cascPath = 'Haar/haarcascade_frontalcatface.xml'
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (160, 120)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(160, 120))
# allow the camera to warmup
time.sleep(0.1)
lastTime = time.time() * 1000.0
num = 0
id = Id.AddName()
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if (num > 200):
break
# Detect faces in the image
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
from picamera.array import PiRGBArray
from picamera import PiCamera
import cv2
import numpy as np
import Id
camera = PiCamera()
camera.resolution = (160, 128)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(160, 128))
face_detector = cv2.CascadeClassifier('Haar/haarcascade_frontalcatface.xml')
eye_cascade = cv2.CascadeClassifier('Haar/haarcascade_eye.xml')
face_id = Id.AddName()
count = 0
WHITE = [255, 255, 255]
while (count < 200):
ret, img = camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if np.average(gray) > 110:
faces = face_detector.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
Face = gray[y - int(h / 2):y + int(h * 1.5),
x - int(x / 2):x + int(w * 1.5)]
eyes = (Id.DetectEyes(Face))
cv2.putText(gray, "Face Detected", (x + (w / 2), y - 5),
cv2.FONT_HERSHEY_DUPLEX, .4, WHITE)
if eyes is not None:
frame = eyes
else:
