changes (27b7f6c2) · Commits · Nathaniel Callens / Image Compression

.ipynb_checkpoints/Error_to_Image-checkpoint.ipynb

+2 −2

Original line number	Original line	Diff line number	Diff line
	%% Cell type:code id:dbef8759 tags:		%% Cell type:code id:dbef8759 tags:

	``` python		``` python
	import numpy as np		import numpy as np
	from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution		from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution
	from matplotlib import pyplot as plt		from matplotlib import pyplot as plt
	from itertools import product		from itertools import product
	import os		import os
	import sys		import sys
	from PIL import Image		from PIL import Image
	from scipy.optimize import minimize		from scipy.optimize import minimize
	from time import time		from time import time
	from numpy import linalg as la		from numpy import linalg as la
	from scipy.stats import gaussian_kde		from scipy.stats import gaussian_kde
	import seaborn as sns		import seaborn as sns
	import pywt		import pywt
	```		```

	%% Cell type:code id:9ed20f84 tags:		%% Cell type:code id:9ed20f84 tags:

	``` python		``` python
	def plot_hist(tiff_list, i=0):		def plot_hist(tiff_list, i=0):
	"""		"""
	This function is the leftovers from the first attempt to plot histograms.		This function is the leftovers from the first attempt to plot histograms.
	As it stands it needs some work in order to function again. We will		As it stands it needs some work in order to function again. We will
	fix this later. 1/25/22		fix this later. 1/25/22
	"""		"""

	image = tiff_list[i]		image = tiff_list[i]
	image = Image.open(image) #Open the image and read it as an Image object		image = Image.open(image) #Open the image and read it as an Image object
	image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data		image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
	image_int = image.astype(np.int_)		image_int = image.astype(np.int_)

	A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation		A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation

	z0 = image_int[0:-2,0:-2] # get all the first pixel for the entire image		z0 = image_int[0:-2,0:-2] # get all the first pixel for the entire image
	z1 = image_int[0:-2,1:-1] # get all the second pixel for the entire image		z1 = image_int[0:-2,1:-1] # get all the second pixel for the entire image
	z2 = image_int[0:-2,2::] # get all the third pixel for the entire image		z2 = image_int[0:-2,2::] # get all the third pixel for the entire image
	z3 = image_int[1:-1,0:-2] # get all the fourth pixel for the entire image		z3 = image_int[1:-1,0:-2] # get all the fourth pixel for the entire image

	# calculate the out put of the system of equation		# calculate the out put of the system of equation
	y0 = np.ravel(-z0+z2-z3)		y0 = np.ravel(-z0+z2-z3)
	y1 = np.ravel(z0+z1+z2)		y1 = np.ravel(z0+z1+z2)
	y2 = np.ravel(-z0-z1-z2-z3)		y2 = np.ravel(-z0-z1-z2-z3)
	y = np.vstack((y0,y1,y2))		y = np.vstack((y0,y1,y2))

	# use numpy solver to solve the system of equations all at once		# use numpy solver to solve the system of equations all at once
	predict = np.linalg.solve(A,y)[-1]		predict = np.linalg.solve(A,y)[-1]
	#predict = []		#predict = []

	# flatten the neighbor pixels and stack them together		# flatten the neighbor pixels and stack them together
	z0 = np.ravel(z0)		z0 = np.ravel(z0)
	z1 = np.ravel(z1)		z1 = np.ravel(z1)
	z2 = np.ravel(z2)		z2 = np.ravel(z2)
	z3 = np.ravel(z3)		z3 = np.ravel(z3)
	neighbor = np.vstack((z0,z1,z2,z3)).T		neighbor = np.vstack((z0,z1,z2,z3)).T

	# calculate the difference		# calculate the difference
	diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)		diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)


	# flatten the image to a vector		# flatten the image to a vector
	small_image = image_int[1:-1,1:-1]		small_image = image_int[1:-1,1:-1]

	#Reshape the predictions to be a 2D array		#Reshape the predictions to be a 2D array
	predict = np.pad(predict.reshape(510,638), pad_width=1)		predict = np.pad(predict.reshape(510,638), pad_width=1)
	"""predict[0,:] = image[0,:]		"""predict[0,:] = image[0,:]
	predict[:,0] = image[:,0]		predict[:,0] = image[:,0]
	predict[:,-1] = image[:,-1]		predict[:,-1] = image[:,-1]
	predict[-1,:] = image[-1,:]"""		predict[-1,:] = image[-1,:]"""


	#Calculate the error between the original image and our predictions		#Calculate the error between the original image and our predictions
	#Note that we only predicted on the inside square of the original image, excluding		#Note that we only predicted on the inside square of the original image, excluding
	#The first row, column and last row, column		#The first row, column and last row, column
	#error = (image_int - predict).astype(int) #Experiment		#error = (image_int - predict).astype(int) #Experiment

	#this one works		#this one works
	error = image_int - predict		error = image_int - predict


	return predict, diff, image_int, error, A		return predict, diff, image_int, error, A
	```		```

