plotting problem #393
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Thanks for the issue. |
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On Tue, Aug 16, 2022 at 5:13 PM Biguri ***@***.***> wrote:
Thanks for the issue.
Coudl you please show the code you have in the jupyter notebook?
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#!/usr/bin/env python
# coding: utf-8
# In[67]:
#%% DEMO 03: Generate sample data and add realistic CT noise to it.
#
# This demo will show how to generate sample data for image reconstruction
# In[90]:
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib.pyplot as pl
import matplotlib
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import scipy
import scipy.sparse.linalg
from tigre.utilities.Ax import Ax
from tigre.utilities import sample_loader
from tigre.utilities import sl3d
# In[91]:
geo = tigre.geometry_default(high_resolution=False)
# In[92]:
#%% Define angles of projection and load phantom image
# define projection angles (in radians)
angles = np.linspace(0, 2 * np.pi, 50)
# load phatnom image
from IPython.display import Image
head = sample_loader.load_head_phantom(geo.nVoxel)
#head = head = scipy.io.loadmat("D:/شقایق جدید 1401/پروژه/code/TIGRE-master/Python/tigre/data/head.mat" , geo.n_proj)
head
# In[93]:
# Simulate forward projection.
# To match with mathematical notation, the projection operation is called Ax
projections = tigre.Ax(head, geo, angles)
# In[94]:
# Add realistic noise. Adds photon scattering noise ('Poisson') and
# electronic noise of the detector ('Gaussian').
#
# 'Poisson' is related to the maximum photon count in the detector. 1e5 is
# a standard clinical nuber, reduce it for more noise
# 'Gaussian' is related to possible electronic noise in the detector. mean
# of 0 and std of 10 is common in clinical scenario. Increase std for more
# noise.
noise_projections = CTnoise.add(projections, Poisson=1e5, Gaussian=np.array([0, 10]))
# In[101]:
#%% Plot Projections
tigre.plotproj(
projections, angles)
# In[96]:
# plot noise
tigre.plotproj(projections - noise_projections)
# In[ ]:
# In[ ]:
# In[ ]:
#!/usr/bin/env python
# coding: utf-8
# In[1]:
##% Demo 5: How to use plotting functions
#
# This demo will demostrate the options for plotting projection and images
# on TIGRE. The functions have been in previous demos, but in here an
# exaustive explanation and usage of them is given.
# In[1]:
#%%Initialize
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import tigre.algorithms as algs
# In[2]:
#%% Geometry
geo = tigre.geometry_default(high_resolution=False)
# In[6]:
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
# Load thorax phatom data
head = sample_loader.load_head_phantom(geo.nVoxel)
head
# In[7]:
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections, Poisson=1e5, Gaussian=np.array([0, 10]))
# In[4]:
#%% Reconstruct image using OS-SART and FDK
# FDK
imgFDK = algs.fdk(noise_projections, geo, angles)
niter = 50
imgOSSART = algs.ossart(noise_projections, geo, angles, 50)
# In[5]:
#%% Lets use PlotProj
#
# plotProj plots the projection data measure on the detector on each angle.
#
# exhaustive list of possible parameters:
# 'Step' : Defines the step size for skippin projections when plotting,
# usefull when there are a big amount of projections. Default is 1
step = 2
# In[8]:
# 'Colormap': Defines the colormap used to plot. Default is 'gray'.
colormap = "viridis"
colormap = "plasma"
colormap = "gray"
# In[9]:
# 'Clims': Defines the data limits for the color, usefull when one wants to
# see some specific range. The default uses the minimum and maximum of the
# data.
clims = [0, 200]
# In[10]:
# 'Savegif': allows to save the plotted figure as an animated gif,
# specified by the given filename.
giffilename = "demo5projections.gif"
# In[11]:
# 'Slice': allows to plot a single projection .Will overwrite the behaviour
# of 'Step'
slice = 5
# In[12]:
# Lets try out.
tigre.plotproj(
noise_projections, angles, step=step, colormap=colormap, clims=clims, savegif=giffilename
) # not using 'Step'
# In[12]:
# Remember you can also plot errors, for example the added noise by:
noise = np.abs(noise_projections - projections) # abs is what we are interested in plotting
tigre.plotproj(
noise, angles, clims=[0, 2]
)
