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Data_analysis Module

The data_anlalysis module is used to visualize the composition of the sample's classes and see how it shapes the training dataset. Using basic statistical analysis, the user can test multiple sampling parameters and immediatly see their impact on the classes' distribution. It can also be used to automatically search optimal sampling parameters and obtain a more balanced class distribution in the dataset. The sampling parameters can then be used in images_to_samples.py to obtain the desired dataset. This way, there is no need to run images_to_samples.py to find out how the classes are distributed.

The data_analysis module is useful for balancing training data in which a class is under-represented.

Using data_analysis.py

Prerequisites

Before running data_analysis.py, the paths to the csv file containing all the information about the images and the data folder must be specified in the prep_csv_file parameter and in the data_pathparameter respectively. The samples_size and samples_dist must also be specified.

data_analysis parameters in YAML file

Here is an example of the dedicated data_analysis section in the YAML file :

# Data analysis parameters; used in data_analysis.py

data_analysis:
  create_csv: True
  optimal_parameters_search : False
  sampling: {'method':['min_annotated_percent', 'class_proportion'], 'map': 0, '0':0, '1':0, '2':0}

  1. create_csv

    This parameter is used to create a csv file containing the class proportion data of each image sample. This first step is mandatory to ensure the proper operation of the module. Once it is created, the same csv file is used for every tests the user wants to perform. Once that is done, the parameter can then be changed to False.

    The create_csv parameter would have to be changed to True again if any changes were made to the content of the prep_csv_file or if the user wishes to change the values of the samples_size or samples_dist parameters. These parameters have a direct effect on the class proportion calculation.

    The csv file is stored in the folder specified in the data_path parameter of the YAML file.

  2. optimal_parameters_search

    When this parameter is set to True, it activates the optimal sampling parameters search function. This function aims to find wich sampling parameters produce the most balanced dataset based on the standard deviation between the proportions of each class in the dataset.

    The sampling method(s) used for the search function must first be specified in the sampling dictionary in the data_analysis section of the YAML file. It does not take into account the values of the other keys in the dictionary.

    Example
    data_analysis:
  create_csv: False
  optimal_parameters_search : True
  sampling: {'method':['min_annotated_percent', 'class_proportion'], 'map': 0, '0':0, '1':0, '2':0}

    The function first returns the optimal threshold(s) for the chosen sampling method(s). It then returns a preview of the proportions of each classes and the size of the final dataset without creating it.

  3. sampling dictionary

    a) 'method'

    To specify the desired sampling method, write one or both of 'min_annotated_percent' and 'class_proportion'. These sampling methods can be used on their own or together in any order. They have to be in quotes and contained in a list.

    This part of the sampling dictionary is also used for the optimal_parameters_search function.

    b) 'map'

    'map' stands for 'minimum annotated percent' : Minimum percent of non background pixels in sample

    Specify the targeted mininum annotated percent with an integer. For this value to be taken into account, the optimal_paramters_search function must be turned off and 'min_annotated_percent' must be listed in the 'method' key of the sampling dictionary.

    Example
      'map':25

    c) following keys

    The followings keys of the dictionary should be the number of each classes in the images in quotes. Specify the minimum class proportion of one or all classes with integer(s) or float(s). For these values to be taken into account, the optimal_paramters_search function must be turned off and 'class_proportion' must be listed in the 'method' key of the sampling dictionary.

    Example
      '0':0, '1':10, '2':5, '3':5, ...

Running data_analysis.py

To launch the program :

python data_analysis.py path/to/config/file/config.yaml