.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_solidification_tracking.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_solidification_tracking.py: ======================================== Track solidification of a metallic alloy ======================================== In this example, we identify and track the solid-liquid (S-L) interface in a nickel-based alloy undergoing solidification. Tracking the solidification over time enables the calculation of the solidification velocity. This is important to characterize the solidified structure of the sample and will be used to inform research into additive manufacturing of metals. The image sequence was obtained by the Center for Advanced Non-Ferrous Structural Alloys (CANFSA) using synchrotron x-radiography at the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). This analysis was first presented at a conference [1]_. .. [1] Corvellec M. and Becker C. G. (2021, May 17-18) "Quantifying solidification of metallic alloys with scikit-image" [Conference presentation]. BIDS ImageXD 2021 (Image Analysis Across Domains). Virtual participation. https://www.youtube.com/watch?v=cB1HTgmWTd8 .. GENERATED FROM PYTHON SOURCE LINES 22-40 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np import pandas as pd import plotly.express as px import plotly.io from skimage import filters, measure, restoration from skimage.data import nickel_solidification image_sequence = nickel_solidification() y0, y1, x0, x1 = 0, 180, 100, 330 image_sequence = image_sequence[:, y0:y1, x0:x1] print(f'shape: {image_sequence.shape}') .. rst-class:: sphx-glr-script-out .. code-block:: none shape: (11, 180, 230) .. GENERATED FROM PYTHON SOURCE LINES 41-48 The dataset is a 2D image stack with 11 frames (time points). We visualize and analyze it in a workflow where the first image processing steps are performed on the entire three-dimensional dataset (i.e., across space and time), such that the removal of localized, transient noise is favored as opposed to that of physical features (e.g., bubbles, splatters, etc.), which exist in roughly the same position from one frame to the next. .. GENERATED FROM PYTHON SOURCE LINES 48-57 .. code-block:: Python fig = px.imshow( image_sequence, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_001.html .. GENERATED FROM PYTHON SOURCE LINES 58-66 Compute image deltas ==================== Let us first apply a Gaussian low-pass filter in order to smooth the images and reduce noise. Next, we compute the image deltas, i.e., the sequence of differences between two consecutive frames. To do this, we subtract the image sequence ending at the second-to-last frame from the image sequence starting at the second frame. .. GENERATED FROM PYTHON SOURCE LINES 66-78 .. code-block:: Python smoothed = filters.gaussian(image_sequence) image_deltas = smoothed[1:, :, :] - smoothed[:-1, :, :] fig = px.imshow( image_deltas, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_002.html .. GENERATED FROM PYTHON SOURCE LINES 79-85 Clip lowest and highest intensities =================================== We now calculate the 5th and 95th percentile intensities of ``image_deltas``: We want to clip the intensities which lie below the 5th percentile intensity and above the 95th percentile intensity, while also rescaling the intensity values to [0, 1]. .. GENERATED FROM PYTHON SOURCE LINES 85-100 .. code-block:: Python p_low, p_high = np.percentile(image_deltas, [5, 95]) clipped = image_deltas - p_low clipped[clipped < 0.0] = 0.0 clipped = clipped / p_high clipped[clipped > 1.0] = 1.0 fig = px.imshow( clipped, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_003.html .. GENERATED FROM PYTHON SOURCE LINES 101-107 Invert and denoise ================== We invert the ``clipped`` images so the regions of highest intensity will include the region we are interested in tracking (i.e., the S-L interface). Then, we apply a total variation denoising filter to reduce noise beyond the interface. .. GENERATED FROM PYTHON SOURCE LINES 107-119 .. code-block:: Python inverted = 1 - clipped denoised = restoration.denoise_tv_chambolle(inverted, weight=0.15) fig = px.imshow( denoised, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_004.html .. GENERATED FROM PYTHON SOURCE LINES 120-132 Binarize ======== Our next step is to create binary images, splitting the images into foreground and background: We want the S-L interface to be the most prominent feature in the foreground of each binary image, so that it can eventually be separated from the rest of the image. We need a threshold value ``thresh_val`` to create our binary images, ``binarized``. This can be set manually, but we shall use an automated minimum threshold method from the ``filters`` submodule of scikit-image (there are other methods that may work better for different applications). .. GENERATED FROM PYTHON SOURCE LINES 132-144 .. code-block:: Python thresh_val = filters.threshold_minimum(denoised) binarized = denoised > thresh_val fig = px.imshow( binarized, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_005.html .. GENERATED FROM PYTHON SOURCE LINES 145-160 Select largest region ===================== In our binary images, the S-L interface appears as the largest region of connected pixels. For this step of the workflow, we will operate on each 2D image separately, as opposed to the entire 3D dataset, because we are interested in a single moment in time for each region. We apply :func:`skimage.measure.label()` on the binary images so that each region has its own label. Then, we select the largest region in each image by computing region properties, including the ``area`` property, and sorting by ``area`` values. Function :func:`skimage.measure.regionprops_table()` returns a table of region properties which can be read into a Pandas ``DataFrame``. To begin with, let us consider the first image delta at this stage of the workflow, ``binarized[0, :, :]``. .. GENERATED FROM PYTHON SOURCE LINES 160-168 .. code-block:: Python labeled_0 = measure.label(binarized[0, :, :]) props_0 = measure.regionprops_table(labeled_0, properties=('label', 'area', 'bbox')) props_0_df = pd.DataFrame(props_0) props_0_df = props_0_df.sort_values('area', ascending=False) # Show top five rows props_0_df.head() .. raw:: html
label area bbox-0 bbox-1 bbox-2 bbox-3
1 2 417.0 60 83 91 144
198 199 235.0 141 141 179 165
11 12 136.0 62 164 87 180
9 10 122.0 61 208 79 229
8 9 114.0 61 183 83 198


.. GENERATED FROM PYTHON SOURCE LINES 169-172 We can thus select the largest region by matching its label with the one found in the first row of the above (sorted) table. Let us visualize it, along with its bounding box (bbox) in red. .. GENERATED FROM PYTHON SOURCE LINES 172-179 .. code-block:: Python largest_region_0 = labeled_0 == props_0_df.iloc[0]['label'] minr, minc, maxr, maxc = (props_0_df.iloc[0][f'bbox-{i}'] for i in range(4)) fig = px.imshow(largest_region_0, binary_string=True) fig.add_shape(type='rect', x0=minc, y0=minr, x1=maxc, y1=maxr, line=dict(color='Red')) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_006.html .. GENERATED FROM PYTHON SOURCE LINES 180-186 We can see how the lower bounds of the box align with the bottom of the S-L interface by overlaying the same bounding box onto the 0th raw image. This bounding box was calculated from the image delta between the 0th and 1st images, but the bottom-most region of the box corresponds to the location of the interface earlier in time (0th image) because the interface is moving upward. .. GENERATED FROM PYTHON SOURCE LINES 186-191 .. code-block:: Python fig = px.imshow(image_sequence[0, :, :], binary_string=True) fig.add_shape(type='rect', x0=minc, y0=minr, x1=maxc, y1=maxr, line=dict(color='Red')) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_007.html .. GENERATED FROM PYTHON SOURCE LINES 192-196 We are now ready to perform this selection for all image deltas in the sequence. We shall also store the bbox information, which will be used to track the position of the S-L interface. .. GENERATED FROM PYTHON SOURCE LINES 196-215 .. code-block:: Python largest_region = np.empty_like(binarized) bboxes = [] for i in range(binarized.shape[0]): labeled = measure.label(binarized[i, :, :]) props = measure.regionprops_table(labeled, properties=('label', 'area', 'bbox')) props_df = pd.DataFrame(props) props_df = props_df.sort_values('area', ascending=False) largest_region[i, :, :] = labeled == props_df.iloc[0]['label'] bboxes.append([props_df.iloc[0][f'bbox-{i}'] for i in range(4)]) fig = px.imshow( largest_region, animation_frame=0, binary_string=True, labels={'animation_frame': 'time point'}, ) plotly.io.show(fig) .. raw:: html :file: images/sphx_glr_plot_solidification_tracking_008.html .. GENERATED FROM PYTHON SOURCE LINES 216-227 Plot interface location over time ================================= The final step in this analysis is to plot the location of the S-L interfaces over time. This can be achieved by plotting ``maxr`` (third element in bbox) over time since this value shows the `y` location of the bottom of the interface. The pixel size in this experiment was 1.93 microns and the framerate was 80,000 frames per second, so these values are used to convert pixels and image number to physical units. We calculate the average solidfication velocity by fitting a linear polynomial to the scatter plot. The velocity is the first-order coefficient. .. GENERATED FROM PYTHON SOURCE LINES 227-240 .. code-block:: Python ums_per_pixel = 1.93 fps = 80000 interface_y_um = [ums_per_pixel * bbox[2] for bbox in bboxes] time_us = 1e6 / fps * np.arange(len(interface_y_um)) fig, ax = plt.subplots(dpi=100) ax.scatter(time_us, interface_y_um) c0, c1 = np.polynomial.polynomial.polyfit(time_us, interface_y_um, 1) ax.plot(time_us, c1 * time_us + c0, label=f'Velocity: {abs(round(c1, 3))} m/s') ax.set_title('S-L interface location vs. time') ax.set_ylabel(r'Location ($\mu$m)') ax.set_xlabel(r'Time ($\mu$s)') plt.show() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_solidification_tracking_009.png :alt: S-L interface location vs. time :srcset: /auto_examples/applications/images/sphx_glr_plot_solidification_tracking_009.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.747 seconds) .. _sphx_glr_download_auto_examples_applications_plot_solidification_tracking.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.23.2?filepath=notebooks/auto_examples/applications/plot_solidification_tracking.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_solidification_tracking.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_solidification_tracking.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_