def main():
mask_path = "../resources/mask_used.png"
fit_path = os.path.join(a.input_fit_dir, "out_for_texture_tex_D.png")
pix2pix_path = os.path.join(a.input_pix2pix_dir,
"out_for_texture_tex_D.png")
unwrap_path = os.path.join(a.input_unwrap_dir, "out_for_texture_tex.png")
mask_img = (
np.asarray(Image.open(mask_path).resize(
(2048, 2048)), np.float32) / 255.0)
mask_img = mask_img[..., np.newaxis]
fit_img = np.asarray(Image.open(fit_path), np.float32)
pix2pix_img = np.asarray(Image.open(pix2pix_path), np.float32)
unwrap_img = np.asarray(
Image.open(unwrap_path).resize((2048, 2048)), np.float32)
fit_mu = np.sum(fit_img * mask_img, axis=(0, 1)) / np.sum(mask_img,
axis=(0, 1))
pix2pix_mu = np.sum(pix2pix_img * mask_img, axis=(0, 1)) / np.sum(
mask_img, axis=(0, 1))
pix2pix_img = pix2pix_img - pix2pix_mu + fit_mu
pix2pix_img = np.clip(pix2pix_img, 0, 255)
mask_img = np.concatenate([mask_img] * 3, axis=-1)
pix2pix_img = blend_uv(fit_img / 255,
pix2pix_img / 255,
mask_img,
match_color=False,
times=7)
pix2pix_img = pix2pix_img * 255
unwrap_img = blend_uv(fit_img / 255,
unwrap_img / 255,
mask_img,
match_color=False,
times=7)
unwrap_img = unwrap_img * 255
if os.path.exists(a.output_dir) is False:
os.makedirs(a.output_dir)
Image.fromarray(pix2pix_img.astype(np.uint8)).save(
os.path.join(a.output_dir, "output_for_texture_tex_D.png"))
Image.fromarray(unwrap_img.astype(np.uint8)).save(
os.path.join(a.output_dir, "output_for_texture_tex.png"))
def main(_):
print('---- step4 start -----')
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.GPU_NO
print('running base:', FLAGS.output_dir)
basis3dmm = load_3dmm_basis(FLAGS.basis3dmm_path,
FLAGS.uv_path,
is_whole_uv=True)
# load data (all : 0-255-float32)
img_list, pro_yx_list, preset_masks_list, base_uv, pro_xyz_list = load_from_npz(
FLAGS.output_dir, basis3dmm)
# set tf datas
base_uv_batch = tf.constant(base_uv[np.newaxis, ...], name='base_uv')
mask_mid_batch = tf.constant(
preset_masks_list[0][np.newaxis, ..., np.newaxis], tf.float32)
mask_left_batch = tf.constant(
preset_masks_list[1][np.newaxis, ..., np.newaxis], tf.float32)
mask_right_batch = tf.constant(
preset_masks_list[2][np.newaxis, ..., np.newaxis], tf.float32)
mask_batch = tf.clip_by_value(
mask_mid_batch + mask_left_batch + mask_right_batch, 0, 1)
imageH = img_list[0].shape[0]
imageW = img_list[0].shape[1]
assert (img_list[0].shape[0] == img_list[1].shape[0]
and img_list[0].shape[1] == img_list[2].shape[1])
image_mid_batch = tf.placeholder(dtype=tf.float32,
shape=[1, imageH, imageW, 3],
name='image_mid')
image_left_batch = tf.placeholder(dtype=tf.float32,
shape=[1, imageH, imageW, 3],
name='image_left')
image_right_batch = tf.placeholder(dtype=tf.float32,
shape=[1, imageH, imageW, 3],
name='image_right')
NV = basis3dmm['basis_shape'].shape[1] // 3
proj_xyz_mid_batch = tf.placeholder(dtype=tf.float32,
shape=[1, NV, 3],
name='proj_xyz_mid')
proj_xyz_left_batch = tf.placeholder(dtype=tf.float32,
shape=[1, NV, 3],
name='proj_xyz_left')
proj_xyz_right_batch = tf.placeholder(dtype=tf.float32,
shape=[1, NV, 3],
name='proj_xyz_right')
ver_normals_mid_batch, _ = Projector.get_ver_norm(proj_xyz_mid_batch,
basis3dmm['tri'],
'normal_mid')
ver_normals_left_batch, _ = Projector.get_ver_norm(proj_xyz_left_batch,
basis3dmm['tri'],
'normal_left')
ver_normals_right_batch, _ = Projector.get_ver_norm(
proj_xyz_right_batch, basis3dmm['tri'], 'normal_right')
