def train_model(name, xml):
'''
requires: the model name, and the path to the xml annotations.
It trains and saves a new model according to the specified
training options and given annotations
example @ https://github.com/Luca96/dlib-minified-models/tree/master/face_landmarks:
options = dlib.shape_predictor_training_options()
options.tree_depth = 4
options.nu = 0.1
options.cascade_depth = 15
options.feature_pool_size = 800 # or even 1000
options.num_test_splits = 200 # 150-200 is enough
'''
# get the training options
options = dlib.shape_predictor_training_options()
options.tree_depth = 4
options.nu = 0.1
options.cascade_depth = 15
options.feature_pool_size = 400
options.num_test_splits = 50
options.oversampling_amount = 5
#
options.be_verbose = True # tells what is happening during the training
options.num_threads = 4 # number of the threads used to train the model
# finally, train the model
dlib.train_shape_predictor(xml, name, options)
def train1():
import os
import cv2
import dlib
import glob
# 训练68个特征点
current_path = os.getcwd()
faces_path = current_path + '/examples/faces'
# 训练部分
# 参数设置
options = dlib.shape_predictor_training_options()
options.oversampling_amount = 300
options.nu = 0.05
options.tree_depth = 2
options.be_verbose = True
# 导入打好了标签的xml文件
training_xml_path = os.path.join(faces_path,
"training_with_face_landmarks.xml")
# 进行训练，训练好的模型将保存为predictor.dat
dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)
# 打印在训练集中的准确率
print("\nTraining accuracy:{0}".format(
dlib.test_shape_predictor(training_xml_path, "predictor.dat")))
# 导入测试集的xml文件
testing_xml_path = os.path.join(faces_path,
"testing_with_face_landmarks.xml")
# 打印在测试集中的准确率
print("\Testing accuracy:{0}".format(
dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))
def train_shape_predictor(self):
self.__print_training_message('shape predictor')
opt = dlib.shape_predictor_training_options()
opt.oversampling_amount = 300
opt.nu = 0.05
opt.tree_depth = 2
opt.num_threads = self.cpu_cores
opt.be_verbose = True
dlib.train_shape_predictor(self.xml, PREDICTOR_DAT, opt)
def dlib(threads=4):
'''same options used by dlib shape predictors'''
options = dlib.shape_predictor_training_options()
options.oversampling_amount = 40
options.num_threads = threads
options.cascade_depth = 15
options.feature_pool_size = 800
options.num_test_splits = 150
options.be_verbose = True
return options
def config_to_training_options(config: dict):
result = dlib.shape_predictor_training_options()
for option, value in config.items():
if not value:
continue
if hasattr(result, option):
setattr(result, option, value)
return result
def train_shape_predictor(self):
self.__print_training_message('shape predictor')
opt = dlib.shape_predictor_training_options()
# opt.oversampling_amount = 300
# opt.nu = 0.05
# opt.tree_depth = 2
opt.num_threads = self.cpu_cores
opt.be_verbose = True
print(dlib.DLIB_USE_CUDA)
dlib.train_shape_predictor(self.xml, 'predictor.dat',
opt) #PREDICTOR_DAT, opt)
def get_training_options(tree_depth=4, nu=0.1, cascade_depth=15, verbose=True,
pool_size=1000, num_test_splits=250, oversampling=10, num_threads=8):
options = dlib.shape_predictor_training_options()
options.tree_depth = tree_depth
options.nu = nu
options.cascade_depth = cascade_depth
options.feature_pool_size = pool_size
options.num_test_splits = num_test_splits
options.oversampling_amount = oversampling
options.be_verbose = verbose
options.num_threads = num_threads
return options
def __init__(self, model):
if isinstance(model, STRING_TYPES) or isinstance(model, Path):
m_path = Path(model)
if not Path(m_path).exists():
raise ValueError('Model {} does not exist.'.format(m_path))
model = dlib.shape_predictor(str(m_path))
# Dlib doesn't expose any information about how the model was built,
# so we just create dummy options
self.algorithm = DlibAlgorithm(dlib.shape_predictor_training_options(),
n_iterations=0)
