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budz_shape_predictor.py
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budz_shape_predictor.py
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import cv2
import dlib
# if len(sys.argv) != 2:
# print(
# "Give the path to the examples/faces directory as the argument to this "
# "program. For example, if you are in the python_examples folder then "
# "execute this program by running:\n"
# " ./train_shape_predictor.py ../examples/faces")
# exit()
# faces_folder = sys.argv[1]
predictor = dlib.shape_predictor("budz3_predictor_landmarks.dat")
# detector = dlib.get_frontal_face_detector()
detector = dlib.fhog_object_detector("b3adetector.svm")
# Now let's run the detector and shape_predictor over the images in the faces
frame_number = 0
stream = cv2.VideoCapture("../../face_recognition-master/examples/bbunny-clip2-1.mp4")
while True:
(grabbed, frame) = stream.read()
frame_number += 1
# if the frame was not grabbed, then we have reached the
# end of the stream
if not grabbed:
break
rgb_image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(rgb_image, 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()))
rect_to_bb(d)
# Get the landmarks/parts for the face in box d.
shape = predictor(rgb_image, d)
for i, part in shape.part:
print("part: {}, xy: {}".format(i, shape.part))
# close the video file pointers
stream.release()