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import os, yaml, platform
def get_default_download_folder():
system_platform = platform.system() # Gets the system platform, e.g., 'Linux', 'Windows', 'Darwin'
if system_platform == "Windows":
# Typically, the Downloads folder for Windows is in the user's profile folder
default_output_folder = os.path.join(os.getenv('USERPROFILE'), 'Downloads')
elif system_platform == "Darwin":
# Typically, the Downloads folder for macOS is in the user's home directory
default_output_folder = os.path.join(os.path.expanduser("~"), 'Downloads')
elif system_platform == "Linux":
# Typically, the Downloads folder for Linux is in the user's home directory
default_output_folder = os.path.join(os.path.expanduser("~"), 'Downloads')
else:
default_output_folder = "set/path/to/downloads/folder"
print("Please manually set the output folder")
return default_output_folder
def build_LM2_config():
dir_home = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
# Initialize the base structure
config_data = {
'leafmachine': {}
}
# Modular sections to be added to 'leafmachine'
do_section = {
'check_for_illegal_filenames': True,
'check_for_corrupt_images_make_vertical': True,
'run_leaf_processing': True
}
print_section = {
'verbose': True,
'optional_warnings': True
}
logging_section = {
'log_level': None
}
default_output_folder = get_default_download_folder()
project_section = {
'dir_output': default_output_folder,
# 'dir_output': 'D:/D_Desktop/LM2',
'run_name': 'test',
'image_location': 'local',
'GBIF_mode': 'all',
'batch_size': 40,
'num_workers': 2,
'dir_images_local': '',
# 'dir_images_local': 'D:\Dropbox\LM2_Env\Image_Datasets\Manuscript_Images',
'path_combined_csv_local': None,
'path_occurrence_csv_local': None,
'path_images_csv_local': None,
'use_existing_plant_component_detections': None,
'use_existing_archival_component_detections': None,
'process_subset_of_images': False,
'dir_images_subset': '',
'n_images_per_species': 10,
'species_list': ''
}
cropped_components_section = {
'do_save_cropped_annotations': False,
'save_cropped_annotations': ['label'],
'save_per_image': False,
'save_per_annotation_class': True,
'binarize_labels': False,
'binarize_labels_skeletonize': False
}
modules_section = {
'armature': False,
'specimen_crop': False
}
data_section = {
'save_json_rulers': False,
'save_json_measurements': False,
'save_individual_csv_files_rulers': False,
'save_individual_csv_files_measurements': False,
'save_individual_csv_files_landmarks': False,
'save_individual_efd_files': False,
'include_darwin_core_data_from_combined_file': False,
'do_apply_conversion_factor': True
}
overlay_section = {
'save_overlay_to_pdf': False,
'save_overlay_to_jpgs': True,
'overlay_dpi': 300, # Between 100 to 300
'overlay_background_color': 'black', # Either 'white' or 'black'
'show_archival_detections': True,
'show_plant_detections': True,
'show_segmentations': True,
'show_landmarks': True,
'ignore_archival_detections_classes': [],
'ignore_plant_detections_classes': ['leaf_whole', 'specimen'], # Could also include 'leaf_partial' and others if needed
'ignore_landmark_classes': [],
'line_width_archival': 12, # Previous value given was 2
'line_width_plant': 12, # Previous value given was 6
'line_width_seg': 12, # 12 is specified as "thick"
'line_width_efd': 12, # 3 is specified as "thick" but 12 is given here
'alpha_transparency_archival': 0.3,
'alpha_transparency_plant': 0,
'alpha_transparency_seg_whole_leaf': 0.4,
'alpha_transparency_seg_partial_leaf': 0.3
}
plant_component_detector_section = {
'detector_type': 'Plant_Detector',
'detector_version': 'PLANT_GroupAB_200',
'detector_iteration': 'PLANT_GroupAB_200',
'detector_weights': 'best.pt',
'minimum_confidence_threshold': 0.3, # Default is 0.5
'do_save_prediction_overlay_images': True,
'ignore_objects_for_overlay': [] # 'leaf_partial' can be included if needed
}
archival_component_detector_section = {
'detector_type': 'Archival_Detector',
'detector_version': 'PREP_final',
'detector_iteration': 'PREP_final',
'detector_weights': 'best.pt',
'minimum_confidence_threshold': 0.5, # Default is 0.5
'do_save_prediction_overlay_images': True,
'ignore_objects_for_overlay': []
}
