Commit 9d6383a4 authored by Unknown's avatar Unknown
Browse files

Image Cleaning notebook bugfix

parent fd1812c2
%% Cell type:markdown id: tags:
# Image cleaning and atom finding using pycroscopy
### Suhas Somnath, Chris R. Smith, Stephen Jesse
The Center for Nanophase Materials Science and The Institute for Functional Imaging for Materials <br>
Oak Ridge National Laboratory<br>
1/19/2017
%% Cell type:markdown id: tags:
## Configure the notebook first
%% Cell type:code id: tags:
``` python
!pip install -U numpy scipy skimage h5py matplotlib Ipython ipywidgets pycroscopy
# set up notebook to show plots within the notebook
% matplotlib notebook
# Import necessary libraries:
# General utilities:
import os
import sys
from time import time
from scipy.misc import imsave
# Computation:
import numpy as np
import h5py
from skimage import measure
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.spatial.distance import pdist
# Visualization:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from mpl_toolkits.axes_grid1 import make_axes_locatable
from IPython.display import display, HTML
import ipywidgets as widgets
from mpl_toolkits.axes_grid1 import ImageGrid
# Finally, pycroscopy itself
sys.path.append('..')
import pycroscopy as px
# Make Notebook take up most of page width
display(HTML(data="""
<style>
div#notebook-container { width: 95%; }
div#menubar-container { width: 65%; }
div#maintoolbar-container { width: 99%; }
</style>
"""))
```
%% Cell type:markdown id: tags:
## Load the image that will be cleaned:
%% Cell type:code id: tags:
``` python
image_path = px.io.uiGetFile('*.png *PNG *TIFF * TIF *tif *tiff *BMP *bmp','Images')
print('Working on: \n{}'.format(image_path))
folder_path, file_name = os.path.split(image_path)
base_name, _ = os.path.splitext(file_name)
```
%% Cell type:markdown id: tags:
## Make the image file pycroscopy compatible
Convert the source image file into a pycroscopy compatible hierarchical data format (HDF or .h5) file. This simple translation gives you access to the powerful data functions within pycroscopy
#### H5 files:
* are like smart containers that can store matrices with data, folders to organize these datasets, images, metadata like experimental parameters, links or shortcuts to datasets, etc.
* are readily compatible with high-performance computing facilities
* scale very efficiently from few kilobytes to several terabytes
* can be read and modified using any language including Python, Matlab, C/C++, Java, Fortran, Igor Pro, etc.
%% Cell type:code id: tags:
``` python
# Check if an HDF5 file with the chosen image already exists.
# Only translate if it does not.
h5_path = os.path.join(folder_path, base_name+'.h5')
need_translation = True
if os.path.exists(h5_path):
try:
h5_file = h5py.File(h5_path, 'r+')
h5_raw = h5_file['Measurement_000']['Channel_000']['Raw_Data']
need_translation = False
print('HDF5 file with Raw_Data found. No need to translate.')
except KeyError:
print('Raw Data not found.')
else:
print('No HDF5 file found.')
if need_translation:
# Initialize the Image Translator
tl = px.ImageTranslator()
# create an H5 file that has the image information in it and get the reference to the dataset
h5_raw = tl.translate(image_path)
# create a reference to the file
h5_file = h5_raw.file
print('HDF5 file is located at {}.'.format(h5_file.filename))
```
%% Cell type:markdown id: tags:
### Inspect the contents of this h5 data file
The file contents are stored in a tree structure, just like files on a contemporary computer.
The data is stored as a 2D matrix (position, spectroscopic value) regardless of the dimensionality of the data.
In the case of these 2D images, the data is stored as a N x 1 dataset
The main dataset is always accompanied by four ancillary datasets that explain the position and spectroscopic value of any given element in the dataset.
In the case of the 2d images, the positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0....
The spectroscopic information is trivial since the data at any given pixel is just a scalar value
%% Cell type:code id: tags:
``` python
print('Datasets and datagroups within the file:')
px.io.hdf_utils.print_tree(h5_file)
print('\nThe main dataset:')
print(h5_file['/Measurement_000/Channel_000/Raw_Data'])
print('\nThe ancillary datasets:')
print(h5_file['/Measurement_000/Channel_000/Position_Indices'])
print(h5_file['/Measurement_000/Channel_000/Position_Values'])
print(h5_file['/Measurement_000/Channel_000/Spectroscopic_Indices'])
print(h5_file['/Measurement_000/Channel_000/Spectroscopic_Values'])
print('\nMetadata or attributes in a datagroup')
for key in h5_file['/Measurement_000'].attrs:
print('{} : {}'.format(key, h5_file['/Measurement_000'].attrs[key]))
```
%% Cell type:markdown id: tags:
## Initialize an object that will perform image windowing on the .h5 file
* Note that after you run this, the H5 file is opened. If you want to re-run this cell, close the H5 file first
%% Cell type:code id: tags:
``` python
# Initialize the windowing class
iw = px.ImageWindow(h5_raw, max_RAM_mb=1024*4)
# grab position indices from the H5 file
h5_pos = h5_raw.parent[h5_raw.attrs['Position_Indices']]
# determine the image size:
num_x = len(np.unique(h5_pos[:,0]))
num_y = len(np.unique(h5_pos[:,1]))
# extract figure data and reshape to proper numpy array
raw_image_mat = np.reshape(h5_raw[()], [num_x,num_y]);
```
%% Cell type:markdown id: tags:
## Visualize the source image:
Though the source file is actually grayscale image, we will visualize it using a color-scale
%% Cell type:code id: tags:
``` python
fig, axis = plt.subplots(figsize=(10,10))
img = axis.imshow(raw_image_mat,cmap=px.plot_utils.cmap_jet_white_center(), origin='lower');
divider = make_axes_locatable(axis)
cax = divider.append_axes("right", size="5%", pad=0.2)
plt.colorbar(img, cax=cax)
axis.set_title('Raw Image', fontsize=16);
```
%% Cell type:markdown id: tags:
## Extract the optimal window size from the image
%% Cell type:code id: tags:
``` python
num_peaks = 2
win_size , psf_width = iw.window_size_extract(num_peaks, save_plots=False, show_plots=True)
print('Window size = {}'.format(win_size))
```
%% Cell type:code id: tags:
``` python
# Uncomment this line if you need to manually specify a window size
# win_size = 8
# plot a single window
row_offset = int(0.5*(num_x-win_size))
col_offset = int(0.5*(num_y-win_size))
plt.figure()
plt.imshow(raw_image_mat[row_offset:row_offset+win_size,
col_offset:col_offset+win_size],
cmap=px.plot_utils.cmap_jet_white_center(),
origin='lower');
# the result should be about the size of a unit cell
# if it is the wrong size, just choose on manually by setting the win_size
plt.show()
```
%% Cell type:markdown id: tags:
## Now break the image into a sequence of small windows
We do this by sliding a small window across the image. This artificially baloons the size of the data.
%% Cell type:code id: tags:
``` python
windowing_parms = {
'fft_mode': None, # Options are None, 'abs', 'data+abs', or 'complex'
'win_x': win_size,
'win_y': win_size,
'win_step_x': 1,
'win_step_y': 1,
}
win_parms_copy = windowing_parms.copy()
if windowing_parms['fft_mode'] is None:
win_parms_copy['fft_mode'] = 'data'
h5_wins_grp = px.hdf_utils.check_for_old(h5_raw, 'Windowing',
win_parms_copy)
if h5_wins_grp is None:
print('Windows either do not exist or were created with different parameters')
t0 = time()
h5_wins = iw.do_windowing(win_x=windowing_parms['win_x'],
win_y=windowing_parms['win_y'],
save_plots=False,
show_plots=False,
win_fft=windowing_parms['fft_mode'])
print( 'Windowing took {} seconds.'.format(round(time()-t0, 2)))
else:
print('Taking existing windows dataset')
h5_wins = h5_wins_grp['Image_Windows']
print('\nRaw data was of shape {} and the windows dataset is now of shape {}'.format(h5_raw.shape, h5_wins.shape))
print('Now each position (window) is descibed by a set of pixels')
```
%% Cell type:code id: tags:
``` python
# Peek at a few random windows
num_rand_wins = 9
rand_positions = np.random.randint(0, high=h5_wins.shape[0], size=num_rand_wins)
example_wins = np.zeros(shape=(windowing_parms['win_x'], windowing_parms['win_y'], num_rand_wins), dtype=np.float32)
for rand_ind, rand_pos in enumerate(rand_positions):
example_wins[:, :, rand_ind] = np.reshape(h5_wins[rand_pos], (windowing_parms['win_x'], windowing_parms['win_y']))
px.plot_utils.plot_map_stack(example_wins, heading='Example Windows', cmap=px.plot_utils.cmap_jet_white_center(),
title=['Window # ' + str(win_pos) for win_pos in rand_positions]);
```
%% Cell type:markdown id: tags:
## Performing Singular Value Decompostion (SVD) on the windowed data
SVD decomposes data (arranged as position x value) into a sequence of orthogonal components arranged in descending order of variance. The first component contains the most significant trend in the data. The second component contains the next most significant trend orthogonal to all previous components (just the first component). Each component consists of the trend itself (eigenvector), the spatial variaion of this trend (eigenvalues), and the variance (statistical importance) of the component.
