Check the image and rotate it

gistfile1.txt

def guess_direction(cut_points):
#     bbs = np.asarray(cut_points)
    bbs = cut_points
    l = bbs[:, 2] - bbs[:, 0]
    # Convert negative length to positive.
    l = np.asarray([-i if i < 0 else i for i in l])
    w = bbs[:, 3] - bbs[:, 1]
    w = np.asarray([-i if i < 0 else i for i in w])

    if l.mean() - w.mean() > 0:
        return 0  # horizontal
    else:
        return 1  # Vertical - need to rotate 90 degree


def rotate_bound(image, angle=90):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)
    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (i.e., the rotation components of the matrix)
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
    # perform the actual rotation and return the image
    return cv2.warpAffine(image, M, (nW, nH))