def display(tfrecord_dir,
bfm_path,
param_mean_std_path,
image_size,
num_images=5,
n_tex_para=40):
print('Loading sdataset %s' % tfrecord_dir)
batch_size = 4
dset = dataset.TFRecordDatasetSupervised(tfrecord_dir=tfrecord_dir,
batch_size=batch_size,
repeat=False,
shuffle_mb=0)
print('Loading BFM model')
bfm = TfMorphableModel(model_path=bfm_path, n_tex_para=n_tex_para)
idx = 0
filename = '/opt/project/output/verify_dataset/supervised/20200525/image_batch_{0}_indx_{1}.jpg'
unnormalize_labels = fn_unnormalize_300W_LP_labels(
param_mean_std_path=param_mean_std_path, image_size=image_size)
while idx < num_images:
try:
image_tensor, labels_tensor = dset.get_minibatch_tf()
except tf.errors.OutOfRangeError:
break
# render images using labels
roi, landmarks, pose_para, shape_para, exp_para, color_para, illum_para, tex_para = split_300W_LP_labels(
labels_tensor)
roi, landmarks, pose_para, shape_para, exp_para, color_para, illum_para, tex_para = unnormalize_labels(
batch_size, roi, landmarks, pose_para, shape_para, exp_para,
color_para, illum_para, tex_para)
image_rendered = render_batch(pose_param=pose_para,
shape_param=shape_para,
exp_param=exp_para,
tex_param=tex_para,
color_param=color_para,
illum_param=illum_para,
frame_height=image_size,
frame_width=image_size,
tf_bfm=bfm,
batch_size=batch_size).numpy().astype(
np.uint8)
for i in range(batch_size):
images = np.concatenate(
(image_tensor[i].numpy().astype(np.uint8), image_rendered[i]),
axis=0)
# images = image_rendered[i]
imageio.imsave(filename.format(idx, i), images)
idx += 1
print('\nDisplayed %d images' % idx)
def example_render_batch3(pic_names: list, tf_bfm: TfMorphableModel,
n_tex_para: int, save_to_folder: str,
resolution: int):
batch_size = len(pic_names)
images_orignal = load_images(pic_names, '/opt/project/examples/Data/80k/')
shape_param_batch, exp_param_batch, pose_param_batch = load_params_80k(
pic_names=pic_names)
shape_param = tf.squeeze(shape_param_batch)
exp_param = tf.squeeze(exp_param_batch)
pose_param = tf.squeeze(pose_param_batch)
pose_param = tf.concat([
pose_param[:, :-1],
tf.constant(0.0, shape=(batch_size, 1), dtype=tf.float32),
pose_param[:, -1:]
],
axis=1)
lm = tf_bfm.get_landmarks(shape_param,
exp_param,
pose_param,
batch_size,
450,
is_2d=True,
is_plot=True)
images_rendered = render_batch(
pose_param=pose_param,
shape_param=shape_param,
exp_param=exp_param,
tex_param=tf.constant(0.0,
shape=(len(pic_names), n_tex_para),
dtype=tf.float32),
color_param=None,
illum_param=None,
frame_height=450,
frame_width=450,
tf_bfm=tf_bfm,
batch_size=batch_size).numpy().astype(np.uint8)
for i, pic_name in enumerate(pic_names):
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
plot_image_w_lm(ax, resolution, images_orignal[i], lm[i])
ax = fig.add_subplot(1, 2, 2)
plot_image_w_lm(ax, resolution, images_rendered[i], lm[i])
plt.savefig(os.path.join(save_to_folder, pic_name))
def inverse_rendering(bfm_path, exp_path, pd_model_path, param_mean_std_path, save_to, image_folder, image_names,
batch_size, resolution,
n_tex_para, n_shape_para):
print('Loading BFM model')
bfm = TfMorphableModel(
model_path=bfm_path,
exp_path=exp_path,
n_shape_para=n_shape_para,
n_tex_para=n_tex_para
)
model = load_model(pd_model_path=pd_model_path)
rendered_filename_tmp = os.path.join(save_to, '{0}_rendered.jpg')
unnormalize_labels = fn_unnormalize_ffhq_labels(param_mean_std_path=param_mean_std_path, image_size=image_size)
