def testDetectFacesAndCreateFiles(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/janice2.jpg',
'./resources/cascade/albert4.jpg',
'./resources/cascade/albert5.jpg',
'./resources/cascade/david1.jpg']
for n_image in range(len(fnames)):
fname = fnames[n_image]
expected_result = expected_results[n_image]
_, base_fname = os.path.split(fname)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces_file = face_finder.create_faces_file(fname, is_overwrite = True, target_file = './outputs/cascade/2/' + base_fname + '.faces.txt')
# get the sub images
sub_images = face_finder.get_sub_images_from_file(original_image_file = fname, faces_file = faces_file)
for n_face, sub_image in enumerate(sub_images):
cv2.imshow('face_%d' %n_face, sub_image)
cv2.waitKey()
# create sub images files
sub_images_file = face_finder.create_sub_images_from_file(original_image_file = fname, faces_file = faces_file, target_folder = None)
with open(faces_file,'r') as fid:
for i in range(len(expected_result)):
line = fid.readline()
def testDetectFacesAndCreateFiles(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = [
'./resources/cascade/Fayssal_Mekdad_0002.jpg',
'./resources/cascade/family-home.png'
]
expected_results = [
['x,y,dx,dy,score,angle,type\n', '61,62,132,132,344,0.0,haar'],
[
'x,y,dx,dy,score,angle,type\n',
'327,101,121,121,154,0.0,lbp\n',
'237,106,113,113,139,0.0,lbp\n', '164,49,91,91,49,0.0,lbp\n',
'434,86,94,94,95,0.0,lbp\n'
]
]
for n_image in range(len(fnames)):
fname = fnames[n_image]
expected_result = expected_results[n_image]
_, base_fname = os.path.split(fname)
face_finder = CascadeFaceFinder(
haar_file='../resources/haarcascade_frontalface_default.xml',
lbp_file='../resources/lbpcascade_frontalface.xml')
faces_file = face_finder.create_faces_file(
fname,
is_overwrite=True,
target_file='./outputs/cascade/2/' + base_fname + '.faces.txt')
# get the sub images
sub_images = face_finder.get_sub_images_from_file(
original_image_file=fname, faces_file=faces_file)
for n_face, sub_image in enumerate(sub_images):
cv2.imshow('face_%d' % n_face, sub_image)
cv2.waitKey()
# create sub images files
sub_images_file = face_finder.create_sub_images_from_file(
original_image_file=fname,
faces_file=faces_file,
target_folder=None)
with open(faces_file, 'r') as fid:
for i in range(len(expected_result)):
line = fid.readline()
self.assertEqual(line.strip(), expected_result[i].strip())
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 testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/Fayssal_Mekdad_0002.jpg', './resources/cascade/family-home.png']
expected_faces = [[],[]]
expected_faces[0].append(CascadeResult(box_with_score = ([61,62,132,132], 344), cascade_type = 'haar', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([327,101,119,119], 244), cascade_type = 'haar', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([238,107,111,111], 135), cascade_type = 'lbp', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([163,48,93,93], 51), cascade_type = 'lbp', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([433,86,95,95], 92), cascade_type = 'lbp', angle = 0.0))
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
self.assertAlmostEqual(face.overlap(expected_faces[n_images][n_face]) / face.area, 1.00, 0.01)
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
new_face_file = os.path.join('./outputs/cascade/1/', base_fname.split('.')[0] + '.face.%d.png' %n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' %n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
cv2.waitKey()
def testDetectFacesAndCreateFiles(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/Fayssal_Mekdad_0002.jpg', './resources/cascade/family-home.png']
expected_results = [['x,y,dx,dy,score,angle,type\n',
'61,62,132,132,344,0.0,haar'],
['x,y,dx,dy,score,angle,type\n',
'327,101,121,121,154,0.0,lbp\n',
'237,106,113,113,139,0.0,lbp\n',
'164,49,91,91,49,0.0,lbp\n',
'434,86,94,94,95,0.0,lbp\n']
]
for n_image in range(len(fnames)):
fname = fnames[n_image]
expected_result = expected_results[n_image]
_, base_fname = os.path.split(fname)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces_file = face_finder.create_faces_file(fname, is_overwrite = True, target_file = './outputs/cascade/2/' + base_fname + '.faces.txt')
# get the sub images
sub_images = face_finder.get_sub_images_from_file(original_image_file = fname, faces_file = faces_file)
for n_face, sub_image in enumerate(sub_images):
cv2.imshow('face_%d' %n_face, sub_image)
cv2.waitKey()
# create sub images files
sub_images_file = face_finder.create_sub_images_from_file(original_image_file = fname, faces_file = faces_file, target_folder = None)
with open(faces_file,'r') as fid:
for i in range(len(expected_result)):
line = fid.readline()
self.assertEqual(line.strip(), expected_result[i].strip())
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 testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/janice2.jpg',
'./resources/cascade/albert4.jpg',
'./resources/cascade/albert5.jpg',
'./resources/cascade/david1.jpg']
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
new_face_file = os.path.join('./outputs/cascade/1/', base_fname.split('.')[0] + '.face.%d.png' %n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' %n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
cv2.waitKey()
def testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = [
'./resources/cascade/Fayssal_Mekdad_0002.jpg',
'./resources/cascade/family-home.png'
]
expected_faces = [[], []]
expected_faces[0].append(
CascadeResult(box_with_score=([61, 62, 132, 132], 344),
cascade_type='haar',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([327, 101, 119, 119], 244),
cascade_type='haar',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([238, 107, 111, 111], 135),
cascade_type='lbp',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([163, 48, 93, 93], 51),
cascade_type='lbp',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([433, 86, 95, 95], 92),
cascade_type='lbp',
angle=0.0))
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(
haar_file='../resources/haarcascade_frontalface_default.xml',
lbp_file='../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
self.assertAlmostEqual(
face.overlap(expected_faces[n_images][n_face]) / face.area,
1.00, 0.01)
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(
img, face, padding_factor=0.25)
new_face_file = os.path.join(
'./outputs/cascade/1/',
base_fname.split('.')[0] + '.face.%d.png' % n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' % n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
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)
