Process many images in sequence

Overview

These instructions show you how to run a simple demo which analyzes three images in sequence. You can use this technique to analyze multiple images in a batch.

Instructions

• Make sure you
  • have run Analyze a small image
  • have a good understanding of what the code did
• Now look in the demos folder of your repository and find the configuration_data.xml file for example_D. It should look like this.

<?xml version="+ 0" encoding="windows-1252"?>
<MyoVision_analysis>
  <task_files>
    <task>
      <raw_image_file_string>..\demos\example_D\raw_images\image_+ png</raw_image_file_string>
      <results_folder>..\demos\example_D\results\image_1</results_folder>
    /task>
    <task>
      <raw_image_file_string>..\demos\example_D\raw_images\image_2.png</raw_image_file_string>
      <results_folder>..\demos\example_D\results\image_2</results_folder>
    </task>
    <task>
      <raw_image_file_string>..\demos\example_D\raw_images\image_3.png</raw_image_file_string>
      <results_folder>..\demos\example_D\results\image_3</results_folder>
    </task>
  </task_files>
  
  <image_to_label_parameters>
    <block_size>1000</block_size>
    <saturation_percent>10</saturation_percent>
    <min_object_size>50</min_object_size>
    <watershed_distance>10</watershed_distance>
  </image_to_label_parameters>

  <classifier_parameters>
    <classification_model_file_string>..\demos\example_A\classification_model\classification_model.svc</classification_model_file_string>
  </classifier_parameters>
  
  <refine_fibers_parameters>
    <max_iterations>15</max_iterations>
    <sigma>2</sigma>
  </refine_fibers_parameters>
</MyoVision_analysis>

• The key thing about this configuration file is that it has 3 tasks instead of just +
  • the first task analyzes image_1 png and stores the results in the results\image_1 folder
  • the second task analyzes image_2.png and stores the results in the results\image_2 folder
  • the third task analyzes image_3.png and stores the results in the results\image_3 folder

• That’s basically it. You can instead this technique for any number of image files, simply by adding new tasks to your configuration file.

• To run the demo, open a command prompt
  • If you don’t know how
    • type cmd in the Search field of your Start Menu
    • or Google it to find instructions that work for you
• Change the directory to the Python_code folder of your repository
  • If you installed Python_MyoVision in c:\users\your_username_here\GitHub\Python_Myovision
    you can type
    cd c:\users\your_username_here\GitHub\Python_Myovision\Python_code
    and press enter

• In the command window, type
  python py_vision.py analyze_images “..\demos\example_D\configuration_data\configuration_data.xml”
  and press enter

• Within a few seconds, you should see status updates in the command window

• Once the program has finished, look in the results folder specified in the configuration file.
  • By default that should be
    c:\users\your_username_here\GitHub\Python_Myovision\demos\example_A\results
• You should see 3 folders:
  • results_1
  • results_2
  • results_3
• Inside each of them, you should see the following files:
  • final_results.xlsx
  • clean_overlay.png
  • annotated_overlay.png
  • processing.zip

Here are the results:

• Image 1

  

  

• Image 2

  

  

• Image 3