Since the data consists of the large sequence of small windows, SVD essentially compares every single window with every other window to find statistically significant trends in the image
%% Cell type:code id: tags:
``` python
# check to make sure number of components is correct:
num_comp = 1024
num_comp = min(num_comp,
min(h5_wins.shape)*len(h5_wins.dtype))
h5_svd = px.hdf_utils.check_for_old(h5_wins, 'SVD', {'num_components':num_comp})
if h5_svd is None:
print('SVD was either not performed or was performed with different parameters')
h5_svd = px.processing.doSVD(h5_wins, num_comps=num_comp)
else:
print('Taking existing SVD results')
h5_U = h5_svd['U']
h5_S = h5_svd['S']
h5_V = h5_svd['V']
# extract parameters of the SVD results
h5_pos = iw.hdf.file[h5_wins.attrs['Position_Indices']]
num_rows = len(np.unique(h5_pos[:, 0]))
num_cols = len(np.unique(h5_pos[:, 1]))
num_comp = h5_S.size
print("There are a total of {} components.".format(num_comp))
print('\nRaw data was of shape {} and the windows dataset is now of shape {}'.format(h5_raw.shape, h5_wins.shape))
print('Now each position (window) is descibed by a set of pixels')
plot_comps = 49
U_map_stack = np.reshape(h5_U[:, :plot_comps], [num_rows, num_cols, -1])
V_map_stack = np.reshape(h5_V, [num_comp, win_size, win_size])
V_map_stack = np.transpose(V_map_stack,(2,1,0))
```
%% Cell type:markdown id: tags:
## Visualize the SVD results
##### S (variance):
The plot below shows the variance or statistical significance of the SVD components. The first few components contain the most significant information while the last few components mainly contain noise.
Note also that the plot below is a log-log plot. The importance of each subsequent component drops exponentially.
%% Cell type:code id: tags:
``` python
fig_S, ax_S = px.plot_utils.plotScree(h5_S[()]);
```
%% Cell type:markdown id: tags:
#### V (Eigenvectors or end-members)
The V dataset contains the end members for each component
%% Cell type:code id: tags:
``` python
for field in V_map_stack.dtype.names:
fig_V, ax_V = px.plot_utils.plot_map_stack(V_map_stack[:,:,:][field], heading='', title='Vector-'+field, num_comps=plot_comps,
color_bar_mode='each', cmap=px.plot_utils.cmap_jet_white_center())
```
%% Cell type:markdown id: tags:
#### U (Abundance maps):
The plot below shows the spatial distribution of each component
%% Cell type:code id: tags:
``` python
fig_U, ax_U = px.plot_utils.plot_map_stack(U_map_stack[:,:,:25], heading='', title='Component', num_comps=plot_comps,
color_bar_mode='each', cmap=px.plot_utils.cmap_jet_white_center())
```
%% Cell type:markdown id: tags:
## Reconstruct image (while removing noise)
Since SVD is just a decomposition technique, it is possible to reconstruct the data with U, S, V matrices.
It is also possible to reconstruct a version of the data with a set of components.
Thus, by reconstructing with the first few components, we can remove the statistical noise in the data.
##### The key is to select the appropriate (number of) components to reconstruct the image without the noise
%% Cell type:code id: tags:
``` python
clean_components = range(36) # np.append(range(5,9),(17,18))
num_components=len(clean_components)
# Check if the image has been reconstructed with the same parameters:
# First, gather all groups created by this tool:
h5_clean_image = None
for item in h5_svd:
if item.startswith('Cleaned_Image_') and isinstance(h5_svd[item],h5py.Group):
grp = h5_svd[item]
old_comps = px.hdf_utils.get_attr(grp, 'components_used')
if old_comps.size == len(list(clean_components)):
if np.all(np.isclose(old_comps, np.array(clean_components))):
h5_clean_image = grp['Cleaned_Image']
print( 'Existing clean image found. No need to rebuild.')