self.algorithm.dlib_model = model
self.scales = [1]
def create(tree_depth, cascades, pool_size, splits,
threads=4, oversampling=20):
'''returns the option obj with the given values'''
options = dlib.shape_predictor_training_options()
options.tree_depth = tree_depth
options.nu = 0.1
options.num_threads = threads
options.cascade_depth = cascades
options.be_verbose = True
options.feature_pool_size = pool_size
options.num_test_splits = splits
options.oversampling_amount = oversampling
return options
def fastest(threads=4, oversampling=20):
'''use to train a minified shape-predictor, but
still accurate'''
options = dlib.shape_predictor_training_options()
options.tree_depth = 3
options.nu = 0.1
options.num_threads = threads
options.cascade_depth = 13
options.be_verbose = True
options.feature_pool_size = 100
options.num_test_splits = 250
options.oversampling_amount = oversampling
return options
def optimal(threads=4, oversampling=20, tree_depth=10):
'''train a balanced shape predictor
in terms of speed, accuracy and size'''
options = dlib.shape_predictor_training_options()
options.tree_depth = 3
options.nu = 0.1
options.num_threads = threads
options.cascade_depth = tree_depth
options.be_verbose = True
options.feature_pool_size = 150
options.num_test_splits = 350
options.oversampling_amount = oversampling
return options
def accurate(threads=4, oversampling=20):
'''use to train an accurate shape-predictor, decrease [oversampling]
for a faster training'''
options = dlib.shape_predictor_training_options()
options.tree_depth = 4
options.nu = 0.1
options.num_threads = threads
options.cascade_depth = 15
options.be_verbose = True
options.feature_pool_size = 800
options.num_test_splits = 150 + 50
options.oversampling_amount = oversampling
return options
def __init__(self, model):
if isinstance(model, STRING_TYPES) or isinstance(model, Path):
m_path = Path(model)
if not Path(m_path).exists():
raise ValueError('Model {} does not exist.'.format(m_path))
model = dlib.shape_predictor(str(m_path))
# Dlib doesn't expose any information about how the model was buit,
# so we just create dummy options
self.algorithm = DlibAlgorithm(dlib.shape_predictor_training_options(),
n_iterations=0)
self.algorithm.dlib_model = model
self.scales = [1]
def _setup_dlib_options(self, feature_padding, n_pixel_pairs,
distance_prior_weighting, regularisation_weight,
n_split_tests, n_trees, n_dlib_perturbations,
n_tree_levels):
check_int = partial(checks.check_multi_scale_param, self.n_scales,
(int,))
check_float = partial(checks.check_multi_scale_param, self.n_scales,
(float,))
feature_padding = check_int('feature_padding', feature_padding)
n_pixel_pairs = check_int('n_pixel_pairs', n_pixel_pairs)
distance_prior_weighting = check_float('distance_prior_weighting',
distance_prior_weighting)
regularisation_weight = check_float('regularisation_weight',
regularisation_weight)
n_split_tests = check_int('n_split_tests', n_split_tests)
n_trees = check_int('n_trees', n_trees)
n_dlib_perturbations = check_int('n_dlib_perturbations',
n_dlib_perturbations)
n_tree_levels = check_int('n_tree_levels', n_tree_levels)
self._dlib_options_templates = []
for j in range(self.n_scales):
new_opts = dlib.shape_predictor_training_options()
# Size of region within which to sample features for the feature
# pool, e.g a padding of 0.5 would cause the algorithm to sample
# pixels from a box that was 2x2 pixels
new_opts.feature_pool_region_padding = feature_padding[j]
# P parameter form Kazemi paper
new_opts.feature_pool_size = n_pixel_pairs[j]
# Controls how tight the feature sampling should be. Lower values
# enforce closer features. Opposite of explanation from Kazemi
# paper, lambda
new_opts.lambda_param = distance_prior_weighting[j]
# Boosting regularization parameter - nu from Kazemi paper, larger
# values may cause overfitting but improve performance on training
# data
new_opts.nu = regularisation_weight[j]
