#================================================================================ #Example: thermocouples on a 2d plate # #pyvale: the python validation engine #License: MIT #Copyright (C) 2024 The Computer Aided Validation Team #================================================================================ """ 2D Plate with a Hole --------------------- This example walks through setting up a DIC and strain calculation for the classic "plate with a hole" problem. The images used are synthetically generated, allowing for comparison to analytically known values. """ import matplotlib.pyplot as plt from pathlib import Path # pyvale modules import pyvale.data as dataset import pyvale.dic as dic # %% # We'll start by defining some variables that will be reused throughout the example: # the reference image, deformed image(s), and the subset size. # # If you're working with a series of deformed images, it's a good idea to place # them in a separate folder or ensure they follow a consistent naming convention. # In such cases, the wildcard operator `*` can be used to select multiple files. # # The images used here are included in the `data `_ folder. # We've provided helper functions to load them regardless of your installation path. subset_size = 31 ref_img = dataset.dic_plate_with_hole_cam0_ref() def_img = dataset.dic_plate_with_hole_cam0_def() # create a directory for the the different outputs output_path = Path.cwd() / "pyvale-output" / "ex02" if not output_path.is_dir(): output_path.mkdir(parents=True, exist_ok=True) # %% # Next, we’ll select our Region of Interest (ROI) using the interactive tool. # Create an instance of the ROI class and pass the reference image # as input. This image will be shown as the underlay during any ROI selection or # visualization. roi = dic.RegionOfInterest(ref_img) roi.read_yaml(dataset.dic_ex10_roi()) # %% # Once you've closed the ROI interactive window, a mask and seed location coordinates # will be generated. These are needed for the DIC engine. # # If you intend to reuse this ROI, it's a good idea to save it. For large images, # setting `binary=True` is recommended to reduce file size and write time. roi_file = output_path / "roi.dat" roi.save_array(filename=roi_file, binary=False) # %% # To load a previously saved ROI for future use, use the `read_array` method. # Make sure the filename and format (binary or human-readable) match what was saved. roi.read_array(filename=roi_file, binary=False) # %% # Now we can run the 2D DIC engine using :func:`pyvale.dic.calculate_2d`. # # This function accepts many optional arguments — consult the documentation for full details. # At a minimum, you’ll need to specify: # # - Reference image # - Deformed image(s) # - ROI mask # - Seed coordinates (If using a Reliability Guided approach) # - Subset size # # By default, the engine uses an affine shape function with the Zero Normalised # Sum of Squared Differences (ZNSSD) correlation criterion. # # At present, the DIC engine doesn't return any results to the user, instead the results are saved to disk. # You can customize the filename, location, format, and delimiter using # the options options `output_basepath`, `output_prefix`, `output_delimiter`, and `output_binary`. # More info on these options can be found in the API documentation for :func:`pyvale.dic.calculate_2d`. # By default, the results will be saved with the prefix `dic_results_` followed # by the original filename. The file extension will be replaced will either ".csv" or "dic2d" # depending on whether the results are being saved in human-readable or binary format. dic.calculate_2d(reference=ref_img, deformed=def_img, roi_mask=roi.mask, seed=roi.seed, subset_size=subset_size, subset_step=10, shape_function="AFFINE", max_displacement=10, correlation_criteria="ZNSSD", output_basepath=output_path, output_delimiter=",", output_prefix="dic_results_") # %% # If you saved the results in a human-readable format, you can use any tool # (e.g., Excel, Python, MATLAB) for post-processing. # # For convenience, we provide a utility function to import results back into Python # for analysis and visualization: :func:`pyvale.dic.import_2d`. # # The returned object is an instance of :class:`pyvale.DICResults`. If the results # were saved in binary format or with a custom delimiter, be sure to specify those parameters. dic_files = output_path / "dic_results_*.csv" dicdata = dic.import_2d(data=dic_files, delimiter=",", binary=False) # %% # As an example, here's a simple visualization of the displacement (u, v) and # correlation cost for the two deformed images using matplotlib. You'll need to # ensure you have `matplotlib.pyplot` installed and imported. fig, axes = plt.subplots(2, 3, figsize=(15, 10)) axes = axes.flatten() # First deformation image im1 = axes[0].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.u_px[1]) im2 = axes[1].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.v_px[1]) im3 = axes[2].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.cost[1]) # Second deformation image im4 = axes[3].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.u_px[2]) im5 = axes[4].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.v_px[2]) im6 = axes[5].pcolor(dicdata.ss_x, dicdata.ss_y, dicdata.cost[2]) # Titles axes[0].set_title('u component (def0000.tiff)') axes[1].set_title('v component (def0000.tiff)') axes[2].set_title('cost (def0000.tiff)') axes[3].set_title('u component (def0001.tiff)') axes[4].set_title('v component (def0001.tiff)') axes[5].set_title('cost (def0001.tiff)') for aa in axes: aa.set_aspect('equal') # Colorbars fig.colorbar(im1, ax=axes[0]) fig.colorbar(im2, ax=axes[1]) fig.colorbar(im3, ax=axes[2]) fig.colorbar(im4, ax=axes[3]) fig.colorbar(im5, ax=axes[4]) fig.colorbar(im6, ax=axes[5]) plt.tight_layout() plt.show() # %% # .. image:: ../../../../_static/plate_with_hole.png # :alt: Displacement and cost values # :width: 800px # :align: center