<jupyter_start><jupyter_text>Generate response of training dataset - input: result of `labelme_json_to_dataset` with labelme annotation in json format. Trees are annotated with circle geometry. - output: images in geotiff (1 band, 0 as background and 255 as tree pixels)<jupyter_code>from urbantree.detectree.segmentation import generate_response
from urbantree.setting import Setting

#SETTING = "settings/by_dop80c_1312/detectree_r1/setting.yaml"
SETTING = "settings/opendata_luftbild_dop60_1312/detectree_r1/setting.yaml"

setting = Setting.load_detectree_setting(SETTING)

generate_response(**setting)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07401574803149606, 'fraction_numerical': 0.029921259842519685, 'mean_word_length': 7.712328767123288, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '43943', 'license': 'MIT', 'n_tokens_mistral': 212, 'n_tokens_neox': 201, 'n_words': 49, 'path': 'wf-segmentation-generate-response.ipynb', 'repo_name': 'easz/urban-tree', 'size': '635'}