# S from Kazemi paper - Number of split features at each node to
# sample. The one that gives the best split is chosen.
new_opts.num_test_splits = n_split_tests[j]
# K from Kazemi paper - number of weak regressors
new_opts.num_trees_per_cascade_level = n_trees[j]
# R from Kazemi paper - amount of times other shapes are sampled
# as example initialisations
new_opts.oversampling_amount = n_dlib_perturbations[j]
# F from Kazemi paper - number of levels in the tree (depth of tree)
new_opts.tree_depth = n_tree_levels[j]
self._dlib_options_templates.append(new_opts)
def _setup_dlib_options(self, feature_padding, n_pixel_pairs,
distance_prior_weighting, regularisation_weight,
n_split_tests, n_trees, n_dlib_perturbations,
n_tree_levels):
check_int = partial(checks.check_multi_scale_param, self.n_scales,
(int, ))
check_float = partial(checks.check_multi_scale_param, self.n_scales,
(float, ))
feature_padding = check_int('feature_padding', feature_padding)
n_pixel_pairs = check_int('n_pixel_pairs', n_pixel_pairs)
distance_prior_weighting = check_float('distance_prior_weighting',
distance_prior_weighting)
regularisation_weight = check_float('regularisation_weight',
regularisation_weight)
n_split_tests = check_int('n_split_tests', n_split_tests)
n_trees = check_int('n_trees', n_trees)
n_dlib_perturbations = check_int('n_dlib_perturbations',
n_dlib_perturbations)
n_tree_levels = check_int('n_tree_levels', n_tree_levels)
self._dlib_options_templates = []
for j in range(self.n_scales):
new_opts = dlib.shape_predictor_training_options()
# Size of region within which to sample features for the feature
# pool, e.g a padding of 0.5 would cause the algorithm to sample
# pixels from a box that was 2x2 pixels
new_opts.feature_pool_region_padding = feature_padding[j]
# P parameter from Kazemi paper
new_opts.feature_pool_size = n_pixel_pairs[j]
# Controls how tight the feature sampling should be. Lower values
# enforce closer features. Opposite of explanation from Kazemi
# paper, lambda
new_opts.lambda_param = distance_prior_weighting[j]
# Boosting regularization parameter - nu from Kazemi paper, larger
# values may cause overfitting but improve performance on training
# data
new_opts.nu = regularisation_weight[j]
# S from Kazemi paper - Number of split features at each node to
# sample. The one that gives the best split is chosen.
new_opts.num_test_splits = n_split_tests[j]
# K from Kazemi paper - number of weak regressors
new_opts.num_trees_per_cascade_level = n_trees[j]
# R from Kazemi paper - amount of times other shapes are sampled
# as example initialisations
new_opts.oversampling_amount = n_dlib_perturbations[j]
# F from Kazemi paper - number of levels in the tree (depth of tree)
new_opts.tree_depth = n_tree_levels[j]
self._dlib_options_templates.append(new_opts)
def train_shape_predictor(self, output_name, input_xml):
print('[INFO] trining shape predictor....')
# get the training options
options = dlib.shape_predictor_training_options()
options.tree_depth = 4
options.nu = 0.1
options.cascade_depth = 15
options.feature_pool_size = 400
options.num_test_splits = 100
options.oversampling_amount = 10
options.be_verbose = True
options.num_threads = 4
# start training the model
dlib.train_shape_predictor(input_xml, output_name, options)
def main():
# generate xml file
with open('helen-dataset.xml', 'w') as out_xml_file:
xml = generate_xml()
out_xml_file.write(xml)
# set options
options = dlib.shape_predictor_training_options()
options.num_threads = multiprocessing.cpu_count() # cpu threads
options.be_verbose = True
train_xml_filename = './helen-dataset.xml'
print("Start training")
dlib.train_shape_predictor(train_xml_filename, "helen-dataset.dat",
options)
print("Finish")
def train_model(name, xml):
"""requires: the model name, and the path to the xml annotations.
