зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-30 05:26:05 +02:00
44e5a987b1
* updated documentation in cropposts * updated documentation in display * updated documentation in faces * added comments to objects.py * updated utils.py docs * updated text.py docs * improve doc display * fix doc for display and remove redundant variable * removed documentation from cropposts.py * removed unused imports * get rid of ipywidgets dependency * remove unused imports, improve type hints * improve doc in utils * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Inga Ulusoy <inga.ulusoy@uni-heidelberg.de> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
110 строки
3.5 KiB
Python
110 строки
3.5 KiB
Python
import glob
|
|
import os
|
|
from pandas import DataFrame
|
|
import pooch
|
|
|
|
|
|
class DownloadResource:
|
|
"""A remote resource that needs on demand downloading.
|
|
|
|
We use this as a wrapper to the pooch library. The wrapper registers
|
|
each data file and allows prefetching through the CLI entry point
|
|
ammico_prefetch_models.
|
|
"""
|
|
|
|
# We store a list of defined resouces in a class variable, allowing
|
|
# us prefetching from a CLI e.g. to bundle into a Docker image
|
|
resources = []
|
|
|
|
def __init__(self, **kwargs):
|
|
DownloadResource.resources.append(self)
|
|
self.kwargs = kwargs
|
|
|
|
def get(self):
|
|
return pooch.retrieve(**self.kwargs)
|
|
|
|
|
|
def ammico_prefetch_models():
|
|
"""Prefetch all the download resources"""
|
|
for res in DownloadResource.resources:
|
|
res.get()
|
|
|
|
|
|
class AnalysisMethod:
|
|
"""Base class to be inherited by all analysis methods."""
|
|
|
|
def __init__(self, subdict: dict) -> None:
|
|
self.subdict = subdict
|
|
# define keys that will be set by the analysis
|
|
|
|
def set_keys(self):
|
|
raise NotImplementedError()
|
|
|
|
def analyse_image(self):
|
|
raise NotImplementedError()
|
|
|
|
|
|
def find_files(
|
|
path: str = None, pattern: str = "*.png", recursive: bool = True, limit: int = 20
|
|
) -> list:
|
|
"""Find image files on the file system.
|
|
|
|
Args:
|
|
path (str, optional): The base directory where we are looking for the images. Defaults
|
|
to None, which uses the XDG data directory if set or the current
|
|
working directory otherwise.
|
|
pattern (str, optional): The naming pattern that the filename should match. Defaults to
|
|
"*.png". Can be used to allow other patterns or to only include
|
|
specific prefixes or suffixes.
|
|
recursive (bool, optional): Whether to recurse into subdirectories. Default is set to False.
|
|
limit (int, optional): The maximum number of images to be found.
|
|
Defaults to 20. To return all images, set to None.
|
|
|
|
Returns:
|
|
list: A list with all filenames including the path.
|
|
"""
|
|
if path is None:
|
|
path = os.environ.get("XDG_DATA_HOME", ".")
|
|
result = list(glob.glob(f"{path}/{pattern}", recursive=recursive))
|
|
if limit is not None:
|
|
result = result[:limit]
|
|
return result
|
|
|
|
|
|
def initialize_dict(filelist: list) -> dict:
|
|
"""Initialize the nested dictionary for all the found images.
|
|
|
|
Args:
|
|
filelist (list): The list of files to be analyzed, including their paths.
|
|
Returns:
|
|
dict: The nested dictionary with all image ids and their paths."""
|
|
mydict = {}
|
|
for img_path in filelist:
|
|
id_ = os.path.splitext(os.path.basename(img_path))[0]
|
|
mydict[id_] = {"filename": img_path}
|
|
return mydict
|
|
|
|
|
|
def append_data_to_dict(mydict: dict) -> dict:
|
|
"""Append entries from nested dictionaries to keys in a global dict."""
|
|
|
|
# first initialize empty list for each key that is present
|
|
outdict = {key: [] for key in list(mydict.values())[0].keys()}
|
|
# now append the values to each key in a list
|
|
for subdict in mydict.values():
|
|
for key in subdict.keys():
|
|
outdict[key].append(subdict[key])
|
|
return outdict
|
|
|
|
|
|
def dump_df(mydict: dict) -> DataFrame:
|
|
"""Utility to dump the dictionary into a dataframe."""
|
|
return DataFrame.from_dict(mydict)
|
|
|
|
|
|
def is_interactive():
|
|
"""Check if we are running in an interactive environment."""
|
|
import __main__ as main
|
|
|
|
return not hasattr(main, "__file__")
|