sem_io Helper functions to extract, view and store metadata from the header of SEM images (.tif) recorded using either Zeiss SmartSEM or Thermo Fisher Scientific xT software. Installation Please clone/download the repository and install with pip cd sem_iopip install . Usage Command line To print an overview of parameters from the image header in the console, at the command line: sem_io path/to/my/image.tif To print an overview from several images one after the other, you can use: sem_io path/to/my/image.tif path/to/my/image2.tif path/to/my/image3.tif To print an overview from all images in a folder, you can use: sem_io path/to/my/folder_of_tif_images You can process any number of individual images and folders at the same time: sem_io path/to/my/image.tif path/to/my/folder_of_tif_images path/to/my/image2.tif The flag -d can be used to additionally dump selected metadata to json: sem_io path/to/my/image.tif -d The json file will be located in the same folder as the image and will have the name my_image_metadata.json.As before, you can process any number of images and/or folders of images with the flag -d. To dump metadata from a folder containing many images, without printing the output to the screen,you can additionally use the flag -s: sem_io path/to/my/folder_of_tif_images -d -s or, more simply: sem_io path/to/my/folder_of_tif_images -ds Either of the above two commands will save a json file for each .tif image in the specified folder. If you prefer a simple text file instead of a json, you can pipe the output of sem_io to file like this: sem_io path/to/my/folder_of_tif_images > other/path/my_text_output.txt The above command will put the output of sem_io for all the .tif images in the folder given into a single text file. You can equally do this for a single .tif image by specifying the path to an image instead of to a folder. Python You can also import the module and use the functions directly in Python. >>> import sem_io To print an overview of parameters from the image header in the console: >>> my_params = sem_io.SEMparams(r"path/to/my/image.tif") If you just want to collect and store the parameters and not print them, you can do: >>> my_params = sem_io.SEMparams(r"path/to/my/image.tif", verbose=False) You can print ALL the header parameters to the console like this: >>> my_params.print_param_dict(my_params.params) You can print A SELECTION OF the header parameters (given in the class definition) to the console like this: >>> my_params.print_param_dict(my_params.params_grouped) Both my_params.params and my_params.params_groups are dictionaries and any parameter can be accessed, e.g. >>> date = my_params.params_grouped["General"]["Date"] Alternatively you can dump all the header parameters to a json file (optionally including a key giving the original image path) like this: >>> my_params.dump_params_to_json(my_params.params, "my_json_path.json", image_path=my_params.img_path) Calling the command line interface from Python You can call the functions from the command line within Python using the subprocess module. For example, to dump metadata from a directory of images to json and print the output to the Python terminal: >>> import subprocess>>> subprocess.run(["sem_io", "path/to/my/folder_of_tif_images", "-d"]) To pipe the output of sem_io to a text file instead: >>> with open("my/output/path/myfile.txt", "w") as output_file:... process = subprocess.Popen(["sem_io", r"path/to/my/image.tif"], stdout=output_file)... process.communicate()... Getting the Image Pixel Size and Adding a Scalebar All the functions are staticmethods, so you don't need to instantiate the SEMparams class at all. For example, there is a bespoke function for getting the image pixel size and its unit in one line of code: >>> pixel_size, unit = sem_io.SEMparams.get_image_pixel_size(r"path/to/my/image.tif") This is useful if you want to plot the SEM image using matplotlib and add a scalebar with the correct dimensions using matplotlib-scalebar. Here's the whole process as an example: >>> import matplotlib.pyplot as plt>>> from matplotlib_scalebar.scalebar import ScaleBar>>> import sem_io>>> my_image = plt.imread("path_to_my_image.tif")>>> fig, ax = plt.subplots()>>> ax.imshow(my_image, cmap='gray')>>> pixel_size, unit = sem_io.SEMparams.get_image_pixel_size("path_to_my_image.tif")>>> my_scalebar = ScaleBar(pixel_size, units=unit, location='lower right', scale_loc='top')>>> ax.add_artist(my_scalebar) Even Electron Channeling Patterns acquired in rocking beam mode are correctly handled by sem_io.SEMparams.get_image_pixel_size() Dependencies Pillow General The selected parameters defined in the class definition of SEMparams form a subset of those available in the header of the .tif image. If you are interested in other parameters, the program can be easily customised - all the header parameters are extracted and are available as the "params" instance attribute.