facetracker_lbp.py

#!/usr/bin/env python

import cv2, sys, time, os
from pantilthat import *

# Load the BCM V4l2 driver for /dev/video0
os.system('sudo modprobe bcm2835-v4l2')
# Set the framerate ( not sure this does anything! )
os.system('v4l2-ctl -p 8')

# Frame Size. Smaller is faster, but less accurate.
# Wide and short is better, since moving your head
# vertically is kinda hard!
FRAME_W = 180
FRAME_H = 100

# Default Pan/Tilt for the camera in degrees.
# Camera range is from -90 to 90
cam_pan = 90
cam_tilt = 60

# Set up the CascadeClassifier for face tracking
#cascPath = 'haarcascade_frontalface_default.xml' # sys.argv[1]
cascPath = '/usr/share/opencv/lbpcascades/lbpcascade_frontalface.xml'
faceCascade = cv2.CascadeClassifier(cascPath)

# Set up the capture with our frame size
video_capture = cv2.VideoCapture(0)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  FRAME_W)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, FRAME_H)
time.sleep(2)

# Turn the camera to the default position
pan(cam_pan-90)
tilt(cam_tilt-90)
light_mode(WS2812)

def lights(r,g,b,w):
    for x in range(18):
        set_pixel_rgbw(x,r if x in [3,4] else 0,g if x in [3,4] else 0,b,w if x in [0,1,6,7] else 0)
    show()

lights(0,0,0,50)

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()
    # This line lets you mount the camera the "right" way up, with neopixels above
    frame = cv2.flip(frame, -1)
    
    if ret == False:
      print("Error getting image")
      continue

    # Convert to greyscale for detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.equalizeHist( gray )

    # Do face detection
    faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
   
    # Slower method 
    '''faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=4,
        minSize=(20, 20),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
    )'''
    
    lights(50 if len(faces) == 0 else 0, 50 if len(faces) > 0 else 0,0,50)

    for (x, y, w, h) in faces:
        # Draw a green rectangle around the face
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

        # Track first face
        
        # Get the center of the face
        x = x + (w/2)
        y = y + (h/2)

        # Correct relative to center of image
        turn_x  = float(x - (FRAME_W/2))
        turn_y  = float(y - (FRAME_H/2))

        # Convert to percentage offset
        turn_x  /= float(FRAME_W/2)
        turn_y  /= float(FRAME_H/2)

        # Scale offset to degrees
        turn_x   *= 2.5 # VFOV
        turn_y   *= 2.5 # HFOV
        cam_pan  += -turn_x
        cam_tilt += turn_y

        print(cam_pan-90, cam_tilt-90)

        # Clamp Pan/Tilt to 0 to 180 degrees
        cam_pan = max(0,min(180,cam_pan))
        cam_tilt = max(0,min(180,cam_tilt))

        # Update the servos
        pan(int(cam_pan-90))
        tilt(int(cam_tilt-90))

        break

    frame = cv2.resize(frame, (540,300))
    frame = cv2.flip(frame, 1)
   
    # Display the image, with rectangle
    # on the Pi desktop 
    cv2.imshow('Video', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()