def imgs_to_out(path):
save_log = os.path.join('train_log', '3')
save_folder = os.path.join(save_log, 'models')
img_names = os.listdir(path)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
r_net = R_Net.R_Net()
o_net = O_Net.O_Net()
img = tf.placeholder(tf.float32, [None, None, None, 3])
r_output = r_net.pr(img)
o_output = o_net.pr(img)
model_pr = tf.train.Saver()
model_pr.restore(
sess, os.path.join(save_folder,
str(4000) + 'save_net.ckpt'))
initialize_uninitialized(sess)
imgs = [Image.open(os.path.join(path, i)) for i in img_names]
imgs = [padding_img_rect(i) for i in imgs]
p_imgs = [np.array(i.resize((24, 24), Image.BILINEAR)) for i in imgs]
r_out = sess.run(r_output, feed_dict={img: p_imgs})
n_r_crops = []
n_imgs = []
n_ori_img = []
for i in range(r_out.shape[0]):
n_out = r_out[i]
n_ori_img.append(imgs[i])
n_crops = [n_out[2], n_out[3], n_out[4], n_out[5]]
n_r_crops.append(n_crops)
im1 = Image.fromarray(p_imgs[i]).crop(n_crops)
im1 = padding_img_rect(im1)
im1 = im1.resize([48, 48], Image.BILINEAR)
n_imgs.append(np.array(im1))
o_out = sess.run(o_output, feed_dict={img: n_imgs})
for i in range(o_out.shape[0]):
output_p = o_out[i, :]
img_show = n_ori_img[i]
draw = ImageDraw.Draw(img_show)
labeln = output_p[2:]
labeln = [
j * (n_r_crops[i][2] - n_r_crops[i][0]) / 48 for j in labeln
]
labeln = [
k + n_r_crops[i][int(j % 2)] for j, k in enumerate(labeln)
]
labeln = [j / 24 * max(img_show.size) for j in labeln]
draw.rectangle(((labeln[0], labeln[1]), (labeln[2], labeln[3])),
outline='black')
draw.ellipse(
(labeln[4] - 2, labeln[5] - 2, labeln[4] + 2, labeln[5] + 2),
fill='blue')
draw.ellipse(
(labeln[6] - 2, labeln[7] - 2, labeln[6] + 2, labeln[7] + 2),
fill='red')
img_show.show()
default="1")
parser.add_argument("-G",
"--gpu-memory",
help="gpu memary used",
type=float,
dest="gpu_memory",
default="0.1")
args = parser.parse_args()
save_log = os.path.join('train_log', args.save_log)
save_folder = os.path.join(save_log, 'models')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
net = R_Net.R_Net()
input_img = tf.placeholder(tf.float32, [None, None, None, 3])
feature_map = net.pr(input_img)
model_pr = tf.train.Saver()
model_pr.restore(sess,
os.path.join(save_folder,
str(10000) + 'save_net.ckpt'))
ge = generator_test_region.generator_test(image_path='/data/images/',
image_size=24,
batch=1,
image_from_each_face=3,
dy=[5, 11, 20])