	%% Cell type:code id:ba2881d9 tags:		%% Cell type:code id:ba2881d9 tags:

	``` python		``` python
	scenes = file_extractor()		scenes = file_extractor()
	images = image_extractor(scenes)		images = image_extractor(scenes)
	num_images = im_distribution(images, "_9")		num_images = im_distribution(images, "_9")
	```		```

	%% Cell type:code id:11e95c34 tags:		%% Cell type:code id:11e95c34 tags:

	``` python		``` python
	predict, diff, im, err, A = plot_hist(num_images, 0)		predict, diff, im, err, A = plot_hist(num_images, 0)
	```		```

	%% Cell type:code id:434e4d2f tags:		%% Cell type:code id:434e4d2f tags:

	``` python		``` python
	def reconstruct(error, A):		def reconstruct(error, A):
	"""		"""
	Function that reconstructs the original image		Function that reconstructs the original image
	from the error matrix and using the predictive		from the error matrix and using the predictive
	algorithm developed in the encoding.		algorithm developed in the encoding.

	Parameters:		Parameters:
	error (array): matrix of errors computed in encoding. Same		error (array): matrix of errors computed in encoding. Same
	shape as the original image (512, 640) in this case		shape as the original image (512, 640) in this case
	A (array): Matrix used for the system of equations to create predictions		A (array): Matrix used for the system of equations to create predictions
	Returns:		Returns:
	image (array): The reconstructed image		image (array): The reconstructed image
	"""		"""
	new_e = error.copy()		new_e = error.copy()
	rows, columns = new_e.shape		rows, columns = new_e.shape

	for r in range(1, rows-1):		for r in range(1, rows-1):
	for c in range(1, columns-1):		for c in range(1, columns-1):
	z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]		z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]
	y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))		y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))

	if r == 345 and c == 421:		if r == 345 and c == 421:
	print(new_e[r][c])		print(new_e[r][c])
	print(np.linalg.solve(A,y)[-1])		print(np.linalg.solve(A,y)[-1])
	print(new_e[r][c] + np.linalg.solve(A,y)[-1])		print(new_e[r][c] + np.linalg.solve(A,y)[-1])
	print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))		print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))

	#Real solution that works, DO NOT DELETE		#Real solution that works, DO NOT DELETE
	new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))		new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))

	#new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])		#new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])

	return new_e.astype(int)		return new_e.astype(int)

	```		```

	%% Cell type:code id:8d835626 tags:		%% Cell type:code id:7f395ab2 tags:

	``` python		``` python
	new_error = reconstruct(err, A)		new_error = reconstruct(err, A)
	```		```

	%% Output		%% Output

	3.499999999992724		3.499999999992724
	[13644.5]		[13644.5]
	[13648.]		[13648.]
	[13648.]		[13648.]

