# Get a single frame.
success = False
while not success:
t0 = time.time()
success, frame = tracker._get_frame()
# Close the connection with the tracker.
tracker.close()
# Cascades
face_cascade = cv2.CascadeClassifier(_FACECASCADE)
eye_cascade = cv2.CascadeClassifier(_EYECASCADE)
# Crop the face and the eyes from the image.
success, facecrop = generic._crop_face(frame, face_cascade, \
minsize=(30, 30))
success, eyes = generic._crop_eyes(facecrop, eye_cascade, \
Lexpect=(0.7,0.4), Rexpect=(0.3,0.4), maxdist=None, maxsize=None)
# Find the pupils in both eyes
B = generic._find_pupils(eyes[0], eyes[1], glint=True, mode='diameter')
t1 = time.time()
# Process results
print("Elapsed time: %.3f ms" % (1000 * (t1 - t0)))
pyplot.figure()
pyplot.imshow(facecrop, cmap='gray')
pyplot.figure()
pyplot.imshow(eyes[0], cmap='gray')
pyplot.figure()
pyplot.imshow(eyes[1], cmap='gray')
# # # # #
else:
# Initialise a new tracker instance.
tracker = ImageTracker(imgdir=IMGDIR, mode=MODE, debug=DEBUG)
# Get a single frame.
success = False
while not success:
t0 = time.time()
success, frame = tracker._get_frame()
# Close the connection with the tracker.
tracker.close()
# Cascades
face_cascade = cv2.CascadeClassifier(_FACECASCADE)
eye_cascade = cv2.CascadeClassifier(_EYECASCADE)
# Crop the face and the eyes from the image.
success, facecrop = generic._crop_face(frame, face_cascade, \
minsize=(30, 30))
success, eyes = generic._crop_eyes(facecrop, eye_cascade, \
Lexpect=(0.7,0.4), Rexpect=(0.3,0.4), maxdist=None, maxsize=None)
# Find the pupils in both eyes
B = generic._find_pupils(eyes[0], eyes[1], glint=True, mode='diameter')
t1 = time.time()
# Process results
print("Elapsed time: %.3f ms" % (1000*(t1-t0)))
pyplot.figure(); pyplot.imshow(facecrop, cmap='gray')
pyplot.figure(); pyplot.imshow(eyes[0], cmap='gray')
pyplot.figure(); pyplot.imshow(eyes[1], cmap='gray')
# # # # #