print("Detection of the face took {}".format(time.time() - start))
start = time.time()
lbpFaceFeature = mlbp.computeFeaturePatchWise(
cv2.cvtColor(alignedFaces[0], cv2.COLOR_RGB2GRAY))
print("Computing the mlbp feature of a face took {}".format(time.time() -
start))
start = time.time()
lbpFeature = mlbp.computeFeaturePatchWise(
cv2.cvtColor(faceImg, cv2.COLOR_RGB2GRAY))
print("Computing the mlbp feature of the entire frame took {}".format(
time.time() - start))
dsift = features.DSIFT()
start = time.time()
gray = cv2.cvtColor(alignedFaces[0], cv2.COLOR_RGB2GRAY)
gray = gray.astype("float32")
kp, dsiftFaceFeature = dsift.compute(gray, 8, 16)
dsiftFaceFeature = np.transpose(dsiftFaceFeature)
print("Computing the dsift feature of the face took {}".format(time.time() -
start))
start = time.time()
kpFull, dsiftFeature = dsift.compute(faceImg, 8, 8)
print("Computing the dsift feature of the full image took {}".format(
time.time() - start))
def __init__(self, lbp, path_to_classifier):
self._lbp = lbp
self._sift = features.DSIFT()
self.clf = joblib.load(path_to_classifier)