It trains and saves a new model according to the specified
training options and given annotations"""
# get the training options
options = dlib.shape_predictor_training_options()
options.tree_depth = 4
options.nu = 0.1
options.cascade_depth = 15
options.feature_pool_size = 400
options.num_test_splits = 50
options.oversampling_amount = 5
#
options.be_verbose = True # tells what is happening during the training
options.num_threads = 4 # number of the threads used to train the model
# finally, train the model
dlib.train_shape_predictor(xml, name, options)
def test_shape_predictor_params(treeDepth, nu, cascadeDepth, featurePoolSize,
numTestSplits, oversamplingAmount,
oversamplingTransJitter, padding, lambdaParam):
# Grab the default options for dlib's shape predictor and then set the values
# based on the current hyperparameter values, casting to ints when appropriate
options = dlib.shape_predictor_training_options()
options.tree_depth = int(treeDepth)
options.nu = nu
options.cascade_depth = int(cascadeDepth)
options.feature_pool_size = int(featurePoolSize)
options.num_test_splits = int(numTestSplits)
options.oversampling_amount = int(oversamplingAmount)
options.oversampling_translation_jitter = oversamplingTransJitter
options.feature_pool_region_padding = padding
options.lambda_param = lambdaParam
# Tell dlib to be verbose when training and utilize our supplied number of threads when training
options.be_verbose = True
options.num_threads = procs
# Display the current set of options to our terminal
print("[INFO] Starting training process...")
print(options)
sys.stdout.flush()
# Train the model using the current set of hyperparameters
dlib.train_shape_predictor(config.TRAIN_PATH, config.TEMP_MODEL_PATH,
options)
# Take the newly trained shape predictor model and evaluate it on both the training and testing set
trainingError = dlib.test_shape_predictor(config.TRAIN_PATH,
config.TEMP_MODEL_PATH)
testingError = dlib.test_shape_predictor(config.TEST_PATH,
config.TEMP_MODEL_PATH)
# Display the training and testing errors for the current trial
print("[INFO] train error: {}".format(trainingError))
print("[INFO] test error: {}".format(testingError))
sys.stdout.flush()
# Return the error on the testing set
return testingError
def test_params(cascade_depth, padding, nu, tree_depth,
num_trees_per_cascade_level, lambda_param, jitter):
options = dlib.shape_predictor_training_options()
options.feature_pool_region_padding = padding
options.cascade_depth = int(cascade_depth)
options.nu = nu
options.tree_depth = int(tree_depth)
options.oversampling_translation_jitter = jitter
options.num_trees_per_cascade_level = int(num_trees_per_cascade_level)
options.lambda_param = lambda_param
options.num_threads = 4
options.be_verbose = True
print("start training")
print(options)
sys.stdout.flush()
dlib.train_shape_predictor(training_xml_path, "bbr_predictor.dat", options)
print("\nTraining error: ",
dlib.test_shape_predictor(training_xml_path, "bbr_predictor.dat"))
err = dlib.test_shape_predictor(testing_xml_path, "bbr_predictor.dat")
print("\nTesting error: ", err)
sys.stdout.flush()
return err
def copy_dlib_options(options):
new_options = dlib.shape_predictor_training_options()
for p in sorted(filter(lambda x: '__' not in x, dir(options))):
setattr(new_options, p, getattr(options, p))
return new_options
def copy_dlib_options(options):
new_options = dlib.shape_predictor_training_options()
for p in sorted(filter(lambda x: '__' not in x, dir(options))):
setattr(new_options, p, getattr(options, p))
return new_options
# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory. This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
if len(sys.argv) != 2:
print(
"Give the path to the examples/faces directory as the argument to this "
"program. For example, if you are in the python_examples folder then "
"execute this program by running:\n"
" ./train_shape_predictor.py ../examples/faces"
)
exit()
faces_folder = sys.argv[1]
options = dlib.shape_predictor_training_options()
# Now make the object responsible for training the model.
# This algorithm has a bunch of parameters you can mess with. The
# documentation for the shape_predictor_trainer explains all of them.
# You should also read Kazemi's paper which explains all the parameters
# in great detail. However, here I'm just setting three of them
# differently than their default values. I'm doing this because we
# have a very small dataset. In particular, setting the oversampling
# to a high amount (300) effectively boosts the training set size, so
# that helps this example.
options.oversampling_amount = 300
# I'm also reducing the capacity of the model by explicitly increasing
# the regularization (making nu smaller) and by using trees with
# smaller depths.
options.nu = 0.05
options.tree_depth = 2
def train_model(name, xml):
'''train and return a dlib shape predictor.
