def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml',
fidu_model_file = '../resources/model_ang_0.txt',
fidu_exec_dir = '../resources/'):
'''
Constructor
'''
self.face_finder = CascadeFaceFinder(haar_file = haar_file,
lbp_file = lbp_file)
self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
self.fidu_exec_dir = fidu_exec_dir
self.valid_angles = [-45,-30,-15,0,15,30,45]
def testAffineAlign(self):
aligner = AffineAligner(fidu_model_file = '../resources/model_ang_0.txt')
img_files = ['./resources/affine_align/Meryl_Streep_0013.jpg', './resources/affine_align/Fayssal_Mekdad_0002.jpg']
for img_file in img_files:
img_file = os.path.abspath(img_file)
detect_landmarks(fname = img_file)
fidu_file = img_file.replace('.jpg','.cfidu')
img = cv2.imread(img_file)
score, yaw_angle, fidu_points = read_fidu(fidu_file)
aligned_img, R = aligner.align(img, fidu_points)
aligned_img_cpy = aligned_img.copy()
draw_fidu(img, fidu_points)
fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
draw_fidu(aligned_img_cpy, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
cv2.imshow('padded_face with landmarks', img)
cv2.imshow('aligned_face', cv2.resize(aligned_img, (320,320), interpolation = cv2.INTER_CUBIC))
cv2.imshow('aligned_face with landmarks', cv2.resize(aligned_img_cpy, (320,320), interpolation = cv2.INTER_CUBIC))
cv2.waitKey()
def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml',
fidu_model_file = '../resources/model_ang_0.txt',
fidu_exec_dir = '../resources/'):
'''
Constructor
'''
self.face_finder = CascadeFaceFinder(haar_file = haar_file,
lbp_file = lbp_file)
self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
self.fidu_exec_dir = fidu_exec_dir
self.valid_angles = [-45,-30,-15,0,15,30,45]
def testAffineAlign(self):
aligner = AffineAligner(fidu_model_file='../resources/model_ang_0.txt')
img_files = [
'./resources/affine_align/Meryl_Streep_0013.jpg',
'./resources/affine_align/Fayssal_Mekdad_0002.jpg'
]
for img_file in img_files:
img_file = os.path.abspath(img_file)
detect_landmarks(fname=img_file)
fidu_file = img_file.replace('.jpg', '.cfidu')
img = cv2.imread(img_file)
score, yaw_angle, fidu_points = read_fidu(fidu_file)
aligned_img, R = aligner.align(img, fidu_points)
aligned_img_cpy = aligned_img.copy()
draw_fidu(img, fidu_points)
fidu_points_in_aligned = unwarp_fidu(orig_fidu_points=fidu_points,
unwarp_mat=R)
draw_fidu(aligned_img_cpy,
fidu_points_in_aligned,
radius=9,
color=(255, 0, 0),
thickness=3)
cv2.imshow('padded_face with landmarks', img)
cv2.imshow(
'aligned_face',
cv2.resize(aligned_img, (320, 320),
interpolation=cv2.INTER_CUBIC))
cv2.imshow(
'aligned_face with landmarks',
cv2.resize(aligned_img_cpy, (320, 320),
interpolation=cv2.INTER_CUBIC))
cv2.waitKey()
class CascadeFaceAligner(object):
'''
classdocs
'''
def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml',
fidu_model_file = '../resources/model_ang_0.txt',
fidu_exec_dir = '../resources/'):
'''
Constructor
'''
self.face_finder = CascadeFaceFinder(haar_file = haar_file,
lbp_file = lbp_file)
self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
self.fidu_exec_dir = fidu_exec_dir
self.valid_angles = [-45,-30,-15,0,15,30,45]
def detect_faces(self, input_folder, output_folder, mark_dones = True):
'''
mark_dones - if True, will create a hidden file, marking this file as done with a hidden file, starting with '.done.'