images = []
images_names = []
plt.gca().set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
for img_name, img in load_images(image_folder=image_folder, image_names=image_names, resolution=resolution):
images.append(img)
images_names.append(img_name)
if len(images) == batch_size:
inference_and_render_images(
images=images,
images_names=images_names,
model=model,
bfm=bfm,
unnormalize_labels=unnormalize_labels,
rendered_filename_tmp=rendered_filename_tmp)
images = []
images_names = []
if images:
inference_and_render_images(
images=images,
images_names=images_names,
model=model,
bfm=bfm,
unnormalize_labels=unnormalize_labels,
rendered_filename_tmp=rendered_filename_tmp)
def example_render_batch2(pic_names: list, tf_bfm: TfMorphableModel, save_to_folder: str, n_tex_para:int):
batch_size = len(pic_names)
images_orignal = load_images(pic_names, '/opt/project/examples/Data/300W_LP/')
shape_param_batch, exp_param_batch, tex_param_batch, color_param_batch, illum_param_batch, pose_param_batch, lm_batch = \
load_params(pic_names=pic_names, n_tex_para=n_tex_para)
# pose_param: [batch, n_pose_param]
# shape_param: [batch, n_shape_para]
# exp_param: [batch, n_exp_para]
# tex_param: [batch, n_tex_para]
# color_param: [batch, n_color_para]
# illum_param: [batch, n_illum_para]
shape_param_batch = tf.squeeze(shape_param_batch)
exp_param_batch = tf.squeeze(exp_param_batch)
tex_param_batch = tf.squeeze(tex_param_batch)
color_param_batch = tf.squeeze(color_param_batch)
illum_param_batch = tf.squeeze(illum_param_batch)
pose_param_batch = tf.squeeze(pose_param_batch)
lm_rended = tf_bfm.get_landmarks(shape_param_batch, exp_param_batch, pose_param_batch, batch_size, 450, is_2d=True, is_plot=True)
images_rendered = render_batch(
pose_param=pose_param_batch,
shape_param=shape_param_batch,
exp_param=exp_param_batch,
tex_param=tex_param_batch,
color_param=color_param_batch,
illum_param=illum_param_batch,
frame_height=450,
frame_width=450,
tf_bfm=tf_bfm,
batch_size=batch_size
).numpy().astype(np.uint8)
for i, pic_name in enumerate(pic_names):
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
plot_image_w_lm(ax, 450, images_orignal[i], lm_batch[i])
ax = fig.add_subplot(1, 2, 2)
plot_image_w_lm(ax, 450, images_rendered[i], lm_rended[i])
plt.savefig(os.path.join(save_to_folder, pic_name))
is_plot=True)
images_rendered = render_batch(
pose_param=pose_param_batch,
shape_param=shape_param_batch,
exp_param=exp_param_batch,
tex_param=tex_param_batch,
color_param=color_param_batch,
illum_param=illum_param_batch,
frame_height=450,
frame_width=450,
tf_bfm=tf_bfm,
batch_size=batch_size
).numpy().astype(np.uint8)
for i, pic_name in enumerate(pic_names):
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
plot_image_w_lm(ax, 450, images_orignal[i], lm_batch[i])
ax = fig.add_subplot(1, 2, 2)
plot_image_w_lm(ax, 450, images_rendered[i], lm_rended[i])
plt.savefig(os.path.join(save_to_folder, pic_name))
if __name__ == '__main__':
n_tex_para = 40
tf_bfm = TfMorphableModel(model_path='./examples/Data/BFM/Out/BFM.mat', n_tex_para=n_tex_para)
save_rendered_to = './output/render_batch/'
pic_names = ['IBUG_image_014_01_2', 'AFW_134212_1_0', 'IBUG_image_008_1_0']
example_render_batch(pic_names=pic_names, tf_bfm=tf_bfm, save_to_folder=save_rendered_to, n_tex_para=n_tex_para)
ax.plot(lm[0, 31:36], lm[1, 31:36], marker='o', markersize=2, linestyle='-',
color='w', lw=2)