Training options are:
cascade_depth:
The number of cascades created to train the model with. The number of
trees in the model is = cascade_depth * num_trees_per_cascade_level.
> default: 10
feature_pool_region_padding:
Size of region within which to sample features for the feature pool,
e.g a padding of 0.5 would cause the algorithm to sample pixels from
a box that was 2x2 pixels
> default: 0
feature_pool_size:
Number of pixels used to generate features for the random trees at
each cascade. So in general larger settings of this parameter give
better accuracy but make the algorithm run slower.
> default: 400
lambda_param:
Controls how tight the feature sampling should be. Lower values enforce
closer features. To decide how to split nodes in the regression tree
the algorithm looks at pairs of pixels in the image. These pixel pairs
are sampled randomly but with a preference for selecting pixels that
are near each other.
> default: 0.1
nu:
The regularization parameter.
Larger values of this parameter will cause the algorithm to fit
the training data better but may also cause overfitting.
The value must be 0 < nu <= 1.
> default: 0.1
num_test_splits:
Number of split features at each node to sample.
Larger values of this parameter will usually give more accurate
outputs but take longer to train.
> default: 20
num_trees_per_cascade_level:
The number of trees created for each cascade.
> default: 500
oversampling_amount:
The number of randomly selected initial starting points sampled
for each training example. This parameter controls the number of
randomly selected deformation applied to the training data.
So the bigger this parameter the better (excepting that larger
values make training take longer).
> default: 20
oversampling_translation_jitter:
When generating the get_oversampling_amount() factor of extra training
samples you can also jitter the bounding box by adding random small
translational shifts. For instance, if you set it to 0.1 then it would
randomly translate the bounding boxes by between 0% and 10% their
width and height in the x and y directions respectively. Doing this is
essentially equivalent to randomly jittering the bounding boxes in the
training data. So doing this kind of jittering can help make the
learned model more robust against slightly misplaced bounding boxes.
> default: 0
random_seed:
The random seed used by the internal random number generator
> default: ""
tree_depth:
The depth of the trees used in each cascade.
There are pow(2, get_tree_depth()) leaves in each tree.
> default: 4
'''
options = dlib.shape_predictor_training_options()
options.tree_depth = 3 # 4
options.nu = 0.1
options.num_threads = 8
options.cascade_depth = 12 # 15
options.be_verbose = True
options.feature_pool_size = 400 + 20 # 400
options.num_test_splits = 20 + 5
options.oversampling_amount = 20
# options.oversampling_translation_jitter = 0.1
dlib.train_shape_predictor(xml, name, options)
def parameter_model(self, tree, nu, threads, cascade_depth,
feature_pool_size, test_splits, os):
"""!
Réglage du modèle
@param tree = [num_trees,tree_depth]
@param --num-trees number of regression trees (default = 500)
@param --tree-depth choice of tree depth (default = 4)
# define the depth of each regression tree -- there will be a total
# of 2^tree_depth leaves in each tree; small values of tree_depth
# will be *faster* but *less accurate* while larger values will
# generate trees that are *deeper*, *more accurate*, but will run
# *far slower* when making predictions
# Typical values for tree_depth are in the range [2, 8].
@param --nu regularization parameter (default = 0.1)
Values closer to 1 will make our model fit the training data closer,
but could potentially lead to overfitting.
Values closer to 0 will help our model generalize;
however, there is a caveat to the generalization power
— the closer nu is to 0, the more training data you’ll need.
Typically, for small values of nu you’ll need 1000s of training examples.
