Lab 5 Q1

import numpy as np

from skimage import io, data

import math

from scipy import interpolate

import matplotlib.pyplot as plt

if __name__ == '__main__':

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'''

Library USE for EE326 2021

'''

import numpy as np

from skimage import io, data

import math

from scipy import interpolate

import matplotlib.pyplot as plt

# General

def format(input_image):

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

    output_image = output_image.astype(np.uint8)

    return output_image

# LAB 4

def convolution_3x3(input_image, operator_3x3):

    col, row = input_image.shape

    output_image = np.zeros([col, row])

    operator = np.zeros(9)

    for i in range(3):

        for j in range(3):

            operator[i*3+j] = operator_3x3[i, j]

    for i in range(col):

        for j in range(row):

            local = np.zeros(9)

            index = 0

            for i2 in [-1, 0, 1]:

                for j2 in [-1, 0, 1]:

                    if (i + i2 < 0 or i + i2 > col - 1 or j + j2 < 0 or j + j2 > row - 1):

                        local[index] = 0

                    else:

                        local[index] = input_image[i + i2, j + j2]

                    index += 1

            output_image[i, j] = np.dot(operator, local)

    output_image = output_image.astype(np.uint8)

    return output_image

def sobel_filter(input_image):

    operator1 = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])

    operator2 = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])

    output_image1 = convolution_3x3(input_image, operator1)

    output_image2 = convolution_3x3(input_image, operator2)

    #

    # output_image1 = np.clip(output_image1, 0, 255)

    # output_image2 = np.clip(output_image2, 0, 255)

    output_image = output_image1 + output_image2 # + input_image

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

    output_image = output_image.astype(np.uint8)

    return output_image

def zero_padding(input_image, P, Q):

    output_image = np.zeros([P, Q])

    return output_image

# LAB 5

def zero_padding_DFT(input_image, P, Q):

    m,n = input_image.shape

    output_image = np.zeros([P, Q])

    output_image[0:m, 0:n] = input_image

    return output_image

def zero_padding_DFT(input_image):

    m,n = input_image.shape

    P = 2*m

    Q = 2*n

    output_image = np.zeros([P, Q])

    output_image[0:m, 0:n] = input_image

    return output_image

def transform_centering(input_image):

    m, n = input_image.shape

    output_image = input_image

    mul1 = 1

    for i in range(m):

        mul2 = mul1

        for j in range(n):

            output_image[i, j] = input_image[i, j] * mul2

            mul2 *= -1

        mul1 *= -1

    return output_image

def generating_from_spatial_filter(input_filter, P, Q):

    output_filter = np.zeros(P, Q)

    return output_filter

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)

    print(test_output)

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import numpy as np

from skimage import io, data

import math

from scipy import interpolate

import matplotlib.pyplot as plt

if __name__ == '__main__':

 No newline at end of file

import numpy as np

from skimage import io, data

import math

from scipy import interpolate

import matplotlib.pyplot as plt

if __name__ == '__main__':

 No newline at end of file