uv_mid_batch, _ = \
unwrap_utils.unwrap_img_into_uv(
image_mid_batch / 255.0,
proj_xyz_mid_batch,
ver_normals_mid_batch,
basis3dmm,
512)
uv_left_batch, _ = \
unwrap_utils.unwrap_img_into_uv(
image_left_batch / 255.0,
proj_xyz_left_batch,
ver_normals_left_batch,
basis3dmm,
512)
uv_right_batch, _ = \
unwrap_utils.unwrap_img_into_uv(
image_right_batch / 255.0,
proj_xyz_right_batch,
ver_normals_right_batch,
basis3dmm,
512)
uv_left_batch.set_shape((1, 512, 512, 3))
uv_mid_batch.set_shape((1, 512, 512, 3))
uv_right_batch.set_shape((1, 512, 512, 3))
# lapulasion pyramid blending
cur_uv = tf_blend_uv(uv_left_batch,
uv_right_batch,
mask_right_batch,
match_color=False)
cur_uv = tf_blend_uv(cur_uv,
uv_mid_batch,
mask_mid_batch,
match_color=False)
uv_batch = tf_blend_uv(base_uv_batch / 255,
cur_uv,
mask_batch,
match_color=True)
uv_batch = uv_batch * 255
uv_batch = tf.identity(uv_batch, name='uv_tex')
print("uv_batch: ", uv_batch.shape)
#------------------------------------------------------------------------------------------
# build fitting graph
uv_bases = basis3dmm['uv']
para_tex = tf.get_variable(shape=[1, uv_bases['basis'].shape[0]],
initializer=tf.zeros_initializer(),
name='para_tex')
uv_rgb, uv_mask = get_region_uv_texture(uv_bases, para_tex)
print("uv_rgb: ", uv_rgb.shape)
# build fitting loss
input_uv512_batch = tf.placeholder(dtype=tf.float32,
shape=[1, 512, 512, 3],
name='gt_uv')
tot_loss = 0.
loss_str = 'total:{}'
if FLAGS.photo_weight > 0:
photo_loss = Losses.photo_loss(uv_rgb / 255.0,
input_uv512_batch / 255.0, uv_mask)
tot_loss = tot_loss + photo_loss * FLAGS.photo_weight
loss_str = '; photo:{}'
if FLAGS.uv_tv_weight > 0:
uv_tv_loss = Losses.uv_tv_loss(uv_rgb / 255.0, uv_mask)
tot_loss = tot_loss + uv_tv_loss * FLAGS.uv_tv_weight
loss_str = loss_str + '; tv:{}'
if FLAGS.uv_reg_tex_weight > 0:
uv_reg_tex_loss = Losses.reg_loss(para_tex)
tot_loss = tot_loss + uv_reg_tex_loss * FLAGS.uv_reg_tex_weight
loss_str = loss_str + '; reg:{}'
optim = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
train_op = optim.minimize(tot_loss)
with tf.Session() as sess:
if FLAGS.write_graph:
tf.train.write_graph(sess.graph_def,
'',
FLAGS.pb_path,
as_text=True)
exit()
sess.run(tf.global_variables_initializer())
start_time = time.time()
uv_extract, o_uv_left_batch, o_uv_mid_batch, o_uv_right_batch, o_mask_right_batch= \
sess.run( [ uv_batch, uv_left_batch, uv_mid_batch, uv_right_batch ,mask_right_batch],
{
image_mid_batch: img_list[0][np.newaxis,...],
image_left_batch: img_list[1][np.newaxis,...],
image_right_batch: img_list[2][np.newaxis,...],
proj_xyz_mid_batch: pro_xyz_list[0][np.newaxis,...],
proj_xyz_left_batch: pro_xyz_list[1][np.newaxis,...],
proj_xyz_right_batch: pro_xyz_list[2][np.newaxis,...] ,
})
uv_extract_dump = np.copy(uv_extract)
uv_extract = uv_extract_dump
print(' ------------- time wrap and merge:',
time.time() - start_time)
# blur will help
uv_extract = np.asarray(uv_extract[0], np.float32)
for _ in range(FLAGS.blur_times):
kernel = np.reshape(np.array([1.] * FLAGS.blur_kernel * FLAGS.blur_kernel, np.float32), \
[FLAGS.blur_kernel, FLAGS.blur_kernel]) / (FLAGS.blur_kernel * FLAGS.blur_kernel)
uv_extract = cv2.filter2D(uv_extract, -1, kernel)
if FLAGS.is_downsize:
uv_extract = cv2.resize(uv_extract, (256, 256))
uv_extract = cv2.resize(uv_extract, (512, 512))
uv_extract = uv_extract[np.newaxis, ...]
