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3D-Room-Layout-Estimation_LGT-Net / evaluation /f1_score.py

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	"""
	@author: Zhigang Jiang
	@time: 2022/01/28
	@description:
	Holistic 3D Vision Challenge on General Room Layout Estimation Track Evaluation Package
	Reference: https://github.com/bertjiazheng/indoor-layout-evaluation
	"""

	from scipy.optimize import linear_sum_assignment
	import numpy as np
	import scipy

	HEIGHT, WIDTH = 512, 1024
	MAX_DISTANCE = np.sqrt(HEIGHT2 + WIDTH2)


	def f1_score_2d(gt_corners, dt_corners, thresholds):
	distances = scipy.spatial.distance.cdist(gt_corners, dt_corners)
	return eval_junctions(distances, thresholds=thresholds)


	def eval_junctions(distances, thresholds=5):
	thresholds = thresholds if isinstance(thresholds, tuple) or isinstance(
	thresholds, list) else list([thresholds])

	num_gts, num_preds = distances.shape

	# filter the matches between ceiling-wall and floor-wall junctions
	mask = np.zeros_like(distances, dtype=np.bool)
	mask[:num_gts//2, :num_preds//2] = True
	mask[num_gts//2:, num_preds//2:] = True
	distances[~mask] = np.inf

	# F-measure under different thresholds
	Fs = []
	Ps = []
	Rs = []
	for threshold in thresholds:
	distances_temp = distances.copy()

	# filter the mis-matched pairs
	distances_temp[distances_temp > threshold] = np.inf

	# remain the rows and columns that contain non-inf elements
	distances_temp = distances_temp[:, np.any(np.isfinite(distances_temp), axis=0)]

	if np.prod(distances_temp.shape) == 0:
	Fs.append(0)
	Ps.append(0)
	Rs.append(0)
	continue

	distances_temp = distances_temp[np.any(np.isfinite(distances_temp), axis=1), :]

	# solve the bipartite graph matching problem
	row_ind, col_ind = linear_sum_assignment_with_inf(distances_temp)
	true_positive = np.sum(np.isfinite(distances_temp[row_ind, col_ind]))

	# compute precision and recall
	precision = true_positive / num_preds
	recall = true_positive / num_gts

	# compute F measure
	Fs.append(2 * precision * recall / (precision + recall))
	Ps.append(precision)
	Rs.append(recall)

	return Fs, Ps, Rs


	def linear_sum_assignment_with_inf(cost_matrix):
	"""
	Deal with linear_sum_assignment with inf according to
	https://github.com/scipy/scipy/issues/6900#issuecomment-451735634
	"""
	cost_matrix = np.copy(cost_matrix)
	cost_matrix[np.isinf(cost_matrix)] = MAX_DISTANCE
	return linear_sum_assignment(cost_matrix)