np.uint8),[320,320,3]).astype(np.float32)-g_mean,0)
		trimap = np.expand_dims(np.expand_dims(misc.imresize(trimap.astype(np.uint8),[320,320],interp = 'nearest').astype(np.float32),2),0)

		feed_dict = {image_batch:rgb,GT_trimap:trimap}
		pred_alpha = sess.run(pred_mattes,feed_dict = feed_dict)
		final_alpha = misc.imresize(np.squeeze(pred_alpha),origin_shape)
		# misc.imshow(final_alpha)
		misc.imsave('./alpha.png',final_alpha)

def parse_arguments(argv):
	parser = argparse.ArgumentParser()

	parser.add_argument('--alpha', type=str,
		help='input alpha')
	parser.add_argument('--rgb', type=str,
		help='input rgb')
	parser.add_argument('--gpu_fraction', type=float,
		help='how much gpu is needed, usually 4G is enough',default = 0.4)
	return parser.parse_args(argv)


if __name__ == '__main__':
    main(parse_arguments(sys.argv[1:]))