def hough_line(img):
    # Rho and Theta ranges
    thetas = np.deg2rad(np.arange(-90.0, 90.0))
    width, height = img.shape
    diag_len = int(np.sqrt(width**2 + height**2))  # max_dist
    rhos = np.linspace(-diag_len, diag_len, int(diag_len * 2.0))

    # Cache some reusable values
    cos_t = np.cos(thetas)
    sin_t = np.sin(thetas)
    num_thetas = len(thetas)

    # Hough accumulator array of theta vs rho
    accumulator = np.zeros((2*diag_len, num_thetas))
    y_idxs, x_idxs = np.nonzero(img)  # (row, col) indexes to edges

    # Vote in the hough accumulator
    for i in range(len(x_idxs)):
        x = x_idxs[i]
        y = y_idxs[i]

        for t_idx in range(num_thetas):
            # Calculate rho. diag_len is added for a positive index
            rho = int(round(x*cos_t[t_idx] + y * sin_t[t_idx]) + diag_len)
            accumulator[rho, t_idx] += 1

    return accumulator, thetas, rhos