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"""This module is like a tool for just eyes and also the alll of the program. There are a lot of functions here"""
import os
import shutil
import numpy as np
import mediapipe as mp
import cv2
import pickle
from sklearn.utils import shuffle
from screeninfo import get_monitors
from codes.base.face_geometry import PCF, procrustes_landmark_basis, get_metric_landmarks
from codes.base.iris_lm_depth import from_landmarks_to_depth as fl2d
import time
import math
from screeninfo import get_monitors
STATIC_IMAGE_MODE = False
MIN_TRACKING_CONFIDENCE = 0.5
MIN_DETECTION_CONFIDENCE = 0.5
EYE_SIZE = (100, 50)
Y_SCALER = 1000.0
X1_SCALER = 255.0
WHITE = (220, 220, 220)
BLACK = (0, 0, 0)
GRAY = (70, 70, 70)
RED = (0, 0, 220)
BLUE = (220, 0, 0)
GREEN = (0, 220, 0)
PATH2ROOT = ""
PATH2ROOT_ABS = os.path.dirname(__file__) + "/../../"
CLB = "clb"
IO = "io"
LTN = "ltn"
ACC = "acc"
SMP = "smp"
MDL = "mdl"
RAW = "raw"
TRAINED = "trained"
T = "t"
X1 = "x1"
X2 = "x2"
Y = "y"
ER = "er"
FV = "fv"
DEFAULT_BLINKING_THRESHOLD = 4.5
LATENCY_WAITING_TIME = 50
def get_mesh():
"""
Creating face mesh model
Parameters:
None
Returns:
face_mesh: The face mesh model
"""
print("Configuring face detection model...")
face_mesh = mp.solutions.face_mesh.FaceMesh(
static_image_mode=STATIC_IMAGE_MODE,
min_tracking_confidence=MIN_TRACKING_CONFIDENCE,
min_detection_confidence=MIN_DETECTION_CONFIDENCE)
return face_mesh
def get_clb_win_prp(clb_win_align=(0, 0)):
"""
Creating calibration window
Parameters:
clb_win_align: The window's top-left location
Returns:
clb_win_size: The window's size
"""
clb_win_w_align, clb_win_h_align = clb_win_align
screen_w = None
screen_h = None
for m in get_monitors():
screen_w = m.width
screen_h = m.height
clb_win_w = screen_w - clb_win_w_align
clb_win_h = screen_h - clb_win_h_align
clb_win_size = (clb_win_w, clb_win_h)
return clb_win_size
def get_some_landmarks_ids():
"""
Getting some landmarks that are needed for calculation of the face rotation and position vectors
Parameters:
None
Returns:
some_landmarks_ids: The landmarks numbers
"""
jaw_landmarks_ids = [61, 291, 199]
some_landmarks_ids = jaw_landmarks_ids + [
key for key, _ in procrustes_landmark_basis
]
some_landmarks_ids.sort()
return some_landmarks_ids
def get_camera_properties(camera_id):
"""
Getting the camera properties.
Parameters:
camera_id: camera ID
Returns:
fr_size: The frame size
camera_matrix: The intrinsic matrix of the camera
dst_cof: distortion coefficients of the camera
pcf: An object that is needed for later calculations
"""
print("Getting camera properties...")
