Update lab5 (bf2bebf2) · Commits · 袁通 / EE326 Digital Image Processing LAB

Lab5_Frequency_Domain_Filtering/EE326_SUSTech.py

+21 −11

Original line number	Original line	Diff line number	Diff line
	@@ -11,7 +11,9 @@ import matplotlib.pyplot as plt

	# General		# General
	def format_image(input_image):		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)		output_image = output_image.astype(np.uint8)

	return output_image		return output_image
	@@ -72,6 +74,13 @@ def zero_padding(input_image, P, Q):

	# LAB 5		# 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):		def zero_padding_DFT(input_image, P, Q):
	m,n = input_image.shape		m,n = input_image.shape

	@@ -80,6 +89,7 @@ def zero_padding_DFT(input_image, P, Q):

	return output_image		return output_image


	def zero_padding_DFT(input_image):		def zero_padding_DFT(input_image):
	m,n = input_image.shape		m,n = input_image.shape

	@@ -91,6 +101,7 @@ def zero_padding_DFT(input_image):

	return output_image		return output_image


	def transform_centering(input_image):		def transform_centering(input_image):
	m, n = input_image.shape		m, n = input_image.shape
	output_image = input_image		output_image = input_image
	@@ -110,18 +121,17 @@ def generating_from_spatial_filter(input_filter, P, Q):
	return output_filter		return output_filter


	def gaussian_filter(shape, sigma):		def gaussian_filter(a, b, sigma):
	x, y = [edge /2 for edge in shape]		x, y = np.meshgrid(np.linspace(0, a-1, a), np.linspace(0, b-1, b))
	grid = np.array([[((i2+j2)/(2.0sigma*2)) for i in range(-x, x+1)] for j in range(-y, y+1)])		x = x - a/2
	g_filter = np.exp(-grid)/(2np.pisigma**2)		y = y - b/2
	g_filter /= np.sum(g_filter)		d = x * x + y * y
	return g_filter		g = np.exp(-(d / (2.0 * sigma ** 2)))
			# g = g/np.sum(g)
			return g


	if __name__ == '__main__':		if __name__ == '__main__':

	test_input = np.array([[1, 2, 3], [1, 2, 3]])		io.imsave("gaussian_filter_test.tif", format_image(gaussian_filter(100, 100, 30)))
	m, n = test_input.shape
	test_output = zero_padding_DFT(test_input, 2m, 2n)

	print(test_output)
	No newline at end of file