ax.plot(lm[0, 36:42], lm[1, 36:42], marker='o', markersize=2, linestyle='-',
color='w', lw=2)
ax.plot(lm[0, 42:48], lm[1, 42:48], marker='o', markersize=2, linestyle='-',
color='w', lw=2)
ax.plot(lm[0, 48:60], lm[1, 48:60], marker='o', markersize=2, linestyle='-',
color='w', lw=2)
ax.plot(lm[0, 60:68], lm[1, 60:68], marker='o', markersize=2, linestyle='-',
color='w', lw=2)
plt.savefig(filename)
if __name__ == '__main__':
n_tex_para = 40
bfm = TfMorphableModel(model_path='/opt/project/examples/Data/BFM/Out/BFM.mat', n_tex_para=n_tex_para)
output_folder = '/opt/project/output/landmarks/landmark2'
pic_names = ['image00002', 'IBUG_image_014_01_2', 'AFW_134212_1_0', 'IBUG_image_008_1_0']
# pic_names = ['IBUG_image_008_1_0']
batch_size = len(pic_names)
images = load_images(pic_names, '/opt/project/examples/Data')
resolution = 450
shape_param, exp_param, _, _, _, pose_param = load_params(pic_names=pic_names, n_tex_para=n_tex_para,
data_folder='/opt/project/examples/Data/')
landmarks = compute_landmarks(
bfm=bfm, shape_param=shape_param, exp_param=exp_param, pose_param=pose_param, batch_size=batch_size,
resolution=resolution)
save_landmarks(images=images, landmarks=landmarks, output_folder=output_folder, resolution=resolution)
batch_size=batch_size).numpy().astype(np.uint8)
for i, pic_name in enumerate(pic_names):
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
plot_image_w_lm(ax, resolution, images_orignal[i], lm[i])
ax = fig.add_subplot(1, 2, 2)
plot_image_w_lm(ax, resolution, images_rendered[i], lm[i])
plt.savefig(os.path.join(save_to_folder, pic_name))
if __name__ == '__main__':
n_shape_para = 100
n_tex_para = 40
resolution = 450
tf_bfm = TfMorphableModel(
model_path='/opt/project/examples/Data/BFM/Out/BFM.mat',
exp_path='/opt/project/examples/Data/BFM/Out/exp_80k.npz',
n_shape_para=n_shape_para,
n_tex_para=n_tex_para)
save_rendered_to = './out'
pic_names = [
'afw_225191079_1_aug_29', 'helen_train_164866565_1_aug_28',
'ibug_image_007_aug_18', 'lfpw_train_image_0465_aug_11'
]
example_render_batch3(pic_names=pic_names,
tf_bfm=tf_bfm,
n_tex_para=n_tex_para,
save_to_folder=save_rendered_to,
resolution=resolution)
from example_utils import load_images, load_params
from sandbox.landmarks.check_get_landmarks2 import save_landmarks
from tf_3dmm.morphable_model.morphable_model import TfMorphableModel
if __name__ == '__main__':
n_tex_para = 40
bfm = TfMorphableModel(model_path='/opt/project/examples/Data/BFM/Out/BFM.mat', n_tex_para=n_tex_para)
output_folder = '/opt/project/output/landmarks/landmark3'
pic_names = ['image00002', 'IBUG_image_014_01_2', 'AFW_134212_1_0', 'IBUG_image_008_1_0']
# pic_names = ['IBUG_image_008_1_0']
batch_size = len(pic_names)
images = load_images(pic_names, '/opt/project/examples/Data')
resolution = 450
shape_param, exp_param, _, _, _, pose_param = load_params(pic_names=pic_names, n_tex_para=n_tex_para,
data_folder='/opt/project/examples/Data/')
landmarks = bfm.get_landmarks(
shape_param=shape_param,
exp_param=exp_param,
pose_param=pose_param,
batch_size=batch_size,
resolution=resolution,
is_2d=True,
is_plot=True
)
save_landmarks(images=images, landmarks=landmarks, output_folder=output_folder, resolution=resolution)
def render_batch(pose_param, shape_param, exp_param, tex_param, color_param,
illum_param, frame_width: int, frame_height: int,
tf_bfm: TfMorphableModel, batch_size: int):
"""
render faces in batch
:param: pose_param: [batch, n_pose_para] or (batch, 1, n_pose_param)
