def get_cropped_face_from_image(
image, image_path, align_path, params, eye_cascade_classifier,
nose_cascade_classifier, face_bbox, delete_files, return_always_face):
"""
Get face cropped and aligned to eyes from image
:type image: openCV image
:param image: image to be cropped
:type image_path: string
:param image_path: path of image to be analyzed
:type align_path: string
:param align_path: path of directory where aligned faces are saved
:type params: dictionary
:param params: dictionary containing the parameters
to be used for the face detection
:type eye_cascade_classifier: CascadeClassifier
:param eye_cascade_classifier: classifier for detecting eyes
:type nose_cascade_classifier: CascadeClassifier
:param nose_cascade_classifier: classifier for detecting nose
:type face_bbox: tuple
:type face_bbox: bbox of face in original image,
represented as (x, y, width, height)
:type delete_files: boolean
:param delete_files: delete(true) or do not delete(false)
saved image files
:type return_always_face: boolean
:type return_always_face: if true,
return face even if no eyes are detected
:rtype: dictionary or None
:returns: dictionary with results
"""
result = {}
# Offset given as percentage of eye-to-eye distance
offset_pct_x = c.OFFSET_PCT_X
offset_pct_y = c.OFFSET_PCT_Y
# Final size of cropped face
cropped_face_width = c.CROPPED_FACE_WIDTH
cropped_face_height = c.CROPPED_FACE_HEIGHT
# If True, check eye positions
check_eye_positions = c.CHECK_EYE_POSITIONS
if params is not None:
if c.CROPPED_FACE_WIDTH_KEY in params:
cropped_face_width = params[c.CROPPED_FACE_WIDTH_KEY]
if c.CROPPED_FACE_HEIGHT_KEY in params:
cropped_face_height = params[c.CROPPED_FACE_HEIGHT_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 c.CHECK_EYE_POSITIONS_KEY in params:
check_eye_positions = params[c.CHECK_EYE_POSITIONS_KEY]
offset_pct = (offset_pct_x, offset_pct_y)
dest_size = (cropped_face_width, cropped_face_height)
# Detect eyes in face
eye_rects = utils.detect_eyes_in_image(image, eye_cascade_classifier)
eye_left = None
eye_right = None
eye_check_ok = False
if len(eye_rects) == 2:
x_first_eye = eye_rects[0][0]
y_first_eye = eye_rects[0][1]
w_first_eye = eye_rects[0][2]
h_first_eye = eye_rects[0][3]
x_second_eye = eye_rects[1][0]
y_second_eye = eye_rects[1][1]
w_second_eye = eye_rects[1][2]
h_second_eye = eye_rects[1][3]
x_left_eye_center = 0
y_left_eye_center = 0
x_right_eye_center = 0
y_right_eye_center = 0
if x_first_eye < x_second_eye:
x_left_eye_center = x_first_eye + w_first_eye / 2
y_left_eye_center = y_first_eye + h_first_eye / 2
x_right_eye_center = x_second_eye + w_second_eye / 2
y_right_eye_center = y_second_eye + h_second_eye / 2
else:
x_right_eye_center = x_first_eye + w_first_eye / 2
y_right_eye_center = y_first_eye + h_first_eye / 2
x_left_eye_center = x_second_eye + w_second_eye / 2
y_left_eye_center = y_second_eye + h_second_eye / 2
eye_left = (int(x_left_eye_center + face_bbox[0]),
int(y_left_eye_center + face_bbox[1]))
eye_right = (int(x_right_eye_center + face_bbox[0]),
int(y_right_eye_center + face_bbox[1]))
if check_eye_positions:
eye_check_ok = utils.check_eye_pos(
eye_left, eye_right, face_bbox, params)
else:
eye_check_ok = True
if eye_check_ok:
# Open whole image as PIL Image
img = Image.open(image_path)
# Store eye positions related to whole image
result[c.LEFT_EYE_POS_KEY] = eye_left
result[c.RIGHT_EYE_POS_KEY] = eye_right
result[c.NOSE_POSITION_KEY] = None
# Align face image
# Create unique file path
tmp_file_name = str(uuid.uuid4())
tmp_file_name_complete = tmp_file_name + '.png'
tmp_file_path = os.path.join(align_path, tmp_file_name_complete)
CropFace(
img, eye_left, eye_right, offset_pct, dest_size).save(tmp_file_path)
face_image = cv2.imread(tmp_file_path, cv2.IMREAD_GRAYSCALE)
