def main(_):
save_model_path = os.path.join(FLAGS.output_dir, FLAGS.save_model_path)
save_sample_path = os.path.join(FLAGS.output_dir, FLAGS.save_sample_path)
save_log_path = os.path.join(FLAGS.output_dir, FLAGS.save_log_path)
# create directories if not exist
if not os.path.exists(save_model_path):
os.mkdir(save_model_path)
if not os.path.exists(save_log_path):
os.mkdir(save_log_path)
if not os.path.exists(save_sample_path) and not FLAGS.sample_to_log:
os.mkdir(save_sample_path)
model = DeepHeatmapsModel(
mode='TRAIN',
train_iter=FLAGS.train_iter,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
l_weight_primary=FLAGS.l_weight_primary,
l_weight_fusion=FLAGS.l_weight_fusion,
l_weight_upsample=FLAGS.l_weight_upsample,
reg=FLAGS.reg,
adam_optimizer=FLAGS.adam_optimizer,
momentum=FLAGS.momentum,
step=FLAGS.step,
gamma=FLAGS.gamma,
weight_initializer=FLAGS.weight_initializer,
weight_initializer_std=FLAGS.weight_initializer_std,
bias_initializer=FLAGS.bias_initializer,
image_size=FLAGS.image_size,
c_dim=FLAGS.c_dim,
num_landmarks=FLAGS.num_landmarks,
sigma=FLAGS.sigma,
scale=FLAGS.scale,
margin=FLAGS.margin,
bb_type=FLAGS.bb_type,
win_mult=FLAGS.win_mult,
augment_basic=FLAGS.augment_basic,
augment_texture=FLAGS.augment_texture,
p_texture=FLAGS.p_texture,
augment_geom=FLAGS.augment_geom,
p_geom=FLAGS.p_geom,
output_dir=FLAGS.output_dir,
save_model_path=save_model_path,
save_sample_path=save_sample_path,
save_log_path=save_log_path,
pre_train_path=FLAGS.pre_train_path,
load_pretrain=FLAGS.load_pretrain,
load_primary_only=FLAGS.load_primary_only,
img_path=FLAGS.img_path,
valid_data=FLAGS.valid_data,
valid_size=FLAGS.valid_size,
log_valid_every=FLAGS.log_valid_every,
train_crop_dir=FLAGS.train_crop_dir,
img_dir_ns=FLAGS.img_dir_ns,
print_every=FLAGS.print_every,
save_every=FLAGS.save_every,
sample_every=FLAGS.sample_every,
sample_grid=FLAGS.sample_grid,
sample_to_log=FLAGS.sample_to_log,
debug_data_size=FLAGS.debug_data_size,
debug=FLAGS.debug,
use_epoch_data=FLAGS.use_epoch_data,
epoch_data_dir=FLAGS.epoch_data_dir)
model.train()
bb_type = 'init'
img_list = load_menpo_image_list(
img_dir=data_dir,
test_data=test_data,
train_crop_dir=data_dir,
img_dir_ns=data_dir,
bb_type=bb_type,
bb_dictionary=bb_dictionary,
mode='TEST',
return_transform=map_landmarks_to_original_image)
# load model
heatmap_model = DeepHeatmapsModel(mode='TEST',
img_path=data_dir,
test_model_path=model_path,
test_data=test_data,
menpo_verbose=False)
# *************** predict landmarks ***************
print("\npredicting landmarks for: " + os.path.join(data_dir, test_data))
print("\nsaving landmarks to: " + out_dir)
for i, img in enumerate(img_list):
if i == 0:
reuse = None
else:
reuse = True
preds = heatmap_model.get_landmark_predictions(
img_list=[img],
pdm_models_dir=pdm_path,
def evaluate_heatmap_fusion_network(model_path, img_path, test_data, batch_size=10, image_size=256, margin=0.25,
bb_type='gt', c_dim=3, scale=1, num_landmarks=68, debug=False,
