def _get_face_embed(img: np.ndarray, proc_size: int,
mode: str) -> Union[np.ndarray, None]:
capt_faces = FaceCapture.cap(img)
if not capt_faces.has_face:
print(
f"[WARN] {__name__} ♦ failed to get face-embed cuz FaceCapture can't capture any faces"
)
return
if capt_faces.face_count != 1:
print(
f"[WARN] {__name__} ♦ failed to get face-embed cuz the count of CapturedFace is not 1"
)
return
face = Cropper.get_cropped_face(img, capt_faces[0], 0)
if mode == 'cv2-dnn':
embed_model = ModelAPI.get('openface-embed')
face_blob = ImgTool.get_dnn_blob(img, proc_size)
if face_blob is not None:
embed_model.setInput(face_blob)
vec = embed_model.forward()
return vec.flatten()
elif mode == 'fr-dlib':
face = ImgTool.get_rgb_img(face)
if face is not None:
face = ImgTool.resize(face, proc_size)
return face_recognition.face_encodings(face)[0]
def get_smart_cropped_face(img: np.ndarray, face_block: dlib.rectangle):
for i in range(10, -1, -1):
margin = i / 10
cropped = Cropper.get_cropped_face(img, face_block, margin)
h, w, _ = cropped.shape
if h > 0 and w > 0:
capt_faces = FaceCapture.cap(cropped)
if capt_faces.face_count == 1:
return cropped
def get_encodes_of_faces_in_frame(img: np.ndarray, mode: str) -> list:
result = list()
capt_faces = FaceCapture.cap(img)
for face_block in capt_faces:
face_encode = FacesProc.get_face_encode_from_raw(
mode, img, face_block)
if face_encode is not None:
result.append(face_encode)
return result
def open_camera(self):
UITool.msg_window('Info', 'Start face distant protect mode.')
flag = True
vs = WebcamVideoStream(src=0)
vs.start()
time.sleep(2.0)
cv2.startWindowThread()
while flag:
check = True
frame = vs.read()
frame = imutils.resize(frame, width=800)
display_frame = frame.copy()
capt_faces = FaceCapture.cap(frame)
if capt_faces.has_face:
display_frame = ImgTool.add_face_block(display_frame,
capt_faces)
face_data = FacesProc.get_all_faces_with_encode_low_filter(
frame, self.mode)
if face_data:
check = self.check_face_encode(face_data)
print(f"status: {check}")
if not check:
print(
f" WARN - find stranger and no master in the same time !!!"
)
self.alert = True
else:
print('fetched face data is not enough')
if self.display:
display_frame = cv2.flip(display_frame, 1)
if check:
text = 'Safe'
print(text)
display_frame = ImgTool.add_text(display_frame, text)
else:
text = 'Detect Stranger!'
display_frame = ImgTool.add_text(display_frame,
text,
color='red')
cv2.imshow('Frame', display_frame)
key = cv2.waitKey(1)
if self.alert or key == 27 or key == ord('q'):
flag = False
cv2.waitKey(500)
cv2.destroyAllWindows()
cv2.waitKey(500)
vs.stop()
cv2.destroyAllWindows()
def get_rotated_face(img: np.ndarray) -> np.ndarray:
img = img.copy()
capt_faces = FaceCapture.cap(img)
if capt_faces.face_count == 1:
face_block = capt_faces[0]
lmk_detector = ModelAPI.get('dlib-lmk68')
landmark = FaceRotate.get_landmarks(lmk_detector, img, face_block)
if landmark:
left_eye_coord = FaceRotate.get_left_eye_center(landmark)
right_eye_coord = FaceRotate.get_right_eye_center(landmark)
angle_to_hrzl = FaceRotate.get_angle_to_hrzl(
left_eye_coord, right_eye_coord)
return ImgTool.get_rotate_img(img, angle_to_hrzl)
def launch(self):
flag = True
vs = WebcamVideoStream(src=0)
vs.start()
time.sleep(2.0)
fetch_face = 0
last_face_ts = 0
cv2.startWindowThread()
while flag:
frame = vs.read()
if frame is None:
continue
frame = imutils.resize(frame, width=800)
display_frame = frame.copy()
capt_faces = FaceCapture.cap(frame)
if capt_faces.face_count == 1:
display_frame = ImgTool.add_face_block(display_frame,
capt_faces)
ts = FileTool.get_curr_ts()
if ts - last_face_ts > RecordMD.INTERVAL:
if self.__record_face(frame, capt_faces, ts):
fetch_face += 1
print(
f'fetch face successfully. ({fetch_face}/{self.fetch_count})'
)
last_face_ts = ts
else:
print(f'failed to record faces')
if self.display:
display_frame = cv2.flip(display_frame, 1)
text = f"{fetch_face} / {self.fetch_count}"
display_frame = ImgTool.add_text(display_frame, text)
cv2.imshow('Frame', display_frame)
key = cv2.waitKey(1)
if fetch_face == self.fetch_count or key == 27 or key == ord('q'):
flag = False
cv2.waitKey(500)
cv2.destroyAllWindows()
cv2.waitKey(500)
vs.stop()
cv2.destroyAllWindows()
# self.__img_meta.to_csv(RecordMD.meta_path, index=False, encoding='utf-8')
msg = 'Finish recoding master data.'