'''
input_files1 = glob.glob(os.path.join(input_folder, '*.jpg'))
input_files2 = glob.glob(os.path.join(input_folder, '*.png'))
input_files = input_files1 + input_files2
N = len(input_files)
for n_file, input_file in enumerate(input_files):
a,b = os.path.split(input_file)
done_file = os.path.join(a, '.done.' + b.rsplit('.',1)[0])
if os.path.exists(done_file):
continue
print ("... processing", input_file)
target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file,
faces_file = faces_file,
target_folder = output_folder,
img_type = 'jpg')
#touch
open(done_file,'w').close()
print ("Detected on %d / %d files" %(n_file, N))
def align_faces(self, input_images, output_path, fidu_max_size = None, fidu_min_size = None, is_align = True, is_draw_fidu = False, delete_no_fidu = False):
'''
input_images - can be either a folder (all *.jpgs in it) or a list of filenames
, fidu_max_size = None, fidu_min_size = None):
'''
if type(input_images) == type(''):
input_images = glob.glob(os.path.join(input_images, '*.jpg'))
for input_image in input_images:
detect_landmarks(fname = os.path.abspath(input_image),
max_size = fidu_max_size,
min_size = fidu_min_size,
fidu_exec_dir = self.fidu_exec_dir)
fidu_file = input_image.rsplit('.',1)[0] + '.cfidu'
fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
if not (fidu_score is not None and yaw_angle in self.valid_angles):
# skip face
if delete_no_fidu:
os.remove(fidu_file)
os.remove(input_image)
continue
if is_align:
# save the aligned image
sub_img = cv2.imread(input_image)
_, base_fname = os.path.split(input_image)
aligned_img, R = self.aligner.align(sub_img, fidu_points)
aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.png')
cv2.imwrite(aligned_img_file, aligned_img)
# save a copy of the aligned image, with the landmarks drawn
if is_draw_fidu:
aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.withpoints.png')
fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
cv2.imwrite(aligned_img_file, aligned_img)
class CascadeFaceAligner(object):
'''
classdocs
'''
def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml',
fidu_model_file = '../resources/model_ang_0.txt',
fidu_exec_dir = '../resources/'):
'''
Constructor
'''
self.face_finder = CascadeFaceFinder(haar_file = haar_file,
lbp_file = lbp_file)
self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
self.fidu_exec_dir = fidu_exec_dir
self.valid_angles = [-45,-30,-15,0,15,30,45]
def detect_faces(self, input_folder, output_folder, mark_dones = True):
'''
mark_dones - if True, will create a hidden file, marking this file as done with a hidden file, starting with '.done.'
'''
input_files1 = glob.glob(os.path.join(input_folder, '*.jpg'))
input_files2 = glob.glob(os.path.join(input_folder, '*.png'))
input_files = input_files1 + input_files2
N = len(input_files)
for n_file, input_file in enumerate(input_files):
a,b = os.path.split(input_file)
done_file = os.path.join(a, '.done.' + b.rsplit('.',1)[0])
if os.path.exists(done_file):
continue
print "... processing", input_file
target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file,
faces_file = faces_file,
target_folder = output_folder,
img_type = 'jpg')
#touch
open(done_file,'w').close()
print "Detected on %d / %d files" %(n_file, N)
def align_faces(self, input_images, output_path, fidu_max_size = None, fidu_min_size = None, is_align = True, is_draw_fidu = False, delete_no_fidu = False):
'''
input_images - can be either a folder (all *.jpgs in it) or a list of filenames
, fidu_max_size = None, fidu_min_size = None):
'''
if type(input_images) == type(''):
input_images = glob.glob(os.path.join(input_images, '*.jpg'))
for input_image in input_images:
detect_landmarks(fname = os.path.abspath(input_image),
max_size = fidu_max_size,
min_size = fidu_min_size,
fidu_exec_dir = self.fidu_exec_dir)
fidu_file = input_image.rsplit('.',1)[0] + '.cfidu'
fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
if not (fidu_score is not None and yaw_angle in self.valid_angles):
# skip face
if delete_no_fidu:
os.remove(fidu_file)
os.remove(input_image)
continue
if is_align:
# save the aligned image
sub_img = cv2.imread(input_image)
_, base_fname = os.path.split(input_image)
aligned_img, R = self.aligner.align(sub_img, fidu_points)
aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.png')
cv2.imwrite(aligned_img_file, aligned_img)
# save a copy of the aligned image, with the landmarks drawn
if is_draw_fidu:
aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.withpoints.png')
fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
cv2.imwrite(aligned_img_file, aligned_img)