@param --threads number of threads to be used (default = 1)
@param --cascade-depth choice of cascade depth (default = 15)
# the cascade_depth will have a dramatic impact on both the accuracy and the output file size of your model.
# in the range [6, 18],
@param --feature-pool-size choice of feature pool size (default = 500) (could be 400)
# The feature_pool_size controls the number of pixels used
# to generate features for the random trees in each cascade.
# My recommendation here is that you should use large values for feature_pools_size
# if inference speed is not a concern. Otherwise, you should use smaller values for faster prediction speed
@param --test-splits number of test splits (default = 20)
# selects best features at each cascade when training -- the larger
# this value is, the *longer* it will take to train but (potentially)
# the more *accurate* your model will be
@param --oversampling oversampling amount (default = 10) (could be 5)
# controls amount of "jitter" (i.e., data augmentation) when training
# the shape predictor -- applies the supplied number of random
# deformations, thereby performing regularization and increasing the
# ability of our model to generalize
# in range (0,50) : 50 means nb_image * 50
# !!!! can increase a lot training time !!!!
"""
num_trees, tree_depth = tree[0], tree[1]
options = dlib.shape_predictor_training_options()
options.num_trees_per_cascade_level = num_trees
options.nu = nu
options.num_threads = threads
options.tree_depth = tree_depth
options.cascade_depth = cascade_depth
options.feature_pool_size = feature_pool_size
options.num_test_splits = test_splits
options.oversampling_amount = os
options.be_verbose = True
self.options = options
return self.options
def main():
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
ap.add_argument("-p", "--path", required=True, help="path to dataset")
ap.add_argument("-v", "--verbose", default=True, help="be_verbose flag")
ap.add_argument("-cd",
"--cascade_depth",
type=int,
default=10,
help="cascade_depth value")
ap.add_argument("-td",
"--tree_depth",
type=int,
default=4,
help="tree_depth value")
ap.add_argument("-ntpcl",
"--num_tress_per_cascade_level",
type=int,
default=500,
help="num_tress_per_cascade_level value")
ap.add_argument("-nu", "--nu", default=0.1, type=float, help="nu value")
ap.add_argument("-oa",
"--oversampling_amount",
type=int,
default=20,
help="oversampling_amount value")
ap.add_argument("-fps",
"--feature_pool_size",
type=int,
default=400,
help="feature_pool_size value")
ap.add_argument("-l",
"--lambda_param",
default=0.1,
type=float,
help="lambda_param value")
ap.add_argument("-nts",
"--num_test_splits",
type=int,
default=20,
help="num_test_splits value")
ap.add_argument("-fprp",
"--feature_pool_region_padding",
default=0,
type=int,
help="feature_pool_region_padding value")
ap.add_argument("-rs",
"--random_seed",
default="",
help="random_seed value")
ap.add_argument("-nt",
"--num_threads",
default=0,
type=int,
help="num_threads value")
ap.add_argument("-m", "--model_name", required=True, help="model_name")
args = ap.parse_args()
# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory. This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
faces_folder = args.path
options = dlib.shape_predictor_training_options()
