def FindFace():
faceCount = 0
while faceCount == 0:
stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.capture("images/LifeFaceDetection.jpg")
camera.close()
stream.seek(0)
image = cv2.imread("images/LifeFaceDetection.jpg")
fd = FaceDetector()
faceRects = fd.detect(image)
faceCount = len(faceRects)
if len(faceRects) > 0:
DrawRectangleAndSave(faceRects, image)
lib.logImages.logFace(image)
post.postText(700, "Hi!")
time.sleep(3)
post.postText(400, "Nice outfit")
time.sleep(2)
post.postText(200, "If you have a tennis ball I can take your picture")
FindTennisBall()
else:
print "no faces found"
ap.add_argument("-c","--cascade",help="path to where the face cascade resides",default="cascades/haarcascade_frontalface_default.xml")
ap.add_argument("-e","--eye",help="path to where the eye cascade resides",default="cascades/haarcascade_eye.xml")
ap.add_argument("-f","--faces",help="path to the image dataset",default="faces_data/")
ap.add_argument("-tf","--testface",help="path to the test image dataset",default="faces_realtime/cloony2.jpg")
args = vars(ap.parse_args())
###############################################
#Load faces classifier model from disk
pkl_file = open('models/faces_classifier.pkl', 'rb')
classifier = pickle.load(pkl_file)
pkl_file.close()
print "Load model from disk successfully"
###############################################
###############################################
fd = FaceDetector(args["cascade"])
desc = RGBHistogram([8,8,8])
###############################################
###############################################
imagePaths = sorted(glob.glob(args["faces"]+"/*.jpg"))
target = []
for (imagePath) in imagePaths:
image = cv2.imread(imagePath)
features = desc.describe(image,None)
target.append(imagePath.split("_")[-2])
targetNames = np.unique(target)
le = LabelEncoder()
target = le.fit_transform(target)
###############################################
ap.add_argument("-f",
"--face",
default="cascades/haarcascade_frontalface_default.xml",
help="path to where the face cascade resides")
ap.add_argument("-i",
"--image",
default="input_face/lena.jpg",
help="path to where the image file resides")
args = vars(ap.parse_args())
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# find faces in the image
fd = FaceDetector(args["face"])
faceRects = fd.detect(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(100, 100))
print "I found %d face(s)" % (len(faceRects))
# loop over the faces and draw a rectangle around each
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
sub_face = image[y:y + h, x:x + w]
sub_face = imutils.resize(sub_face, width=200, height=200)
sub_face_file_name = "input_face/subface.jpg"
cv2.imwrite(sub_face_file_name, sub_face)
detected_face_file = "input_face/detected.jpg"
from PIL import Image, ImageFilter
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", default="cascades/haarcascade_frontalface_default.xml",
help = "path to where the face cascade resides")
ap.add_argument("-i", "--image", default="input_face/lena.jpg",
help = "path to where the image file resides")
args = vars(ap.parse_args())
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# find faces in the image
fd = FaceDetector(args["face"])
faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5,
minSize = (100, 100))
print "I found %d face(s)" % (len(faceRects))
# loop over the faces and draw a rectangle around each
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
sub_face = image[y:y+h, x:x+w]
sub_face = imutils.resize(sub_face,width=200,height=200)
sub_face_file_name = "input_face/subface.jpg"
cv2.imwrite(sub_face_file_name, sub_face)
detected_face_file = "input_face/detected.jpg"
print "Write detected file %s" % detected_face_file
cv2.imwrite(detected_face_file,image)