def test_invalid_image():
model_loc = "./models"
ob = FaceDetectorDlib(model_loc=model_loc, model_type="hog")
img = np.zeros((100,100,5), dtype='float32')
with pytest.raises(InvalidImage):
ob.detect_faces(img)
def test_incorrect_model_path():
"""
Test object init with the incorrect model path
"""
ob = None
incorrect_model_loc = "./wrong_models"
with pytest.raises(ModelFileMissing):
ob = FaceDetectorDlib(model_loc=incorrect_model_loc, model_type="mmod")
def __init__(
self,
model_loc: str = "./models",
persistent_data_loc="data/facial_data.json",
face_detection_threshold: int = 0.99,
face_detector: str = "dlib",
) -> None:
"""Constructor
Args:
model_loc (str, optional): Path where model files are saved. Defaults to "./models".
persistent_data_loc (str, optional): Path to save the persistence storage file.
Defaults to 'data/facial_data.json'.
face_detection_threshold (int, optional): Threshold facial model confidence to consider a detection.
Defaults to 0.99.
face_detector (str, optional): Type of face detector to use. Options:
Dlib-HOG and MMOD, MTCNN, OpenCV CNN. Defaults to 'dlib'.
Raises:
ModelFileMissing: Raised when model file is not found
"""
keypoints_model_path = os.path.join(
model_loc, FaceRecognition.keypoints_model_path
)
face_recog_model_path = os.path.join(
model_loc, FaceRecognition.face_recog_model_path
)
if not (
path_exists(keypoints_model_path) or path_exists(face_recog_model_path)
):
raise ModelFileMissing
if face_detector == "opencv":
self.face_detector = FaceDetectorOpenCV(
model_loc=model_loc, crop_forehead=True, shrink_ratio=0.2
)
elif face_detector == "mtcnn":
self.face_detector = FaceDetectorMTCNN(crop_forehead=True, shrink_ratio=0.2)
else:
self.face_detector = FaceDetectorDlib()
self.face_detection_threshold = face_detection_threshold
self.keypoints_detector = dlib.shape_predictor(keypoints_model_path)
self.face_recognizor = dlib.face_recognition_model_v1(face_recog_model_path)
self.datastore = FaceDataStore(persistent_data_loc=persistent_data_loc)
def test_correct_model_path():
"""
Test object init with the correct model path
"""
ob = None
model_loc = "./models"
try:
ob = FaceDetectorDlib(model_loc=model_loc, model_type="mmod")
except Exception:
pass
finally:
assert isinstance(ob, FaceDetectorDlib)
def __init__(
self,
face_detector: str = "dlib",
model_loc: str = "models",
persistent_db_path: str = "data/facial_data.json",
face_detection_threshold: float = 0.8,
) -> None:
self.face_recognizer = FaceRecognition(
model_loc=model_loc,
persistent_data_loc=persistent_db_path,
face_detection_threshold=face_detection_threshold,
face_detector=face_detector,
)
if face_detector == "opencv":
self.face_detector = FaceDetectorOpenCV(
model_loc=model_loc, crop_forehead=True, shrink_ratio=0.2
)
elif face_detector == "mtcnn":
self.face_detector = FaceDetectorMTCNN(crop_forehead=True, shrink_ratio=0.2)
elif face_detector == "dlib":
self.face_detector = FaceDetectorDlib()
class FaceRecognition:
"""Class for Face Recognition related methods.
Main operations: Register and Recognize face.
Raises:
ModelFileMissing: [description]
NoNameProvided: [description]
NoFaceDetected: [description]
FaceMissing: [description]
"""
keypoints_model_path = "shape_predictor_5_face_landmarks.dat"
face_recog_model_path = "dlib_face_recognition_resnet_model_v1.dat"
def __init__(
self,
model_loc: str = "./models",
persistent_data_loc="data/facial_data.json",
face_detection_threshold: int = 0.99,
face_detector: str = "dlib",
) -> None:
"""Constructor
Args:
model_loc (str, optional): Path where model files are saved. Defaults to "./models".
persistent_data_loc (str, optional): Path to save the persistence storage file.
Defaults to 'data/facial_data.json'.
face_detection_threshold (int, optional): Threshold facial model confidence to consider a detection.
Defaults to 0.99.
face_detector (str, optional): Type of face detector to use. Options:
Dlib-HOG and MMOD, MTCNN, OpenCV CNN. Defaults to 'dlib'.
Raises:
ModelFileMissing: Raised when model file is not found
"""
keypoints_model_path = os.path.join(
model_loc, FaceRecognition.keypoints_model_path)
face_recog_model_path = os.path.join(
model_loc, FaceRecognition.face_recog_model_path)
if not (path_exists(keypoints_model_path)
or path_exists(face_recog_model_path)):
raise ModelFileMissing
if face_detector == "opencv":
self.face_detector = FaceDetectorOpenCV(model_loc=model_loc,
crop_forehead=True,
shrink_ratio=0.2)
elif face_detector == "mtcnn":
self.face_detector = FaceDetectorMTCNN(crop_forehead=True,
shrink_ratio=0.2)
else:
self.face_detector = FaceDetectorDlib()
self.face_detection_threshold = face_detection_threshold
self.keypoints_detector = dlib.shape_predictor(keypoints_model_path)
self.face_recognizor = dlib.face_recognition_model_v1(
face_recog_model_path)
self.datastore = FaceDataStore(persistent_data_loc=persistent_data_loc)
def register_face(self,
image=None,
name: str = None,
bbox: List[int] = None):
"""Method to register a face via the facial encoding.