break
if h5_clean_image is None:
t0 = time()
#h5_clean_image = iw.clean_and_build_batch(h5_win=h5_wins, components=clean_components)
h5_clean_image = iw.clean_and_build_separate_components(h5_win=h5_wins, components=clean_components)
print( 'Cleaning and rebuilding image took {} seconds.'.format(round(time()-t0, 2)))
```
%% Cell type:code id: tags:
``` python
# Building a stack of images from here:
image_vec_components = h5_clean_image[()]
# summing over the components:
for comp_ind in range(1, h5_clean_image.shape[1]):
image_vec_components[:, comp_ind] = np.sum(h5_clean_image[:, :comp_ind+1], axis=1)
# converting to 3D:
image_components = np.reshape(image_vec_components, [num_x, num_y, -1])
# calculating the removed noise:
noise_components = image_components - np.reshape(np.tile(h5_raw[()], [1, h5_clean_image.shape[1]]), image_components.shape)
# defining a helper function to get the FFTs of a stack of images
def get_fft_stack(image_stack):
blackman_window_rows = np.blackman(image_stack.shape[0])
blackman_window_cols = np.blackman(image_stack.shape[1])
fft_stack = np.zeros(image_stack.shape, dtype=np.float)
for image_ind in range(image_stack.shape[2]):
layer = image_stack[:, :, image_ind]
windowed = blackman_window_rows[:, np.newaxis] * layer * blackman_window_cols[np.newaxis, :]
fft_stack[:, :, image_ind] = np.abs(np.fft.fftshift(np.fft.fft2(windowed, axes=(0,1)), axes=(0,1)))
return fft_stack
# get the FFT of the cleaned image and the removed noise:
fft_image_components = get_fft_stack(image_components)
fft_noise_components = get_fft_stack(noise_components)
```
%% Cell type:code id: tags:
``` python
fig_U, ax_U = px.plot_utils.plot_map_stack(image_components[:,:,:25], heading='', evenly_spaced=False,
title='Upto component', num_comps=plot_comps, color_bar_mode='single',
cmap=px.plot_utils.cmap_jet_white_center())
```
%% Cell type:markdown id: tags:
## Reconstruct the image with the first N components
slide the bar to pick the the number of components such that the noise is removed while maintaining the integrity of the image
%% Cell type:code id: tags:
``` python
num_comps = min(16, image_components.shape[2])
img_stdevs = 3
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 14))
axes.flat[0].loglog(h5_S[()], '*-')
axes.flat[0].set_xlim(left=1, right=h5_S[()].size)
axes.flat[0].set_ylim(bottom=np.min(h5_S[()]), top=np.max(h5_S[()]))
axes.flat[0].set_title('Variance', fontsize=16)
vert_line = axes.flat[0].axvline(x=num_comps, color='r')
clean_image_mat = image_components[:, :, num_comps]
img_clean = axes.flat[1].imshow(clean_image_mat, cmap=px.plot_utils.cmap_jet_white_center(), origin='lower')
mean_val = np.mean(clean_image_mat)
std_val = np.std(clean_image_mat)
img_clean.set_clim(vmin=mean_val-img_stdevs*std_val, vmax=mean_val+img_stdevs*std_val)
axes.flat[1].get_yaxis().set_visible(False)
axes.flat[1].get_xaxis().set_visible(False)
axes.flat[1].set_title('Cleaned Image', fontsize=16)
fft_std_dev = np.max(np.std(fft_image_components[:, :, num_comps]))
img_noise_fft = axes.flat[2].imshow(fft_noise_components[:, :, num_comps], cmap=plt.cm.jet,
vmin=0, vmax=4*fft_std_dev, origin='lower')
axes.flat[2].get_yaxis().set_visible(False)
axes.flat[2].get_xaxis().set_visible(False)
axes.flat[2].set_title('FFT of removed noise', fontsize=16)
img_clean_fft = axes.flat[3].imshow(fft_image_components[:, :, num_comps], cmap=plt.cm.jet,
vmin=0, vmax=4*fft_std_dev, origin='lower')
axes.flat[3].set_title('FFT of cleaned image', fontsize=16)
axes.flat[3].get_yaxis().set_visible(False)
axes.flat[3].get_xaxis().set_visible(False)
plt.show()
def move_comp_line(num_comps):
vert_line.set_xdata((num_comps, num_comps))
clean_image_mat = image_components[:, :, num_comps]
img_clean.set_data(clean_image_mat)
mean_val = np.mean(clean_image_mat)
std_val = np.std(clean_image_mat)
img_clean.set_clim(vmin=mean_val-img_stdevs*std_val, vmax=mean_val+img_stdevs*std_val)
img_noise_fft.set_data(fft_noise_components[:, :, num_comps])
img_clean_fft.set_data(fft_image_components[:, :, num_comps])
clean_components = range(num_comps)
