def save_single_video(self):
my_multi_test_datasets = multi_test_datasets(
batch_size=self.batch_size,
video_num=self.video_imgs_num,
frame_interval=2,
is_frame=True,
is_Optical=False,
crop_size=4,
img_size=self.img_size_h)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
self.restore_model_weghts(sess)
seletced_dataset_idx = 3
selected_video_idx = 2
video_label = my_multi_test_datasets.init_test_single_videos(
seletced_dataset_idx, selected_video_idx)
video_lenth = 0
image_batches_list = []
while 1:
batches = my_multi_test_datasets.get_single_videos_batches()
if not (batches == []):
# print(batches.shape)
video_lenth += (batches.shape[0] * batches.shape[1])
out_image1, out_image2 = sess.run(
[self.out_image_con1, self.out_image_con2],
feed_dict={
self.train_in_ph: batches,
self.phase: False
})
image_batches_list.append(out_image1)
image_batches_list.append(out_image2)
else:
break
image_batches_list = np.concatenate(image_batches_list, axis=0)
save_batch_images(
image_batches_list, self.gray_img_save_path,
'datasets_%d_video_%d.jpg' %
(seletced_dataset_idx, selected_video_idx))
print('test')
return
def test_video(self):
def reshape_batches(inputs_batches):
return np.reshape(
inputs_batches,
newshape=[
inputs_batches.shape[0] * inputs_batches.shape[1],
inputs_batches.shape[2], inputs_batches.shape[3],
inputs_batches.shape[4]
])
my_multi_test_datasets = multi_test_datasets(batch_size=4,
video_num=4,
frame_interval=2,
is_frame=True,
is_Optical=False,
crop_size=4,
img_size=self.img_size_h)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
self.restore_model_weghts(sess)
video_label = my_multi_test_datasets.init_test_single_videos(
seletced_dataset_idx=2, selected_video_idx=2)
video_lenth = 0
psnr1_list = []
optical_loss_list = []
gray_loss_list = []
optical_frame_list = []
gray_frame_list = []
optical_frame_label_list = []
gray_frame_label_list = []
while 1:
batches = my_multi_test_datasets.get_single_videos_batches()
if not (batches == []):
print(batches.shape)
video_lenth += (batches.shape[0] * batches.shape[1])
batch_data_gray = batches[:, :, :, :, 0:1]
gray_loss, gray_frames, gray_psnr1 = sess.run(
[
self.gray_loss_sequences_frame_mean,
self.gray_train_out_ph,
self.gray_loss_sequences_frame_psnr1
],
feed_dict={
self.gray_train_in_ph: batch_data_gray,
self.phase: False
})
# print('optical loss shape',optical_loss.shape)
print('gray loss shape', gray_loss.shape)
print('psnr shape', gray_psnr1.shape)
# print('gray loss shape',gray_loss.shape)
# optical_loss = optical_loss.flatten()
gray_loss = gray_loss.flatten()
gray_psnr1 = gray_psnr1.flatten()
# optical_frame_list.append(reshape_batches(optical_frames))
gray_frame_list.append(reshape_batches(gray_frames))
gray_frame_label_list.append(
reshape_batches(batch_data_gray))
# optical_frame_label_list.append(reshape_batches(batch_data_optical))
# optical_loss_list.append(optical_loss)
gray_loss_list.append(gray_loss)
psnr1_list.append(gray_psnr1)
else:
break
# print('optical-loss')
# optical_loss_list = max_min_np(np.concatenate(optical_loss_list,axis=0))
# save_roc_auc_plot_img('',optical_loss_list, video_label)
print('gray-loss')
gray_loss_list = max_min_np(np.concatenate(gray_loss_list, axis=0))
print(gray_loss_list)
print(video_label)
save_roc_auc_plot_img('', gray_loss_list, video_label)
print('psnr1-auc')
gray_psnr1 = max_min_np(np.concatenate(psnr1_list, axis=0))
# print(gray_psnr1)
save_roc_auc_plot_img('', gray_psnr1, video_label)
gray_frame_list = np.concatenate(gray_frame_list, axis=0)
# optical_frame_list = np.concatenate(optical_frame_list, axis=0)
gray_frame_label_list = np.concatenate(gray_frame_label_list,
axis=0)
# optical_frame_label_list = np.concatenate(optical_frame_label_list, axis=0)
gray_frame_list = np.concatenate(
[gray_frame_list, gray_frame_label_list], axis=1)
# optical_frame_list = np.concatenate([optical_frame_list, optical_frame_label_list], axis=2)
save_batch_images(gray_frame_list, self.gray_img_save_path,
'test_gray.jpg')
return