Siamese neural network is used to generate 128 numbers
for a given facial region. These encodings can be used to identify a
facial ROI for identification later.
Args:
image (numpy array, optional): Defaults to None.
name (str, optional): Name to associate with the face. Defaults to None.
bbox (List[int], optional): Facial ROI bounding box. Defaults to None.
Raises:
NoNameProvided:
NoFaceDetected:
Returns:
Dict: Facial encodings along with an unique identifier and name
"""
if not is_valid_img(image) or name is None:
raise NoNameProvided if name is None else InvalidImage
image = image.copy()
face_encoding = None
try:
if bbox is None:
bboxes = self.face_detector.detect_faces(image=image)
if len(bboxes) == 0:
raise NoFaceDetected
bbox = bboxes[0]
face_encoding = self.get_facial_fingerprint(image, bbox)
# Convert the numpy array to normal python float list
# to make json serialization simpler
facial_data = {
"id": str(uuid.uuid4()),
"encoding": tuple(face_encoding.tolist()),
"name": name,
}
# save the encoding with the name
self.save_facial_data(facial_data)
logger.info("Face registered with name: {}".format(name))
except Exception as exc:
raise exc
return facial_data
def save_facial_data(self, facial_data: Dict = None) -> bool:
"""Saves facial data to cache and persistent storage
Args:
facial_data (Dict, optional): [description]. Defaults to None.
Returns:
bool: status of saving
"""
if facial_data is not None:
self.datastore.add_facial_data(facial_data=facial_data)
return True
return False
def get_registered_faces(self) -> List[Dict]:
"""Returns the list of all facial data of all registered users
Returns:
List[Dict]: List of facial data
"""
return self.datastore.get_all_facial_data()
def recognize_faces(self,
image,
threshold: float = 0.6,
bboxes: List[List[int]] = None):
"""Finds matching registered users for the
face(s) in the input image. The input image should be cropped to contain
only one face and then sent to this method.
Args:
image (numpy array): [description]
threshold (float, optional): Max threshold euclidean distance to
consider two people to be a match. Defaults to 0.6.
bboxes (List[List[int]], optional): List of facial ROI bounding box.
If this is None, then face detection is performed on the image
and facial recognition is run for all the detected faces, otherwise
if a bounding box is sent, then facial recognition is only
done for that bounding box. Defaults to None.
Raises:
NoFaceDetected: [description]
Returns:
List[Tuple]: List of information of matching
"""
if image is None:
return InvalidImage
image = image.copy()
if bboxes is None:
bboxes = self.face_detector.detect_faces(image=image)
if len(bboxes) == 0:
raise NoFaceDetected
# Load the data of existing registered faces
# compare using the metric the closest match
all_facial_data = self.datastore.get_all_facial_data()
matches = []
for bbox in bboxes:
face_encoding = self.get_facial_fingerprint(image, bbox)
match, min_dist = None, 10000000
for face_data in all_facial_data:
dist = self.euclidean_distance(face_encoding,
face_data["encoding"])
if dist <= threshold and dist < min_dist:
match = face_data
min_dist = dist
# bound box, matched face details, dist from closest match
matches.append((bbox, match, min_dist))
return matches
def get_facial_fingerprint(self,
image,
bbox: List[int] = None) -> List[float]:
"""Driver method for generating the facial encoding for an input image.
Input image bbox -> facial keypoints detection -> keypoints used for
face alignment -> Siamese NN -> Encoding
Args:
image (numpy array): [description]
bbox (List[int], optional): List of facial ROI bounding box. Defaults to None.
Raises:
FaceMissing: [description]
Returns:
List[float]: Facial Encoding
"""
if bbox is None:
raise FaceMissing
# Convert to dlib format rectangle
bbox = convert_to_dlib_rectangle(bbox)
# Get the facial landmark coordinates
face_keypoints = self.keypoints_detector(image, bbox)
# Compute the 128D vector that describes the face in an img identified by
# shape. In general, if two face descriptor vectors have a Euclidean
# distance between them less than 0.6 then they are from the same
# person, otherwise they are from different people.
face_encoding = self.get_face_encoding(image, face_keypoints)
return face_encoding
def get_face_encoding(self, image, face_keypoints: List):
"""Method for generating the facial encoding for
a face in an input image.
