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 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))
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 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