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video_eye_tracker_02.py
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video_eye_tracker_02.py
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import cv2
import time
import dlib
import numpy as np
import eye_tracker_05 as eyeTrk #detect 68 point
PERPROMANCE_TEST = 0
def timelap_check(title, start):
if(PERPROMANCE_TEST == 1):
print('\tTimeLap - {:s} {:.6f}'.format(title, time.time() - start))
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# loop over all facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 3);
cap.set(cv2.CAP_PROP_FRAME_WIDTH,640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
time.sleep(1) #warming up
if not cap.isOpened():
exit()
#set camera param
tdistCoeffs = np.zeros((5, 1))
twidth = 640
theight = 480
tmaxSize = max(twidth, theight)
tcameraMatrix = np.array([[tmaxSize, 0, twidth / 2.0], [0, tmaxSize, theight / 2.0], [0, 0, 1]], np.float32)
def select_camera_calib(num):
tempDistCoeffs = np.zeros((5, 1))
tempCameraMatrix = np.eye(3)
if (num == 1):
tempDistCoeffs[0][0] = -0.015136023194323986
tempDistCoeffs[1][0] = 0.2177351340933552
tempDistCoeffs[2][0] = 0.0025235154109215703
tempDistCoeffs[3][0] = 0.0022730661434222452
tempDistCoeffs[4][0] = -0.7167190677252845
tempCameraMatrix[0][0] = 641.8333531354511
tempCameraMatrix[1][1] = 641.8333531354511
tempCameraMatrix[0][2] = 309.4109382434117
tempCameraMatrix[1][2] = 258.82858265848694
# print(tempDistCoeffs, tempCameraMatrix)
return tempCameraMatrix, tempDistCoeffs
#set 3d face model
faceModel3D = np.zeros((7, 3), dtype=np.float32)
# RIGHTHEAR
faceModel3D[0] = [-7.5, 0., -8. ]
# LEFTHEAR
faceModel3D[1] = [ 7.5, 0., -8. ]
# NOSE
faceModel3D[2] = [ 0., 3.5, 2.5]
# RIGHTMOUTH
faceModel3D[3] = [-3., 6., 0. ]
# LEFTMOUTH
faceModel3D[4] = [ 3., 6., 0. ]
# RIGHTEYE
faceModel3D[5] = [-3.5, 0., -1. ]
# LEFTEYE
faceModel3D[6] = [ 3.5, 0., -1. ]
# predictor_path = "./dlib/shape_predictor_68_face_landmarks.dat"
# predictor_path = "./dlib/type2mini.dat"
# predictor_path = "./dlib/eye_predictor.dat"
predictor_path = "./dlib/type1_21_facefull.dat"
objEyeTrack = eyeTrk.eyeTracker(predictor_path)
tcameraMatrix, tdistCoeffs = select_camera_calib(1)
objEyeTrack.initilaize_calib(tcameraMatrix, tdistCoeffs)
# objEyeTrack.initilaize_training_path(predictor_path)
# objEyeTrack.initialize_p3dmodel(faceModel3D)
tcnt = 1
# detector = dlib.get_frontal_face_detector()
# predictor = dlib.shape_predictor(predictor_path)
# predictor = dlib.shape_predictor("./dlib/type2mini.dat")
ttimecount = 0
FRAME_REPEAT = 30
tfps = 30
available = 0
viewType = 0
while True:
if(ttimecount == 0):
starttime = time.time()
ttimecount += 1
cap.retrieve()
ret, image = cap.read()
# if(ttimecount%2 == 0):
# continue
# if ((ttimecount % 4 == 0) or (ttimecount % 4 == 1) or (ttimecount % 4 == 2)):
# continue
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# if not ret:
# print("Can't read frame")
# break
# print('ret', ret)
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
time_s = time.time()
tempWidth = 120
available = objEyeTrack.preprocess(img, (160-tempWidth,120-tempWidth,400+tempWidth,340+tempWidth))
# available = objEyeTrack.preprocess(img, (0,0, 640 , 480))
timelap_check('1.detect face ', time_s)
if(available > 0 ):
time_s = time.time()
objEyeTrack.algo_run(img, tSelect=viewType )
timelap_check('2.calc eye gaze ', time_s)
time_s = time.time()
objEyeTrack.rendering(image, tSelect=viewType )
timelap_check('3.rendering ', time_s)
# faces = detector(img)
# for face in faces:
# x1, y1, x2, y2 = face.left(), face.top(), face.right(), face.bottom()
# cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), thickness=2)
#
# landmarks = predictor(img, face)
# tlandmark = shape_to_np(landmarks)
# # for n in range(0, 68):
# # x = landmarks.part(n).x
# # y = landmarks.part(n).y
# # cv2.circle(image, (x, y), 4, (255, 0, 0), -1)
# for (sX, sY) in tlandmark:
# cv2.circle(image, (sX, sY), 1, (255, 0, 0), -1)
# # image = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if (ttimecount >= FRAME_REPEAT):
tfps = ttimecount / (time.time() - starttime)
ttimecount = 0
cv2.putText(image, 'FPS={:.1f} {:s}'.format(tfps, " "),
(10, 460),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), thickness=2, lineType=8)
cv2.imshow('image', image)
key = cv2.waitKey(1)
if key == ord('q'):
break
elif key == ord('s'):
cv2.imwrite('out{:03d}.png'.format(tcnt), image)
tcnt+=1
print("save")
elif key == ord('1'):
if(viewType%10==1):
viewType -= 1
else:
viewType += 1
elif key == ord('2'):
if(viewType//10%10==1):
viewType -= 10
else:
viewType += 10
elif key == ord('3'):
if(viewType//100%10==1):
viewType -= 100
else:
viewType += 100
elif key == ord('4'):
if(viewType//1000%10==1):
viewType -= 1000
else:
viewType += 1000
elif key == ord('0'):
if(viewType//10000%10==1):
viewType -= 10000
else:
viewType += 10000
elif key == ord('w'):
objEyeTrack.initilaize_training_path("./dlib/type1_21_facefull.dat")
elif key == ord('e'):
objEyeTrack.initilaize_training_path("./dlib/shape_predictor_68_face_landmarks.dat")
# time.sleep(0.001)
cap.release()
cv2.destroyAllWindows()