Source code for deepforest.IoU

IoU Module, with help from
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("truth_id", "prediction_id", "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