:param: shape_param: [batch, n_shape_para, 1] or [batch, n_shape_para]
:param: exp_param: [batch, n_exp_para, 1] or [batch, n_exp_para]
:param: tex_param: [batch, n_tex_para, 1] or [batch, n_tex_para]
:param: color_param: [batch, 1, n_color_para] or [batch, n_color_para]
:param: illum_param: [batch, 1, n_illum_para] or [batch, n_illum_para]
:param: frame_width: rendered image width
:param: frame_height: rendered image height
:param: tf_bfm: basel face model
:param: batch_size: batch size
:return: images, [batch, frame_width, frame_height, 3]
"""
assert is_tf_expression(pose_param)
pose_shape = tf.shape(pose_param)
if pose_shape.shape[0] == 2:
tf.debugging.assert_shapes(
[(pose_param, (batch_size, tf_bfm.get_num_pose_param()))],
message='pose_param shape wrong, dim != ({batch}, {dim})'.format(
batch=batch_size, dim=tf_bfm.get_num_pose_param()))
pose_param = tf.expand_dims(pose_param, 1)
elif pose_shape.shape[0] == 3:
tf.debugging.assert_shapes(
[(pose_param, (batch_size, 1, tf_bfm.get_num_pose_param()))],
message='pose_param shape wrong, dim != ({batch}, 1, {dim})'.
format(batch=batch_size, dim=tf_bfm.get_num_pose_param()))
else:
raise ValueError(
'pose_param shape wrong, dim != ({batch}, 1, {dim}) or ({batch}, {dim})'
.format(batch=batch_size, dim=tf_bfm.get_num_pose_param()))
vertices = tf_bfm.get_vertices(shape_param=shape_param,
exp_param=exp_param,
batch_size=batch_size)
# vertex_norm = lighting.vertex_normals(vertices, tf_bfm.triangles)
vertex_norm = tfg.geometry.representation.mesh.normals.vertex_normals(
vertices=vertices,
indices=tf.repeat(tf.expand_dims(tf_bfm.triangles, 0),
batch_size,
axis=0),
clockwise=True)
colors = tf_bfm.get_vertex_colors(tex_param=tex_param,
color_param=color_param,
illum_param=illum_param,
vertex_norm=-vertex_norm,
batch_size=batch_size)
colors = tf.clip_by_value(colors / 255., 0., 1.)
transformed_vertices = affine_transform(vertices=vertices,
scaling=pose_param[:, 0, 6:],
angles_rad=pose_param[:, 0, 0:3],
t3d=pose_param[:, 0:, 3:6])
transformed_vertices_x = transformed_vertices[:, :,
0] * 2 / frame_width - 1
transformed_vertices_y = transformed_vertices[:, :,
1] * 2 / frame_height - 1
transformed_vertices_z = -transformed_vertices[:, :, 2] / tf.reduce_max(
tf.abs(transformed_vertices[:, :, 2]))
# Convert vertices to homogeneous coordinates
transformed_vertices = tf.concat([
tf.expand_dims(transformed_vertices_x, axis=2),
tf.expand_dims(transformed_vertices_y, axis=2),
tf.expand_dims(transformed_vertices_z, axis=2),
tf.ones_like(transformed_vertices[:, :, -1:])
],
axis=2)
# Render the G-buffer
image = dirt.rasterise_batch(
vertices=transformed_vertices,
faces=tf.tile(tf.expand_dims(tf_bfm.triangles, axis=0),
(batch_size, 1, 1)),
# faces=tf.expand_dims(tf_bfm.triangles, axis=0),
vertex_colors=colors,
background=tf.zeros([batch_size, frame_height, frame_width, 3]),
width=frame_width,
height=frame_height,
channels=3)
return image * 255
def load_3dmm(pic_name):
mat_filename = '../../examples/Data/{0}.mat'.format(pic_name)
mat_data = sio.loadmat(mat_filename)
sp = tf.constant(mat_data['Shape_Para'], dtype=tf.float32)
ep = tf.constant(mat_data['Exp_Para'], dtype=tf.float32)
tp = tf.constant(mat_data['Tex_Para'], dtype=tf.float32)
cp = tf.constant(mat_data['Color_Para'], dtype=tf.float32)
ip = tf.constant(mat_data['Illum_Para'], dtype=tf.float32)
pp = tf.constant(mat_data['Pose_Para'], dtype=tf.float32)
