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face_landmark_detection.py
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face_landmark_detection.py
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from imutils.video import VideoStream
from imutils import face_utils
import imutils
import time
import cv2
import dlib
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
print("[INFO] camera sensor warming up...")
vs = VideoStream(0).start()
time.sleep(2.0)
# dets = detector(img, 1)
# print("Number of faces detected: {}".format(len(dets)))
# for k, d in enumerate(dets):
# print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
# k, d.left(), d.top(), d.right(), d.bottom()))
# # Get the landmarks/parts for the face in box d.
# shape = predictor(img, d)
# print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
# shape.part(1)))
# # Draw the face landmarks on the screen.
# # win.add_overlay(shape)
# 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, height=600)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayayscale frame
rects = detector(gray, 0)
# loopop over the face detections
for rect in rects:
(x,y,w,h) = face_utils.rect_to_bb(rect)
cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255, 0), 3)
shape = predictor(frame, rect)
shape = face_utils.shape_to_np(shape)
# Draw the face landmarks on the screen.
# loop over the (x, y)-coordinates for the facial landmarks
# and draw each of them
for (i, (x, y)) in enumerate(shape):
cv2.circle(frame, (x, y), 2, (0, 255, 0), -1)
# cv2.putText(frame, str(i + 1), (x - 10, y - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 255, 0), 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()