Update lab5

# General

def format_image(input_image):

    output_image = np.clip(input_image, 0, 255)

    output_image = input_image

    output_image -= np.min(output_image)

    output_image = (output_image/np.max(output_image))*255

    output_image = output_image.astype(np.uint8)

    return output_image

# LAB 5

def extract_result(input_image):

    x, y = input_image.shape

    output_image = input_image[int(x/2):x, int(y/2):y]

    return output_image

def zero_padding_DFT(input_image, P, Q):

    m,n = input_image.shape

    return output_image

def zero_padding_DFT(input_image):

    m,n = input_image.shape

    return output_image

def transform_centering(input_image):

    m, n = input_image.shape

    output_image = input_image

    return output_filter

def gaussian_filter(shape, sigma):

    x, y = [edge /2 for edge in shape]

    grid = np.array([[((i**2+j**2)/(2.0*sigma**2)) for i in range(-x, x+1)] for j in range(-y, y+1)])

    g_filter = np.exp(-grid)/(2*np.pi*sigma**2)

    g_filter /= np.sum(g_filter)

    return g_filter

def gaussian_filter(a, b, sigma):

    x, y = np.meshgrid(np.linspace(0, a-1, a), np.linspace(0, b-1, b))

    x = x - a/2

    y = y - b/2

    d = x * x + y * y

    g = np.exp(-(d / (2.0 * sigma ** 2)))

    # g = g/np.sum(g)

    return g

if __name__ == '__main__':

    test_input = np.array([[1, 2, 3], [1, 2, 3]])

    m, n = test_input.shape

    test_output = zero_padding_DFT(test_input, 2*m, 2*n)

    io.imsave("gaussian_filter_test.tif", format_image(gaussian_filter(100, 100, 30)))

    print(test_output)

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