return sp, ep, tp, cp, ip, pp
tf_bfm = TfMorphableModel('../../examples/Data/BFM/Out/BFM.mat')
image_size = 450
# --load mesh data
pic_name1 = 'IBUG_image_014_01_2'
pic_name2 = 'AFW_134212_1_0'
sp1, ep1, tp1, cp1, ip1, pp1 = load_3dmm(pic_name1)
sp2, ep2, tp2, cp2, ip2, pp2 = load_3dmm(pic_name2)
mix_ratio = 0.2
spm = sp1 * mix_ratio + sp2 * (1 - mix_ratio)
epm = ep1 * mix_ratio + ep2 * (1 - mix_ratio)
image1 = render(pose_param=pp1,
shape_param=sp1,
n_tex_para = 40
mat_data = sio.loadmat(mat_filename)
shape_param = tf.constant(mat_data['Shape_Para'], dtype=tf.float32)
shape_param = tf.expand_dims(shape_param, 0)
exp_param = tf.constant(mat_data['Exp_Para'], dtype=tf.float32)
exp_param = tf.expand_dims(exp_param, 0)
tex_param = tf.constant(mat_data['Tex_Para'][:n_tex_para, :], dtype=tf.float32)
tex_param = tf.expand_dims(tex_param, 0)
color_param = tf.constant(mat_data['Color_Para'], dtype=tf.float32)
color_param = tf.expand_dims(color_param, 0)
illum_param = tf.constant(mat_data['Illum_Para'], dtype=tf.float32)
illum_param = tf.expand_dims(illum_param, 0)
pose_param = tf.constant(mat_data['Pose_Para'], dtype=tf.float32)
pose_param = tf.expand_dims(pose_param, 0)
tf_bfm = TfMorphableModel(model_path='../../examples/Data/BFM/Out/BFM.mat',
n_tex_para=n_tex_para)
vertices = tf_bfm.get_vertices(shape_param=shape_param,
exp_param=exp_param,
batch_size=1)
vertex_norm = lighting.vertex_normals(vertices, tf_bfm.triangles)
bvertex_colors = tf_bfm.get_vertex_colors(tex_param, color_param, illum_param,
-vertex_norm, 1)
vertex_colors = tf_bfm.get_vertex_colors(tex_param[0], color_param[0],
illum_param[0], -vertex_norm[0])
print('done')
from tf_3dmm.mesh.transform import affine_transform
from tf_3dmm.morphable_model.morphable_model import TfMorphableModel
from scipy import io as sio
import tensorflow as tf
tf_bfm = TfMorphableModel('../../examples/Data/BFM/Out/BFM.mat')
# --load mesh data
pic_name = 'IBUG_image_008_1_0'
# pic_name = 'IBUG_image_014_01_2'
mat_filename = '../../examples/Data/{0}.mat'.format(pic_name)
mat_data = sio.loadmat(mat_filename)
sp = tf.constant(mat_data['Shape_Para'], dtype=tf.float32)
ep = tf.constant(mat_data['Exp_Para'], dtype=tf.float32)
vertices = tf_bfm.get_vertices(sp, ep)
triangles = tf_bfm.triangles
pp = tf.constant(mat_data['Pose_Para'], dtype=tf.float32)
s = pp[0, 6]
angles = pp[0, 0:3]
t = pp[0, 3:6]
transformed_vertices = affine_transform(vertices, s, angles, t)
landmarks_raw = tf.gather_nd(
transformed_vertices, tf.reshape(tf_bfm.get_landmark_indices(), (-1, 1)))
landmarks = tf.concat([
tf.reshape(landmarks_raw[:, 0],
(-1, 1)), 450 - tf.reshape(landmarks_raw[:, 1], (-1, 1)) - 1
],
axis=1)
def display(tfrecord_dir,
bfm_path,
exp_path,
param_mean_std_path,
image_size,
num_images=5,
n_tex_para=40,
n_shape_para=100):
print('Loading sdataset %s' % tfrecord_dir)
batch_size = 4
dset = dataset.TFRecordDatasetSupervised(tfrecord_dir=tfrecord_dir,
batch_size=batch_size,
repeat=False,
shuffle_mb=0)
print('Loading BFM model')
bfm = TfMorphableModel(model_path=bfm_path,
exp_path=exp_path,
n_shape_para=n_shape_para,
n_tex_para=n_tex_para)
idx = 0
filename = '/opt/project/output/verify_dataset/supervised-80k/20200717/image_batch_{0}_indx_{1}.jpg'
unnormalize_labels = fn_unnormalize_80k_labels(
param_mean_std_path=param_mean_std_path, image_size=image_size)
while idx < num_images:
try:
image_tensor, labels_tensor = dset.get_minibatch_tf()