# iter fit textrue paras
start_time = time.time()
for i in range(FLAGS.train_step):
l1, l2, l3, l4, _ = sess.run(
[tot_loss, photo_loss, uv_tv_loss, uv_reg_tex_loss, train_op],
{input_uv512_batch: uv_extract})
if i % 50 == 0:
print(i, loss_str.format(l1, l2, l3, l4))
para_tex_out = sess.run(para_tex, {input_uv512_batch: uv_extract})
print(' ------------- time fit uv paras:', time.time() - start_time)
uv_fit, face_mask = construct(uv_bases, para_tex_out, 512)
uv_fit = blend_uv(base_uv / 255, uv_fit / 255, face_mask, True, 5)
uv_fit = np.clip(uv_fit * 255, 0, 255)
# save
prefix = "bapi"
Image.fromarray(np.squeeze(uv_extract_dump).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_tex_merge.png'))
Image.fromarray(np.squeeze(uv_fit).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_tex_fit.png'))
Image.fromarray(np.squeeze(o_uv_mid_batch).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_uv_mid_batch.png'))
Image.fromarray(np.squeeze(o_uv_left_batch).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_uv_left_batch.png'))
Image.fromarray(np.squeeze(o_uv_right_batch).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_uv_right_batch.png'))
Image.fromarray(np.squeeze(o_mask_right_batch).astype(np.uint8)).save(
os.path.join(FLAGS.output_dir, prefix + '_mask_right_batch.png'))
np.save(os.path.join(FLAGS.output_dir, "para_tex_init.npy"), para_tex_out)
print('---- step4 succeed -----')
def main(_):
# save parameters
save_params()
mask_batch = tf.placeholder(
dtype=tf.float32, shape=[1, 512, 512, 1], name="uv_mask"
)
tex_batch = tf.placeholder(dtype=tf.float32, shape=[1, 512, 512, 3], name="uv_tex")
var_mask_batch = tf.get_variable(
shape=[1, 512, 512, 1], dtype=tf.float32, name="var_mask", trainable=False
)
var_tex_batch = tf.get_variable(
shape=[1, 512, 512, 3], dtype=tf.float32, name="var_tex", trainable=False
)
assign_op = tf.group(
[tf.assign(var_mask_batch, mask_batch), tf.assign(var_tex_batch, tex_batch)],
name="assign_op",
)
# arrange images by name (load 3dmm)
basis3dmm = load_3dmm_basis(
FLAGS.basis3dmm_path,
FLAGS.uv_path,
uv_weight_mask_path=FLAGS.uv_weight_mask_path,
is_train=False,
is_whole_uv=False,
)
# build fitting graph
uv_region_bases = basis3dmm["uv"]
para_uv_dict = {}
for region_name in uv_region_bases:
region_basis = uv_region_bases[region_name]
para = tf.get_variable(
shape=[1, region_basis["basis"].shape[0]],
initializer=tf.zeros_initializer(),
name="para_" + region_name,
)
para_uv_dict[region_name] = para
uv_rgb, uv_mask = get_uv_texture(uv_region_bases, para_uv_dict)
photo_weight_mask = get_weighted_photo_mask(uv_region_bases)
# build fitting loss
tot_loss = 0.0
loss_str = ""
if FLAGS.photo_weight > 0:
photo_loss = Losses.ws_photo_loss(
var_tex_batch, uv_rgb / 255.0, uv_mask * photo_weight_mask * var_mask_batch
)
photo_loss = tf.identity(photo_loss, name="photo_loss")
tot_loss = tot_loss + photo_loss * FLAGS.photo_weight
loss_str = "photo:{}"
if FLAGS.uv_tv_weight > 0:
uv_tv_loss = Losses.uv_tv_loss2(
uv_rgb / 255, uv_mask, basis3dmm["uv_weight_mask"]
)
uv_tv_loss = tf.identity(uv_tv_loss, name="uv_tv_loss")
tot_loss = tot_loss + uv_tv_loss * FLAGS.uv_tv_weight
loss_str = loss_str + ";tv:{}"
