def recalc_detectors(self, haar_file, lbp_file):
self.haar_dtct = CascadeDetector(cascade_file=haar_file,
min_size=self.min_size,
min_neighbors=20,
scale_factor=1.03,
cascade_type='haar',
thr=0.4,
angles=self.hangles)
self.lbp_dtct = CascadeDetector(cascade_file=lbp_file,
min_size=self.min_size,
min_neighbors=15,
scale_factor=1.04,
cascade_type='lbp',
thr=0.4,
angles=self.langles)
def recalc_detectors(self, haar_file, lbp_file):
self.haar_dtct = CascadeDetector(
cascade_file=haar_file,
min_size=self.min_size,
min_neighbors=20,
scale_factor=1.03,
cascade_type="haar",
thr=0.4,
angles=self.hangles,
)
self.lbp_dtct = CascadeDetector(
cascade_file=lbp_file,
min_size=self.min_size,
min_neighbors=15,
scale_factor=1.04,
cascade_type="lbp",
thr=0.4,
angles=self.langles,
)
class CascadeFaceFinder(object):
def __init__(self,
min_size=32,
drawn_target_res=360 * 360,
hangles=[0, -22, 22],
langles=[0, -45, -22, 22, 45],
haar_file='haarcascade_frontalface_default.xml',
lbp_file='lbpcascade_frontalface.xml'):
'''
finder = CascadeFaceFinder(min_size = 32, drawn_target_res = 360*360, hangles = [0], langles = [0,-45,-22,22,45], parts_threshold = 0)
finder.get_faces_in_folder(input_folder, output_dir, drawn_folder, is_small_drawn)
or
finder.get_faces_in_photo(full_file, output_dir, drawn_folder, is_small_drawn)
'''
self.min_size = (min_size, min_size)
self.drawn_target_res = drawn_target_res
self._hangles = hangles
self._langles = langles
self.recalc_detectors(haar_file, lbp_file)
# self.funnel = FaceFunnel()
@property
def hangles(self):
return self._hangles
@hangles.setter
def hangles(self, hangles):
self._hangles = hangles
self.recalc_detectors()
@property
def langles(self):
return self._langles
@langles.setter
def langles(self, langles):
self._langles = langles
self.recalc_detectors()
def recalc_detectors(self, haar_file, lbp_file):
self.haar_dtct = CascadeDetector(cascade_file=haar_file,
min_size=self.min_size,
min_neighbors=20,
scale_factor=1.03,
cascade_type='haar',
thr=0.4,
angles=self.hangles)
self.lbp_dtct = CascadeDetector(cascade_file=lbp_file,
min_size=self.min_size,
min_neighbors=15,
scale_factor=1.04,
cascade_type='lbp',
thr=0.4,
angles=self.langles)
def get_faces_list_in_photo(self, img):
if self.hangles:
haar_faces = self.haar_dtct.detectWithAngles(img, resolve=True)
else:
haar_faces = []
lbp_faces = self.lbp_dtct.detectWithAngles(img, resolve=True)
faces = resolve_boxes({
'haar': haar_faces,
'lbp': lbp_faces
},
min_overlap=0.6)
return faces
def create_faces_file(self, fname, is_overwrite=False, target_file=None):
'''
Runs facial detection on fname (say a.jpg, or a.png), and creates a results file (a.faces.txt)
target_file - override, and specify a specific target file
is_overwrite - allow overwriting an existing results file
'''
faces = self.get_faces_list_in_photo(cv2.imread(fname))
results_file = fname.rsplit(
'.', 1)[0] + '.faces.txt' if target_file is None else target_file
if os.path.exists(results_file) and not is_overwrite:
print("Warning, faces result file", results_file, "exists")
else:
with open(results_file, 'w') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
header = ['x', 'y', 'dx', 'dy', 'score', 'angle', 'type']
csv_writer.writerow(header)
for face in faces:
csv_writer.writerow([
str(i) for i in [
int(face.x),
int(face.y),
int(face.dx),
int(face.dy), face.score, face.angle,
face.cascade_type
]
])
return results_file
def get_sub_images_from_file(self, original_image_file, faces_file):
'''
extracts all the face sub-images from an image file, based on the results in a faces file
returns - the list of face images (numpy arrays)
'''
img = cv2.imread(original_image_file)
faces_reader = csv.reader(open(faces_file))
faces_reader.next() # discard the headings
padded_face_images = []
for line in faces_reader:
x, y, dx, dy, score, angle, cascade_type = line
[x, y, dx, dy, score,
angle] = [int(float(i)) for i in [x, y, dx, dy, score, angle]]
face = CascadeResult(([x, y, dx, dy], score), cascade_type, angle)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(
img, face, padding_factor=0.25)
padded_face_images.append(padded_face)
return padded_face_images
def create_sub_images_from_file(self,
original_image_file,
faces_file,
target_folder=None,
img_type='png'):
'''
reads a faces file, created by "self.create_faces_file" and extracts padded faces from the original image
The faces will be created in the same folder as the faces file, unless specified otherwise by "target_folder"
returns - the list of face files (strings)
'''
target_folder = os.path.split(
faces_file)[0] if target_folder is None else target_folder
