Signature extraction from a JPEG File Code Setup

Question

I am trying to extract a signature from a document using some code that I found on GIT.

The error is as follows: "ValueError: Image RGB array must be uint8 or floating point; found int32"

No matter what image I use, I get the error. I am a novice at image extraction so any help would be appreciated.

The code used:

def extract_signature(source_image):
    """Extract signature from an input image.

    Parameters
    ----------
    source_image : numpy ndarray
        The pinut image.

    Returns
    -------
    numpy ndarray
        An image with the extracted signatures.

    """
    # read the input image
    img = source_image
    img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary

    # connected component analysis by scikit-learn framework
    blobs = img > img.mean()
    blobs_labels = measure.label(blobs, background=1)
    # image_label_overlay = label2rgb(blobs_labels, image=img)

    fig, ax = plt.subplots(figsize=(10, 6))

    '''
    # plot the connected components (for debugging)
    ax.imshow(image_label_overlay)
    ax.set_axis_off()
    plt.tight_layout()
    plt.show()
    '''

    the_biggest_component = 0
    total_area = 0
    counter = 0
    average = 0.0
    for region in regionprops(blobs_labels):
        if (region.area > 10):
            total_area = total_area + region.area
            counter = counter + 1
        # print region.area # (for debugging)
        # take regions with large enough areas
        if (region.area >= 250):
            if (region.area > the_biggest_component):
                the_biggest_component = region.area

    average = (total_area/counter)
    print("the_biggest_component: " + str(the_biggest_component))
    print("average: " + str(average))

    # experimental-based ratio calculation, modify it for your cases
    # a4_small_size_outliar_constant is used as a threshold value to remove connected outliar connected pixels
    # are smaller than a4_small_size_outliar_constant for A4 size scanned documents
    a4_small_size_outliar_constant = ((average/constant_parameter_1)*constant_parameter_2)+constant_parameter_3
    print("a4_small_size_outliar_constant: " + str(a4_small_size_outliar_constant))

    # experimental-based ratio calculation, modify it for your cases
    # a4_big_size_outliar_constant is used as a threshold value to remove outliar connected pixels
    # are bigger than a4_big_size_outliar_constant for A4 size scanned documents
    a4_big_size_outliar_constant = a4_small_size_outliar_constant*constant_parameter_4
    print("a4_big_size_outliar_constant: " + str(a4_big_size_outliar_constant))

    # remove the connected pixels are smaller than a4_small_size_outliar_constant
    pre_version = morphology.remove_small_objects(blobs_labels, a4_small_size_outliar_constant)
    # remove the connected pixels are bigger than threshold a4_big_size_outliar_constant 
    # to get rid of undesired connected pixels such as table headers and etc.
    component_sizes = np.bincount(pre_version.ravel())
    too_small = component_sizes > (a4_big_size_outliar_constant)
    too_small_mask = too_small[pre_version]
    pre_version[too_small_mask] = 0
    # save the the pre-version which is the image is labelled with colors
    # as considering connected components
    plt.imsave('pre_version.png', pre_version)

    # read the pre-version
    img = cv2.imread('pre_version.png', 0)
    # ensure binary
    img = cv2.threshold(img, 0, 255,
                        cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    # save the the result
    # cv2.imwrite("output.png", img)
    return img

If anyone has any suggestions on tips on what to change or improve, that would be greatly appreciated.