def analyze_image(image_path, params=None):
"""
Execute caption recognition on given image
:type image_path: string
:param image_path: path of image to be analyzed
:type params: dictionary
:param params: configuration parameters (see table)
:rtype: tuple
:returns: a (label, tag, face) tuple,
where label is the assigned label,
tag the assigned tag
and face the detected face in image (as an OpenCV image)
============================================ ======================================== =============================
Key Value Default value
============================================ ======================================== =============================
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
============================================ ======================================== =============================
"""
label = -1
tag = -1
align_path = c.ALIGNED_FACES_PATH
if (params is not None) and (c.ALIGNED_FACES_PATH_KEY in params):
align_path = params[c.ALIGNED_FACES_PATH_KEY]
# Face detection
face = get_detected_cropped_face(image_path, align_path, params)
if face is not None:
cap_rec_res = get_tag_from_image(image_path, params)
label = cap_rec_res[c.ASSIGNED_LABEL_KEY]
tag = cap_rec_res[c.ASSIGNED_TAG_KEY]
return label, tag, face
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