fr_w, fr_h = 1280, 720
cap = cv2.VideoCapture(camera_id) # (tp.CAMERA_ID, cv2.CAP_DSHOW)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, fr_w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, fr_h)
new_fr_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
new_fr_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
cap.release()
fr_size = new_fr_w, new_fr_h
fr_center = (new_fr_w // 2, new_fr_h // 2)
focal_length = new_fr_w
camera_matrix = np.array([
[focal_length, 0, fr_center[0]],
[0, focal_length, fr_center[1]],
[0, 0, 1]], dtype="double")
dst_cof = np.zeros((4, 1))
pcf = PCF(
frame_height=fr_h,
frame_width=fr_w,
fy=fr_w)
return fr_size, camera_matrix, dst_cof, pcf
def get_camera(camera_id, frame_size):
"""
Setting the camera
Parameters:
camera_id: Camera ID
frame_size: The frame size
Returns:
cap: The capture object"""
frame_w, frame_h = frame_size
cap = cv2.VideoCapture(camera_id) # (camera_id, cv2.CAP_DSHOW)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, frame_w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, frame_h)
return cap
def get_frame(cap):
"""
Getting the frame
Parameters:
cap: The capture object
Returns:
success: whether or not the frame is received
img: the frame (BGR)
img_rgb: the frame (RGB). It is needed for face mesh model
"""
success, img = cap.read()
if success:
img = cv2.flip(img, 1)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
else:
img = None
img_rgb = None
return success, img, img_rgb
def get_eyes_pixels(eye_pixels):
"""
Get eyes locations
Parameters:
eyes_pixels: eyes pixels
Returns:
eye_top_left: eye top left
eye_bottom_right: eyes bottom right
"""
pxl = np.min(eye_pixels[:, 0])
pxr = np.max(eye_pixels[:, 0])
pyt = np.min(eye_pixels[:, 1])
pyb = np.max(eye_pixels[:, 1])
ew = max(pxr - pxl, 25)
ht = int(0.35 * ew)
hb = int(0.25 * ew)
wl = int(0.2 * ew)
wr = int(0.1 * ew)
eye_top_left = pxl - wl, pyt - ht
eye_bottom_right = pxr + wr, pyb + hb
return eye_top_left, eye_bottom_right
def get_face(all_landmarks_pixels):
"""
Getting the face
Parameters:
all_landmarks_pixels: the landmarks
Retruns:
face_left: face left
face_right: face_right
face_top: face top
face_bottom: face bottom
"""
face_left = all_landmarks_pixels[:, 0].min()
face_right = all_landmarks_pixels[:, 0].max()
face_top = all_landmarks_pixels[:, 1].min()
face_bottom = all_landmarks_pixels[:, 1].max()
return face_left, face_right, face_top, face_bottom
def get_eyes_ratio(all_landmarks):
"""
Getting the eyes ratio
Parameters:
all_landmarks: all of the landmarks
Returns:
ear: The eyes aspect ratio
"""
wl = np.sqrt(((all_landmarks[33,:2]-all_landmarks[133,:2])**2).sum())
hl1 = np.sqrt(((all_landmarks[159,:2]-all_landmarks[145,:2])**2).sum())
hl2 = np.sqrt(((all_landmarks[158,:2]-all_landmarks[153,:2])**2).sum())
hl = (hl1 + hl2) / 2
wr = np.sqrt(((all_landmarks[362,:2]-all_landmarks[263,:2])**2).sum())
hr1 = np.sqrt(((all_landmarks[385,:2]-all_landmarks[380,:2])**2).sum())
hr2 = np.sqrt(((all_landmarks[386,:2]-all_landmarks[374,:2])**2).sum())
hr = (hr1 + hr2) / 2
ear = ((wl / hl + wr / hr) / 2)
return ear
def get_model_inputs(
image,
image_rgb,
face_mesh,
camera_matrix,
pcf,
image_size,
dst_cof,
some_landmarks_ids,
show_features=False,
return_face=False
