def _handler_rgb(ir_path, vis_path, model_path, model_pre_path, ssim_weight, index, output_path=None):
# ir_img = get_train_images(ir_path, flag=False)
# vis_img = get_train_images(vis_path, flag=False)
ir_img = get_train_images_rgb(ir_path, flag=False)
vis_img = get_train_images_rgb(vis_path, flag=False)
dimension = ir_img.shape
ir_img = ir_img.reshape([1, dimension[0], dimension[1], dimension[2]])
vis_img = vis_img.reshape([1, dimension[0], dimension[1], dimension[2]])
ir_img = np.transpose(ir_img, (0, 2, 1, 3))
vis_img = np.transpose(vis_img, (0, 2, 1, 3))
ir_img1 = ir_img[:, :, :, 0]
ir_img1 = ir_img1.reshape([1, dimension[0], dimension[1], 1])
ir_img2 = ir_img[:, :, :, 1]
ir_img2 = ir_img2.reshape([1, dimension[0], dimension[1], 1])
ir_img3 = ir_img[:, :, :, 2]
ir_img3 = ir_img3.reshape([1, dimension[0], dimension[1], 1])
vis_img1 = vis_img[:, :, :, 0]
vis_img1 = vis_img1.reshape([1, dimension[0], dimension[1], 1])
vis_img2 = vis_img[:, :, :, 1]
vis_img2 = vis_img2.reshape([1, dimension[0], dimension[1], 1])
vis_img3 = vis_img[:, :, :, 2]
vis_img3 = vis_img3.reshape([1, dimension[0], dimension[1], 1])
print('img shape final:', ir_img1.shape)
with tf.Graph().as_default(), tf.Session() as sess:
infrared_field = tf.placeholder(
tf.float32, shape=ir_img1.shape, name='content')
visible_field = tf.placeholder(
tf.float32, shape=ir_img1.shape, name='style')
dfn = DenseFuseNet(model_pre_path)
output_image = dfn.transform_addition(infrared_field, visible_field)
# restore the trained model and run the style transferring
saver = tf.train.Saver()
saver.restore(sess, model_path)
output1 = sess.run(output_image, feed_dict={infrared_field: ir_img1, visible_field: vis_img1})
output2 = sess.run(output_image, feed_dict={infrared_field: ir_img2, visible_field: vis_img2})
output3 = sess.run(output_image, feed_dict={infrared_field: ir_img3, visible_field: vis_img3})
output1 = output1.reshape([1, dimension[0], dimension[1]])
output2 = output2.reshape([1, dimension[0], dimension[1]])
output3 = output3.reshape([1, dimension[0], dimension[1]])
output = np.stack((output1, output2, output3), axis=-1)
output = np.transpose(output, (0, 2, 1, 3))
save_images(ir_path, output, output_path,
prefix='fused' + str(index), suffix='_densefuse_addition_'+str(ssim_weight))
def _handler_rgb_patch_based(images_path,
model_path,
model_pre_path,
index,
output_path=None):
size = len(images_path)
images = ["" for x in range(size)]
ir_img1 = ["" for x in range(size)]
ir_img2 = ["" for x in range(size)]
ir_img3 = ["" for x in range(size)]
for x in range(0, size):
images[x] = get_train_images_rgb(images_path[x], flag=False)
dimension = images[x].shape
images[x] = images[x].reshape(
[1, dimension[0], dimension[1], dimension[2]])
images[x] = np.transpose(images[x], (0, 2, 1, 3))
ir_img1[x] = images[x][:, :, :, 0]
ir_img1[x] = ir_img1[x].reshape([1, dimension[0], dimension[1], 1])
ir_img2[x] = images[x][:, :, :, 1]
ir_img2[x] = ir_img2[x].reshape([1, dimension[0], dimension[1], 1])
ir_img3[x] = images[x][:, :, :, 2]
ir_img3[x] = ir_img3[x].reshape([1, dimension[0], dimension[1], 1])
print('img shape final:', ir_img1[0].shape)
dimension = images[0].shape
TRAIN_TAIL_SIZE_X = 32
TRAIN_TAIL_SIZE_Y = 32
TILES_X = (int)(dimension[1] / TRAIN_TAIL_SIZE_X)
TILES_Y = (int)(dimension[2] / TRAIN_TAIL_SIZE_Y)
INPUT_SHAPE_TILE = (1, TRAIN_TAIL_SIZE_X, TRAIN_TAIL_SIZE_Y, 1)
with tf.Graph().as_default(), tf.Session() as sess:
images_field = ["" for x in range(size)]
for x in range(0, size):
images_field[x] = tf.placeholder(tf.float32,
shape=INPUT_SHAPE_TILE)
dfn = DenseFuseNet(model_pre_path)
output_image = dfn.transform_addition(images_field)
# restore the trained model and run the style transferring
saver = tf.train.Saver()
saver.restore(sess, model_path)
dimension = images[0].shape
