def fr_experiments(params, show_results):
"""
Execute face recognition experiments
:type params: dictionary
:param params: configuration parameters
to be used for the experiment (see table)
:type show_results: boolean
:param show_results: show (True) or do not show (False)
images with detected faces
============================================ ======================================== ==============================
Key Value Default value
============================================ ======================================== ==============================
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'
test_set_path path of directory
containing test set
use_nose_pos_in_detection If True, detections with no good False
nose position are discarded
aligned_faces_path Default path of directory
for aligned faces
cropped_face_height Height of aligned faces (in pixels) 400
cropped_face_width Width of aligned faces (in pixels) 200
dataset_already_divided If True, dataset is already divided False
between training and test set
dataset_path Path of whole dataset, used if dataset
is not already divided between
training and test set
db_name Name of single file
containing face models
db_models_path Path of directory containing face models
face_model_algorithm Algorithm for face recognition 'LBP'
('Eigenfaces', 'Fisherfaces' or 'LBP')
face_recognition_results_path Path of directory where
test results will be saved
test_set_path Path of directory containing
test set
training_set_path Path of directory containing
training set
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)
offset_pct_x % of the image to keep next to 0.20
the eyes in the horizontal direction
offset_pct_y % of the image to keep next to 0.50
the eyes in the vertical direction
software_test_file Path of image to be used for
software test
training_images_nr Number of images per person used in
training set
use_eye_detection If True, use eye detection for detecting True
eye position for aligning faces in
test images
use_eye_detection_in_training If True, use eye detection for detecting True
eye position for aligning faces in
training images
use_eyes_position If True, align faces in test images True
by using eye positions
use_eyes_position_in_training If True, align faces in training images True
by using eye positions
use_face_detection_in_training If True, use face detection False
for images in training set
use_NBNN If True, False
use Naive Bayes Nearest Neighbor
use_one_file_for_face_models If True, use one file for face models True
use_resizing If True, resize images True
use_weighted_regions If True, use weighted LBP False
============================================ ======================================== ==============================
"""
rec_images_nr = 0 # Number of correctly recognized images
test_images_nr = 0 # Number of total test images
mean_rec_time = 0
# List of confidence values for true positives
true_pos_confidence_list = []
# List of confidence values for false positives
false_pos_confidence_list = []
fm = FaceModels(params)
training_images_nr = ce.TRAINING_IMAGES_NR
if params is not None:
# Number of images for each person to be used for the training
training_images_nr = params[ce.TRAINING_IMAGES_NR_KEY]
# Number of people
people_nr = fm.get_people_nr()
rec_dict = {} # Dictionary containing all results for this experiment
# List used for creating YAML file with list of images
images_list_for_YAML = []
# List used for creating YAML file with list of people
people_list_for_YAML = []
# List containing recognition rates
rec_rate_list = []
# Initialize dictionaries with people
people_true_positives_dict = {}
people_false_positives_dict = {}
people_test_images_nr_dict = {}
tags = fm.get_tags()
for tag in tags:
people_true_positives_dict[tag] = 0
people_false_positives_dict[tag] = 0
people_test_images_nr_dict[tag] = 0
dataset_already_divided = ce.DATASET_ALREADY_DIVIDED
# directory with test set
test_set_path = ce.FACE_RECOGNITION_TEST_SET_PATH
if not dataset_already_divided:
test_set_path = ce.FACE_RECOGNITION_DATASET_PATH
results_path = ce.FACE_RECOGNITION_RESULTS_PATH
if params is not None:
# Get path of directories with used files from params
if ce.DATASET_ALREADY_DIVIDED_KEY in params:
dataset_already_divided = params[ce.DATASET_ALREADY_DIVIDED_KEY]
if dataset_already_divided:
if ce.TEST_SET_PATH_KEY in params:
test_set_path = params[ce.TEST_SET_PATH_KEY]
else:
if ce.DATASET_PATH_KEY in params:
test_set_path = params[ce.DATASET_PATH_KEY]
# directory with results
if ce.FACE_RECOGNITION_RESULTS_PATH_KEY in params:
results_path = params[ce.FACE_RECOGNITION_RESULTS_PATH_KEY]
# Iterate over all directories with images
images_dirs = os.listdir(test_set_path)
total_test_images_nr = 0
for images_dir in images_dirs:
ann_face_tag = images_dir
images_dir_complete_path = os.path.join(test_set_path, images_dir)
# Iterate over all images in this directory
image_counter = 0
person_test_images = 0
person_rec_images = 0
for image in os.listdir(images_dir_complete_path):
# If dataset is not already divided,
# first training_images_nr images are used for training,
# the remaining for test
if(dataset_already_divided