if delete_files:
os.remove(tmp_file_path)
else:
result[c.ALIGNED_FACE_FILE_NAME_KEY] = tmp_file_name
# Check nose position
nose_check_ok = True
use_nose_pos_in_detection = c.USE_NOSE_POS_IN_DETECTION
use_nose_pos_in_recognition = c.USE_NOSE_POS_IN_RECOGNITION
if params is not None:
if c.USE_NOSE_POS_IN_DETECTION_KEY in params:
use_nose_pos_in_detection = params[c.USE_NOSE_POS_IN_DETECTION_KEY]
if c.USE_NOSE_POS_IN_RECOGNITION_KEY in params:
use_nose_pos_in_recognition = params[c.USE_NOSE_POS_IN_RECOGNITION_KEY]
if use_nose_pos_in_detection or use_nose_pos_in_recognition:
noses = utils.detect_nose_in_image(
face_image, nose_cascade_classifier)
x_right_eye = offset_pct[0] * dest_size[0]
x_left_eye = dest_size[0] - x_right_eye
y_eyes = offset_pct[1] * dest_size[1]
good_noses = 0
for(x_nose, y_nose, w_nose, h_nose) in noses:
# Coordinates of bounding box center in face image
x_center = float(x_nose + w_nose / 2)
y_center = float(y_nose + h_nose / 2)
# Store nose position relative to face image
nose_x_pct = x_center / cropped_face_width
nose_y_pct = y_center / cropped_face_height
nose = (nose_x_pct, nose_y_pct)
result[c.NOSE_POSITION_KEY] = nose
# Nose must be between eyes in horizontal direction
# and below eyes in vertical direction
if((x_center > x_right_eye) and(x_center < x_left_eye)
and (y_center > y_eyes)):
good_noses += 1
if good_noses != 1:
nose_check_ok = False
result[c.NOSE_POSITION_KEY] = None
if nose_check_ok or not use_nose_pos_in_detection:
if c.USE_HIST_EQ_IN_CROPPED_FACES:
face_image = cv2.equalizeHist(face_image)
if c.USE_NORM_IN_CROPPED_FACES:
face_image = cv2.normalize(
face_image, alpha=0, beta=255,
norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
if c.USE_CANNY_IN_CROPPED_FACES:
face_image = cv2.Canny(face_image, 0.1, 100)
if c.USE_TAN_AND_TRIGG_NORM:
face_image = utils.normalize_illumination(face_image)
# Insert oval mask in image
if c.USE_OVAL_MASK:
face_image = utils.add_oval_mask(face_image)
if not delete_files:
tmp_file_name_gray = (
tmp_file_name + c.ALIGNED_FACE_GRAY_SUFFIX + '.png')
tmp_file_gray_path = os.path.join(
align_path, tmp_file_name_gray)
cv2.imwrite(tmp_file_gray_path, face_image)
return result
else:
result[c.FACE_KEY] = None
return result
else:
if return_always_face:
result[c.LEFT_EYE_POS_KEY] = None
result[c.RIGHT_EYE_POS_KEY] = None
return result
else:
return None
def detect_faces_in_image(resource_path, align_path, params, show_results,
delete_files=False, return_always_faces=False):
"""
Detect faces in image
:type resource_path: string
:param resource_path: path of image to be analyzed
:type align_path: string
:param align_path: path of directory where aligned faces are saved
:type params: dictionary
:param params: dictionary containing the parameters
to be used for the face detection
:type delete_files: boolean
:param delete_files: delete(true) or do not delete(false)
saved image files
:type show_results: boolean
:param show_results: show (true) or do not show (false)
image with detected faces
:rtype: dictionary
:returns: dictionary with results
"""
# Saving processing time for face detection
start_time = cv2.getTickCount()
result = {}
# Open image
file_check = os.path.isfile(resource_path)
if not file_check:
print('Image file does not exist')
result[c.ERROR_KEY] = 'Image file does not exist'
return result
try:
image = cv2.imread(resource_path, cv2.IMREAD_GRAYSCALE)
PADDING_BORDER = 0
# Pad image with zeros
if PADDING_BORDER != 0:
image = cv2.copyMakeBorder(
image, PADDING_BORDER, PADDING_BORDER, PADDING_BORDER,
PADDING_BORDER, cv2.BORDER_CONSTANT, 0)
# Load classifier files
classifier_file = ''
classifier_file_2 = ''
use_one_classifier_file = True
# Algorithm to be used for the face detection
algorithm = c.FACE_DETECTION_ALGORITHM
