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merge3.py
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merge3.py
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from lib.device import Camera
from lib.processors_noopenmdao import findFaceGetPulse
from lib.interface import plotXY, imshow, waitKey, destroyWindow
import argparse
import numpy as np
import datetime
#TODO: work on serial port comms, if anyone asks for it
#from serial import Serial
import socket
import sys
from cv2 import moveWindow
from scipy.spatial import distance as dist
from imutils.video import VideoStream
from imutils import face_utils
from threading import Thread
import pyglet
import imutils
import time
import dlib
import cv2
import serial
COM_PORT = '/dev/cu.usbserial-14510' # 請自行修改序列埠名稱
BAUD_RATES = 9600
ser = serial.Serial(COM_PORT, BAUD_RATES)
def sound_alarm(path):
# play an alarm sound
music = pyglet.resource.media('alarm.wav')
music.play()
pyglet.app.run()
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 horizon
# 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
class getPulseApp(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
# 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 for to set off the
# alarm
EYE_AR_THRESH = 0.20
EYE_AR_CONSEC_FRAMES = 30
# initialize the frame counter as well as a boolean used to
# indicate if the alarm is going off
COUNTER = 0
ALARM_ON = False
# 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("68 face landmarks.dat")
# 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 = VideoStream(src=args["webcam"]).start()
time.sleep(1.0)
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
"f": self.write_csv}
def toggle_cam(self):
if len(self.cameras) > 1:
self.processor.find_faces = True
self.bpm_plot = False
destroyWindow(self.plot_title)
self.selected_cam += 1
self.selected_cam = self.selected_cam % len(self.cameras)
def write_csv(self):
"""
Writes current data to a csv file
"""
fn = "Webcam-pulse" + str(datetime.datetime.now())
fn = fn.replace(":", "_").replace(".", "_")
data = np.vstack((self.processor.times, self.processor.samples)).T
np.savetxt(fn + ".csv", data, delimiter=',')
print("Writing csv")
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
#state = self.processor.find_faces.toggle()
state = self.processor.find_faces_toggle()
print("face detection lock =", not state)
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print("bpm plot disabled")
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print("bpm plot enabled")
if self.processor.find_faces:
self.toggle_search()
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w, 0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.times,
self.processor.samples],
[self.processor.freqs,
self.processor.fft]],
labels=[False, True],
showmax=[False, "bpm"],
label_ndigits=[0, 0],
showmax_digits=[0, 1],
skip=[3, 3],
name=self.plot_title,
bg=self.processor.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
self.pressed = waitKey(10) & 255 # wait for keypress for 10 ms
if self.pressed == 27: # exit program on 'esc'
print("Exiting")
for cam in self.cameras:
cam.cam.release()
if self.send_serial:
self.serial.close()
sys.exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def eye_ratio(self,rect,gray,frame):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = self.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[self.lStart:self.lEnd]
rightEye = shape[self.rStart:self.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 < self.EYE_AR_THRESH:
self.COUNTER += 1
# if the eyes were closed for a sufficient number of
# then sound the alarm
if self.COUNTER >= self.EYE_AR_CONSEC_FRAMES:
# if the alarm is not on, turn it on
if not self.ALARM_ON:
self.ALARM_ON = True
# check to see if an alarm file was supplied,
# and if so, start a thread to have the alarm
# sound played in the background
'''
if args["alarm"] != "":
t = Thread(target=sound_alarm,
args=(args["alarm"],))
t.deamon = True
t.start()
'''
ser.write(b'a')
# draw an alarm on the frame
cv2.putText(frame, "DROWSINESS ALERT!", (400, 180),
cv2.FONT_HERSHEY_SIMPLEX, 1.5 , (0, 0, 255), 2)
# otherwise, the eye aspect ratio is not below the blink
# threshold, so reset the self.counter and alarm
else:
self.COUNTER = 0
self.ALARM_ON = False
# draw the computed eye aspect ratio on the frame to help
# with debugging and setting the correct eye aspect ratio
# thresholds and frame counters
cv2.putText(frame, "EAR: {:.2f}".format(ear), (500, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1.2 , (0, 0, 255), 2)
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.cameras[self.selected_cam].get_frame()
self.h, self.w, _c = frame.shape
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #DD
rects = self.detector(gray, 0)
for rect in rects:
self.eye_ratio(rects[0],gray,frame)
# display unaltered frame
# imshow("Original",frame)
# set current image frame to the processor's input
self.processor.frame_in = frame
# process the image frame to perform all needed analysis
self.processor.run(self.selected_cam)
# collect the output frame for display
output_frame = self.processor.frame_out
# show the processed/annotated output frame
imshow("Processed", output_frame)
# create and/or update the raw data display if needed
if self.bpm_plot:
self.make_bpm_plot()
if self.send_serial:
self.serial.write(str(self.processor.bpm) + "\r\n")
if self.send_udp:
self.sock.sendto(str(self.processor.bpm), self.udp)
# handle any key presses
self.key_handler()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Webcam pulse detector.')
parser.add_argument('--serial', default=None,
help='serial port destination for bpm data')
parser.add_argument('--baud', default=None,
help='Baud rate for serial transmission')
parser.add_argument('--udp', default=None,
help='udp address:port destination for bpm data')
args_1 = parser.parse_args()
App = getPulseApp(args_1)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-w", "--webcam", type=int, default=0,
help="index of webcam on system")
args = vars(ap.parse_args())
try:
while True:
App.main_loop()
except(KeyboardInterrupt):
serial.close()