lab5-1

import matplotlib.pyplot as plt

import numpy as np

from PIL import Image

from joblib import Parallel, delayed

import time

import lab5_lib

from skimage import io, data

def gaussian_pass_11812418(input_image, sigma):

    input_image = lab5_lib.FFT_zero_padding(input_image)

    row, col = input_image.shape

    input_image = np.fft.fft2(input_image)

    input_image = np.fft.fftshift(input_image)

    # define gaussian

    gaussian_filter = lab5_lib.generate_butterworth(row, col, 1, sigma)

    plt.imshow(gaussian_filter)

    plt.show()

    filtered_img = np.multiply(input_image, gaussian_filter)

    output_img = np.fft.ifft2(np.fft.ifftshift(filtered_img))

    output_img = lab5_lib.transform_centering(output_img)

    plt.imshow(np.real(output_img))

    plt.show()

if __name__ == '__main__':

    gaussian_pass_11812418(io.imread("Q5_3.tif"), 100)

import matplotlib.pyplot as plt

import numpy as np

from PIL import Image

from joblib import Parallel, delayed

import time

import lab5_lib

from skimage import io, data

def gaussian_pass_11812418(input_image, sigma):

    input_image = lab5_lib.FFT_zero_padding(input_image)

    row, col = input_image.shape

    input_image = np.fft.fft2(input_image)

    input_image = np.fft.fftshift(input_image)

    # define gaussian

    gaussian_filter = lab5_lib.generate_gaussian(row, col, sigma)

    plt.imshow(gaussian_filter)

    plt.show()

    filtered_img = np.multiply(input_image, gaussian_filter)

    output_img = np.fft.ifft2(np.fft.ifftshift(filtered_img))

    output_img = lab5_lib.transform_centering(output_img)

    plt.imshow(np.real(output_img))

    plt.show()

if __name__ == '__main__':

    gaussian_pass_11812418(io.imread("Q5_2.tif"), 0.01)

import matplotlib.pyplot as plt

import numpy as np

from PIL import Image

from joblib import Parallel, delayed

import time

import lab5_lib

def conv(input_img, kernel_flat):

    row, col = input_img.shape

    def pad_with(vector, pad_width, iaxis, kwargs):

        pad_value = kwargs.get('padder', 0)

        vector[:pad_width[0]] = pad_value

        vector[-pad_width[1]:] = pad_value

    input_img_padding = np.pad(input_img, 1, pad_with, padder=0)

    # def par func

    def single_conv(i, j):

        im_arr = input_img_padding[(i - 1):(i + 2), (j - 1):(j + 2)].flatten()

        return np.sum(im_arr * kernel_flat)

    par_output = np.array(Parallel(n_jobs=6)(delayed(single_conv)(i, j)

                                             for i in range(1, row + 1)

                                             for j in range(1, col + 1)

                                             ))

    Lap = np.reshape(par_output, input_img.shape)

    return Lap

def sobel_11812418(input_img):

    pic_num = 1

    # Reading of the image into numpy array:

    img = Image.open(input_img)

    # FFT transform for img arr

    img_arr = np.asarray(img)

    row, col = img_arr.shape

    # FFT Padding

    img_arr = lab5_lib.FFT_zero_padding(img_arr)

    img_arr = lab5_lib.multiply_center(img_arr)

    img_arr_fft = np.fft.fft2(img_arr)

    # plt.imshow(20*np.log(np.abs((np.fft.fft2(img_arr)))))

    # plt.show()

    kernel1 = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])

    DFT_kernel_1_fft = lab5_lib.DFT_kernel(img_arr,kernel1)

    # K1

    filtered_1 = np.real(np.fft.ifft2(img_arr_fft * DFT_kernel_1_fft))

    filtered_1 = lab5_lib.FFT_extract_padding(filtered_1)

    filtered_1 = lab5_lib.multiply_center(filtered_1)

    # plt.imshow(filtered_1)

    # plt.show()

    # conv

    # op_img = np.fft.ifft2(conv(img_arr, kernel_flat))

    # print(filtered_1)

    op_img = lab5_lib.normalize(filtered_1)*255

    op_img = np.clip(op_img, 110, 140)

    print(op_img)

    plt.imshow(op_img)

    plt.show()

    return op_img

start_time = time.time()

A = sobel_11812418("Q5_1.tif")

op_image = Image.fromarray(A.astype(np.uint8))

print("--- %s seconds ---" % (time.time() - start_time))

op_image.save("output/Q5_1_M.tif")