):
"""
Preparing the models inputs. Eyes images, face rotation, face position, iris locations in image
Parameters:
image: the frame (BGR)
image_rgb: the frame (RGB) for face mesh model
face_mesh: The face mesh model
camera_matrix: The intrinsic matrix
pcf: pcf object for calculating the face vectors
image_size: frame size
dst_cof: distortion coefficients of the camera
some_landmarks_ids: the landmarks needed for calculation of face vectors
show_features: whether or not show the inputs
return_face: whether or not return the face image
Returns:
success: whether or not the eyes extraction was successful
image: the frame
eyes_gray: the eyes image which is gray scale
features_vector: 10 values for face rotation, face position and iris locations
eye_ratio: The eyes aspect ratio
face_img: face image
"""
left_eye_landmarks_ids = (33, 133)
right_eye_landmarks_ids = (362, 263)
jaw_landmarks_ids = (61, 291, 199)
focal_length = camera_matrix[0, 0]
success = False
eyes_gray = None
features = []
features_vector = []
eye_ratio = 0.0
face_img = []
face_size = (300, 350)
mfl = face_mesh.multi_face_landmarks
# If there is any landmark
if mfl:
all_landmarks = np.array([(lm.x, lm.y, lm.z) for lm in mfl[0].landmark])
all_landmarks_pixels = np.array(all_landmarks[:,:2] * image_size, np.uint32)
eye_ratio = get_eyes_ratio(all_landmarks)
head_pose_landmarks = all_landmarks.T
metric_landmarks, _ = get_metric_landmarks(head_pose_landmarks.copy(), pcf)
some_landmarks_model = metric_landmarks[:, some_landmarks_ids].T
some_landmarks_image = (all_landmarks[some_landmarks_ids, :2] * image_size)
# Caluculating the face vector and face position
(
_,
rotation_vector,
translation_vector
) = cv2.solvePnP(
some_landmarks_model,
some_landmarks_image,
camera_matrix,
dst_cof,
flags=cv2.SOLVEPNP_ITERATIVE
)
features.append(rotation_vector.reshape((3,)))
features.append(translation_vector.reshape((3,)))
# calculating iris location
(
success_left,
_,
_,
left_iris_landmarks,
_,
left_iris_landmarks_respect_face
) = fl2d(
image_rgb,
all_landmarks[left_eye_landmarks_ids, :].T,
image_size,
is_right_eye=False,
focal_length=focal_length
)
(
success_right,
_,
_,
right_iris_landmarks,
_,
right_iris_landmarks_respect_face
) = fl2d(
image_rgb,
all_landmarks[right_eye_landmarks_ids, :].T,
image_size,
is_right_eye=True,
focal_length=focal_length
)
if success_left and success_right:
left_eye_pixels = all_landmarks_pixels[left_eye_landmarks_ids, :2]
left_eye_tl, left_eye_br = get_eyes_pixels(left_eye_pixels)
eye_left = image_rgb[left_eye_tl[1]:left_eye_br[1], left_eye_tl[0]:left_eye_br[0]]
right_eye_pixels = all_landmarks_pixels[right_eye_landmarks_ids, :2]
right_eye_tl, right_eye_br = get_eyes_pixels(right_eye_pixels)
eye_right = image_rgb[right_eye_tl[1]:right_eye_br[1], right_eye_tl[0]:right_eye_br[0]]
if eye_left.any() and eye_right.any():
success = True
features.append(left_iris_landmarks_respect_face[0, :2])
features.append(right_iris_landmarks_respect_face[0, :2])
for feats in features:
for feat in feats:
features_vector.append(feat)
features_vector = np.array(features_vector)
eye_left_resize = cv2.resize(eye_left, EYE_SIZE, interpolation=cv2.INTER_AREA)
eye_right_resize = cv2.resize(eye_right, EYE_SIZE, interpolation=cv2.INTER_AREA)
