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)
class Camera(object):
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)
def show_camera(self):
# capture frames from the camera
for f in self.camera.capture_continuous(self.rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image
self.frame = f.array
# resize the frame and convert it to grayscale
self.frame = self.rot180(imutils.resize(self.frame, width=300))
self.gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
# detect faces in the image and then clone the frame
# so that we can draw on it
self.faceRects = self.fd.detect(self.gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
self.frameClone = self.frame.copy()
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in self.faceRects:
cv2.rectangle(self.frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
# Desactivo el laser si detecto una cara
if len(self.faceRects) > 0:
GPIO.output(laser, 0)
else:
GPIO.output(laser, 1)
# show our detected faces, then clear the frame in
# preparation for the next frame
cv2.imshow("Robopot", self.frameClone)
self.rawCapture.truncate(0)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
def rot180(self, frame):
self.frame = frame
return numpy.rot90(numpy.rot90(self.frame))
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
print("Screen Width: %s" % screenWidth)
print("Screen Height: %s" % screenHeight)
# if a video path was not supplied, grab the reference
# to the gray
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, load the video
else:
camera = cv2.VideoCapture(args["video"])
# construct the face detector and allow the camera to warm
# up
fd = FaceDetector(args["face"])
time.sleep(0.1)
lastTime = time.time()
# send to 0
ser.write('0\n')
# keep looping
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
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)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = fd.detect(gray, 1.3, 5)
import argparse
import cv2
import numpy as np
#variables
cx = 0
cy = 0
larghezza_foto = 85
blank_image_raw = np.zeros((larghezza_foto,larghezza_foto), np.uint8)
blank_image = cv2.cvtColor(blank_image_raw, cv2.COLOR_GRAY2BGR)
proportional_w = 10
proportional_h = 8
offset_constant = 2.5
# construct the face detector
fd = FaceDetector("cascades/haarcascade_frontalface_default.xml")
# open video capture
camera = cv2.VideoCapture(0)
#load demo_image
image_patente = cv2.imread("patente-fronte.png")
#think on use tag for place text and picture in demo_image
# merge image with text
font = cv2.FONT_HERSHEY_SIMPLEX # good
cv2.putText(image_patente,'PIPPO',(130,40), font, 0.4,(0,0,0),1,16)
cv2.putText(image_patente,'PAPERINO',(130,55), font, 0.4,(0,0,0),1,16)
cv2.putText(image_patente,'08/07/97 Forli (FC)',(130,72), font, 0.3,(0,0,0),1,16)
cv2.putText(image_patente,'03/02/1995 MIT-UCO',(130,89), font, 0.3,(0,0,0),1,16)
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)
"r2D2": "b",
"closeEncounters": "c",
"ariel": "d",
"laugh2": "e",
"squeak": "f",
"waka": "g",
"catcall": "h",
"ohhh": "i",
"uhoh": "j",
"laugh": "k"
}
#### connect
Arduino = connectArduino("/dev/cu.usbmodem1411")
# construct the face detector
fd = FaceDetector("./cascades/haarcascade_frontalface_default.xml")
camera = cv2.VideoCapture(0)
while True:
numberOfFace = detectFace(camera, fd)
print(numberOfFace)
if (numberOfFace == 1):
messageArduino(commands["laugh"])
elif (numberOfFace == 2):
messageArduino(commands["closeEncounters"])
# messageArduino(commands["laugh"])
time.sleep(1)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
camera.release()
# import the necessary packages
from pyimagesearch.facedetector import FaceDetector
from pyimagesearch import imutils
import argparse
import cv2
# 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")
# ap.add_argument("-v", "--video",
# help = "path to the (optional) video file")
# args = vars(ap.parse_args())
# construct the face detector
fd = FaceDetector('cascades/haarcascade_frontalface_default.xml')
# if a video path was not supplied, grab the reference
# to the gray
# if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, load the video
# else:
# camera = cv2.VideoCapture(args["video"])
# Load our overlay image: mustache.png
imgMustache = cv2.imread('images/mustache.png', -1)
# Create the mask for the mustache
orig_mask = imgMustache[:, :, 3]