# Path of directory containing classifier files
classifiers_folder_path = c.CLASSIFIERS_DIR_PATH + os.sep
# Cascade classifier for eye detection
eye_detection_classifier = c.EYE_DETECTION_CLASSIFIER
# Cascade classifiers for nose detection
nose_detection_classifier = c.NOSE_DETECTION_CLASSIFIER
use_eyes_position = c.USE_EYES_POSITION
if params is not None:
if c.FACE_DETECTION_ALGORITHM_KEY in params:
algorithm = params[c.FACE_DETECTION_ALGORITHM_KEY]
if c.CLASSIFIERS_DIR_PATH_KEY in params:
classifiers_folder_path = params[c.CLASSIFIERS_DIR_PATH_KEY] + os.sep
if c.EYE_DETECTION_CLASSIFIER_KEY in params:
eye_detection_classifier = params[c.EYE_DETECTION_CLASSIFIER_KEY]
if c.NOSE_DETECTION_CLASSIFIER_KEY in params:
nose_detection_classifier = params[c.NOSE_DETECTION_CLASSIFIER_KEY]
if c.USE_EYES_POSITION_KEY in params:
use_eyes_position = params[c.USE_EYES_POSITION_KEY]
if algorithm == 'HaarCascadeFrontalFaceAlt':
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_ALT_CLASSIFIER
elif algorithm == 'HaarCascadeFrontalFaceAltTree':
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_ALT_TREE_CLASSIFIER
elif algorithm == 'HaarCascadeFrontalFaceAlt2':
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_ALT2_CLASSIFIER
elif algorithm == 'HaarCascadeFrontalFaceDefault':
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_DEFAULT_CLASSIFIER
elif algorithm == 'HaarCascadeProfileFace':
classifier_file = classifiers_folder_path + HAARCASCADE_PROFILEFACE_CLASSIFIER
elif algorithm == 'HaarCascadeFrontalAndProfileFaces':
use_one_classifier_file = False
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_ALT_CLASSIFIER
classifier_file_2 = classifiers_folder_path + HAARCASCADE_PROFILEFACE_CLASSIFIER
elif algorithm == 'LBPCascadeFrontalface':
classifier_file = classifiers_folder_path + LBPCASCADE_FRONTALFACE_CLASSIFIER
elif algorithm == 'LBPCascadeProfileFace':
classifier_file = classifiers_folder_path + LBPCASCADE_PROFILEFACE_CLASSIFIER
elif algorithm == 'LBPCascadeFrontalAndProfileFaces':
use_one_classifier_file = False
classifier_file = classifiers_folder_path + LBPCASCADE_FRONTALFACE_CLASSIFIER
classifier_file_2 = classifiers_folder_path + LBPCASCADE_PROFILEFACE_CLASSIFIER
elif algorithm == 'HaarCascadeFrontalAndProfileFaces2':
use_one_classifier_file = False
classifier_file = classifiers_folder_path + HAARCASCADE_FRONTALFACE_ALT2_CLASSIFIER
classifier_file_2 = classifiers_folder_path + HAARCASCADE_PROFILEFACE_CLASSIFIER
else:
print '\nAlgorithm %s is not available' % algorithm
result[c.ERROR_KEY] = 'Detection algorithm is not available'
return result
faces = []
if use_one_classifier_file:
face_cascade_classifier = cv2.CascadeClassifier(classifier_file)
if face_cascade_classifier.empty():
print('Error loading face cascade classifier file')
result[c.ERROR_KEY] = 'Error loading face cascade classifier file'
return result
else:
if algorithm == 'LBPCascadeProfileFace':
# lbpcascade_profileface classifier
# only detects faces rotated to the right,
# so it must be used on the original
# and on the flipped image
faces_from_orig_image = detect_faces_in_image_with_single_classifier(
image, face_cascade_classifier, params)
# Flip image around y-axis
flipped_image = cv2.flip(image, 1)
faces_from_flipped_image = detect_faces_in_image_with_single_classifier(
flipped_image, face_cascade_classifier, params)
# Transform coordinates of faces from flipped image
image_width = len(image[0, :])
for i in range(len(faces_from_flipped_image)):
faces_from_flipped_image[i][0] = image_width + 1 - faces_from_flipped_image[i][0] - faces_from_flipped_image[i][2]
# Merge results
faces = merge_classifier_results(
faces_from_orig_image, faces_from_flipped_image)
else:
