video_facial_landmarks.py

# import the necessary packages
from imutils.video import VideoStream
from imutils import face_utils
import datetime
import argparse
import imutils
import time
import dlib
import cv2


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
    help="path to facial landmark predictor")
ap.add_argument("-r", "--picamera", type=int, default=-1,
    help="whether or not the Raspberry Pi camera should be used")
args = vars(ap.parse_args())


# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])


# initialize the video stream and allow the cammera sensor to warmup
print("[INFO] camera sensor warming up...")
vs = VideoStream(usePiCamera=args["picamera"] > 0).start()
time.sleep(2.0)


# loop over the frames from the video stream
while True:
    # grab the frame from the threaded video stream, resize it to
    # have a maximum width of 400 pixels, and convert it to
    # grayscale
    frame = vs.read()
    frame = imutils.resize(frame, width=400)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
    # detect faces in the grayscale frame
    rects = detector(gray, 0)

    # loop over the face detections
    for rect in rects:
        # determine the facial landmarks for the face region, then
        # convert the facial landmark (x, y)-coordinates to a NumPy
        # array
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape)
 
        # loop over the (x, y)-coordinates for the facial landmarks
        # and draw them on the image
        for (x, y) in shape:
            cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)
      
    # show the frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
 
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()