Args:
image (numpy array): [description]
face_keypoints (List): [description]
Returns:
[type]: [description]
"""
encoding = self.face_recognizor.compute_face_descriptor(
image, face_keypoints, 1)
return np.array(encoding)
def euclidean_distance(self, vector1: Tuple, vector2: Tuple):
"""Computes Euclidean distance between two vectors
Args:
vector1 (Tuple): [description]
vector2 (Tuple): [description]
Returns:
[type]: [description]
"""
return np.linalg.norm(np.array(vector1) - np.array(vector2))
def test_detect_face_mmod(img2_data, img2_facebox_dlib_mmod):
model_loc = "./models"
ob = FaceDetectorDlib(model_loc=model_loc, model_type="mmod")
assert img2_facebox_dlib_mmod == ob.detect_faces(img2_data)
def test_detect_face_hog(img2_data, img2_facebox_dlib_hog):
model_loc = "./models"
ob = FaceDetectorDlib(model_loc=model_loc, model_type="hog")
assert img2_facebox_dlib_hog == ob.detect_faces(img2_data)
class FaceRecognitionVideo:
"""Class with methods to do facial recognition on video or webcam feed."""
def __init__(
self,
face_detector: str = "dlib",
model_loc: str = "models",
persistent_db_path: str = "data/facial_data.json",
face_detection_threshold: float = 0.8,
) -> None:
self.face_recognizer = FaceRecognition(
model_loc=model_loc,
persistent_data_loc=persistent_db_path,
face_detection_threshold=face_detection_threshold,
face_detector=face_detector,
)
if face_detector == "opencv":
self.face_detector = FaceDetectorOpenCV(
model_loc=model_loc, crop_forehead=True, shrink_ratio=0.2
)
elif face_detector == "mtcnn":
self.face_detector = FaceDetectorMTCNN(crop_forehead=True, shrink_ratio=0.2)
elif face_detector == "dlib":
self.face_detector = FaceDetectorDlib()
def recognize_face_video(
self,
video_path: str = None,
detection_interval: int = 15,
save_output: bool = False,
preview: bool = False,
output_path: str = "data/output.mp4",
resize_scale: float = 0.5,
verbose: bool = True,
) -> None:
if video_path is None:
# If no video source is given, try
# switching to webcam
video_path = 0
elif not path_exists(video_path):
raise FileNotFoundError
cap, video_writer = None, None
try:
cap = cv2.VideoCapture(video_path)
# To save the video file, get the opencv video writer
video_writer = get_video_writer(cap, output_path)
frame_num = 1
matches, name, match_dist = [], None, None
t1 = time.time()
logger.info("Enter q to exit...")
while True:
status, frame = cap.read()
if not status:
break
try:
# Flip webcam feed so that it looks mirrored
if video_path == 0:
frame = cv2.flip(frame, 2)
if frame_num % detection_interval == 0:
# Scale down the image to increase model
# inference time.
smaller_frame = convert_to_rgb(
cv2.resize(frame, (0, 0), fx=resize_scale, fy=resize_scale)
)
# Detect faces
matches = self.face_recognizer.recognize_faces(
image=smaller_frame, threshold=0.6, bboxes=None
)
if verbose:
self.annotate_facial_data(matches, frame, resize_scale)
if save_output:
video_writer.write(frame)
if preview:
cv2.imshow("Preview", cv2.resize(frame, (680, 480)))
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
except Exception:
pass
frame_num += 1
t2 = time.time()
logger.info("Time:{}".format((t2 - t1) / 60))
logger.info("Total frames: {}".format(frame_num))
logger.info("Time per frame: {}".format((t2 - t1) / frame_num))
except Exception as exc:
raise exc
finally:
cv2.destroyAllWindows()
cap.release()
video_writer.release()
def register_face_webcam(
self, name: str = None, detection_interval: int = 5
) -> bool:
if name is None:
raise NoNameProvided
cap = None
try:
cap = cv2.VideoCapture(0)
frame_num = 0
while True:
status, frame = cap.read()
if not status:
break
if frame_num % detection_interval == 0:
# detect faces
bboxes = self.face_detector.detect_faces(image=frame)
try:
if len(bboxes) == 1:
facial_data = self.face_recognizer.register_face(
image=frame, name=name, bbox=bboxes[0]
)
if facial_data:
draw_bounding_box(frame, bboxes[0])
cv2.imshow("Registered Face", frame)
cv2.waitKey(0)
logger.info("Press any key to continue......")
break
except Exception as exc:
traceback.print_exc(file=sys.stdout)
frame_num += 1
except Exception as exc:
raise exc
finally:
cv2.destroyAllWindows()
cap.release()
def register_face_path(self, img_path: str, name: str) -> None:
if not path_exists(img_path):
raise PathNotFound
try:
img = cv2.imread(img_path)
facial_data = self.face_recognizer.register_face(
image=convert_to_rgb(img), name=name
)
if facial_data:
logger.info("Face registered...")
return True
return False
except Exception as exc:
raise exc
def annotate_facial_data(
self, matches: List[Dict], image, resize_scale: float
) -> None:
for face_bbox, match, dist in matches:
name = match["name"] if match is not None else "Unknown"
# match_dist = '{:.2f}'.format(dist) if dist < 1000 else 'INF'
# name = name + ', Dist: {}'.format(match_dist)
# draw face labels
draw_annotation(image, name, int(1 / resize_scale) * np.array(face_bbox))