eyes = np.concatenate([eye_left_resize, eye_right_resize])
eyes_gray = np.expand_dims(cv2.cvtColor(eyes, cv2.COLOR_RGB2GRAY), 2)
if return_face:
fp = get_face(all_landmarks_pixels)
face_img = image[fp[2]:fp[3], fp[0]:fp[1]]
face_img = cv2.resize(face_img, face_size, interpolation=cv2.INTER_AREA)
if show_features:
cv2.rectangle(image, left_eye_tl, left_eye_br, (190, 100, 40), 2)
cv2.rectangle(image, right_eye_tl, right_eye_br, (190, 100, 40), 2)
jaw_landmarks_pixels = all_landmarks_pixels[jaw_landmarks_ids, :2]
for pix in jaw_landmarks_pixels:
cv2.circle(image, pix, 2, (0, 255, 255), cv2.FILLED)
left_eye_landmarks_pixels = all_landmarks_pixels[left_eye_landmarks_ids, :2]
for pix in left_eye_landmarks_pixels:
cv2.circle(image, pix, 2, (255, 0, 255), cv2.FILLED)
right_eye_landmarks_pixels = all_landmarks_pixels[right_eye_landmarks_ids, :2]
for pix in right_eye_landmarks_pixels:
cv2.circle(image, pix, 2, (255, 0, 255), cv2.FILLED)
left_iris_pixel = np.array(
left_iris_landmarks[0, :2] * image_size).astype(np.uint32)
cv2.circle(image, left_iris_pixel, 4, (255, 255, 0), cv2.FILLED)
right_iris_pixel = np.array(
right_iris_landmarks[0, :2] * image_size).astype(np.uint32)
cv2.circle(image, right_iris_pixel, 4, (255, 255, 0), cv2.FILLED)
(nose_end_point2D, _) = cv2.projectPoints(
np.array([(0.0, 0.0, 25.0)]),
rotation_vector,
translation_vector,
camera_matrix,
dst_cof,
)
p1 = (int(some_landmarks_image[0][0]), int(some_landmarks_image[0][1]))
p2 = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
cv2.line(image, p1, p2, (127, 64, 255), 2)
return success, image, eyes_gray, features_vector, eye_ratio, face_img
def get_time(i, t, print_time=False):
"""
getting time
Parameters:
i: the iterator
t: the time
print_time: whether or not print the time
Returns:
fps: Frame per second
"""
el_t = time.perf_counter() - t
fps = round(i / el_t, 2)
if print_time:
print(f"Elapsed time: {int(el_t / 60)}:{int(el_t % 60)}")
return fps
def create_dir(folders_list):
"""
creating direcotry
Parameters:
folders_list: folders' list
Returns:
fol_dir: folder directory
"""
fol_dir = ""
for fol in folders_list:
if fol[-1] != "/":
fol += "/"
fol_dir += fol
if not os.path.exists(fol_dir):
os.mkdir(fol_dir)
return fol_dir
def load(fol_dir, data_name):
"""
Loading the the data
Parameters:
fol_dir: folder directory
data_name: the data name
Returns:
data: the loaded data
"""
print("Loading data from " + fol_dir)
data = []
for dn in data_name:
with open(fol_dir + dn + ".pickle", 'rb') as f:
data.append(pickle.load(f))
return data
def save(data, fol_dir, data_name):
"""
Saving the data
Parameters:
data: the data that we want to save
fol_dir: the folder directory
data_nmae: name of the file that we want put
Returns:
None
"""
print("Saving data in " + fol_dir)
for (d, dn) in zip(data, data_name):
with open(fol_dir + dn + ".pickle", 'wb') as f:
pickle.dump(d, f)
def remove(fol_dir, files=None):
"""
Removing the files
Parameters:
fol_dir: folder directory
files: the file that we want to remove
Returns:
None
"""
if files:
for fn in files:
file_dir = fol_dir + fn + ".pickle"
print("Removing " + file_dir)
os.remove(file_dir)
else:
print("Removing " + fol_dir)
shutil.rmtree(fol_dir)
def file_existing(fol_dir, file_name):