debug_data_size=20):
t = time()
from deep_heatmaps_model_fusion_net import DeepHeatmapsModel
import logging
logging.getLogger('tensorflow').disabled = True
# load test image menpo list
test_menpo_img_list = load_menpo_test_list(
img_path, test_data=test_data, image_size=image_size, margin=margin, bb_type=bb_type)
if debug:
test_menpo_img_list = test_menpo_img_list[:debug_data_size]
print ('\n*** FUSION NETWORK: calculating normalized mean error on: ' + test_data +
' set (%d images - debug mode) ***' % debug_data_size)
else:
print ('\n*** FUSION NETWORK: calculating normalized mean error on: ' + test_data + ' set (%d images) ***' %
(len(test_menpo_img_list)))
# create heatmap model
tf.reset_default_graph()
model = DeepHeatmapsModel(mode='TEST', batch_size=batch_size, image_size=image_size, c_dim=c_dim,
num_landmarks=num_landmarks, img_path=img_path, test_model_path=model_path,
test_data=test_data, menpo_verbose=False)
# add placeholders
model.add_placeholders()
# build model
model.build_model()
# create loss ops
model.create_loss_ops()
num_batches = int(1. * len(test_menpo_img_list) / batch_size)
if num_batches == 0:
batch_size = len(test_menpo_img_list)
num_batches = 1
reminder = len(test_menpo_img_list) - num_batches * batch_size
num_batches_reminder = num_batches + 1 * (reminder > 0)
img_inds = np.arange(len(test_menpo_img_list))
with tf.Session() as session:
# load trained parameters
saver = tf.train.Saver()
saver.restore(session, model_path)
print ('\nnum batches: ' + str(num_batches_reminder))
err = []
for j in range(num_batches):
print ('batch %d / %d ...' % (j + 1, num_batches_reminder))
batch_inds = img_inds[j * batch_size:(j + 1) * batch_size]
batch_images, _, batch_landmarks_gt = load_images_landmarks(
test_menpo_img_list, batch_inds=batch_inds, image_size=image_size,
c_dim=c_dim, num_landmarks=num_landmarks, scale=scale)
batch_maps_pred = session.run(model.pred_hm_f, {model.images: batch_images})
batch_pred_landmarks = batch_heat_maps_to_landmarks(
batch_maps_pred, batch_size=batch_size, image_size=image_size, num_landmarks=num_landmarks)
batch_err = session.run(
model.nme_per_image, {model.lms: batch_landmarks_gt, model.pred_lms: batch_pred_landmarks})
err = np.hstack((err, batch_err))
if reminder > 0:
print ('batch %d / %d ...' % (j + 2, num_batches_reminder))
reminder_inds = img_inds[-reminder:]
batch_images, _, batch_landmarks_gt = load_images_landmarks(
test_menpo_img_list, batch_inds=reminder_inds, image_size=image_size,
c_dim=c_dim, num_landmarks=num_landmarks, scale=scale)
batch_maps_pred = session.run(model.pred_hm_f, {model.images: batch_images})
batch_pred_landmarks = batch_heat_maps_to_landmarks(
batch_maps_pred, batch_size=reminder, image_size=image_size, num_landmarks=num_landmarks)
batch_err = session.run(
model.nme_per_image, {model.lms: batch_landmarks_gt, model.pred_lms: batch_pred_landmarks})
err = np.hstack((err, batch_err))
print ('\ndone!')
