Vectorized HOG feature detector for SimpleCV images

Image.findHOGFeatures

def findHOGFeatures(self, n_divs=3, n_bins=6):
    """
    **SUMMARY**
    Get HOG(Histogram of Oriented Gradients) features from the image.


    **PARAMETERS**
    * *n_divs* - the number of divisions(cells).
    * *n_divs* - the number of orientation bins.

    **RETURNS**
    Returns the HOG vector in a numpy array

    """
    n_HOG = n_divs * n_divs * n_bins  # Size of HOG vector

    HOG = np.zeros((n_HOG, 1))  # Initialize output HOG vector

    # Apply sobel on image to find x and y orientations of the image
    Icv = self.getNumpyCv2()
    Ix = cv2.Sobel(Icv, ddepth=cv.CV_32F, dx=1, dy=0, ksize=3)
    Iy = cv2.Sobel(Icv, ddepth=cv.CV_32F, dx=0, dy=1, ksize=3)

    Ix = Ix.transpose(1, 0, 2)
    Iy = Iy.transpose(1, 0, 2)
    cellx = self.width / n_divs  # width of each cell(division)
    celly = self.height / n_divs  # height of each cell(division)

    # Area of image
    img_area = self.height * self.width

    #Range of each bin
    BIN_RANGE = (2 * pi) / n_bins

    angles = np.arctan2(Iy, Ix)
    magnit = ((Ix ** 2) + (Iy ** 2)) ** 0.5

    height, width = self.height, self.width
    bins = (angles[...,0] % (2 * pi) / BIN_RANGE).astype(int)
    x, y = np.mgrid[:width, :height]
    x = x * n_divs // width
    y = y * n_divs // height
    labels = (x * n_divs + y) * n_bins + bins
    index = np.arange(n_HOG)
    HOG = ndi_sum(magnit[..., 0], labels, index)

    return HOG / self_area

i want to get return a featureset like the other function in the imageclass that i can draw() or crop().