except tf.errors.OutOfRangeError:
break
# render images using labels
pose_para, shape_para, exp_para, _, _, _ = split_80k_labels(
labels_tensor)
pose_para, shape_para, exp_para, _, _, _ = unnormalize_labels(
batch_size, pose_para, shape_para, exp_para, None, None, None)
# add 0 to t3d z axis
# 80k dataset only have x, y translation
pose_para = tf.concat([
pose_para[:, :-1],
tf.constant(0.0, shape=(batch_size, 1), dtype=tf.float32),
pose_para[:, -1:]
],
axis=1)
landmarks = bfm.get_landmarks(shape_para,
exp_para,
pose_para,
batch_size,
image_size,
is_2d=True,
is_plot=True)
image_rendered = render_batch(
pose_param=pose_para,
shape_param=shape_para,
exp_param=exp_para,
tex_param=tf.constant(0.0,
shape=(batch_size, n_tex_para),
dtype=tf.float32),
color_param=None,
illum_param=None,
frame_height=image_size,
frame_width=image_size,
tf_bfm=bfm,
batch_size=batch_size).numpy().astype(np.uint8)
for i in range(batch_size):
fig = plt.figure()
# input image
ax = fig.add_subplot(1, 2, 1)
ax.imshow(image_tensor[i].numpy().astype(np.uint8))
ax.plot(landmarks[i, 0, 0:17],
landmarks[i, 1, 0:17],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 17:22],
landmarks[i, 1, 17:22],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 22:27],
landmarks[i, 1, 22:27],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 27:31],
landmarks[i, 1, 27:31],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 31:36],
landmarks[i, 1, 31:36],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 36:42],
landmarks[i, 1, 36:42],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 42:48],
landmarks[i, 1, 42:48],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 48:60],
landmarks[i, 1, 48:60],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 60:68],
landmarks[i, 1, 60:68],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2 = fig.add_subplot(1, 2, 2)
ax2.imshow(image_rendered[i])
ax2.plot(landmarks[i, 0, 0:17],
landmarks[i, 1, 0:17],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 17:22],
landmarks[i, 1, 17:22],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 22:27],
landmarks[i, 1, 22:27],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 27:31],
landmarks[i, 1, 27:31],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 31:36],
landmarks[i, 1, 31:36],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 36:42],
landmarks[i, 1, 36:42],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 42:48],
landmarks[i, 1, 42:48],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 48:60],
landmarks[i, 1, 48:60],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 60:68],
landmarks[i, 1, 60:68],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
plt.savefig(filename.format(idx, i))
idx += 1
import time
import scipy.io as sio
import tensorflow as tf
import tensorflow_graphics as tfg
from dirt import lighting
from tf_3dmm.morphable_model.morphable_model import TfMorphableModel
tf.debugging.set_log_device_placement(True)
n_tex_para = 40
tf_bfm = TfMorphableModel(
model_path='/opt/project/examples/Data/BFM/Out/BFM.mat',
n_tex_para=n_tex_para)
pic_names = [
'image00002', 'IBUG_image_014_01_2', 'AFW_134212_1_0', 'IBUG_image_008_1_0'
] * 8
batch_size = len(pic_names)
def my_load_params(pic_names, n_tex_para):
# --load mesh data
shape_param_batch = []
exp_param_batch = []
tex_param_batch = []
color_param_batch = []
illum_param_batch = []
pose_param_batch = []
def display(tfrecord_dir,
bfm_path,
