def train_by_images(path, db_file_name, params=None):
"""
Train an LBP face recognizer by using captions in images
:type path: string
:param path: path of directory with images
:type db_file_name: string
:param db_file_name: path of file that will contain the face models
:type params: dictionary
:param params: configuration parameters (see table)
:rtype: tuple
:return: a (model, tags) tuple, where model is an LBPHFaceRecognizer
and tags is the list of found tags
============================================ ======================================== =============================
Key Value Default value
============================================ ======================================== =============================
LBP_grid_x Number of columns in grid 4
used for calculating LBP
LBP_grid_y Number of columns in grid 8
used for calculating LBP
LBP_neighbors Number of neighbors 8
used for calculating LBP
LBP_radius Radius used 1
for calculating LBP (in pixels)
use_one_file_for_face_models If True, use one file for face models True
align_path Path of directory
where aligned faces are saved
check_eye_positions If True, check eye positions True
classifiers_dir_path Path of directory with OpenCV
cascade classifiers
eye_detection_classifier Classifier for eye detection 'haarcascade_mcs_lefteye.xml'
face_detection_algorithm Classifier for face detection 'HaarCascadeFrontalFaceAlt2'
('HaarCascadeFrontalFaceAlt',
'HaarCascadeFrontalFaceAltTree',
'HaarCascadeFrontalFaceAlt2',
'HaarCascadeFrontalFaceDefault',
'HaarCascadeProfileFace',
'HaarCascadeFrontalAndProfileFaces',
'HaarCascadeFrontalAndProfileFaces2',
'LBPCascadeFrontalface',
'LBPCascadeProfileFace' or
'LBPCascadeFrontalAndProfileFaces')
flags Flags used in face detection 'DoCannyPruning'
('DoCannyPruning', 'ScaleImage',
'FindBiggestObject', 'DoRoughSearch').
If 'DoCannyPruning' is used, regions
that do not contain lines are discarded.
If 'ScaleImage' is used, image instead
of the detector is scaled
(it can be advantegeous in terms of
memory and cache use).
If 'FindBiggestObject' is used,
only the biggest object is returned
by the detector.
'DoRoughSearch', used together with
'FindBiggestObject',
terminates the search as soon as
the first candidate object is found
min_neighbors Mininum number of neighbor bounding 5
boxes for retaining face detection
min_size_height Minimum height of face detection 20
bounding box (in pixels)
min_size_width Minimum width of face detection 20
bounding box (in pixels)
scale_factor Scale factor between two scans 1.1
in face detection
max_eye_angle Maximum inclination of the line 0.125
connecting the eyes
(in % of pi radians)
min_eye_distance Minimum distance between eyes 0.25
(in % of the width of the face
bounding box)
nose_detection_classifier Classifier for nose detection 'haarcascade_mcs_nose.xml'
software_test_file Path of image to be used for
software test
use_nose_pos_in_detection If True, detections with no good False
nose position are discarded
lev_ratio_pct_threshold Minimum threshold for considering 0.8
captions in frame
min_tag_length Minimum length of tags considered 10
in caption recognition
tags_file_path Path of text file containing
list of tags
tesseract_parent_dir_path Path of directory containing
'tesseract' directory
use_blacklist If True, use blacklist of items True
that make the results of the
caption recognition on a frame
rejected
use_levenshtein If True, words found in image True
by caption recognition and tags
are compared by using
the Levenshtein distance
============================================ ======================================== =============================
"""
# Set parameters
lbp_radius = c.LBP_RADIUS
lbp_neighbors = c.LBP_NEIGHBORS
lbp_grid_x = c.LBP_GRID_X
lbp_grid_y = c.LBP_GRID_Y
use_one_file = ce.USE_ONE_FILE_FOR_FACE_MODELS
if params:
if c.LBP_RADIUS_KEY in params:
lbp_radius = params[c.LBP_RADIUS_KEY]
if c.LBP_NEIGHBORS_KEY in params:
lbp_radius = params[c.LBP_NEIGHBORS_KEY]
if c.LBP_GRID_X_KEY in params:
lbp_radius = params[c.LBP_GRID_X]
if c.LBP_GRID_Y_KEY in params:
lbp_radius = params[c.LBP_GRID_Y_KEY]
if ce.USE_ONE_FILE_FOR_FACE_MODELS_KEY in params:
use_one_file = params[ce.USE_ONE_FILE_FOR_FACE_MODELS_KEY]
# Save processing time
start_time = cv2.getTickCount()
model = None
X, y = [], []
tags = {}
image_counter = 0
for image in os.listdir(path):
image_complete_path = path + os.sep + image
[label, tag, face] = analyze_image(image_complete_path, params)
if label != -1:
X.append(np.asarray(face, dtype=np.uint8))
y.append(label)
tags[label] = tag
image_counter += 1
# Save file with face models
if use_one_file:
model = cv2.createLBPHFaceRecognizer(
lbp_radius,
lbp_neighbors,
lbp_grid_x,
lbp_grid_y)
model.train(np.asarray(X), np.asarray(y))
model.save(db_file_name)
else:
y_set = set(y)
for label in y_set:
person_X = []
person_y = []
for i in range(0, len(y)):
if y[i] == label:
person_X.append(X[i])
person_y.append(label)
save_model_file(person_X, person_y, params, db_file_name)
# Save labels in YAML file
save_YAML_file(db_file_name + "-Tags", tags)
# Calculate processing time in seconds
time_in_clocks = cv2.getTickCount() - start_time
time_in_s = time_in_clocks / cv2.getTickFrequency()
print('Creation time: ' + str(time_in_s) + ' s\n')
return model, tags
self._params[ce.TRAINING_IMAGES_NR_KEY])
if((not already_div) and
(file_counter >= training_images_nr)):
# Number of training images has been reached
break
use_one_file = ce.USE_ONE_FILE_FOR_FACE_MODELS
if self._params is not None:
use_one_file = (
self._params[ce.USE_ONE_FILE_FOR_FACE_MODELS_KEY])
if not use_one_file:
save_model_file(X, y)
X, y = [], []
l += 1
return [X, y]
def read_images(self, path, sz=None):
"""
Reads the images in a given folder,
resizes images on the fly if size is given.
:type path: String
:param path: Path to a folder with subfolders
representing the subjects (people)
:type sz: tuple
