"""
IoU Module, with help from https://github.com/SpaceNetChallenge/utilities/blob/spacenetV3/spacenetutilities/evalTools.py
"""
import numpy as np
import rtree
import pandas as pd
from scipy.optimize import linear_sum_assignment
[docs]def create_rtree_from_poly(poly_list):
# create index
index = rtree.index.Index(interleaved=True)
for idx, geom in enumerate(poly_list):
index.insert(idx, geom.bounds)
return index
def _overlap_(test_poly, truth_polys, rtree_index):
"""Calculate overlap between one polygon and all ground truth by area"""
prediction_id = []
truth_id = []
area = []
matched_list = list(rtree_index.intersection(test_poly.geometry.bounds))
for index in truth_polys.index:
if index in matched_list:
# get the original index just to be sure
intersection_result = test_poly.geometry.intersection(
truth_polys.loc[index].geometry)
intersection_area = intersection_result.area
else:
intersection_area = 0
prediction_id.append(test_poly.prediction_id)
truth_id.append(truth_polys.loc[index].truth_id)
area.append(intersection_area)
results = pd.DataFrame({
"prediction_id": prediction_id,
"truth_id": truth_id,
"area": area
})
return results
def _overlap_all(test_polys, truth_polys, rtree_index):
"""Find area of overlap among all sets of ground truth and prediction"""
results = []
for index, row in test_polys.iterrows():
result = _overlap_(test_poly=row,
truth_polys=truth_polys,
rtree_index=rtree_index)
results.append(result)
results = pd.concat(results, ignore_index=True)
return results
def _iou_(test_poly, truth_poly):
"""Intersection over union"""
intersection_result = test_poly.intersection(truth_poly.geometry)
intersection_area = intersection_result.area
union_area = test_poly.union(truth_poly.geometry).area
return (intersection_area / union_area)
[docs]def compute_IoU(ground_truth, submission):
"""
Args:
ground_truth: a projected geopandas dataframe with geoemtry
submission: a projected geopandas dataframe with geometry
Returns:
iou_df: dataframe of IoU scores
"""
# Create index columns for ease
ground_truth["truth_id"] = ground_truth.index.values
submission["prediction_id"] = submission.index.values
# rtree_index
rtree_index = create_rtree_from_poly(ground_truth.geometry)
# find overlap among all sets
overlap_df = _overlap_all(test_polys=submission,
truth_polys=ground_truth,
rtree_index=rtree_index)
# Create cost matrix for assignment
matrix = overlap_df.pivot(index="truth_id", columns="prediction_id",
values="area").values
row_ind, col_ind = linear_sum_assignment(matrix, maximize=True)
# Create IoU dataframe, match those predictions and ground truth, IoU = 0
# for all others, they will get filtered out
iou_df = []
for index, row in ground_truth.iterrows():
if index in row_ind:
matched_id = col_ind[np.where(index == row_ind)[0][0]]
iou = _iou_(submission[submission.prediction_id == matched_id],
ground_truth.loc[index])
score = submission[submission.prediction_id == matched_id].score.values[0]
else:
iou = 0
matched_id = None
score = None
iou_df.append(
pd.DataFrame({
"prediction_id": [matched_id],
"truth_id": [index],
"IoU": iou,
"score": score
}))
iou_df = pd.concat(iou_df)
iou_df = iou_df.merge(ground_truth[["truth_id", "xmin", "xmax", "ymin", "ymax"]])
return iou_df