optimize butterworse (a9cb27ad) · Commits · Sparkf / SUSTech EE326 Digital Image Processing

lab5/butterworse_notch.py

+31 −10

Original line number	Diff line number	Diff line
		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 butterworse_pass_11812418(input_img, sigma):
		# Reading of the image into numpy array:
		img = Image.open(input_img)
		# FFT transform for img arr
		input_image = np.asarray(img)

		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)
		#492,336

		# define butterworth
		butterworth_filter = 1-lab5_lib.generate_butterworth(40, 40, 2, sigma)
		bf_x = [70,150,310,395]
		bf_y = [90,210]

		for x in bf_x:
		for y in bf_y:
		input_image[x:x+40,y:y+40] = np.multiply(input_image[x:x+40,y:y+40],butterworth_filter)
		plt.imshow(np.log(np.abs(input_image)))
		plt.show()
		filtered_img = np.multiply(input_image, gaussian_filter)

		# plt.imshow(input_image)
		# plt.show()
		filtered_img = input_image
		# filtered_img = np.multiply(input_image, butterworth_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()
		output_img = lab5_lib.normalize(output_img)*255
		# plt.imshow(np.real(output_img))
		# plt.show()

		return np.abs(output_img)


		if __name__ == '__main__':
		gaussian_pass_11812418(io.imread("Q5_3.tif"), 100)
		start_time = time.time()
		A = butterworse_pass_11812418("Q5_3.tif", 100)
		op_image = Image.fromarray(A.astype(np.uint8))
		print("--- %s seconds ---" % (time.time() - start_time))
		op_image.save("output/Q5_3_M.tif")

lab5/gaussian.py

+35 −7

Original line number	Diff line number	Diff line
		@@ -5,10 +5,14 @@ from joblib import Parallel, delayed
		import time
		import lab5_lib

		from skimage import io, data

		def gaussian_pass_11812418(input_img, sigma):
		# Reading of the image into numpy array:
		img = Image.open(input_img)
		# FFT transform for img arr
		input_image_origin = np.asarray(img)
		input_image = np.asarray(img)

		def gaussian_pass_11812418(input_image, sigma):
		input_image = lab5_lib.FFT_zero_padding(input_image)
		row, col = input_image.shape

		@@ -16,14 +20,38 @@ def gaussian_pass_11812418(input_image, sigma):
		input_image = np.fft.fftshift(input_image)
		# define gaussian
		gaussian_filter = lab5_lib.generate_gaussian(row, col, sigma)
		plt.imshow(gaussian_filter)
		plt.show()
		# print(gaussian_filter)
		# 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()
		# print(np.min(np.abs(output_img)))
		# output_img = lab5_lib.normalize(output_img)
		# plt.imshow(np.real(output_img))
		# plt.show()

		return np.abs(output_img), np.abs(input_image_origin-output_img)


		if __name__ == '__main__':
		gaussian_pass_11812418(io.imread("Q5_2.tif"), 0.01)
		start_time = time.time()
		A_LP,A_HP = gaussian_pass_11812418("Q5_2.tif", 30)
		op_image_LP = Image.fromarray(A_LP.astype(np.uint8))
		op_image_LP.save("output/Q5_2_LP30.tif")
		op_image_HP = Image.fromarray(A_HP.astype(np.uint8))
		op_image_HP.save("output/Q5_2_HP30.tif")
		A_LP,A_HP = gaussian_pass_11812418("Q5_2.tif", 60)
		op_image_LP = Image.fromarray(A_LP.astype(np.uint8))
		op_image_LP.save("output/Q5_2_LP60.tif")
		op_image_HP = Image.fromarray(A_HP.astype(np.uint8))
		op_image_HP.save("output/Q5_2_HP60.tif")
		A_LP,A_HP = gaussian_pass_11812418("Q5_2.tif", 160)
		op_image_LP = Image.fromarray(A_LP.astype(np.uint8))
		op_image_LP.save("output/Q5_2_LP160.tif")
		op_image_HP = Image.fromarray(A_HP.astype(np.uint8))
		op_image_HP.save("output/Q5_2_HP160.tif")


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

lab5/sobel.py

+12 −12

Original line number	Diff line number	Diff line
		@@ -6,9 +6,6 @@ import time
		import lab5_lib





		def conv(input_img, kernel_flat):
		row, col = input_img.shape

		@@ -40,6 +37,7 @@ def sobel_11812418(input_img):

		# FFT transform for img arr
		img_arr = np.asarray(img)
		input_image_origin = np.asarray(img)

		row, col = img_arr.shape

		@@ -56,8 +54,6 @@ def sobel_11812418(input_img):

		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)
		@@ -65,23 +61,27 @@ def sobel_11812418(input_img):
		# 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)
		op_img = np.clip(op_img, 115, 145)
		op_img = lab5_lib.normalize(op_img)*255
		print(op_img)

		plt.imshow(op_img)
		plt.show()

		return op_img
		op_img_spacial = conv(input_image_origin,kernel1.flatten())
		op_img_spacial = lab5_lib.normalize(op_img_spacial)*255

		return op_img, op_img_spacial


		start_time = time.time()
		A = sobel_11812418("Q5_1.tif")
		A, A_S = sobel_11812418("Q5_1.tif")
		op_image = Image.fromarray(A.astype(np.uint8))
		op_image_S = Image.fromarray(A_S.astype(np.uint8))
		print("--- %s seconds ---" % (time.time() - start_time))
		op_image.save("output/Q5_1_M.tif")
		op_image_S.save("output/Q5_1_M_S.tif")

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