fig.canvas.draw()
# display(fig)
widgets.interact(move_comp_line, num_comps=(1, image_components.shape[2]-1, 1));
```
%% Cell type:markdown id: tags:
## Check the cleaned image now:
%% Cell type:code id: tags:
``` python
num_comps = 12
fig, axis = plt.subplots(figsize=(7, 7))
clean_image_mat = image_components[:, :, num_comps]
img_clean = axis.imshow(clean_image_mat, cmap=px.plot_utils.cmap_jet_white_center(), origin='lower')
mean_val = np.mean(clean_image_mat)
std_val = np.std(clean_image_mat)
img_clean.set_clim(vmin=mean_val-img_stdevs*std_val, vmax=mean_val+img_stdevs*std_val)
axis.get_yaxis().set_visible(False)
axis.get_xaxis().set_visible(False)
axis.set_title('Cleaned Image', fontsize=16);
```
%% Cell type:markdown id: tags:
# Atom Finding
We will attempt to find the positions and the identities of atoms in the image now
## Perform clustering on the dataset
Clustering divides data into k clusters such that the variance within each cluster is minimized.<br>
Here, we will be performing k-means clustering on a set of components in the U matrix from SVD.<br>
We want a large enough number of clusters so that K-means identifies fine nuances in the data. At the same time, we want to minimize computational time by reducing the number of clusters. We recommend 32 - 64 clusters.
%% Cell type:code id: tags:
``` python
clean_components = 12
num_clusters = 32
# Check for existing Clustering results
estimator = px.Cluster(h5_U, 'KMeans', num_comps=clean_components, n_clusters=num_clusters)
do_cluster = False
# See if there are existing cluster results
try:
h5_kmeans = h5_svd['U-Cluster_000']
print( 'Clustering results loaded. Will now check parameters')
except:
except Exception:
print( 'Could not load Clustering results.')
do_cluster = True
# Check that the same components are used
if not do_cluster:
new_clean = estimator.data_slice[1]
if isinstance(new_clean, np.ndarray):
new_clean = new_clean.tolist()
else:
# print(new_clean)
if new_clean.step is None:
new_clean = range(new_clean.start, new_clean.stop)
else:
new_clean = range(new_clean.start, new_clean.stop, new_clean.step)
if all(h5_kmeans.attrs['components_used']==new_clean):
print( 'Clustering results used the same components as those requested.')
else:
do_cluster = True
print( 'Clustering results used the different components from those requested.')
# Check that the same number of clusters were used
if not do_cluster:
old_clusters = len(np.unique(h5_kmeans['Cluster_Indices']))
if old_clusters==num_clusters:
print( 'Clustering results used the same number of clusters as requested.')
else:
do_cluster = True
print( 'Clustering results used a different number of clusters from those requested.')
# Perform k-means clustering on the U matrix now using the list of components only if needed:
if do_cluster:
t0 = time()
h5_kmeans = estimator.do_cluster()
print( 'kMeans took {} seconds.'.format(round(time()-t0, 2)))
else:
print( 'Using existing results.')
print( 'Clustering results in {}.'.format(h5_kmeans.name))
half_wind = int(win_size*0.5)
# generate a cropped image that was effectively the area that was used for pattern searching
# Need to get the math righ on the counting
cropped_clean_image = clean_image_mat[half_wind:-half_wind + 1, half_wind:-half_wind + 1]
# Plot cluster results Get the labels dataset
labels_mat = np.reshape(h5_kmeans['Labels'][()], [num_rows, num_cols])
fig, axes = plt.subplots(ncols=2, figsize=(14,7))
axes[0].imshow(cropped_clean_image,cmap=px.plot_utils.cmap_jet_white_center(), origin='lower')
axes[0].set_title('Cleaned Image', fontsize=16)
axes[1].imshow(labels_mat, aspect=1, interpolation='none',cmap=px.plot_utils.cmap_jet_white_center(), origin='lower')
axes[1].set_title('K-means cluster labels', fontsize=16);
for axis in axes:
axis.get_yaxis().set_visible(False)
axis.get_xaxis().set_visible(False)
```
%% Cell type:markdown id: tags:
#### Visualize the hierarchical clustering
The vertical length of the branches indicates the relative separation between neighboring clusters.
%% Cell type:code id: tags:
``` python
# Plot dendrogram here