Statistics: Posted by pageauc — Wed Sep 03, 2014 8:19 pm

Statistics: Posted by pageauc — Wed Sep 03, 2014 8:06 pm

Statistics: Posted by Alan — Wed Sep 03, 2014 6:58 pm

Code:

#!/usr/bin/env python
# opencv-find-face : Opencv face tracking with pan/tilt search and lock
# written by Claude Pageau -
# This is a little laggy but does work OK.
# Uses pipan.py module from openelectrons.com RPI camera pan/tilt to control
# camera tracking or use your own pan/tilt module and modify code accordingly.
# if you are not using openelectrons.com pan/tilt hardware.
# Also picamera python module must be installed as well as opencv
# To install opencv and python for opencv
# sudo apt-get install libopencv-dev python-opencv
# To install picamera python module
# sudo apt-get install python-picamera
# You will also need to install python picamera.array tha includes numpy
# sudo pip install "picamera[array]"
# copy /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml
# to same folder that this python script is in.
#    Note
# v4l2 driver is not used since stream is created using picamera module
# using picamera.array
# If you have any questions email pageauc@gmail.com
 
print "Initializing ...."
import io
import time
import picamera
import picamera.array
import cv2
import numpy as np

# openelectron.com python module and files from the OpenElectron RPI camera pan/tilt
# Copy pipan.py to same folder as this script.
import pipan

p = pipan.PiPan()

# To speed things up, lower the resolution of the camera
CAMERA_WIDTH = 320
CAMERA_HEIGHT = 240
# Show opencv window
show_window = False

# Camera center of image
cam_cx = CAMERA_WIDTH / 2
cam_cy = CAMERA_HEIGHT / 2
inch = 9.0

# bounds checking for pan/tilt search.
limit_y_bottom = 90
limit_y_top = 150
limit_y_level = 140
limit_x_left = 60
limit_x_right = 240

# Approx Center of Pan/Tilt motion
pan_x_c = 120
pan_y_c = 130

# Set Initial starting position of pan/tilt
pan_cx = pan_x_c
pan_cy = pan_y_c

# Amount pan/tilt moves when searching
pan_move_x = 30
pan_move_y = 20

# Timer seconds to wait before starting pan/tilt search for face.
# local face search
face_timer1 = 15
# Wide face search
face_timer2 = 30
# Stop pan/tilt and start Motion Detect.
face_timer3 = 45

# Motion scan settings
motion_detected = False
# sensitivity - How much the color value (0-255) needs to change to be considered a change
sensitivity = 25
# threshold - How many pixels must change to be considered motion
threshold = CAMERA_WIDTH * CAMERA_HEIGHT * 2 / 110

# Move the pan/tilt to a specific location. has built in limit checks.
def pan_goto(x,y):
   p.do_pan (int(x))
   p.do_tilt (int(y))

def motion_scan(x,y):
   print "motion_scan - Scan for Motion at cx=%d cy=%d" % (x, y)
   pan_goto(x,y)
   numImages = 0
   step = 1  # use this to toggle where the image gets saved
   captureCount = 0 # flag used to begin a sequence capture
   stream = io.BytesIO()
   with picamera.PiCamera() as camera:
      camera.resolution = (CAMERA_WIDTH, CAMERA_HEIGHT)
      time.sleep(2)

      # begin motion scan
      while sensitivity > 0:
         camera.resolution = (CAMERA_WIDTH, CAMERA_HEIGHT)
 
         if step == 1:
             stream.seek(0)
             camera.capture(stream, 'bgra', True)
             data1 = np.fromstring(stream.getvalue(), dtype=np.uint8)
             step = 2
         else:
             stream.seek(0)
             camera.capture(stream, 'bgra', True)
             data2 = np.fromstring(stream.getvalue(), dtype=np.uint8)
             step = 1
         numImages = numImages + 1
         if numImages > 4:  # ignore first few images because if the camera is not quite ready it will register as motion right away
             if captureCount <= 0:
                 # not capturing, test for motion (very simplistic, but works good enough for my purposes)
                 data3 = np.abs(data1 - data2)  # get difference between 2 successive images

                 # There are 4 times the number of pixels due to rgba
                 numTriggers = np.count_nonzero(data3 > sensitivity) / 4 / sensitivity

                 if numTriggers > threshold:
                    print "motion_scan - Motion Detected. Threshold=%d Triggers=%d"  % (threshold, numTriggers)
                    stream.close()
                    return True   
   
def face_scan(x,y):
  print "face_scan - Start Scan at cx=%d cy=%d" % (x, y)
  pan_parked = False
  # Face detection opencv center of face box
  face_cx = x
  face_cy = y
  # Pan/Tilt motion center point
  pan_cx = x
  pan_cy = y
   