print ('run time: ' + str(time() - t))
return err
def main(_):
for i, param in enumerate(params):
test_dir = os.path.join(FLAGS.output_dir, str(param))
if not os.path.exists(test_dir):
os.mkdir(test_dir)
print('\n##### RUNNING TESTS FUSION (%d/%d) #####' %
(i + 1, len(params)))
print('##### current directory: ' + test_dir)
save_model_path = os.path.join(test_dir, 'model')
save_sample_path = os.path.join(test_dir, 'sample')
save_log_path = os.path.join(test_dir, 'logs')
# create directories if not exist
if not os.path.exists(save_model_path):
os.mkdir(save_model_path)
if not os.path.exists(save_log_path):
os.mkdir(save_log_path)
if not os.path.exists(save_sample_path) and not FLAGS.sample_to_log:
os.mkdir(save_sample_path)
tf.reset_default_graph() # reset graph
model = DeepHeatmapsModel(
mode='TRAIN',
train_iter=FLAGS.train_iter,
batch_size=FLAGS.batch_size,
learning_rate=param,
l_weight_primary=FLAGS.l_weight_primary,
l_weight_fusion=FLAGS.l_weight_fusion,
l_weight_upsample=FLAGS.l_weight_upsample,
reg=FLAGS.reg,
adam_optimizer=FLAGS.adam_optimizer,
momentum=FLAGS.momentum,
step=FLAGS.step,
gamma=FLAGS.gamma,
weight_initializer=FLAGS.weight_initializer,
weight_initializer_std=FLAGS.weight_initializer_std,
bias_initializer=FLAGS.bias_initializer,
image_size=FLAGS.image_size,
c_dim=FLAGS.c_dim,
num_landmarks=FLAGS.num_landmarks,
sigma=FLAGS.sigma,
scale=FLAGS.scale,
margin=FLAGS.margin,
bb_type=FLAGS.bb_type,
approx_maps=FLAGS.approx_maps,
win_mult=FLAGS.win_mult,
augment_basic=FLAGS.augment_basic,
basic_start=FLAGS.basic_start,
augment_texture=FLAGS.augment_texture,
p_texture=FLAGS.p_texture,
augment_geom=FLAGS.augment_geom,
p_geom=FLAGS.p_geom,
artistic_step=FLAGS.artistic_step,
artistic_start=FLAGS.artistic_start,
output_dir=FLAGS.output_dir,
save_model_path=save_model_path,
save_sample_path=save_sample_path,
save_log_path=save_log_path,
test_model_path=FLAGS.test_model_path,
pre_train_path=os.path.join(save_model_path,
FLAGS.pre_train_model_name),
load_pretrain=FLAGS.load_pretrain,
load_primary_only=FLAGS.load_primary_only,
img_path=FLAGS.img_path,
test_data=FLAGS.test_data,
valid_data=FLAGS.valid_data,
valid_size=FLAGS.valid_size,
log_valid_every=FLAGS.log_valid_every,
train_crop_dir=FLAGS.train_crop_dir,
img_dir_ns=FLAGS.img_dir_ns,
print_every=FLAGS.print_every,
save_every=FLAGS.save_every,
sample_every=FLAGS.sample_every,
sample_grid=FLAGS.sample_grid,
sample_to_log=FLAGS.sample_to_log,
debug_data_size=FLAGS.debug_data_size,
debug=FLAGS.debug,
use_epoch_data=FLAGS.use_epoch_data,
epoch_data_dir=FLAGS.epoch_data_dir)
model.train()
# directory containing test sets
data_dir = '/Users/arik/Dropbox/a_mac_thesis/artistic_faces/artistic_face_dataset/'
test_sets = ['all_AF'] # test sets to evaluate
# data_dir = '/Users/arik/Desktop/Thesis_mac/semi_art_sets/semi_art_sets_wiki_train_2/'
# test_sets = [
# 'challenging_set_aug_geom_texture',
# 'common_set_aug_geom_texture',
# 'test_set_aug_geom_texture',
# 'full_set_aug_geom_texture'
# ]
# load heatmap model
heatmap_model = DeepHeatmapsModel(mode='TEST',
img_path=conv_dir,
test_model_path=model_path,
menpo_verbose=False,
scale=1)
bb_dir = os.path.join(conv_dir, 'Bounding_Boxes')
# predict landmarks for input test sets
for i, test_data in enumerate(test_sets):
if i == 0:
reuse = None
else:
reuse = True
out_temp = os.path.join(out_dir, test_data)
if not os.path.exists(out_temp):