	%% Cell type:code id:b54c4328 tags:		%% Cell type:code id:06ccaf8e tags:

	``` python		``` python
	e = np.round(err, 1)		e = np.round(err, 1)
	len(np.unique(e[1:-1, 1:-1]))		len(np.unique(e[1:-1, 1:-1]))
	```		```

	%% Output		%% Output

	518		518

Error_to_Image.ipynb

+2 −2

Original line number	Original line	Diff line number	Diff line
	%% Cell type:code id:dbef8759 tags:		%% Cell type:code id:dbef8759 tags:

	``` python		``` python
	import numpy as np		import numpy as np
	from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution		from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution
	from matplotlib import pyplot as plt		from matplotlib import pyplot as plt
	from itertools import product		from itertools import product
	import os		import os
	import sys		import sys
	from PIL import Image		from PIL import Image
	from scipy.optimize import minimize		from scipy.optimize import minimize
	from time import time		from time import time
	from numpy import linalg as la		from numpy import linalg as la
	from scipy.stats import gaussian_kde		from scipy.stats import gaussian_kde
	import seaborn as sns		import seaborn as sns
	import pywt		import pywt
	```		```

	%% Cell type:code id:9ed20f84 tags:		%% Cell type:code id:9ed20f84 tags:

	``` python		``` python
	def plot_hist(tiff_list, i=0):		def plot_hist(tiff_list, i=0):
	"""		"""
	This function is the leftovers from the first attempt to plot histograms.		This function is the leftovers from the first attempt to plot histograms.
	As it stands it needs some work in order to function again. We will		As it stands it needs some work in order to function again. We will
	fix this later. 1/25/22		fix this later. 1/25/22
	"""		"""

	image = tiff_list[i]		image = tiff_list[i]
	image = Image.open(image) #Open the image and read it as an Image object		image = Image.open(image) #Open the image and read it as an Image object
	image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data		image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
	image_int = image.astype(np.int_)		image_int = image.astype(np.int_)

	A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation		A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation

	z0 = image_int[0:-2,0:-2] # get all the first pixel for the entire image		z0 = image_int[0:-2,0:-2] # get all the first pixel for the entire image
	z1 = image_int[0:-2,1:-1] # get all the second pixel for the entire image		z1 = image_int[0:-2,1:-1] # get all the second pixel for the entire image
	z2 = image_int[0:-2,2::] # get all the third pixel for the entire image		z2 = image_int[0:-2,2::] # get all the third pixel for the entire image
	z3 = image_int[1:-1,0:-2] # get all the fourth pixel for the entire image		z3 = image_int[1:-1,0:-2] # get all the fourth pixel for the entire image

	# calculate the out put of the system of equation		# calculate the out put of the system of equation
	y0 = np.ravel(-z0+z2-z3)		y0 = np.ravel(-z0+z2-z3)
	y1 = np.ravel(z0+z1+z2)		y1 = np.ravel(z0+z1+z2)
	y2 = np.ravel(-z0-z1-z2-z3)		y2 = np.ravel(-z0-z1-z2-z3)
	y = np.vstack((y0,y1,y2))		y = np.vstack((y0,y1,y2))

	# use numpy solver to solve the system of equations all at once		# use numpy solver to solve the system of equations all at once
	predict = np.linalg.solve(A,y)[-1]		predict = np.linalg.solve(A,y)[-1]
	#predict = []		#predict = []

	# flatten the neighbor pixels and stack them together		# flatten the neighbor pixels and stack them together
	z0 = np.ravel(z0)		z0 = np.ravel(z0)
	z1 = np.ravel(z1)		z1 = np.ravel(z1)
	z2 = np.ravel(z2)		z2 = np.ravel(z2)
	z3 = np.ravel(z3)		z3 = np.ravel(z3)
	neighbor = np.vstack((z0,z1,z2,z3)).T		neighbor = np.vstack((z0,z1,z2,z3)).T

	# calculate the difference		# calculate the difference
	diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)		diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)


	# flatten the image to a vector		# flatten the image to a vector
	small_image = image_int[1:-1,1:-1]		small_image = image_int[1:-1,1:-1]

	#Reshape the predictions to be a 2D array		#Reshape the predictions to be a 2D array
	predict = np.pad(predict.reshape(510,638), pad_width=1)		predict = np.pad(predict.reshape(510,638), pad_width=1)
	"""predict[0,:] = image[0,:]		"""predict[0,:] = image[0,:]
	predict[:,0] = image[:,0]		predict[:,0] = image[:,0]
	predict[:,-1] = image[:,-1]		predict[:,-1] = image[:,-1]
	predict[-1,:] = image[-1,:]"""		predict[-1,:] = image[-1,:]"""


	#Calculate the error between the original image and our predictions		#Calculate the error between the original image and our predictions
	#Note that we only predicted on the inside square of the original image, excluding		#Note that we only predicted on the inside square of the original image, excluding
	#The first row, column and last row, column		#The first row, column and last row, column
	#error = (image_int - predict).astype(int) #Experiment		#error = (image_int - predict).astype(int) #Experiment

	#this one works		#this one works
	error = image_int - predict		error = image_int - predict


	return predict, diff, image_int, error, A		return predict, diff, image_int, error, A
	```		```