  # Put pan/tilt in a known good position.
  pan_goto(pan_x_c, pan_y_c)   
  face_found = False
  start_time = time.time()
 
  # load a cascade file for detecting faces. This file must be in
  # same folder as this script. or loaded as part of opencv at /usr/share/opencv/haarcascades/
  face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')
  # face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_profileface.xml')
  # Saving the picture to an in-program stream rather than a file
  stream = io.BytesIO()
  with picamera.PiCamera() as camera:
    camera.resolution = (CAMERA_WIDTH, CAMERA_HEIGHT)
    camera.vflip = True
   
    while(True):
      with picamera.array.PiRGBArray(camera) as stream:
        camera.capture(stream, format='bgr')
        # At this point the image is available as stream.array
        image = stream.array
       
      # Convert to grayscale, which is easier
      gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
      # Look for faces over the given image using the loaded cascade file
      faces = face_cascade.detectMultiScale(gray, 1.3, 5)
   
      for (x,y,w,h) in faces:
        face_found = True
        #print "face_scan - Face Found at x=%d y=%d w=%d h=%d" % (x, y, w, h)
        pan_parked = False
        start_time = time.time()
        distance = ((CAMERA_WIDTH/w) * inch)/12
        face_cx = x + w/2
        Nav_LR = (cam_cx - face_cx) / 5
        pan_cx = pan_cx - Nav_LR
        face_cy = y + h/2
        Nav_UD = (cam_cy - face_cy) / 4
        pan_cy = pan_cy - Nav_UD
        # Print Navigation required to center face in image
        if ( abs(Nav_LR)>5 or abs(Nav_UD)>3 ):
           pan_goto(pan_cx, pan_cy)
           print "face_scan - Nav LR=%s UD=%s  Range=%.1f ft" % (Nav_LR, Nav_UD, distance)   
       
        if show_window:
          # Opencv has built in image manipulation functions
            cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2)

      # Use opencv built in window to show the image
      # Leave out if your Raspberry Pi isn't set up to display windows
      if show_window:
         cv2.imshow('Face Image', image)

      elapsed_time = time.time() - start_time         
      # start pan/tilt search for face if timer runs out
      if elapsed_time > face_timer3:
          if not pan_parked:
             pan_parked = True
             stream.close()
             # Return and start motion capture
             return (pan_cx, pan_cy, False)
         
      elif elapsed_time > face_timer2:
          face_found = False
#          print "face_scan - Wide Search Timer2=%d  > %s seconds" % (elapsed_time, face_timer2)
          pan_cx = pan_cx + pan_move_x
          if pan_cx > limit_x_right:
             pan_cx = limit_x_left         
             pan_cy = pan_cy + pan_move_y
             if pan_cy > limit_y_top:
                pan_cy = limit_y_bottom
          pan_goto (pan_cx, pan_cy)
   
      elif elapsed_time > face_timer1:
          face_found = False
#          print "face_scan - Local Search  Timer1=%d  > %s seconds" % (elapsed_time, face_timer1)
          pan_cx = pan_cx + pan_move_x
          if (pan_cx > limit_x_right - (pan_move_x * 2)):
             pan_cx = limit_x_left + pan_move_x         
             pan_cy = pan_cy + pan_move_y
             if (pan_cy > limit_y_top - pan_move_y):
                pan_cy = limit_y_bottom + pan_move_y
          pan_goto (pan_cx, pan_cy)


         
      if cv2.waitKey(1) & 0xFF == ord('q'):
         quit()
         #return (pan_cx, pan_cy, True)
      
# ---------------- Start Main Program

motion_detected = False
q_pressed = False
while (not q_pressed):
  if motion_detected:
    (pan_cx, pan_cy, motion_detected) = face_scan(pan_x_c, pan_y_c)
    if motion_detected:
       quit()
       #q_pressed = True   
  else:
    motion_detected = motion_scan(pan_x_c, pan_y_c)
       
# Close Window
print "Exiting Program ...."
cv2.destroyAllWindows()
camera.close()
quit()
 


Statistics: Posted by pageauc — Wed Sep 03, 2014 6:48 pm

Statistics: Posted by pageauc — Wed Sep 03, 2014 2:40 pm