# Now make the object responsible for training the model.
# This algorithm has a bunch of parameters you can mess with. The
# documentation for the shape_predictor_trainer explains all of them.
# You should also read Kazemi's paper which explains all the parameters
# in great detail. However, here I'm just setting three of them
# differently than their default values. I'm doing this because we
# have a very small dataset. In particular, setting the oversampling
# to a high amount (300) effectively boosts the training set size, so
# that helps this example.
# options.oversampling_amount = args.oversampling_amount
# I'm also reducing the capacity of the model by explicitly increasing
# the regularization (making nu smaller) and by using trees with
# smaller depths.
# options.nu = 0.05
# options.lambda_param = 0.1
# options.tree_depth = 5
# options.be_verbose = True
# options.num_threads = 0
# model_name = "predictor_1.dat"
options.be_verbose = args.verbose
options.cascade_depth = args.cascade_depth
options.tree_depth = args.tree_depth
options.num_tress_per_cascade_level = args.num_tress_per_cascade_level
options.nu = args.nu
options.oversampling_amount = args.oversampling_amount
options.feature_pool_size = args.feature_pool_size
options.lambda_param = args.lambda_param
options.num_test_splits = args.num_test_splits
options.feature_pool_region_padding = args.feature_pool_region_padding
options.random_seed = args.random_seed
options.num_threads = args.num_threads
model_name = args.model_name
# dlib.train_shape_predictor() does the actual training. It will save the
# final predictor to predictor.dat. The input is an XML file that lists the
# images in the training dataset and also contains the positions of the face
# parts.
training_xml_path = os.path.join(faces_folder,
"labels_ibug_300W_train.xml")
dlib.train_shape_predictor(training_xml_path, model_name, options)
# Now that we have a model we can test it. dlib.test_shape_predictor()
# measures the average distance between a face landmark output by the
# shape_predictor and where it should be according to the truth data.
print("\nTraining accuracy: {}".format(
dlib.test_shape_predictor(training_xml_path, model_name)))
# The real test is to see how well it does on data it wasn't trained on. We
# trained it on a very small dataset so the accuracy is not extremely high, but
# it's still doing quite good. Moreover, if you train it on one of the large
# face landmarking datasets you will obtain state-of-the-art results, as shown
# in the Kazemi paper.
testing_xml_path = os.path.join(faces_folder, "labels_ibug_300W.xml")
print("Testing accuracy: {}".format(
dlib.test_shape_predictor(testing_xml_path, model_name)))
# Now let's use it as you would in a normal application. First we will load it
# from disk. We also need to load a face detector to provide the initial
# estimate of the facial location.
predictor = dlib.shape_predictor(model_name)
detector = dlib.get_frontal_face_detector()
# Now let's run the detector and shape_predictor over the images in the faces
# folder and display the results.
print(
"Showing detections and predictions on the images in the faces folder..."
)
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
print("Processing file: {}".format(f))
img = io.imread(f)
win.clear_overlay()
win.set_image(img)
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
# Get the landmarks/parts for the face in box d.
shape = predictor(img, d)
print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
shape.part(1)))
# Draw the face landmarks on the screen.
win.add_overlay(shape)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory. This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
if len(sys.argv) != 2:
print(
"Give the path to the examples/faces directory as the argument to this "
"program. For example, if you are in the python_examples folder then "
"execute this program by running:\n"
" ./train_shape_predictor.py ../examples/faces")
exit()
faces_folder = sys.argv[1]
options = dlib.shape_predictor_training_options()
# Now make the object responsible for training the model.
# This algorithm has a bunch of parameters you can mess with. The
# documentation for the shape_predictor_trainer explains all of them.
# You should also read Kazemi's paper which explains all the parameters
# in great detail. However, here I'm just setting three of them
# differently than their default values. I'm doing this because we
# have a very small dataset. In particular, setting the oversampling
# to a high amount (300) effectively boosts the training set size, so
# that helps this example.
options.oversampling_amount = 300
# I'm also reducing the capacity of the model by explicitly increasing
# the regularization (making nu smaller) and by using trees with
# smaller depths.
options.nu = 0.05
options.tree_depth = 2
''' import sys import os.path import multiprocessing import dlib # root of project repository THE_FILE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) PROJECT_ROOT = os.path.abspath(os.path.join(THE_FILE_DIR, '..', '..')) sys.path.append(PROJECT_ROOT) from src.train_test.landmarks.sets import LANDMARKS_MODELS from src.models.source import get_model_dlib_shape_predictor_filename, get_landmark_filename DEFAULT_SHAPE_PREDICTOR_OPTIONS = dlib.shape_predictor_training_options() # define the depth of each regression tree -- there will be a total # of 2^tree_depth leaves in each tree; small values of tree_depth # will be *faster* but *less accurate* while larger values will # generate trees that are *deeper*, *more accurate*, but will run # *far slower* when making predictions DEFAULT_SHAPE_PREDICTOR_OPTIONS.tree_depth = 4 # regularization parameter in the range [0, 1] that is used to help # our model generalize -- values closer to 1 will make our model fit # the training data better, but could cause overfitting; values closer # to 0 will help our model generalize but will require us to have # training data in the order of 1000s of data points DEFAULT_SHAPE_PREDICTOR_OPTIONS.nu = 0.1