CensorArchivalComponents.yaml

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# LeafMachine2 SpecimenCropping Module
# William N. Weaver 2023
# University of Michigan
# 
# 
# Cite:
# Weaver, W. N., and S. A.Smith. 2023. From leaves to labels: Building modular
#     machine learning networks for rapid herbarium specimen analysis with LeafMachine2.
#     Applications in Plant Sciences. e11548. https://doi.org/10.1002/aps3.11548

leafmachine:
    do:
        check_for_illegal_filenames: False 
        check_for_corrupt_images_make_vertical: False

    # Overall Project Input Settings
    project:
        # Image to Process
        dir_images_local: 'C:/ML_Projects/Dan_Park/annotated_images/Dioscorea-HNC-ColombianHerbarium/Annotated_Dioscorea_HNCOL' #'path/to/input/images'
        run_name: 'Censored__Annotated_Dioscorea_HNCOL'

        # Project Output Dir
        # Can set this to a temp location if the intermediate files are not needed
        dir_output: 'C:/ML_Projects/Dan_Park/annotated_images/Dioscorea-HNC-ColombianHerbarium' #'path/to/output/dir'

        censor_archival_components: True
        hide_archival_components: ['ruler', 'barcode', 'label', 'colorcard', 'map', 'photo', 'weights',]
        replacement_color: '#FFFFFF'

        # Processing Options
        batch_size: 50000                   # Default: 50    | Determines RAM usage
        num_workers: 8                    # Default: 4     | Most hardware loses performance beyond 4 workers
        image_location: 'local'

    cropped_components:
        # empty list for all, add to list to IGNORE, lowercase, comma seperated
        # archival |FROM| 
        #           ruler, barcode, colorcard, label, map, envelope, photo, attached_item, weights
        # plant |FROM| 
        #           leaf_whole, leaf_partial, leaflet, seed_fruit_one, seed_fruit_many, flower_one, flower_many, bud, specimen, roots, wood
        
        include_these_objects_in_specimen_crop: ['ruler','barcode','colorcard', 'label', 'map', 'envelope', 'photo', 'attached_item', 'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud', 'specimen', 'roots', 'wood']
        
        save_per_image: False # creates a folder for each image, saves crops into class-names folders # TODO
        save_per_annotation_class: True # saves crops into class-names folders

        # Optional: Save individual components as images 
        #          - Save jpgs for each identified object
        #          - e.g. Save a jpg of each barcode that is identified from the input images
        #          - can pick from any of the items listed in archival |FROM| and plant |FROM| 
        do_save_cropped_annotations: False # Saves individual cropped components 
        save_cropped_annotations: ['ruler','barcode','colorcard', 'label',] # Saves individual cropped components of these classes



    # For almost all use cases, the settings below do not need to be changed
    #
    # minimum_confidence_threshold 
    #       - By default the detector confidence is set quite low to make sure that
    #         the detector actually finds the objects in the image.
    #       - If you expereince issues and the detector is skipping or detecting too many objects, 
    #         try increaseing or decreasing the 'minimum_confidence_threshold' 
    #         values. They must be between 0 and 1. 
    #       - Higher values will yeild fewer but more confident detections
    #
    # do_save_prediction_overlay_images
    #       - To save space, set:
    #                do_save_prediction_overlay_images = False
    #       - False will not save the intermediate object detection images. 
    #       - These intermediate images are useful for debugging, but are otherwise unimportant. 
    #
    #       NOTE: For everyday use, we recommend setting 'do_save_prediction_overlay_images to False 
    #             so that you are not saving hundreds of overlay images.
    modules:
        specimen_crop: True

    # Configure Plant Component Detector
    plant_component_detector:
        # ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
        detector_type: 'Plant_Detector' 
        detector_version: 'PLANT_LeafPriority'
        detector_iteration: 'PLANT_LeafPriority'
        detector_weights: 'LeafPriority.pt'
        minimum_confidence_threshold: 0.2 # 0.2 = default
        do_save_prediction_overlay_images: True
        ignore_objects_for_overlay: []
    
    # Configure Archival Component Detector
    archival_component_detector:
        # ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
        detector_type: 'Archival_Detector' 
        detector_version: 'PREP_final'
        detector_iteration: 'PREP_final'
        detector_weights: 'best.pt'
        minimum_confidence_threshold: 0.2 # 0.2 = default
        do_save_prediction_overlay_images: True
        ignore_objects_for_overlay: [] 

    print:
        verbose: True
        optional_warnings: True
    
    leaf_segmentation:
        segment_whole_leaves: False
        segment_partial_leaves: False
    landmark_detector:
        landmark_whole_leaves: False
        landmark_partial_leaves: False

    logging:
        log_level: null

    overlay:
        save_overlay_to_pdf: False
        save_overlay_to_jpgs: True
        overlay_dpi: 300 # int |FROM| 100 to 300
        overlay_background_color: 'black' # str |FROM| 'white' or 'black'

        show_archival_detections: True
        ignore_archival_detections_classes: []
        show_plant_detections: True
        ignore_plant_detections_classes: [] #['leaf_whole', 'specimen'] #['leaf_whole', 'leaf_partial', 'specimen']
        show_segmentations: True
        show_landmarks: True
        ignore_landmark_classes: []

        line_width_archival: 12 # int
        line_width_plant: 12 # int
        line_width_seg: 12 # int # thick = 12
        line_width_efd: 12 # int # thick = 3
        alpha_transparency_archival: 0.3  # float between 0 and 1
        alpha_transparency_plant: 0
        alpha_transparency_seg_whole_leaf: 0.4
        alpha_transparency_seg_partial_leaf: 0.3