if FLAGS.uv_reg_tex_weight > 0:
uv_reg_tex_loss = 0.0
for key in para_uv_dict:
para = para_uv_dict[key]
reg_region_loss = Losses.reg_loss(para)
uv_reg_tex_loss = uv_reg_tex_loss + reg_region_loss
uv_reg_tex_loss = uv_reg_tex_loss / len(para_uv_dict)
uv_reg_tex_loss = tf.identity(uv_reg_tex_loss, name="uv_reg_tex_loss")
tot_loss = tot_loss + uv_reg_tex_loss * FLAGS.uv_reg_tex_weight
loss_str = loss_str + ";reg:{}"
optim = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
train_op = optim.minimize(tot_loss, name="train_op")
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
if FLAGS.write_graph:
tf.train.write_graph(sess.graph_def, "", FLAGS.pb_path, as_text=True)
exit()
uv_paths = sorted(glob.glob(os.path.join(FLAGS.data_dir, "*tex.png")))
mask_paths = sorted(glob.glob(os.path.join(FLAGS.data_dir, "*mask.png")))
# mask_paths = ['../resources/mask_front_face.png'] * len(uv_paths)
# uv_paths = sorted(glob.glob(os.path.join(FLAGS.data_dir,'*delight_512.png')))
# mask_paths = sorted(glob.glob(os.path.join(FLAGS.data_dir, '*mask_512.png')))
# base uv
base_uv = Image.open("../resources/base_tex.png")
base_uv = np.asarray(base_uv, np.float32) / 255.0
base_normal = Image.open("../resources/base_normal.png")
base_normal = np.asarray(base_normal, np.float32) / 255.0
for uv_path, mask_path in zip(uv_paths, mask_paths):
uv_input = np.asarray(Image.open(uv_path)).astype(np.float32) / 255.0
mask_input = np.asarray(Image.open(mask_path)).astype(np.float32) / 255.0
if mask_input.shape[0] != 512:
mask_input = cv2.resize(mask_input, (512, 512))
if uv_input.shape[0] != 512:
uv_input = cv2.resize(uv_input, (512, 512))
if len(mask_input.shape) != 3:
mask_input = mask_input[..., np.newaxis]
mask_input = mask_input[:, :, 0:1]
uv_input = uv_input[np.newaxis, ...]
mask_input = mask_input[np.newaxis, ...]
sess.run(init_op)
sess.run(assign_op, {tex_batch: uv_input, mask_batch: mask_input})
for i in range(FLAGS.train_step):
# l1, l2, l3, l4, l5, _ = sess.run([tot_loss, photo_loss, uv_tv_loss, uv_reg_tex_loss, uv_consistency_loss, train_op])
l1, l2, l3, l4, _ = sess.run(
[tot_loss, photo_loss, uv_tv_loss, uv_reg_tex_loss, train_op]
)
if i == 1:
start_time = time.time()
if i % 100 == 0:
print(i, loss_str.format(l1, l2, l3, l4))
para_out_dict = {}
para_out_dict = sess.run(para_uv_dict)
face_uv, face_mask = np_get_uv_texture(basis3dmm["uv2k"], para_out_dict)
face_normal, face_mask = np_get_uv_texture(
basis3dmm["normal2k"], para_out_dict
)
face_uv = np.clip(face_uv, 0, 255)
face_normal = np.clip(face_normal, 0, 255)
face_mask = np.clip(face_mask, 0, 1)
prefix = uv_path.split("/")[-1].split(".")[0]
print(prefix)
# Image.fromarray(face_uv.astype(np.uint8)).save(
# os.path.join(FLAGS.out_dir, prefix + '_face_uv.png'))
out_uv = blend_uv(base_uv, face_uv / 255, face_mask, True)
out_normal = blend_uv(base_normal, face_normal / 255, face_mask, False)
out_uv = np.clip(out_uv * 255, 0, 255)
out_normal = np.clip(out_normal * 255, 0, 255)
out_uv = Image.fromarray(out_uv.astype(np.uint8))
out_normal = Image.fromarray(out_normal.astype(np.uint8))
out_uv.save(os.path.join(FLAGS.out_dir, prefix + "_D.png"))
out_normal.save(os.path.join(FLAGS.out_dir, prefix + "_N.png"))