# In[13]:
#%% PlotImg
# plotImg plots the image slice by slice.
#
# List of optional parameters:
# 'Dim': specifies the dimension for plotting.
# Dim can be 'X','Y','Z'
#
dimension = "Z"
# In[14]:
# 'Step': step size of the plotting. Useful when images are big or one just
# wants an overview of the result
step = 2
# In[15]:
# 'Colormap': Defines the colormap used to plot. Default is 'gray'.
colormap = "plasma"
colormap = "magma"
colormap = "gray"
colormap = "viridis"
# In[16]:
# 'Clims': Defines the data limits for the color, usefull when one wants to
# see some specific range. The default computes the 5% and 95% percentiles
# of the data and uses that as limit.
clims = [0, 1]
# In[17]:
# 'Savegif': allows to save the plotted figure as an animated gif,
# specified by the given filename.
giffilename = "demo5image.gif"
# In[18]:
# 'Slice': allows to plot a single slice .Will overwrite the behaviour
# of 'Step'
slice = 64
# In[19]:
# Lets go for it
tigre.plotimg(imgFDK, dim=dimension, step=step, clims=clims, colormap=colormap, savegif=giffilename)
# In[20]:
# Remember: You can always plot more than 1 image together!
tigre.plotimg(np.concatenate([head, imgFDK, imgOSSART], axis=1), dim="z")
# Or even the errors!
tigre.plotImg(np.concatenate([np.abs(head - imgFDK), np.abs(head - imgOSSART)]), dim="z")
# In[13]:
projections
# In[ ]:
#!/usr/bin/env python
# coding: utf-8
# In[2]:
#%% Demo 4: Simple Image reconstruction
#
#
# This demo will show how a simple image reconstruction can be performed,
# by using OS-SART and FDK
# In[1]:
#%%Initialize
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import tigre.algorithms as algs
# In[9]:
#%% Geometry
geo = tigre.geometry_default(high_resolution=False)
# In[3]:
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
# In[10]:
head = sample_loader.load_head_phantom(geo.nVoxel)
head
# In[11]:
# Load thorax phantom data
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections, Poisson=1e5, Gaussian=np.array([0, 10]))
# In[12]:
#%% Reconstruct image using OS-SART and FDK
# FDK
imgFDK = algs.fdk(noise_projections, geo, angles)
# In[13]:
# OS-SART
niter = 50
imgOSSART = algs.ossart(noise_projections, geo, angles, niter)
# In[14]:
#%% Show the results
tigre.plotimg(
np.concatenate([imgFDK, imgOSSART], axis=1), dim="z"
)
# In[ ]:
# In[ ]:
#!/usr/bin/env python
# coding: utf-8
# In[2]:
# This demo presents the Total variation algorithms in TIGRE. Total
# variation algorithms try to minimize the variation (gradient) of the
# image, assuming its piecewise smooth, as most things in nature are (i.e.
# human body).
# This set of algorithms is specially good performing when the noise is
# very big or the number of projections is small, however, they require more
# computational time and memory than the other algorithms to run.
# In[3]:
# - AwASD_POCS: Edge preserving ASD_POCS (Adaptative weigthed).
# - OS_AwASD_POCS: OS-version of the previous algorithm
# - PCSD: A version of ASD_POCS that heuristically select some of the
# parameters, particularly epsilon (maxL2norm)
# - AwPCSD: Edge preserving version of the previous algorithm
# - OS_AwPCSD: block-wise version of the previous
# In[4]:
#%%Initialize
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import tigre.algorithms as algs
from matplotlib import pyplot as plt
from tigre.utilities.im3Dnorm import im3DNORM
# In[5]:
#%% Geometry
geo = tigre.geometry_default(high_resolution=False)
# In[6]:
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
# Load thorax phatom data
head = sample_loader.load_head_phantom(geo.nVoxel)
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections, Poisson=1e5, Gaussian=np.array([0, 10]))
# In[7]:
#%% Lets create a OS-SART test for comparison
imgOSSART = algs.ossart(noise_projections, geo, angles, 10)
# In[8]:
#%% Total Variation algorithms
#
# ASD-POCS: Adaptative Steeppest Descent-Projection On Convex Subsets
# Often called POCS
# ==========================================================================
# ==========================================================================
# ASD-POCS minimizes At-B and the TV norm separately in each iteration,
# i.e. reconstructs the image first and then reduces the TV norm, every
# iteration. As the other algorithms the mandatory inputs are projections,
# geometry, angles and maximum iterations.
#
# ASD-POCS has a veriety of optional arguments, and some of them are crucial
# to determine the behaviour of the algorithm. The advantage of ASD-POCS is
# the power to create good images from bad data, but it needs a lot of
# tunning.
#
# Optional parameters that are very relevant:
# ----------------------------------------------
# 'maxL2err' Maximum L2 error to accept an image as valid. This
# parameter is crucial for the algorithm, determines at
# what point an image should not be updated further.
# Default is 20% of the FDK L2 norm.
# its called epsilon in the paper
epsilon = (
im3DNORM(tigre.Ax(algs.fdk(noise_projections, geo, angles), geo, angles) - noise_projections, 2)
* 0.15
)
