def __init__(self):
self.size = (640, 480)
# initialize the camera and grab a reference to the raw camera
# capture
self.camera = PiCamera()
self.camera.resolution = self.size
self.camera.framerate = 32
self.rawCapture = PiRGBArray(self.camera, size=self.size)
# construct the face detector and allow the camera to warm
# up
self.fd = FaceDetector("cascades/haarcascade_frontalface_default.xml")
time.sleep(0.1)
import os
from subprocess import call
setproctitle.setproctitle("FacialDetectProcess")
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", required = True,
help = "path to where the face cascade resides")
args = vars(ap.parse_args())
faceimg = "/dev/shm/face.jpg"
facetext = "/dev/shm/face_text"
facelocation = "/dev/shm/face.txt"
faceframe = "/dev/shm/face_frame"
# construct the face detector
fd = FaceDetector(args["face"])
# keep looping
while True:
if (os.path.exists(faceframe)):
fX = 0
fY = 0
fW = 0
fH = 0
# grab the current frame
print "reading image"
gray = cv2.imread('/dev/shm/face.jpg',0)
height, width = gray.shape #get resolution for plotting location to screen thirds in facedetect.py
# detect faces in the image and then clone the frame
# so that we can draw on it
import numpy as np
import cv2
from pyimagesearch.facedetector import FaceDetector
fd = FaceDetector('cascades/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
# Load our overlay image: hat.png
hat_default = cv2.imread('images/crown.png', -1)
# Create the mask for the hat
orig_mask = hat_default[:, :, 3]
# Create the inverted mask for the hat
orig_mask_inv = cv2.bitwise_not(orig_mask)
# Convert hat image to BGR
# and save the original image size (used later when re-sizing the image)
hat = hat_default[:, :, 0:3]
orighatHeight, origHatWidth = hat.shape[:2]
while True:
ret, frame = cap.read()
frame = cv2.resize(frame,
None,
fx=scaling_factor,
fy=scaling_factor,
interpolation=cv2.INTER_AREA)
from __future__ import print_function
from pyimagesearch.facedetector import FaceDetector
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-f",
"face",
required=True,
help='path to where the face cascade resides')
ap.add_argument("-i",
"image",
required=True,
help='path to where the image file resides')
args = vars(ap.parse_arg())
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOER_BGR2GRAY)
fd = FaceDetector(args['face'])
faceRects = fd.detect(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
print("I found {} face(s)".format(len(faceRects)))
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('face', image)