output1 = np.zeros([1, dimension[1], dimension[2]])
output2 = np.zeros([1, dimension[1], dimension[2]])
output3 = np.zeros([1, dimension[1], dimension[2]])
for tile_x in range(TILES_X):
for tile_y in range(TILES_Y):
x1 = tile_x * TRAIN_TAIL_SIZE_X
y1 = tile_y * TRAIN_TAIL_SIZE_Y
x2 = x1 + TRAIN_TAIL_SIZE_X
y2 = y1 + TRAIN_TAIL_SIZE_Y
tile_img1 = ["" for x in range(size)]
tile_img2 = ["" for x in range(size)]
tile_img3 = ["" for x in range(size)]
for x in range(0, size):
tile_img1[x] = ir_img1[x][:, x1:x2, y1:y2, :]
tile_img2[x] = ir_img2[x][:, x1:x2, y1:y2, :]
tile_img3[x] = ir_img3[x][:, x1:x2, y1:y2, :]
output1_t = sess.run(
output_image,
feed_dict={i: d
for i, d in zip(images_field, tile_img1)})
output2_t = sess.run(
output_image,
feed_dict={i: d
for i, d in zip(images_field, tile_img2)})
output3_t = sess.run(
output_image,
feed_dict={i: d
for i, d in zip(images_field, tile_img3)})
output1_t = output1_t.reshape(
[1, TRAIN_TAIL_SIZE_X, TRAIN_TAIL_SIZE_Y])
output2_t = output2_t.reshape(
[1, TRAIN_TAIL_SIZE_X, TRAIN_TAIL_SIZE_Y])
output3_t = output3_t.reshape(
[1, TRAIN_TAIL_SIZE_X, TRAIN_TAIL_SIZE_Y])
output1[0, x1:x2, y1:y2] = output1_t
output2[0, x1:x2, y1:y2] = output2_t
output3[0, x1:x2, y1:y2] = output3_t
output = np.stack((output1, output2, output3), axis=-1)
output = np.transpose(output, (0, 2, 1, 3))
save_images(images_path,
output,
output_path,
prefix='fused' + str(index),
suffix='_densefuse_addition_patch_based')
def _handler_rgb_l1(images_path,
model_path,
model_pre_path,
index,
output_path=None):
size = len(images_path)
images = ["" for x in range(size)]
ir_img1 = ["" for x in range(size)]
ir_img2 = ["" for x in range(size)]
ir_img3 = ["" for x in range(size)]
for x in range(0, size):
images[x] = get_train_images_rgb(images_path[x], flag=False)
dimension = images[x].shape
images[x] = images[x].reshape(
[1, dimension[0], dimension[1], dimension[2]])
images[x] = np.transpose(images[x], (0, 2, 1, 3))
ir_img1[x] = images[x][:, :, :, 0]
ir_img1[x] = ir_img1[x].reshape([1, dimension[0], dimension[1], 1])
ir_img2[x] = images[x][:, :, :, 1]
ir_img2[x] = ir_img2[x].reshape([1, dimension[0], dimension[1], 1])
ir_img3[x] = images[x][:, :, :, 2]
ir_img3[x] = ir_img3[x].reshape([1, dimension[0], dimension[1], 1])
print('img shape final:', ir_img1[0].shape)
with tf.Graph().as_default(), tf.Session() as sess:
images_field = ["" for x in range(size)]
for x in range(0, size):
images_field[x] = tf.placeholder(tf.float32,
shape=ir_img1[0].shape)
dfn = DenseFuseNet(model_pre_path)
enc_irs = ["" for x in range(size)]
enc_irs = dfn.transform_encoder(images_field)
target = tf.placeholder(tf.float32,
shape=enc_irs[0].shape,
name='target')
output_image = dfn.transform_decoder(target)
# restore the trained model and run the style transferring
saver = tf.train.Saver()
saver.restore(sess, model_path)
enc_ir_temps = sess.run(
enc_irs, feed_dict={i: d
for i, d in zip(images_field, ir_img1)})
feature = L1_norm(enc_ir_temps)
output1 = sess.run(output_image, feed_dict={target: feature})
enc_ir_temps = sess.run(
enc_irs, feed_dict={i: d
for i, d in zip(images_field, ir_img2)})
feature = L1_norm(enc_ir_temps)
output2 = sess.run(output_image, feed_dict={target: feature})
enc_ir_temps = sess.run(
enc_irs, feed_dict={i: d
for i, d in zip(images_field, ir_img3)})
feature = L1_norm(enc_ir_temps)
output3 = sess.run(output_image, feed_dict={target: feature})
output1 = output1.reshape([1, dimension[0], dimension[1]])
output2 = output2.reshape([1, dimension[0], dimension[1]])
output3 = output3.reshape([1, dimension[0], dimension[1]])
output = np.stack((output1, output2, output3), axis=-1)
output = np.transpose(output, (0, 2, 1, 3))
save_images(images_path,
output,
output_path,
prefix='fused' + str(index),
suffix='_densefuse_l1norm')