# In[9]:
# 'alpha': Defines the TV hyperparameter. default is 0.002.
# However the paper mentions 0.2 as good choice
alpha = 0.002
# In[10]:
# 'tviter': Defines the amount of TV iterations performed per SART
# iteration. Default is 20
ng = 25
# In[11]:
# Other optional parameters
# ----------------------------------------------
# 'lambda': Sets the value of the hyperparameter for the SART iterations.
# Default is 1
#
# 'lambdared': Reduction of lambda Every iteration
# lambda=lambdared*lambda. Default is 0.99
#
lmbda = 1
lambdared = 0.9999 # you generally want 1
# In[12]:
# 'alpha_red': Defines the reduction rate of the TV hyperparameter
alpha_red = 0.95
# In[13]:
# 'Ratio': The maximum allowed image/TV update ration. If the TV
# update changes the image more than this, the parameter
# will be reduced.default is 0.95
ratio = 0.94
# In[14]:
# 'Verbose' 1 or 0. Default is 1. Gives information about the
# progress of the algorithm.
verb = True
# In[15]:
imgASDPOCS = algs.asd_pocs(
noise_projections,
geo,
angles,
10, # these are very important
tviter=ng,
maxl2err=epsilon,
alpha=alpha, # less important.
lmbda=lmbda,
lmbda_red=lambdared,
rmax=ratio,
verbose=verb,
)
# In[16]:
# OS_ASD_POCS: Odered Subset-TV algorithm
# ==========================================================================
# ==========================================================================
#
# The logical next step to imporce ASD-POCS is substituting SART with a
# faster algorithm, such as OS-SART
#
# The parameters are the same as in ASD-POCS, but also have 'BlockSize'
# In[17]:
imgOSASDPOCS = algs.os_asd_pocs(
noise_projections,
geo,
angles,
10, # these are very important
tviter=ng,
maxl2err=epsilon,
alpha=alpha, # less important.
lmbda=lmbda,
lmbda_red=lambdared,
rmax=ratio,
verbose=verb,
# OSC params
blocksize=10,
)
# In[18]:
# AwASD_POCS: adaptative weighted ASD_POCS
# ==========================================================================
# ==========================================================================
#
# This is a more edge preserving algorithms than ASD_POCS, in theory.
# delta is the cuttof vlaue of anromalized edge exponential weight....
# not super clear, but it cotnrols at which point you accept something as real vs noise edge.
# In[19]:
imgAWASDPOCS = algs.awasd_pocs(
noise_projections,
geo,
angles,
10, # these are very important
tviter=ng,
maxl2err=epsilon,
alpha=alpha, # less important.
lmbda=lmbda,
lmbda_red=lambdared,
rmax=ratio,
verbose=verb, # AwASD_POCS params
delta=np.array([-0.005]),
)
# In[20]:
#%% plot results
# plot images
tigre.plotimg(
np.concatenate([imgAWASDPOCS, imgOSASDPOCS, imgASDPOCS, imgOSSART], axis=1), dim="z", step=2
)
# In[ ]:
|
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@shaghayegh1376 please can you shorten this to the minimal example that reproduces the case? Reduce the code as much as possible |
|
yes its the simplest example :
On Tue, Aug 16, 2022 at 5:19 PM Biguri ***@***.***> wrote:
@shaghayegh1376 <https://github.com/shaghayegh1376> please can you
shorten this to the minimal example that reproduces the case? Reduce the
code as much as possible
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#!/usr/bin/env python
# coding: utf-8
# In[67]:
#%% DEMO 03: Generate sample data and add realistic CT noise to it.
#
# This demo will show how to generate sample data for image reconstruction
# In[90]:
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib.pyplot as pl
import matplotlib
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import scipy
import scipy.sparse.linalg
from tigre.utilities.Ax import Ax
from tigre.utilities import sample_loader
from tigre.utilities import sl3d
# In[91]:
geo = tigre.geometry_default(high_resolution=False)
# In[92]:
#%% Define angles of projection and load phantom image
# define projection angles (in radians)
angles = np.linspace(0, 2 * np.pi, 50)
# load phatnom image
from IPython.display import Image
head = sample_loader.load_head_phantom(geo.nVoxel)
#head = head = scipy.io.loadmat("D:/شقایق جدید 1401/پروژه/code/TIGRE-master/Python/tigre/data/head.mat" , geo.n_proj)
head
# In[93]:
# Simulate forward projection.
# To match with mathematical notation, the projection operation is called Ax
projections = tigre.Ax(head, geo, angles)