UITool.msg_window(msg=msg)
print('finish saving img meta.')
def get_all_faces_with_encode_low_filter(img: np.ndarray,
mode: str) -> list:
result = list()
capt_faces = FaceCapture.cap(img)
for face_block in capt_faces:
cropped_face = Cropper.get_smart_cropped_face(img, face_block)
face_grid = FacesProc.get_face_grid(cropped_face)
if face_grid is None:
print('failed to get face grid')
continue
face_encode = FacesProc.get_face_encode(img, mode)
if face_encode is not None:
record = {'portrait': cropped_face, 'encode': face_encode}
result.append(record)
else:
print('failed to get face encode')
return result
def get_face_encode_from_raw(mode: str,
img: np.ndarray,
face_block=None) -> Union[np.ndarray, None]:
if face_block is not None:
portrait = FacesProc.get_portrait(img, face_block)
if portrait is None:
return
face_grid = FacesProc.get_face_grid(portrait)
if face_grid is None:
return
return FacesProc.get_face_encode(face_grid, mode)
else:
capt_faces = FaceCapture.cap(img)
if capt_faces.face_count != 1:
return
face_block = capt_faces[0]
return FacesProc.get_face_encode_from_raw(mode, img, face_block)
def save_image(self, data):
result = list()
image_id = data[0]
image = data[1]
count = 0
capt_faces = FaceCapture.cap(image)
for i, fb in enumerate(capt_faces, start=1):
cropped_face = Cropper.get_smart_cropped_face(image, fb)
if cropped_face is not None:
rotated = FaceRotate.get_rotated_face(cropped_face)
if rotated is not None:
if RecogTool.can_get_embed(rotated, 'cv2-dnn'):
image_name = f"{FileTool.gen_random_token()}.jpg"
img_path = f"{self.img_dir}/{image_name}"
cv2.imwrite(img_path, rotated)
count += 1
result.append(image_id)
result.append(count)
return result
def get_face_encode(img: np.ndarray,
mode: str,
proc_size=96) -> Union[np.ndarray, None]:
"""
1. cv2-dnn
2. fr-dlib
:param img:
:param mode:
:param proc_size:
:return:
"""
if mode == 'cv2-dnn':
embed_model = ModelAPI.get('openface-embed')
img = face_recognition.load_image_file(img)
face_blob = ImgTool.get_dnn_blob(img, proc_size)
if face_blob is not None:
embed_model.setInput(face_blob)
vec = embed_model.forward()
print(vec)
return vec.flatten()
elif mode == 'fr-dlib':
face = ImgTool.get_rgb_img(img)
if face is None:
return
face = ImgTool.resize(face, proc_size)
if face is None:
return
capt_faces = FaceCapture.cap(face)
if capt_faces.face_count != 1:
return
fb = capt_faces[0]
box = FacesProc.dlib_rect_to_boxes(fb)
result = face_recognition.face_encodings(face, [box])
if len(result):
return result[0]
def get_all_faces_with_encode(img: np.ndarray, mode: str) -> list:
result = list()
capt_faces = FaceCapture.cap(img)
for face_block in capt_faces:
portrait = FacesProc.get_portrait(img, face_block)
if portrait is None:
print('failed to get portrait from image')
continue
face_grid = FacesProc.get_face_grid(portrait)
if face_grid is None:
print('failed to get face grid from image')
continue
face_encode = FacesProc.get_face_encode(img, mode)
if face_encode is not None:
record = {'portrait': portrait, 'encode': face_encode}
result.append(record)
else:
print('failed to get face encode from image')
return result
import cv2
from face_ult.cropper import Cropper
from face_ult.face_capture import FaceCapture
from face_ult.face_rotate import FaceRotate
img_path = 'b.jpg'
image = cv2.imread(img_path)
if __name__ == '__main__':
capt_faces = FaceCapture.cap(image)
for i, fb in enumerate(capt_faces, start=1):
# cropped_face = Cropper.get_cropped_face(image, fb, 0.9)
cropped_face = Cropper.get_smart_cropped_face(image, fb)
rotated = FaceRotate.get_rotated_face(cropped_face)
cf = FaceCapture.cap(rotated)
print(cf.face_count)
def get_face_grid(img: np.ndarray) -> np.ndarray:
capt_faces = FaceCapture.cap(img)
if capt_faces.has_face:
face_block = capt_faces[0]
return Cropper.get_cropped_face(img, face_block, 0)
def is_single_face(img: np.ndarray) -> bool:
if FaceCapture.cap(img).face_count == 1:
return True
else:
return False