# Use classifier on original image only
faces = detect_faces_in_image_with_single_classifier(
image, face_cascade_classifier, params)
else:
face_cascade_classifier_1 = cv2.CascadeClassifier(classifier_file)
face_cascade_classifier_2 = cv2.CascadeClassifier(classifier_file_2)
if face_cascade_classifier_1.empty() | face_cascade_classifier_2.empty():
print('Error loading face cascade classifier file')
result[c.ERROR_KEY] = 'Error loading face cascade classifier file'
return
else:
# Detect faces in image using first classifier
faces_from_classifier_1 = detect_faces_in_image_with_single_classifier(
image, face_cascade_classifier_1, params)
# Detect faces in image using second classifier
faces_from_classifier_2 = detect_faces_in_image_with_single_classifier(
image, face_cascade_classifier_2, params)
# Merge results
faces = merge_classifier_results(
faces_from_classifier_1, faces_from_classifier_2)
detection_time_in_clocks = cv2.getTickCount() - start_time
detection_time_in_seconds = detection_time_in_clocks / cv2.getTickFrequency()
# Cascade classifier for eye detection
eye_classifier_file = classifiers_folder_path + eye_detection_classifier
eye_cascade_classifier = cv2.CascadeClassifier(eye_classifier_file)
# Cascade classifiers for nose detection
nose_classifier_file = classifiers_folder_path + nose_detection_classifier
nose_cascade_classifier = cv2.CascadeClassifier(nose_classifier_file)
if eye_cascade_classifier.empty():
print('Error loading eye cascade classifier file')
result[c.ERROR_KEY] = 'Error loading eye cascade classifier file'
return result
if nose_cascade_classifier.empty():
print('Error loading nose cascade classifier file')
result[c.ERROR_KEY] = 'Error loading nose cascade classifier file'
return result
# Populate dictionary with detected faces and elapsed CPU time
result[c.ELAPSED_CPU_TIME_KEY] = detection_time_in_seconds
result[c.ERROR_KEY] = None
# Create face images from original image
faces_final = []
for (x, y, width, height) in faces:
image_height, image_width = image.shape
face_image = image[y: y + height, x: x + width]
face_dict = {}
face_list = (int(x), int(y), int(width), int(height))
face_dict[c.BBOX_KEY] = face_list
if use_eyes_position:
crop_result = get_cropped_face_from_image(
face_image, resource_path, align_path, params,
eye_cascade_classifier, nose_cascade_classifier,
(x, y, width, height), delete_files, return_always_faces)
if crop_result:
face_dict[c.LEFT_EYE_POS_KEY] = crop_result[c.LEFT_EYE_POS_KEY]
face_dict[c.RIGHT_EYE_POS_KEY] = crop_result[c.RIGHT_EYE_POS_KEY]
face_dict[c.NOSE_POSITION_KEY] = crop_result[c.NOSE_POSITION_KEY]
if not delete_files:
face_dict[c.ALIGNED_FACE_FILE_NAME_KEY] = (
crop_result[c.ALIGNED_FACE_FILE_NAME_KEY])
faces_final.append(face_dict)
else:
faces_final.append(face_dict)
result[c.FACES_KEY] = faces_final
if show_results:
image = cv2.imread(resource_path, cv2.IMREAD_COLOR)
for face_dict in faces_final:
(x, y, w, h) = face_dict[c.BBOX_KEY]
face = image[y: y + h, x: x + w]
eye_rects = utils.detect_eyes_in_image(
face, eye_cascade_classifier)
for(x_eye, y_eye, w_eye, h_eye) in eye_rects:
cv2.rectangle(
face, (x_eye, y_eye), (x_eye + w_eye, y_eye + h_eye),
(0, 0, 255), 3, 8, 0)
noses = utils.detect_nose_in_image(
face, nose_cascade_classifier)
for(x_ear, y_ear, w_ear, h_ear) in noses:
cv2.rectangle(
face, (x_ear, y_ear), (x_ear + w_ear, y_ear + h_ear),
(0, 0, 255), 3, 8, 0)
cv2.rectangle(
image, (x, y), (x + w, y + h), (0, 0, 255), 3, 8, 0)
cv2.namedWindow('Result', cv2.WINDOW_AUTOSIZE)
cv2.imshow('Result', image)
cv2.waitKey(0)
except IOError as e:
error_str = "I/O error({0}): {1}".format(e.errno, e.strerror)
print error_str
result[c.ERROR_KEY] = error_str
return result
except:
print "Unexpected error:", sys.exc_info()[0]
raise
return result