https://www.jianshu.com/p/848014d8dea9
https://www.pyimagesearch.com/2017/05/01/install-dlib-raspberry-pi/
Download Library
https://github.com/davisking/dlib
Identification code
https://gitee.com/Andrew_Qian/face/blob/master/from_video.py
Dependent on the weight
Third, the dependent libraries
dlib need to rely on the following:
- Boost
- Boost.Python
- CMake
- X11
installation method:
$ sudo apt-get update
$ sudo apt-get install build-essential cmake libgtk-3-dev libboost-all-dev -y Fourth, install additional dlib dependent libraries run by pip3
$ pip3 install numpy
$ pip3 install scipy
$ pip3 install scikit-image
Fifth, the official installation
After unzip the downloaded dlib, enter dlib directory
$ sudo python3 setup.py install
This step takes the longest, and be patient.
Sixth, verification
$ python3
Python 3.4.2 (default, Oct 19 2014, 13:31:11) [GCC 4.9.1] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import dlib >>> Seven, the use of virtual memory and GPU memory back to the original value
Modification methods, see "Second, pre-modification preparatory work"
knock off.
python code
Anaconda # / Anaconda / Python!
#Coding: UTF-. 8
"" "
from Screen in face recognition, and real-time facial feature points marked
" ""
Import dlib recognition library # dlib
Import numpy AS # NP data processing library numpy
Import CV2 # image processing library OpenCv
class face_emotion ():
DEF __init __ (Self):
# use the feature extractor get_frontal_face_detector
self.detector = dlib.get_frontal_face_detector ()
# 68 dlib point model, the use of trained forecast feature is
self.predictor = dlib.shape_predictor ( "shape_predictor_68_face_landmarks.dat")
# cv2 camera built objects, as used herein, computer's own camera, then if the external camera, the camera automatically switches to the external
self.cap = cv2.VideoCapture (0)
# set video parameters, propId set video parameters, parameter value set
self.cap.set (. 3, 480) 480)
# Screenshot screenshoot counter
self.cnt = 0
DEF learning_face (Self):
# eyebrows linear fit to the data buffer
line_brow_x = []
line_brow_y = []
# cap.isOpened () Returns true / false check is successful initialization
the while (self.cap.isOpened ()):
# cap.read ()
# returns two values:
# a boolean true / false, reading the video to determine whether the success / whether to end the video
# image object image three dimensional matrix
in Flag, im_rd self.cap.read = ()
# data per frame delay 1ms, the read delay of 0 is a static frame
K cv2.waitKey = (. 1)
# fetch gradation
img_gray = cv2.cvtColor (im_rd , cv2.COLOR_RGB2GRAY)
# using the face detector detects human faces in an image of each frame. And return to face several rects
Faces = self.detector (img_gray, 0)
# question will be displayed on the screen font on
font = cv2.FONT_HERSHEY_SIMPLEX
# If a face is detected
IF (len (Faces) = 0!):
# face mark for each feature point 68
for I in Range (len (Faces)):
# the enumerate method of returning the data objects and the index data, k is the index, d is the faces of the object
for K, d in the enumerate (faces):
# human face with the red rectangle
cv2.rectangle (im_rd, (d.left () , d.top ()), (d.right () , d.bottom ()), (0, 0, 255))
# do not heat calculation block face edge length
self.face_width = d.right () - d.left ()
# circles show each feature point
for i in range (68):
# 68 obtained using the predictor data point coordinates
= self.predictor Shape (im_rd, D)
cv2.circle (im_rd, (shape.part (I) .x, shape.part (I) .y), 2, (0, 255, 0), -1,. 8 )
