Expand Up @@ -36,9 +36,6 @@ def _format_gt_data(h5file): df = pd.read_hdf(h5file)
def _get_unique_level_values(header, level): return header.get_level_values(level).unique().to_list()
animals = _get_unique_level_values(df.columns, "individuals") kpts = _get_unique_level_values(df.columns, "bodyparts") try: Expand All @@ -64,27 +61,16 @@ def _get_unique_level_values(header, level): }

def _get_unique_level_values(header, level): return header.get_level_values(level).unique().to_list()

def calc_prediction_errors(preds, gt): kpts_gt = gt["metadata"]["keypoints"] kpts_pred = preds["metadata"]["keypoints"] map_ = {kpts_gt.index(kpt): i for i, kpt in enumerate(kpts_pred)} annot = gt["annotations"]
# Map image paths from predicted data to GT as the first are typically # absolute whereas the latter are relative to the project path. def _map(strings, substrings): lookup = dict() strings_ = strings.copy() substrings_ = substrings.copy() while strings_: string = strings_.pop() for s in substrings_: if string.endswith(s): lookup[string] = s substrings_.remove(s) break return lookup
map_images = _map(list(preds["predictions"]), list(annot))
errors = np.full( Expand Down Expand Up @@ -124,6 +110,26 @@ def _map(strings, substrings): return errors


def _map(strings, substrings): """ Map image paths from predicted data to GT as the first are typically absolute whereas the latter are relative to the project path. """
lookup = dict() strings_ = strings.copy() substrings_ = substrings.copy() while strings_: string = strings_.pop() for s in substrings_: if string.endswith(s): lookup[string] = s substrings_.remove(s) break return lookup

def conv_obj_to_assemblies(eval_results_obj, keypoint_names): """Convert predictions to deeplabcut assemblies.""" assemblies = {} Expand Down