def prepare_database():
database = {}
# load all the images of individuals to recognize into the database
for file in glob.glob("images/*"):
identity = os.path.splitext(os.path.basename(file))[0]
database[identity] = fr_utils.img_path_to_encoding(file, FRmodel)
print( f'prepare_database: file="{file}", identiti="{identity}"')
return database
def reload_database(self):
self.database = {}
# load all the images of individuals to recognize into the database
for photo_filename in glob.glob("%s/*" % (self.outputPath)):
photo_object = cv2.imread(photo_filename)
identity = os.path.splitext(os.path.basename(photo_filename))[0]
self.database[identity] = fr_utils.img_path_to_encoding(
photo_filename, self.FRmodel)
def get_face_dists(FR_model):
face_database = {}
for name in os.listdir('images'):
for image in os.listdir(os.path.join('images', name)):
identity = os.path.splitext(os.path.basename(image))[0]
face_database[identity] = fr_utils.img_path_to_encoding(
os.path.join('images', name, image), FR_model)
print(face_database)
return face_database
def myMainMethod():
FRmodel = inception_blocks_v2.faceRecoModel(input_shape=(3, 96, 96))
encoding = fr_utils.img_path_to_encoding("crop_frame.jpg", FRmodel)
print(encoding)
# minNeighbors=5,
# minSize=(30, 30),
# flags=cv2.cv2.CV_HAAR_SCALE_IMAGE
# )
faces = faceCascade.detectMultiScale(gray, 1.3, 4)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
print(x, y, w, h, frame.shape)
# roi = frame[640:1279, 360:719, :]
cv2.imwrite("frame.jpg", frame)
file = cv2.imread("frame.jpg")
temp_img = file[y-50:y + w+50, x-50:x + h+50]
cv2.imwrite("temp_file.jpg", temp_img)
encoding = fr_utils.img_path_to_encoding("temp_file.jpg", FRmodel)
print(encoding)
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()