opencv 人脸识别交互与眨眼检测

opencv 人脸识别交互与眨眼检测
直接上代码

客户端如下　　　
import socket
import cv2
import numpy

address = ('127.0.0.1', 8002)
sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
sock.connect(address)

capture = cv2.VideoCapture(0)
ret, frame = capture.read()
encode_param=[int(cv2.IMWRITE_JPEG_QUALITY),90]

while ret:
    result, imgencode = cv2.imencode('.jpg', frame, encode_param)
    data = numpy.array(imgencode)
    stringData = data.tostring()
    sock.send( str(len(stringData)).ljust(16));
    sock.send( stringData );
    ret, frame = capture.read()
    decimg=cv2.imdecode(data,1)
    cv2.imshow('CLIENT',decimg)
    if cv2.waitKey(10) == 27:
        break
sock.close()
cv2.destroyAllWindows()




服务端如下　：
import socket
import cv2
import numpy


from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils.video import VideoStream
from imutils import face_utils
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2

try:
    import face_recognition_models
except Exception:
    print("Please install `face_recognition_models` with this command before using `face_recognition`:\n")
    print("pip install git+https://github.com/ageitgey/face_recognition_models")
    quit()

face_detector = dlib.get_frontal_face_detector()

predictor_68_point_model = face_recognition_models.pose_predictor_model_location()
# pose_predictor_68_point = dlib.shape_predictor(predictor_68_point_model)



def eye_aspect_ratio(eye):
    # compute the euclidean distances between the two sets of
    # vertical eye landmarks (x, y)-coordinates
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])

    # compute the euclidean distance between the horizontal
    # eye landmark (x, y)-coordinates
    C = dist.euclidean(eye[0], eye[3])

    # compute the eye aspect ratio
    ear = (A + B) / (2.0 * C)

    # return the eye aspect ratio
    return ear


# construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-v", "--video", type=str, default="",
# 	help="path to input video file")
# args = vars(ap.parse_args())

# define two constants, one for the eye aspect ratio to indicate
# blink and then a second constant for the number of consecutive
# frames the eye must be below the threshold
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 3

# initialize the frame counters and the total number of blinks
COUNTER = 0
TOTAL = 0


# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_68_point_model)

# grab the indexes of the facial landmarks for the left and
# right eye, respectively
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]

# start the video stream thread
print("[INFO] starting video stream thread...")
# vs = FileVideoStream(args["video"]).start()
# fileStream = True
# vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
# fileStream = False
time.sleep(1.0)


address = ('10.5.35.203', 8009)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(address)
s.listen(True)


def recvall(sock, count):
    buf = b''
    while count:
        newbuf = sock.recv(count)
        if not newbuf: return None
        buf += newbuf
        count -= len(newbuf)
    return buf


conn, addr = s.accept()

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
while 1:
    length = recvall(conn, 16)
    stringData = recvall(conn, int(length))
    data = numpy.fromstring(stringData, dtype='uint8')
    decimg = cv2.imdecode(data, 1)
    frame = cv2.flip(decimg, 1)
    frame = imutils.resize(frame, width=450)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # detect faces in the grayscale frame
    rects = detector(gray, 0)

    # loop over the face detections
    for rect in rects:
        # determine the facial landmarks for the face region, then
        # convert the facial landmark (x, y)-coordinates to a NumPy
        # array
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape)

        # extract the left and right eye coordinates, then use the
        # coordinates to compute the eye aspect ratio for both eyes
        leftEye = shape[lStart:lEnd]
        rightEye = shape[rStart:rEnd]
        leftEAR = eye_aspect_ratio(leftEye)
        rightEAR = eye_aspect_ratio(rightEye)

        # average the eye aspect ratio together for both eyes
        ear = (leftEAR + rightEAR) / 2.0

        # compute the convex hull for the left and right eye, then
        # visualize each of the eyes
        leftEyeHull = cv2.convexHull(leftEye)
        rightEyeHull = cv2.convexHull(rightEye)
        cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
        cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)

        # check to see if the eye aspect ratio is below the blink
        # threshold, and if so, increment the blink frame counter
        if ear < EYE_AR_THRESH:
            COUNTER += 1

        # otherwise, the eye aspect ratio is not below the blink
        # threshold
        else:
            # if the eyes were closed for a sufficient number of
            # then increment the total number of blinks
            if COUNTER >= EYE_AR_CONSEC_FRAMES:
                TOTAL += 1

            # reset the eye frame counter
            COUNTER = 0

        # draw the total number of blinks on the frame along with
        # the computed eye aspect ratio for the frame
        cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    # show the frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
s.close()
cv2.destroyAllWindows()