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

lab5/butterworse_notch.py

+31 −10

Changes for lab5/butterworse_notch.py: 31 added lines, 10 removed lines.
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

Changes for lab5/gaussian.py: 35 added lines, 7 removed lines.
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

+10 −10

Changes for lab5/sobel.py: 10 added lines, 10 removed lines.
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")

Admin message