"""
Checking the existance of the file
Parameters:
fol_dir: folder directory
file_name: file name
Returns:
file_exist: whether or not the file exists
"""
files = os.listdir(fol_dir)
file_exist = False
if files:
for f in files:
if f == file_name:
file_exist = True
return file_exist
def pass_frames(cap, n_frames=5):
"""
Skipping the some frames
Parameters:
cap: camera objec
n_frames: number of frames that we want to pass
Returns:
None
"""
for _ in range(n_frames):
get_frame(cap)
def show_clb_win(
win_name,
pnt=None,
pnt_prd=None,
texts=None,
win_color=BLACK,
win_size=(640, 480),
pnt_color=WHITE,
pnt_prd_color=BLUE
):
"""
Showing the calibration window
Parameters:
win_name: the windows name
pnt: the point for calibration
pnt_prd: the predicted point
texts: the texts that we want to put in the window
win_color: the window color
win_size: window size
pnt_color: the calibration point color
pnt_prd_color: the predicted point color
Returns:
None
"""
pnt_d = int(win_size[0] / 80.0)
clb_img = np.ones((win_size[1], win_size[0], 3))
clb_img[:, :, 0] = clb_img[:, :, 0] * win_color[0]
clb_img[:, :, 1] = clb_img[:, :, 1] * win_color[1]
clb_img[:, :, 2] = clb_img[:, :, 2] * win_color[2]
clb_img = clb_img.astype(np.uint8)
if np.array(pnt).any():
pxl = (np.array(pnt) * np.array(win_size)).astype(np.uint32)
cv2.circle(clb_img, pxl, pnt_d, pnt_color, cv2.FILLED)
if np.array(pnt_prd).any():
pxl_prd = (np.array(pnt_prd) * np.array(win_size)).astype(np.uint32)
cv2.circle(clb_img, pxl_prd, int(pnt_d / 2), pnt_prd_color, cv2.FILLED)
if texts:
for tx in texts:
cv2.putText(clb_img,
tx[0],
(int(win_size[0]*tx[1][0]), int(win_size[1]*tx[1][1])),
cv2.FONT_HERSHEY_SIMPLEX,
tx[2],
tx[3],
tx[4])
cv2.imshow(win_name, clb_img)
def big_win(win_name="", x_disp=0, y_disp=0):
"""
Make the calibration window full size
Paramters:
win_name: window name
x_disp: x coordinate
y_disp: y coordinate
Returns:
None
"""
cv2.namedWindow(win_name, cv2.WND_PROP_FULLSCREEN)
cv2.moveWindow(win_name, x_disp, y_disp)
cv2.setWindowProperty(win_name, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
def get_blink_v(t_vec, eyes_ratio):
"""
Calculating the blinking vector's velocity
Parameters:
t_vec: time vector
eyes_ratio: eyes aspect ratio vector
Returns:
blink_v: velocity of eyes aspect ratio vector
"""
blink_v = eyes_ratio.copy()
blink_v[1:] = (eyes_ratio[1:] - eyes_ratio[:-1]) / (t_vec[1:] - t_vec[:-1])
blink_v[0] = blink_v[1]
return blink_v
def get_blink_duration(t_vec, blinking_period):
"""
Calculating the blink duration
Parameters:
t_vec: time vector
blinking_period: blinking period
Returns:
before_closing: before closing sample
after_closing: after closing sample
"""
dt = 1 / (t_vec[1:] - t_vec[:-1])
fps = dt.mean()
sampling_period = 1/fps
n_smp_blink = round(blinking_period/sampling_period)
before_closing = math.floor(n_smp_blink / 3)
after_closing = math.floor(2 * n_smp_blink / 3) - 1
return before_closing, after_closing
def get_blinking_vec(eyes_ratio_v, bc, ac, threshold):
"""
getting blinking vector
Parameters:
eyes_ratio_v: the vector of eye aspect ratio velocity
bc: before closing
ac: after closing
threshold: blinking threshold
Returns:
blinking: whether or not the user is blinking
