class WebsocketChat(basic.LineReceiver):
p = pipan.PiPan()
def connectionMade(self):
print "Got new client!"
self.transport.write('connected ....\n')
self.factory.clients.append(self)
def connectionLost(self, reason):
print "Lost a client!"
self.factory.clients.remove(self)
def dataReceived(self, data):
if data.split(" ")[0] == "COO":
coo = data.split(" ")[1].split("=")
if coo[0] == "x":
self.p.do_pan(int(coo[1]))
print "Update X=%s" % coo[1]
if coo[0] == "y":
self.p.do_tilt(int(coo[1]))
print "Update Y=%s" % coo[1]
self.factory.messages[float(time.time())] = data
self.updateClients(data)
def updateClients(self, data):
for c in self.factory.clients:
c.message(data)
def message(self, message):
self.transport.write(message + '\n')
def neutral():
p = pipan.PiPan()
p.neutral_pan()
p.neutral_panc()
p.neutral_panu()
p.neutral_tiltc()
sleep(1)
def main():
#Center the camera's position
servo = pipan.PiPan()
servo.neutral_pan()
servo.neutral_tilt()
raw_input("press enter to start searching")
print 'Search Begin!'
# Start position of pan and tilt servo
tilt_pos = 120
pan_pos = 150
# Infinite Loop
while True:
# Subtle serach when no face is found
if (find_face is False):
tilt_pos += random.randint(-10, 10)
pan_pos += random.randint(-15, 15)
# Obvious serach when a face is found
else:
tilt_pos = random.randint(TILT_MIN_POS, TILT_MAX_POS)
pan_pos = random.randint(PAN_MIN_POS, PAN_MAX_POS)
# make sure the servos also in the range
if tilt_pos > TILT_MIN_POS or tilt_pos < TILT_MAX_POS:
servo.do_pan(pan_pos)
else:
servo.do_pan(150)
if pan_pos > PAN_MIN_POS or pan_pos < PAN_MAX_POS:
servo.do_tilt(tilt_pos)
else:
servo.do_tilt(120)
face_recognition()
import io
import picamera
import cv2
import numpy
import pipan
# center the camera
p = pipan.PiPan()
p.neutral_pan()
p.neutral_tilt()
# Pan -> (80, 220), middle position -> 150
# Tilt -> (50, 250) , middle position -> 150
#p.do_pan(100)
#p.do_tilt(100)
#Create a memory stream so photos doesn't need to be saved in a file
stream = io.BytesIO()
#Get the picture (low resolution, so it should be quite fast)
#Here you can also specify other parameters (e.g.:rotate the image)
with picamera.PiCamera() as camera:
camera.resolution = (1280, 720)
camera.hflip = True
camera.vflip = True
camera.capture(stream, format='jpeg')
#Convert the picture into a numpy array
buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)
#Now creates an OpenCV image
#!/usr/bin/env python3
import time
import os, sys
import pipan
p_servo = pipan.PiPan()
while True:
fifoLocation = os.environ['PIPAN_FIFO_LOCATION']
pipein = open(fifoLocation, 'r')
line = pipein.readline()
print(line)
line_array = line.split(' ')
if line_array[0] == "servo":
p_servo.do_pan(int(line_array[1]))
p_servo.do_tilt(int(line_array[2]))
pipein.close()
try:
wgetfile = urllib2.urlopen(config_url)
except:
print("ERROR - Download of config.py Failed")
print(" Try Rerunning the face-track-install.sh Again.")
print(" or")
print(" Perform GitHub curl install per Readme.md")
print(" and Try Again")
print("Exiting %s" % PROG_NAME)
quit()
f = open('config.py', 'wb')
f.write(wgetfile.read())
f.close()
from config import *
p = pipan.PiPan(
) # Initialize pipan driver My Servo Controller is a Dagu mega
# Create Calculated Variables
cam_cx = int(CAMERA_WIDTH / 2)
cam_cy = int(CAMERA_HEIGHT / 2)
big_w = int(CAMERA_WIDTH * WINDOW_BIGGER)
big_h = int(CAMERA_HEIGHT * WINDOW_BIGGER)
# Setup haar_cascade variables
face_cascade = cv2.CascadeClassifier(fface1_haar_path)
frontalface = cv2.CascadeClassifier(fface2_haar_path)
profileface = cv2.CascadeClassifier(pface1_haar_path)
# Color data for OpenCV Markings
blue = (255, 0, 0)
green = (0, 255, 0)
red = (0, 0, 255)
print("Exiting %s" % (progName))
quit()
f = open('config.py', 'wb')
f.write(wgetfile.read())
f.close()
from config import *
# import the necessary python libraries
import io
import time
import cv2
from picamera.array import PiRGBArray
from picamera import PiCamera
from threading import Thread
import pipan
p = pipan.PiPan() # Initialize pipan driver
# Create Calculated Variables
cam_cx = CAMERA_WIDTH / 2
cam_cy = CAMERA_HEIGHT / 2
big_w = int(CAMERA_WIDTH * WINDOW_BIGGER)
big_h = int(CAMERA_HEIGHT * WINDOW_BIGGER)
# Setup haar_cascade variables
face_cascade = cv2.CascadeClassifier(fface1_haar_path)
frontalface = cv2.CascadeClassifier(fface2_haar_path)
profileface = cv2.CascadeClassifier(pface1_haar_path)
# Color data for OpenCV Markings
blue = (255, 0, 0)
green = (0, 255, 0)