from view.python_core.processing_pipelines import PipelineManager

# this tells view all settings including the folder structure of your project
# On Windows, if you copy paths from the file explorer, make sure the string below is always of the form r"......"
ymlfile = r"E:\MR_Till\usage_till_windows.yml"

# specify the pipeline definition file to use
pipelines_definition_file = r"E:\MR_Till\progs\pipelines_settings\processing_pipelines.yml"

# specify the animals whose data is to be processed; and their corresponding pipelines
animals = {
    # "<animal name>": ['pipeline1', 'pipeline2', 'pipeline3'],
    "MR_190510b_or47a": ['pipeline1', 'pipeline2'],
}

# ----------------------------------------------------------------------------------------------------------------------
# visualization flags
# These flags control some aspects of the graphs and plots of evaluation reports
# -----------------------------------------------------------------------------------------------------------------------
# Specifies a threshold that is used when creating and drawing contours of detected components
# from their spatial footprints. Footprints are normalized by dividing by their maximum before being thresholded.
# NOTE: Since this flag is also used when overlaying ROI information in TIF on overviews and movies,
# please set for consistency the flag "RM_ROIThreshold" in your project YML file.
roi_threshold = 0.75  # a float value between 0 and 1, like 0.75

# A maximum-correlation summary image is created from the data and used as the background for showing
# detected components. Maximum-correlation summary image is created by binning frames into non-overlapping bins, and the
# size of these bins are controlled by this flag
frame_bin_size = 200  # an integer value, like 200

# ----------------------------------------------------------------------------------------------------------------------

# required on windows when code spawns subprocesses
# https://stackoverflow.com/questions/18204782/runtimeerror-on-windows-trying-python-multiprocessing
if __name__ == '__main__':

    for animal, pipelines in animals.items():

        visualization_flags = {
            "roi_threshold": roi_threshold,
            "frame_bin_size": frame_bin_size
        }

        pipline_manager = PipelineManager(
            project_yml_file=ymlfile, animal=animal, pipelines_config_file=pipelines_definition_file,
            pipeline_names=pipelines, visualization_flags=visualization_flags)

        pipline_manager.run_all_pipelines(analyse_value_to_use=(1,))