#!/usr/bin/env python3
import numpy as np
from skimage.util import img_as_float
from skimage.io import imread
from pyEdgeEval.common.binary_label.evaluate_boundaries import (
evaluate_boundaries_threshold,
)
from pyEdgeEval.common.utils import check_thresholds
from pyEdgeEval.utils import loadmat
[docs]def load_bsds_gt_boundaries(path: str, new_loader: bool = False):
"""BSDS GT Boundaries
- there are multiple boundaries because there are multiple annotators
- uint8
"""
if new_loader:
from pymatreader import read_mat
gt = read_mat(path)["groundTruth"] # list
num_gts = len(gt)
return [gt[i]["Boundaries"] for i in range(num_gts)]
else:
# FIXME: confusing data organization with scipy
gt = loadmat(path, False)["groundTruth"] # np.ndarray
num_gts = gt.shape[1]
return [gt[0, i]["Boundaries"][0, 0] for i in range(num_gts)]
[docs]def load_predictions(path: str):
img = imread(path)
assert (
img.dtype == np.uint8
), f"ERR: img needs to be uint8, but got{img.dtype}"
return img_as_float(img)
def _evaluate_single(
gt_path,
pred_path,
scale,
max_dist,
thresholds,
apply_thinning,
apply_nms,
**kwargs,
):
"""Evaluate a single sample (sub-routine)
NOTE: don't set defaults for easier debugging
"""
# checks and converts thresholds
thresholds = check_thresholds(thresholds)
pred = load_predictions(pred_path)
gt = load_bsds_gt_boundaries(gt_path)
# TODO: scale inputs
# evaluate multi-label boundaries
count_r, sum_r, count_p, sum_p = evaluate_boundaries_threshold(
thresholds=thresholds,
pred=pred,
gt=gt,
max_dist=max_dist,
apply_thinning=apply_thinning,
apply_nms=apply_nms,
nms_kwargs=dict(
r=1,
s=5,
m=1.01,
half_prec=False,
),
)
return count_r, sum_r, count_p, sum_p
[docs]def bsds_eval_single(kwargs):
"""Wrapper function to unpack all the kwargs"""
return _evaluate_single(**kwargs)