You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
190 lines
7.0 KiB
190 lines
7.0 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Functions for computing metrics like precision, recall, CorLoc and etc."""
|
|
from __future__ import division
|
|
|
|
import numpy as np
|
|
|
|
|
|
def compute_precision_recall(scores, labels, num_gt):
|
|
"""Compute precision and recall.
|
|
|
|
Args:
|
|
scores: A float numpy array representing detection score
|
|
labels: A float numpy array representing weighted true/false positive labels
|
|
num_gt: Number of ground truth instances
|
|
|
|
Raises:
|
|
ValueError: if the input is not of the correct format
|
|
|
|
Returns:
|
|
precision: Fraction of positive instances over detected ones. This value is
|
|
None if no ground truth labels are present.
|
|
recall: Fraction of detected positive instance over all positive instances.
|
|
This value is None if no ground truth labels are present.
|
|
|
|
"""
|
|
if not isinstance(labels, np.ndarray) or len(labels.shape) != 1:
|
|
raise ValueError("labels must be single dimension numpy array")
|
|
|
|
if labels.dtype != np.float and labels.dtype != np.bool:
|
|
raise ValueError("labels type must be either bool or float")
|
|
|
|
if not isinstance(scores, np.ndarray) or len(scores.shape) != 1:
|
|
raise ValueError("scores must be single dimension numpy array")
|
|
|
|
if num_gt < np.sum(labels):
|
|
raise ValueError("Number of true positives must be smaller than num_gt.")
|
|
|
|
if len(scores) != len(labels):
|
|
raise ValueError("scores and labels must be of the same size.")
|
|
|
|
if num_gt == 0:
|
|
return None, None
|
|
|
|
sorted_indices = np.argsort(scores)
|
|
sorted_indices = sorted_indices[::-1]
|
|
true_positive_labels = labels[sorted_indices]
|
|
false_positive_labels = (true_positive_labels <= 0).astype(float)
|
|
cum_true_positives = np.cumsum(true_positive_labels)
|
|
cum_false_positives = np.cumsum(false_positive_labels)
|
|
precision = cum_true_positives.astype(float) / (
|
|
cum_true_positives + cum_false_positives)
|
|
recall = cum_true_positives.astype(float) / num_gt
|
|
return precision, recall
|
|
|
|
|
|
def compute_average_precision(precision, recall):
|
|
"""Compute Average Precision according to the definition in VOCdevkit.
|
|
|
|
Precision is modified to ensure that it does not decrease as recall
|
|
decrease.
|
|
|
|
Args:
|
|
precision: A float [N, 1] numpy array of precisions
|
|
recall: A float [N, 1] numpy array of recalls
|
|
|
|
Raises:
|
|
ValueError: if the input is not of the correct format
|
|
|
|
Returns:
|
|
average_precison: The area under the precision recall curve. NaN if
|
|
precision and recall are None.
|
|
|
|
"""
|
|
if precision is None:
|
|
if recall is not None:
|
|
raise ValueError("If precision is None, recall must also be None")
|
|
return np.NAN
|
|
|
|
if not isinstance(precision, np.ndarray) or not isinstance(
|
|
recall, np.ndarray):
|
|
raise ValueError("precision and recall must be numpy array")
|
|
if precision.dtype != np.float or recall.dtype != np.float:
|
|
raise ValueError("input must be float numpy array.")
|
|
if len(precision) != len(recall):
|
|
raise ValueError("precision and recall must be of the same size.")
|
|
if not precision.size:
|
|
return 0.0
|
|
if np.amin(precision) < 0 or np.amax(precision) > 1:
|
|
raise ValueError("Precision must be in the range of [0, 1].")
|
|
if np.amin(recall) < 0 or np.amax(recall) > 1:
|
|
raise ValueError("recall must be in the range of [0, 1].")
|
|
if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
|
|
raise ValueError("recall must be a non-decreasing array")
|
|
|
|
recall = np.concatenate([[0], recall, [1]])
|
|
precision = np.concatenate([[0], precision, [0]])
|
|
|
|
# Preprocess precision to be a non-decreasing array
|
|
for i in range(len(precision) - 2, -1, -1):
|
|
precision[i] = np.maximum(precision[i], precision[i + 1])
|
|
|
|
indices = np.where(recall[1:] != recall[:-1])[0] + 1
|
|
average_precision = np.sum(
|
|
(recall[indices] - recall[indices - 1]) * precision[indices])
|
|
return average_precision
|
|
|
|
|
|
def compute_cor_loc(num_gt_imgs_per_class,
|
|
num_images_correctly_detected_per_class):
|
|
"""Compute CorLoc according to the definition in the following paper.
|
|
|
|
https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf
|
|
|
|
Returns nans if there are no ground truth images for a class.
|
|
|
|
Args:
|
|
num_gt_imgs_per_class: 1D array, representing number of images containing
|
|
at least one object instance of a particular class
|
|
num_images_correctly_detected_per_class: 1D array, representing number of
|
|
images that are correctly detected at least one object instance of a
|
|
particular class
|
|
|
|
Returns:
|
|
corloc_per_class: A float numpy array represents the corloc score of each
|
|
class
|
|
"""
|
|
return np.where(
|
|
num_gt_imgs_per_class == 0, np.nan,
|
|
num_images_correctly_detected_per_class / num_gt_imgs_per_class)
|
|
|
|
|
|
def compute_median_rank_at_k(tp_fp_list, k):
|
|
"""Computes MedianRank@k, where k is the top-scoring labels.
|
|
|
|
Args:
|
|
tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
|
|
detection on a single image, where the detections are sorted by score in
|
|
descending order. Further, each numpy array element can have boolean or
|
|
float values. True positive elements have either value >0.0 or True;
|
|
any other value is considered false positive.
|
|
k: number of top-scoring proposals to take.
|
|
|
|
Returns:
|
|
median_rank: median rank of all true positive proposals among top k by
|
|
score.
|
|
"""
|
|
ranks = []
|
|
for i in range(len(tp_fp_list)):
|
|
ranks.append(
|
|
np.where(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])] > 0)[0])
|
|
concatenated_ranks = np.concatenate(ranks)
|
|
return np.median(concatenated_ranks)
|
|
|
|
|
|
def compute_recall_at_k(tp_fp_list, num_gt, k):
|
|
"""Computes Recall@k, MedianRank@k, where k is the top-scoring labels.
|
|
|
|
Args:
|
|
tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
|
|
detection on a single image, where the detections are sorted by score in
|
|
descending order. Further, each numpy array element can have boolean or
|
|
float values. True positive elements have either value >0.0 or True;
|
|
any other value is considered false positive.
|
|
num_gt: number of groundtruth anotations.
|
|
k: number of top-scoring proposals to take.
|
|
|
|
Returns:
|
|
recall: recall evaluated on the top k by score detections.
|
|
"""
|
|
|
|
tp_fp_eval = []
|
|
for i in range(len(tp_fp_list)):
|
|
tp_fp_eval.append(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])])
|
|
|
|
tp_fp_eval = np.concatenate(tp_fp_eval)
|
|
|
|
return np.sum(tp_fp_eval) / num_gt
|