or (image_counter >= training_images_nr)):
total_test_images_nr += 1
person_test_images += 1
# Complete path of image
image_complete_path = os.path.join(
images_dir_complete_path, image)
try:
assigned_tag = 'Undefined'
confidence = -1
if USE_FACEEXTRACTOR:
fe = FaceExtractor(fm, params)
handle = fe.extract_faces_from_image(
image_complete_path)
results = fe.get_results(handle)
faces = results[c.FACES_KEY]
if len(faces) != 0:
face = faces[0]
mean_rec_time = (
mean_rec_time +
results[c.ELAPSED_CPU_TIME_KEY])
assigned_tag = face[c.ASSIGNED_TAG_KEY]
confidence = face[c.CONFIDENCE_KEY]
else:
face = cv2.imread(
image_complete_path, cv2.IMREAD_GRAYSCALE)
sz = None
use_resizing = ce.USE_RESIZING
use_eyes_position = c.USE_EYES_POSITION
use_eye_detection = ce.USE_EYE_DETECTION
offset_pct_x = c.OFFSET_PCT_X
offset_pct_y = c.OFFSET_PCT_Y
if params is not None:
if ce.USE_RESIZING_KEY in params:
use_resizing = params[ce.USE_RESIZING_KEY]
if c.USE_EYES_POSITION_KEY in params:
use_eyes_position = (
params[c.USE_EYES_POSITION_KEY])
if ce.USE_EYE_DETECTION_KEY in params:
use_eye_detection = (
params[ce.USE_EYE_DETECTION_KEY])
if c.OFFSET_PCT_X_KEY in params:
offset_pct_x = params[c.OFFSET_PCT_X_KEY]
if c.OFFSET_PCT_Y_KEY in params:
offset_pct_y = params[c.OFFSET_PCT_Y_KEY]
if use_resizing:
width = c.CROPPED_FACE_WIDTH
height = c.CROPPED_FACE_HEIGHT
if params is not None:
if c.CROPPED_FACE_WIDTH_KEY in params:
width = params[c.CROPPED_FACE_WIDTH_KEY]
if c.CROPPED_FACE_HEIGHT_KEY in params:
height = params[c.CROPPED_FACE_HEIGHT_KEY]
sz = (width, height)
if use_eyes_position:
align_path = c.ALIGNED_FACES_PATH
if params is not None:
align_path = params[c.ALIGNED_FACES_PATH_KEY]
if use_eye_detection:
face = fd.get_cropped_face(
image_complete_path, align_path, params,
return_always_face=False)
else:
face = fd.get_cropped_face_using_fixed_eye_pos(
image_complete_path, align_path,
offset_pct=(offset_pct_x, offset_pct_y),
dest_size=sz)
if face is not None:
face = face[c.FACE_KEY]
else:
if sz is not None:
face = cv2.resize(face, sz)
if face is not None:
rec_results = recognize_face(
face, fm, params, show_results)
assigned_tag = rec_results[c.ASSIGNED_TAG_KEY]
confidence = rec_results[c.CONFIDENCE_KEY]
# Add recognition time to total
mean_rec_time = (
mean_rec_time +
rec_results[c.ELAPSED_CPU_TIME_KEY])
image_dict = {ce.IMAGE_KEY: image,
c.ANN_TAG_KEY: images_dir,
c.ASSIGNED_TAG_KEY: assigned_tag,
c.CONFIDENCE_KEY: confidence}
if assigned_tag == ann_face_tag:
image_dict[ce.PERSON_CHECK_KEY] = 'TP'
people_true_positives_dict[assigned_tag] += 1
rec_images_nr += 1
true_pos_confidence_list.append(confidence)
person_rec_images += 1
else:
image_dict[ce.PERSON_CHECK_KEY] = 'FP'
if assigned_tag != 'Undefined':
people_false_positives_dict[assigned_tag] += 1
false_pos_confidence_list.append(confidence)
image_dict_extended = {ce.IMAGE_KEY: image_dict}
images_list_for_YAML.append(image_dict_extended)
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()[0]
raise
def __read_images(self, path, sz=None):
l = 0
X, y = [], []
# Set parameters
align_path = c.ALIGNED_FACES_PATH
use_eyes_pos_in_training = ce.USE_EYES_POSITION_IN_TRAINING
use_eye_det_in_training = ce.USE_EYE_DETECTION_IN_TRAINING
use_face_det_in_training = ce.USE_FACE_DETECTION_IN_TRAINING
offset_pct_x = c.OFFSET_PCT_X
offset_pct_y = c.OFFSET_PCT_Y
if self._params is not None:
align_path = self._params[c.ALIGNED_FACES_PATH_KEY]
use_eyes_pos_in_training = (self._params
[ce.USE_EYES_POSITION_IN_TRAINING_KEY])
use_eye_det_in_training = (
self._params[ce.USE_EYE_DETECTION_IN_TRAINING_KEY])
use_face_det_in_training = (
self._params[ce.USE_FACE_DETECTION_IN_TRAINING_KEY])
offset_pct_x = self._params[c.OFFSET_PCT_X_KEY]
offset_pct_y = self._params[c.OFFSET_PCT_Y_KEY]
for dirname, dirnames, filenames in os.walk(path):
for subdirname in dirnames:
# print "creating model for", subdirname
subject_path = os.path.join(dirname, subdirname)
# print "subject path:", subject_path
file_counter = 0
for filename in os.listdir(subject_path):
# print "image path", os.path.join(subject_path, filename)
try:
if use_face_det_in_training:
im = fd.get_detected_cropped_face(
os.path.join(subject_path, filename),
align_path, self._params,
return_always_face=False)
elif use_eyes_pos_in_training:
if use_eye_det_in_training:
im = None
crop_result = fd.get_cropped_face(
os.path.join(subject_path, filename),
align_path, self._params,
return_always_face=False)
if crop_result:
im = crop_result[c.FACE_KEY]
else:
im = fd.get_cropped_face_using_fixed_eye_pos(
os.path.join(subject_path, filename),
align_path,
offset_pct=(offset_pct_x, offset_pct_y),
dest_size=sz)
else:
im = cv2.imread(
os.path.join(subject_path, filename),
cv2.IMREAD_GRAYSCALE)
# resize to given size (if given)
if (im is not None) and (sz is not None):
im = cv2.resize(im, sz)
if im is not None:
X.append(np.asarray(im, dtype=np.uint8))
y.append(l)
self._tags[l] = str(subdirname)
else:
print "Image", os.path.join(subject_path, filename), "not considered"
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()[0]
raise