# cv2.putText (im_rd, STR (I), (shape.part (I) .x, shape.part (I) .y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
# (255, 255, 255))
# analysis a positional relationship between an arbitrary point n as expression recognition based
mouth_width = (shape.part (54) .x - shape.part (48) .x) / self.face_width # mouth god degree
mouth_higth = (shape.part (66 ) .y - shape.part (62) .y ) / self.face_width # mouth opening degree
# print ( "width of the mouth width ratio of the identification frame:", mouth_width_arv)
# print ( "mouth height identification frame height ratio: ", mouth_higth_arv)
# by two 10 feature points on the eyebrows, eyebrow and analyze the extent degree frown
brow_sum = 0 # height and
frown_sum = 0 # sides of the eyebrows and the distances
for J in Range (. 17, 21 is):
brow_sum + = (shape.part (J) .y - d.top ()) + (shape.part (+ J. 5) .y - d.top ())
frown_sum shape.part + = (J +. 5) .x - shape.part (J) .x
line_brow_x.append (shape.part (J) .x)
line_brow_y .append (shape.part (J) .y)
# self.brow_k, self.brow_d = self.fit_slr (line_brow_x, line_brow_y) # calculates the inclination of the eyebrows
tempx = np.array (line_brow_x)
tempy = np.array (line_brow_y)
Z1 = np.polyfit (tempx , tempy, 1) # fit to a straight line
self.brow_k = -Round (Z1 [0],. 3) # fitted tilt direction and the actual slope of the curve are opposite eyebrows
brow_hight = (brow_sum / 10) / self.face_width # the height of the eyebrows proportion
brow_width = (frown_sum / 5) / self.face_width # eyebrow distance accounted
# print ( "eyebrow height than the height of the identification frame:", round (brow_arv / self.face_width,. 3))
# print ( " eyebrow spacing than the height of the identification frame: ", round (frown_arv / self.face_width,. 3))
# eye opening degree
eye_sum = (shape.part (41) .y - shape.part (37) .y + shape. Part (40) .y - shape.part (38 is) .y +
shape.part (47) .y - shape.part (43 is) .y + shape.part (46 is) .y - shape.part (44 is). y)
= eye_hight (eye_sum /. 4) / self.face_width
# Print ( "open eye than the distance of the identified frame height:", round (eye_open / self.face_width , 3))
# min discussions
# mouth, upset or may be surprised
IF round (mouth_higth> = 0.03):
IF eye_hight> = 0.056:
cv2.putText (im_rd, "Amazing", (D .left (), d.bottom () + 20 is), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2,. 4)
the else:
cv2.putText (im_rd, "Happy", (d.left () , d.bottom () + 20 is), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2,. 4)
# no mouth, may be normal and angry
the else:
IF self.brow_k <= -0.3:
cv2.putText (im_rd, "Angry", (d.left (), d.bottom () + 20 is), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2,. 4)
the else:
cv2.putText ( im_rd, "Nature", (d.left (), d.bottom () + 20 is), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2,. 4)
# indicated face number
cv2.putText ( im_rd, "faces:" + STR (len (faces)), (20,50), font,. 1, (0, 0, 255),. 1, cv2.LINE_AA)
the else:
# does not detect a human face
cv2.putText (im_rd, "No Face", (20 is, 50), font,. 1, (0, 0, 255),. 1, cv2.LINE_AA)
# add a description
= cv2.putText im_rd (im_rd, "S: Screenshot", (20 is, 400), font, 0.8, (0, 0, 255),. 1, cv2.LINE_AA)
im_rd = cv2.putText (im_rd, "Q: quit ", (20 is, 450), font, 0.8, (0, 0, 255),. 1, cv2.LINE_AA)
# save theme key is pressed s
IF (K == the ord ( 's')):
self.cnt + = . 1
cv2.imwrite ( "screenshoot" + STR (self.cnt) +. "JPG", im_rd)
# q is pressed to exit
IF (K == the ord ( 'q')):
BREAK
# window
cv2.imshow ( "camera", im_rd)
# release camera
self.cap.release ()
# delete establish a window
cv2.destroyAllWindows ()
IF __name__ == "__main__":
my_face = face_emotion()
my_face.learning_face()