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Test_reconstruct_DMB_numberadjust_FGADR.py
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Test_reconstruct_DMB_numberadjust_FGADR.py
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import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', default='FGADR')
parser.add_argument('--img_name', default='0516.jpg')
parser.add_argument('--gpus')
args = parser.parse_args()
print(args)
import os
if args.gpus:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
print('CUDA_VISIBLE_DEVICES:', os.environ["CUDA_VISIBLE_DEVICES"])
import keras.backend as K
import tensorflow as tf
import Net
import numpy as np
import StyleFeature
import scipy.io as sio
import cv2
from DMB_fragment import rebuild_AMaps_by_img
import pickle
import random
import yaml
from Opts import save_images, matTonpy
# =============================== path set =============================================== #
out_dir = args.dataset_name + '_NumberAdjust_' + args.img_name
load_model = args.dataset_name
db_dataset = args.dataset_name
real_img_dataset = args.dataset_name # 'DRIVE'
real_img_test_dataset = args.dataset_name # 'DRIVE'
result_dir = 'Test' + '/' + out_dir + ''
model_directory = 'Model_and_Result' + '/' + load_model + '/models' # Directory to restore trained model from.
if tf.gfile.Exists(result_dir):
print('Result dir exists! Press Enter to OVERRIDE...', end='')
input()
tf.gfile.DeleteRecursively(result_dir)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
os.system('cp {} {}'.format(__file__, result_dir))
# ============================== parameters set ========================================== #
max_epoch = 20
img_channel = 3
img_size = 512
img_x = 512
img_y = 512
gt_channel = 1
z_size = 400
# =============================== model and data definition ================================ #
generator = Net.generator
tf.reset_default_graph()
gt = tf.placeholder(shape=[None, img_size, img_size, gt_channel], dtype=tf.float32)
img = tf.placeholder(shape=[None, img_size, img_size, img_channel], dtype=tf.float32)
mask = tf.placeholder(shape=[None, img_size, img_size, 1], dtype=tf.float32)
z = tf.placeholder(shape=[None, z_size], dtype=tf.float32)
# gt_mask = tf.concat([gt, mask], 3)
img_detector = StyleFeature.get_style_model(img, mask, with_feature_mask_from=('dense_2', []))
act_input = {
size: tf.image.resize_images((img_detector.get_layer(layer_name).related_projection.output - mean)/std, [size,size])
for layer_name, size, mean, std in zip(StyleFeature.STYLE_LAYERS,
StyleFeature.STYLE_LAYERS_SIZE,
StyleFeature.STYLE_LAYERS_MEAN,
StyleFeature.STYLE_LAYERS_STD)
}
syn = generator(gt, act_input, z)
# =============================== init ============================================= #
t_vars = tf.trainable_variables()
g_vars = list(set(t_vars) & set(tf.global_variables('generator')))
init = tf.variables_initializer(g_vars)
sess = K.get_session()
saver = tf.train.Saver(g_vars, max_to_keep=None)
sess.run(init)
#writer = tf.train.SummaryWriter(model_directory, sess.graph)
# ==================================== restore weights ================================ #
ckpt = tf.train.get_checkpoint_state(model_directory)
restore_path = ckpt.model_checkpoint_path.replace('-9000', '-9000')
print(restore_path)
saver.restore(sess, restore_path)
# ==================================== start training ===================================== #
if real_img_test_dataset in ['FGADR', 'retinal-lesions', 'IDRiD']:
# img_sample = np.load('data/{}_test_image.npy'.format(real_img_test_dataset))
gt_sample = np.load('data/{}_test_gt.npy'.format(real_img_test_dataset))[..., [0]]
mask_sample = np.load('data/{}_test_mask.npy'.format(real_img_test_dataset))
elif real_img_test_dataset == 'DRIVE':
img_sample, gt_sample, mask_sample = Net.matTonpy_35()
# img_sample = (np.reshape(img_sample, [-1, img_x, img_y, img_channel]) - 0.5) * 2.0
gt_sample = (np.reshape(gt_sample, [-1, img_x, img_y, gt_channel]) - 0.5) * 2.0
mask_sample = (np.reshape(mask_sample, [-1, img_x, img_y, 1]) - 0.5) * 2.0
per_part = 250
part_id = -1
with open('data/'+real_img_test_dataset+'_test.list', 'r') as f:
fname_list = yaml.safe_load(f)
# amaps = rebuild_AMaps_by_img(0, fragments_DB)
# generate images with fragmentDB
for epoch in range(max_epoch+16):
print('epoch:', epoch)
batchNum = 1
if True: # for i, (gt_array, mask_array) in enumerate(zip(gt_sample, mask_sample)):
i = fname_list.index(args.img_name)
i, (gt_array, mask_array) = i, (gt_sample[i], mask_sample[i])
print('img:', batchNum)
if i//per_part != part_id:
part_id = i//per_part
with open('DMB/{}.by_img.{}'.format(db_dataset, part_id), 'rb') as file:
fragments_DB = pickle.load(file)
zs = np.random.normal(0, 0.001, size=[1, z_size]).astype(np.float32)
if epoch <= max_epoch:
amaps, lesion_map = rebuild_AMaps_by_img(i, fragments_DB, lesion_map=True, quantity=epoch/max_epoch)
else:
amaps, lesion_map = rebuild_AMaps_by_img(i, fragments_DB, randomize=True, lesion_map=True, multiple=epoch-max_epoch+1)
syn_array = sess.run(syn, feed_dict={gt: [gt_array], z:zs, mask: [mask_array],
act_input[256]: [amaps[256]],
act_input[64]: [amaps[64]]})
syn_array = (syn_array + 1) / 2
syn_sample_m = syn_array * ((mask_array + 1) / 2)
syn_sample_m = np.reshape(syn_sample_m, [img_x, img_y, img_channel])
save_images(np.reshape(syn_sample_m, [1, img_x, img_y, img_channel]),
[1, 1],
result_dir + '/{}_{}.jpg'.format(fname_list[i], epoch))
lesion_map = cv2.resize(lesion_map, (512, 512), interpolation=cv2.INTER_NEAREST)
lesion_map = lesion_map * ((mask_array + 1) / 2) # crop by mask
_, binary = cv2.threshold((((mask_array + 1) / 2) * 255).astype('uint8'), 0, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
lesion_map = cv2.drawContours(lesion_map, contours, -1, (255, 255, 255))
cv2.imwrite(result_dir + '/{}_{}_lesion_map.jpg'.format(fname_list[i], epoch),
lesion_map)
batchNum += 1
sess.close()