param_mean_std_path,
image_size,
num_images=5,
n_tex_para=40):
print('Loading sdataset %s' % tfrecord_dir)
batch_size = 4
dset = dataset.TFRecordDatasetSupervised(tfrecord_dir=tfrecord_dir,
batch_size=batch_size,
repeat=False,
shuffle_mb=0)
print('Loading BFM model')
bfm = TfMorphableModel(model_path=bfm_path, n_tex_para=n_tex_para)
idx = 0
filename = '/opt/project/output/verify_dataset/supervised/20200525/image_batch_{0}_indx_{1}.jpg'
unnormalize_labels = fn_unnormalize_300W_LP_labels(
param_mean_std_path=param_mean_std_path, image_size=image_size)
while idx < num_images:
try:
image_tensor, labels_tensor = dset.get_minibatch_tf()
except tf.errors.OutOfRangeError:
break
# render images using labels
roi, landmarks, pose_para, shape_para, exp_para, color_para, illum_para, tex_para = split_300W_LP_labels(
labels_tensor)
roi, landmarks, pose_para, shape_para, exp_para, color_para, illum_para, tex_para = unnormalize_labels(
batch_size, roi, landmarks, pose_para, shape_para, exp_para,
color_para, illum_para, tex_para)
image_rendered = render_batch(pose_param=pose_para,
shape_param=shape_para,
exp_param=exp_para,
tex_param=tex_para,
color_param=color_para,
illum_param=illum_para,
frame_height=image_size,
frame_width=image_size,
tf_bfm=bfm,
batch_size=batch_size).numpy().astype(
np.uint8)
for i in range(batch_size):
fig = plt.figure()
# input image
ax = fig.add_subplot(1, 2, 1)
ax.imshow(image_tensor[i].numpy().astype(np.uint8))
ax.plot(landmarks[i, 0, 0:17],
landmarks[i, 1, 0:17],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 17:22],
landmarks[i, 1, 17:22],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 22:27],
landmarks[i, 1, 22:27],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 27:31],
landmarks[i, 1, 27:31],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 31:36],
landmarks[i, 1, 31:36],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 36:42],
landmarks[i, 1, 36:42],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 42:48],
landmarks[i, 1, 42:48],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 48:60],
landmarks[i, 1, 48:60],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax.plot(landmarks[i, 0, 60:68],
landmarks[i, 1, 60:68],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2 = fig.add_subplot(1, 2, 2)
ax2.imshow(image_rendered[i])
ax2.plot(landmarks[i, 0, 0:17],
landmarks[i, 1, 0:17],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 17:22],
landmarks[i, 1, 17:22],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 22:27],
landmarks[i, 1, 22:27],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 27:31],
landmarks[i, 1, 27:31],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 31:36],
landmarks[i, 1, 31:36],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 36:42],
landmarks[i, 1, 36:42],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 42:48],
landmarks[i, 1, 42:48],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 48:60],
landmarks[i, 1, 48:60],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
ax2.plot(landmarks[i, 0, 60:68],
landmarks[i, 1, 60:68],
marker='o',
markersize=2,
linestyle='-',
color='w',
lw=2)
plt.savefig(filename.format(idx, i))
idx += 1
print('\nDisplayed %d images' % idx)
def init_bfm(self):
bfm_path = os.path.join(self.bfm_dir, 'BFM.mat')
self.bfm = TfMorphableModel(model_path=bfm_path,
exp_path=self.exp_path,
n_tex_para=self.n_tex_para,
n_shape_para=self.n_shape_para)