armature_component_detector_section = {
'detector_type': 'Armature_Detector',
'detector_version': 'ARM_A_1000',
'detector_iteration': 'ARM_A_1000',
'detector_weights': 'best.pt',
'minimum_confidence_threshold': 0.5, # Optionally: 0.2
'do_save_prediction_overlay_images': True,
'ignore_objects_for_overlay': []
}
landmark_detector_section = {
'landmark_whole_leaves': True,
'landmark_partial_leaves': False,
'detector_type': 'Landmark_Detector_YOLO',
'detector_version': 'Landmarks',
'detector_iteration': 'Landmarks_V2',
'detector_weights': 'best.pt',
'minimum_confidence_threshold': 0.02,
'do_save_prediction_overlay_images': True,
'ignore_objects_for_overlay': [],
'use_existing_landmark_detections': None, # Example path provided
'do_show_QC_images': False,
'do_save_QC_images': True,
'do_show_final_images': False,
'do_save_final_images': True
}
landmark_detector_armature_section = {
'upscale_factor': 10,
'detector_type': 'Landmark_Detector_YOLO',
'detector_version': 'Landmarks_Arm_A_200',
'detector_iteration': 'Landmarks_Arm_A_200',
'detector_weights': 'last.pt',
'minimum_confidence_threshold': 0.06,
'do_save_prediction_overlay_images': True,
'ignore_objects_for_overlay': [],
'use_existing_landmark_detections': None, # Example path provided
'do_show_QC_images': True,
'do_save_QC_images': True,
'do_show_final_images': True,
'do_save_final_images': True
}
ruler_detection_section = {
'detect_ruler_type': True,
'ruler_detector': 'ruler_classifier_38classes_v-1.pt',
'ruler_binary_detector': 'model_scripted_resnet_720_withCompression.pt',
'minimum_confidence_threshold': 0.4,
'save_ruler_validation': False,
'save_ruler_validation_summary': True,
'save_ruler_processed': False
}
leaf_segmentation_section = {
'segment_whole_leaves': True,
'segment_partial_leaves': False,
'keep_only_best_one_leaf_one_petiole': True,
'save_segmentation_overlay_images_to_pdf': True,
'save_each_segmentation_overlay_image': True,
'save_individual_overlay_images': True, # Not recommended due to potential file count
'overlay_line_width': 1, # Default is 1
'use_efds_for_png_masks': False, # Requires calculate_elliptic_fourier_descriptors to be True
'save_masks_color': True,
'save_full_image_masks_color': True,
'save_rgb_cropped_images': True,
'find_minimum_bounding_box': True,
'calculate_elliptic_fourier_descriptors': True, # Default is True
'elliptic_fourier_descriptor_order': 40, # Default is 40
'segmentation_model': 'GroupB_Dataset_100000_Iter_1176PTS_512Batch_smooth_l1_LR00025_BGR',
'minimum_confidence_threshold': 0.7, # Alternatively: 0.9
'generate_overlay': True,
'overlay_dpi': 300, # Range: 100 to 300
'overlay_background_color': 'black' # Options: 'white' or 'black'
}
# Add the sections to the 'leafmachine' key
config_data['leafmachine']['do'] = do_section
config_data['leafmachine']['print'] = print_section
config_data['leafmachine']['logging'] = logging_section
config_data['leafmachine']['project'] = project_section
config_data['leafmachine']['cropped_components'] = cropped_components_section
config_data['leafmachine']['modules'] = modules_section
config_data['leafmachine']['data'] = data_section
config_data['leafmachine']['overlay'] = overlay_section
config_data['leafmachine']['plant_component_detector'] = plant_component_detector_section
config_data['leafmachine']['archival_component_detector'] = archival_component_detector_section
config_data['leafmachine']['armature_component_detector'] = armature_component_detector_section
config_data['leafmachine']['landmark_detector'] = landmark_detector_section
config_data['leafmachine']['landmark_detector_armature'] = landmark_detector_armature_section
config_data['leafmachine']['ruler_detection'] = ruler_detection_section
config_data['leafmachine']['leaf_segmentation'] = leaf_segmentation_section
return config_data, dir_home
def write_config_file(config_data, dir_home, filename="LeafMachine2.yaml"):
file_path = os.path.join(dir_home, filename)
# Write the data to a YAML file
with open(file_path, "w") as outfile:
yaml.dump(config_data, outfile, default_flow_style=False)
if __name__ == '__main__':
config_data, dir_home = build_LM2_config()
write_config_file(config_data, dir_home)
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