# In[94]:
# Add realistic noise. Adds photon scattering noise ('Poisson') and
# electronic noise of the detector ('Gaussian').
#
# 'Poisson' is related to the maximum photon count in the detector. 1e5 is
# a standard clinical nuber, reduce it for more noise
# 'Gaussian' is related to possible electronic noise in the detector. mean
# of 0 and std of 10 is common in clinical scenario. Increase std for more
# noise.
noise_projections = CTnoise.add(projections, Poisson=1e5, Gaussian=np.array([0, 10]))
# In[101]:
#%% Plot Projections
tigre.plotproj(
projections, angles)
# In[96]:
# plot noise
tigre.plotproj(projections - noise_projections)
# In[ ]:
# In[ ]:
# In[ ]:
|
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I will have a look, but meanwhile I suggest trying it in a normal python enviroment and not in jupyter if you want the plots. I myself dont have it installed and its not officially supported, so I will try to test this but can't promise anything. |
|
Thank you for your response
And i will try it with normal python environment
…On Tue, Aug 16, 2022 at 17:24 Biguri ***@***.***> wrote:
I will have a look, but meanwhile I suggest trying it in a normal python
enviroment and not in jupyter if you want the plots.
I myself dont have it installed and its not officially supported, so I
will try to test this but can't promise anything.
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Dear mr Biguri
I have problem with implementing of tigre toolbox in environment except
jupyter notebook.
How can i write code in other environment for example visual studio code
On Tue, Aug 16, 2022 at 17:29 Shaghayegh Afshari <
***@***.***> wrote:
… Thank you for your response
And i will try it with normal python environment
On Tue, Aug 16, 2022 at 17:24 Biguri ***@***.***> wrote:
> I will have a look, but meanwhile I suggest trying it in a normal python
> enviroment and not in jupyter if you want the plots.
>
> I myself dont have it installed and its not officially supported, so I
> will try to test this but can't promise anything.
>
> —
> Reply to this email directly, view it on GitHub
> <#393 (comment)>, or
> unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AYD4MB3GJDLBP4KWZ4NRT4LVZNM35ANCNFSM56U5VVDQ>
> .
> You are receiving this because you were mentioned.Message ID:
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>
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@shaghayegh1376 you will need to be more specific. There are tutorials in the installation page for python envoroments, have you looked at that? You don't need to "write code" to make TIGRE work in VSC (note that VSC is not an "enviroment"). |
|
Dear Mr. Biguri
as you said before to use originally python environment instead of anaconda
( jupyter notebook and spyder)
I followed the step as it said in the tigre installment and ran steps in
cmd and it installed successfully.
but when i run demo 3 , it would be the result :
[image: image.png]
…On Mon, Aug 29, 2022 at 7:27 PM Biguri ***@***.***> wrote:
@shaghayegh1376 <https://github.com/shaghayegh1376> you will need to be
more specific. There are tutorials in the installation page for python
envoroments, have you looked at that?
what are the problems you are having?
You don't need to "write code" to make TIGRE work in VSC (note that VSC is
not an "enviroment").
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@shaghayegh1376 I can't see the image, please post it in the github issue. |
|
|
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@shaghayegh1376 please next time share text as text, not as an image, it is quite unhelpful as an image! I would suggest trying an older version of python, perhaps 3.7 or so. I had similar issues in the past with 3.9, but I thought we solved this (are you running a recent version of TIGRE??). If you try python 3.7 and it works, let me know. |
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it was with python 3.10 , let me check the 3.7 version |
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Traceback (most recent call last): this was the result with python 3.7 |
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Thats just that you installed it wrong in python 3.7. |
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but i follow the steps like tutorial. |
|
Sorry, you will need to be more specific. Unfortunately as it currently
stands I have no information to help you, I only know it doesn't work for
you.
…On Thu, 1 Sept 2022, 07:13 shaghayegh1376, ***@***.***> wrote:
but i follow the steps like tutorial.
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ok, another question is that: can we replace |
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Absolutely.
As you can see, Tigre uses np.arrays to store the data. Therefore you can
use any plotting technique to plot np.arrays. In fact that is what
tigre.plotimg does, just uses plt.imshow in a nice way. You are more than
welcome to use your own functions!
Ander
…On Fri, 2 Sept 2022, 03:45 shaghayegh1376, ***@***.***> wrote:
ok, another question is that: can we replace
this function:
tigre.plotimg(np.concatenate([imgFDK, imgOSSART], axis=1), dim="z")
with other library like plt.show() or .imshow() and how
i mean , what alternative to tigre.plotting can i use to display the
result.
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This importing error is already fixed by #387 |
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hello
when i rewrite demos in jupyter notebook , in some demos like number 3/4/7/... , in plotting part , code run without error but for result it just show the blank , but in other demo number 5 it shows the result as image or gif.
i use python 3.8
anaconda 3
cuda 11.7
nvidia getforce rtx 3050
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