Marmelator/cv-analysis-service
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This commit is contained in:
Matthias Bisping 2023-01-09 16:22:52 +01:00
parent 65e9735bd9
commit a97f8def7c
1 changed files with 18 additions and 16 deletions
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cv_analysis/utils/test_metrics.py
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@ -1,8 +1,9 @@
from functools import reduce
from operator import itemgetter from operator import itemgetter
from typing import Iterable from typing import Iterable
import numpy as np import numpy as np
from funcy import lmap, lpluck from funcy import lmap, lpluck, first
from cv_analysis.utils import lift from cv_analysis.utils import lift
from cv_analysis.utils.rectangle import Rectangle from cv_analysis.utils.rectangle import Rectangle
@ -30,25 +31,26 @@ def compute_document_score(result_dict, ground_truth_dicts):
def rectangle_from_dict(d): def rectangle_from_dict(d):
x1, y1, w, h = itemgetter("x", "y", "width", "height")(d) x1, y1, w, h = itemgetter("x", "y", "width", "height")(d)
x2 = x1 + w return Rectangle(x1, y1, x1 + w, y1 + h)
y2 = y1 + h
return Rectangle(x1, y1, x2, y2)
def compute_page_iou(results_boxes: Iterable[Rectangle], ground_truth_boxes: Iterable[Rectangle]): def compute_page_iou(predicted_rectangles: Iterable[Rectangle], true_rectangles: Iterable[Rectangle]):
results = list(results_boxes) def find_best_iou(sum_so_far_and_candidate_rectangles, true_rectangle):
truth = list(ground_truth_boxes) sum_so_far, predicted_rectangles = sum_so_far_and_candidate_rectangles
best_match, best_iou = find_max_overlap(true_rectangle, predicted_rectangles)
return sum_so_far + best_iou, predicted_rectangles - {best_match}
def find_best_iou(gt_box): predicted_rectangles = set(predicted_rectangles)
best_match, best_iou = find_max_overlap(gt_box, results) true_rectangles = set(true_rectangles)
results.remove(best_match)
return best_iou iou_sum = first(reduce(find_best_iou, true_rectangles, (0, predicted_rectangles)))
normalizing_factor = 1 / max(len(predicted_rectangles), len(true_rectangles))
score = normalizing_factor * iou_sum
score = sum(map(find_best_iou, truth)) / max(len(results), len(truth))
return score return score
def find_max_overlap(box: Rectangle, box_list: Iterable[Rectangle]): def find_max_overlap(rectangle: Rectangle, candidate_rectangles: Iterable[Rectangle]):
best_candidate = max(box_list, key=lambda x: box.iou(x)) best_candidate_rectangle = max(candidate_rectangles, key=rectangle.iou)
iou = box.iou(best_candidate) iou = rectangle.iou(best_candidate_rectangle)
return best_candidate, iou return best_candidate_rectangle, iou