padded_face_images = self.get_sub_images_from_file(
original_image_file, faces_file)
base_image_name = os.path.split(faces_file)[1].split('.')[0]
face_files = []
for n_face, face_img in enumerate(padded_face_images):
face_file = os.path.join(
target_folder,
base_image_name + '_face_%d.%s' % (n_face, img_type))
cv2.imwrite(face_file, face_img)
face_files.append(face_file)
return face_files
class CascadeFaceFinder(object):
def __init__(
self,
min_size=32,
drawn_target_res=360 * 360,
hangles=[0, -22, 22],
langles=[0, -45, -22, 22, 45],
haar_file="haarcascade_frontalface_default.xml",
lbp_file="lbpcascade_frontalface.xml",
):
"""
finder = CascadeFaceFinder(min_size = 32, drawn_target_res = 360*360, hangles = [0], langles = [0,-45,-22,22,45], parts_threshold = 0)
finder.get_faces_in_folder(input_folder, output_dir, drawn_folder, is_small_drawn)
or
finder.get_faces_in_photo(full_file, output_dir, drawn_folder, is_small_drawn)
"""
self.min_size = (min_size, min_size)
self.drawn_target_res = drawn_target_res
self._hangles = hangles
self._langles = langles
self.recalc_detectors(haar_file, lbp_file)
# self.funnel = FaceFunnel()
@property
def hangles(self):
return self._hangles
@hangles.setter
def hangles(self, hangles):
self._hangles = hangles
self.recalc_detectors()
@property
def langles(self):
return self._langles
@langles.setter
def langles(self, langles):
self._langles = langles
self.recalc_detectors()
def recalc_detectors(self, haar_file, lbp_file):
self.haar_dtct = CascadeDetector(
cascade_file=haar_file,
min_size=self.min_size,
min_neighbors=20,
scale_factor=1.03,
cascade_type="haar",
thr=0.4,
angles=self.hangles,
)
self.lbp_dtct = CascadeDetector(
cascade_file=lbp_file,
min_size=self.min_size,
min_neighbors=15,
scale_factor=1.04,
cascade_type="lbp",
thr=0.4,
angles=self.langles,
)
def get_faces_list_in_photo(self, img):
if self.hangles:
haar_faces = self.haar_dtct.detectWithAngles(img, resolve=True)
else:
haar_faces = []
lbp_faces = self.lbp_dtct.detectWithAngles(img, resolve=True)
faces = resolve_boxes({"haar": haar_faces, "lbp": lbp_faces}, min_overlap=0.6)
return faces
def create_faces_file(self, fname, is_overwrite=False, target_file=None):
"""
Runs facial detection on fname (say a.jpg, or a.png), and creates a results file (a.faces.txt)
target_file - override, and specify a specific target file
is_overwrite - allow overwriting an existing results file
"""
faces = self.get_faces_list_in_photo(cv2.imread(fname))
results_file = fname.rsplit(".", 1)[0] + ".faces.txt" if target_file is None else target_file
if os.path.exists(results_file) and not is_overwrite:
print "Warning, faces result file", results_file, "exists"
else:
with open(results_file, "w") as csvfile:
csv_writer = csv.writer(csvfile, delimiter=",")
header = ["x", "y", "dx", "dy", "score", "angle", "type"]
csv_writer.writerow(header)
for face in faces:
csv_writer.writerow(
[
str(i)
for i in [
int(face.x),
int(face.y),
int(face.dx),
int(face.dy),
face.score,
face.angle,
face.cascade_type,
]
]
)
return results_file
def get_sub_images_from_file(self, original_image_file, faces_file):
"""
extracts all the face sub-images from an image file, based on the results in a faces file
returns - the list of face images (numpy arrays)
"""
img = cv2.imread(original_image_file)
faces_reader = csv.reader(open(faces_file))
faces_reader.next() # discard the headings
padded_face_images = []
for line in faces_reader:
x, y, dx, dy, score, angle, cascade_type = line
[x, y, dx, dy, score, angle] = [int(float(i)) for i in [x, y, dx, dy, score, angle]]
face = CascadeResult(([x, y, dx, dy], score), cascade_type, angle)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor=0.25)
padded_face_images.append(padded_face)
return padded_face_images
def create_sub_images_from_file(self, original_image_file, faces_file, target_folder=None, img_type="png"):
"""
reads a faces file, created by "self.create_faces_file" and extracts padded faces from the original image
The faces will be created in the same folder as the faces file, unless specified otherwise by "target_folder"
returns - the list of face files (strings)
"""
target_folder = os.path.split(faces_file)[0] if target_folder is None else target_folder
padded_face_images = self.get_sub_images_from_file(original_image_file, faces_file)
base_image_name = os.path.split(faces_file)[1].split(".")[0]
face_files = []
for n_face, face_img in enumerate(padded_face_images):
face_file = os.path.join(target_folder, base_image_name + "_face_%d.%s" % (n_face, img_type))
cv2.imwrite(face_file, face_img)
face_files.append(face_file)
return face_files