eys_ratio_v_blink: the vector of blinking
"""
closed_eyes = (eyes_ratio_v > threshold)
blinking = closed_eyes.copy()
n_smp = blinking.shape[0]
eyes_ratio_v_blink = np.zeros((n_smp,))
for (i, ce) in enumerate(closed_eyes):
if ce and (i > bc) and (i < n_smp-ac):
for j in range(1, bc+1):
blinking[i-j] = True
for j in range(1, ac+1):
blinking[i+j] = True
eyes_ratio_v_blink[blinking] = threshold
return blinking, eyes_ratio_v_blink
def get_blinking(t_mat, eyes_ratio_mat, threshold=DEFAULT_BLINKING_THRESHOLD, normal_blinking_period=0.4):
"""
Getting blinking
Parameters:
t_mat: a list of time vectors
eyes_ratio_mat: a list of eyes aspect ratio vectors
threshold: blinking threshold
normal_blinking_period: normal blinking threshold
Returns:
eyes_ratio_v_mat: a list of eyes aspect ratio velocity vector
blinking_mat: a list of blinking vectors
eyes_ratio_v_blink_mat: a list of eyes aspect ratio boolians vectors
"""
eyes_ratio_v_mat = []
blinking_mat = []
eyes_ratio_v_blink_mat = []
for (k, eyes_ratio) in enumerate(eyes_ratio_mat):
t_vec = t_mat[k]
eyes_ratio_v_mat.append(get_blink_v(t_vec, eyes_ratio).copy())
bc, ac = get_blink_duration(t_vec, normal_blinking_period)
blinking, eyes_ratio_v_blink = get_blinking_vec(eyes_ratio_v_mat[-1], bc, ac, threshold)
blinking_mat.append(blinking)
eyes_ratio_v_blink_mat.append(eyes_ratio_v_blink)
return eyes_ratio_v_mat, blinking_mat, eyes_ratio_v_blink_mat
def find_max_mdl(fol_dir, a=3, b=-3):
"""
finding the maximum model number
Parameters:
fol_dir: folder directory
a: the first index of the model number
b: the last index of the model number
Returns:
max_num: maximum model number
"""
mdl_numbers = []
mdl_name = os.listdir(fol_dir)
if mdl_name:
for mn in mdl_name:
if mn[-3:] == ".h5":
mdl_num = int(mn[a:b])
mdl_numbers.append(mdl_num)
max_num = max(mdl_numbers)
else:
max_num = 0
return max_num
def get_threshold(er_dir, threshold):
"""
Getting the threshold. default, user offered or application offered
Parameters:
er_dir: directory of er file
thereshold: the threshold, 'd', 'ao', or 'uo'
Returns:
threshold: the threshold value
"""
if threshold == "d":
threshold = DEFAULT_BLINKING_THRESHOLD
elif threshold == "ao":
oth = "oth_app"
fe = file_existing(er_dir, oth + ".pickle")
if fe:
threshold = load(er_dir , [oth])[0]
else:
print("App offered threshold does not exist!! We use default threshold.")
threshold = DEFAULT_BLINKING_THRESHOLD
elif threshold == "uo":
oth = "oth_usr"
fe = file_existing(er_dir, oth + ".pickle")
if fe:
threshold = load(er_dir , [oth])[0]
else:
print("User offered threshold does not exist!! We use default threshold.")
threshold = DEFAULT_BLINKING_THRESHOLD
else:
threshold = float(threshold)
print(f"blinking threshold: {threshold}")
return threshold
# Getting some directories
models_dir = create_dir([PATH2ROOT_ABS+"models"])
io_dir = create_dir([models_dir+"io"])
et_dir = create_dir([models_dir+"et"])
io_raw_dir = create_dir([io_dir, RAW])
io_trained_dir = create_dir([io_dir, TRAINED])
et_raw_dir = create_dir([et_dir, RAW])
et_trained_dir = create_dir([et_dir, TRAINED])
files_dir = create_dir([PATH2ROOT_ABS+"other_files"])
scalers_dir = create_dir([files_dir, "scalers"])
subjects_dir = create_dir([PATH2ROOT+"subjects"])
monitors = get_monitors() |