	%% Cell type:code id:ba2881d9 tags:		%% Cell type:code id:ba2881d9 tags:

	``` python		``` python
	scenes = file_extractor()		scenes = file_extractor()
	images = image_extractor(scenes)		images = image_extractor(scenes)
	num_images = im_distribution(images, "_9")		num_images = im_distribution(images, "_9")
	```		```

	%% Cell type:code id:11e95c34 tags:		%% Cell type:code id:11e95c34 tags:

	``` python		``` python
	predict, diff, im, err, A = plot_hist(num_images, 0)		predict, diff, im, err, A = plot_hist(num_images, 0)
	```		```

	%% Cell type:code id:434e4d2f tags:		%% Cell type:code id:434e4d2f tags:

	``` python		``` python
	def reconstruct(error, A):		def reconstruct(error, A):
	"""		"""
	Function that reconstructs the original image		Function that reconstructs the original image
	from the error matrix and using the predictive		from the error matrix and using the predictive
	algorithm developed in the encoding.		algorithm developed in the encoding.

	Parameters:		Parameters:
	error (array): matrix of errors computed in encoding. Same		error (array): matrix of errors computed in encoding. Same
	shape as the original image (512, 640) in this case		shape as the original image (512, 640) in this case
	A (array): Matrix used for the system of equations to create predictions		A (array): Matrix used for the system of equations to create predictions
	Returns:		Returns:
	image (array): The reconstructed image		image (array): The reconstructed image
	"""		"""
	new_e = error.copy()		new_e = error.copy()
	rows, columns = new_e.shape		rows, columns = new_e.shape

	for r in range(1, rows-1):		for r in range(1, rows-1):
	for c in range(1, columns-1):		for c in range(1, columns-1):
	z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]		z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]
	y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))		y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))

	if r == 345 and c == 421:		if r == 345 and c == 421:
	print(new_e[r][c])		print(new_e[r][c])
	print(np.linalg.solve(A,y)[-1])		print(np.linalg.solve(A,y)[-1])
	print(new_e[r][c] + np.linalg.solve(A,y)[-1])		print(new_e[r][c] + np.linalg.solve(A,y)[-1])
	print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))		print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))

	#Real solution that works, DO NOT DELETE		#Real solution that works, DO NOT DELETE
	new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))		new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))

	#new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])		#new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])

	return new_e.astype(int)		return new_e.astype(int)

	```		```

	%% Cell type:code id:8d835626 tags:		%% Cell type:code id:7f395ab2 tags:

	``` python		``` python
	new_error = reconstruct(err, A)		new_error = reconstruct(err, A)
	```		```

	%% Output		%% Output

	3.499999999992724		3.499999999992724
	[13644.5]		[13644.5]
	[13648.]		[13648.]
	[13648.]		[13648.]

	%% Cell type:code id:b54c4328 tags:		%% Cell type:code id:06ccaf8e tags:

	``` python		``` python
	e = np.round(err, 1)		e = np.round(err, 1)
	len(np.unique(e[1:-1, 1:-1]))		len(np.unique(e[1:-1, 1:-1]))
	```		```

	%% Output		%% Output

	518		518

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