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@RayHY RayHY/evaluate.py
Last active Feb 26, 2019

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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
import argparse
import scipy.io
def get_right_and_junk_index(query_label, gallery_labels, query_camera_label=None, gallery_camera_labels=None):
same_label_index = np.argwhere(gallery_labels == query_label)
if (query_camera_label is not None) and (gallery_camera_labels is not None):
same_camera_label_index = np.argwhere(gallery_camera_labels == query_camera_label)
# the index of mis-detected images, which contain the body parts.
junk_index1 = np.argwhere(gallery_labels == -1)
# find index that are both in query_index and camera_index
# the index of the images, which are of the same identity in the same cameras.
junk_index2 = np.intersect1d(same_label_index, same_camera_label_index)
junk_index = np.append(junk_index2, junk_index1)
# find index that in query_index but not in camera_index
# which means the same lable but different camera
right_index = np.setdiff1d(same_label_index, same_camera_label_index, assume_unique=True)
return right_index, junk_index
else:
return same_label_index, None
def evaluate_with_index(sorted_similarity_index, right_result_index, junk_result_index=None):
"""calculate cmc curve and Average Precision for a single query with index
:param sorted_similarity_index: index of all returned items. typically get with
function `np.argsort(similarity)`
:param right_result_index: index of right items. such as items in gallery
that have the same id but different camera with query
:param junk_result_index: index of junk items. such as items in gallery
that have the same camera and id with query
:return: single cmc, Average Precision
"""
# initial a numpy array to store the AccK(like [0, 0, 0, 1, 1, ...,1]).
cmc = np.zeros(len(sorted_similarity_index), dtype=np.int32)
ap = 0.0
if len(right_result_index) == 0:
cmc[0] = -1
return cmc, ap
if junk_result_index is not None:
# remove junk_index
# all junk_result_index in sorted_similarity_index has been removed.
# for example:
# (sorted_similarity_index, junk_result_index)
# ([3, 2, 0, 1, 4], [0, 1]) -> [3, 2, 4]
need_remove_mask = np.in1d(sorted_similarity_index, junk_result_index, invert=True)
sorted_similarity_index = sorted_similarity_index[need_remove_mask]
mask = np.in1d(sorted_similarity_index, right_result_index)
right_index_location = np.argwhere(mask == True).flatten()
# [0,0,0,...0, 1,1,1,...,1]
# |
# right answer first appearance
cmc[right_index_location[0]:] = 1
for i in range(len(right_result_index)):
precision = float(i + 1) / (right_index_location[i] + 1)
if right_index_location[i] != 0:
# last rank precision, not last match precision
old_precision = float(i) / (right_index_location[i])
else:
old_precision = 1.0
ap = ap + (1.0 / len(right_result_index)) * (old_precision + precision) / 2
return cmc, ap
def calculate_similarity_distance(query_feature, gallery_features):
"""calculate the distance between query and gallery
:param gallery_features: the feature's list for gallery
:param query_feature: the feature for query
:return: similarity_distance, size = N*1
"""
if isinstance(query_feature, np.ndarray):
return np.dot(gallery_features, query_feature)
else:
return torch.mm(gallery_features, query_feature.view(-1, 1)).squeeze(1).cpu().numpy()
def evaluate(query_features, query_labels, query_camera_labels,
gallery_features, gallery_labels, gallery_camera_labels):
total_cmc = np.zeros(len(gallery_labels), dtype=np.int32)
total_average_precision = 0.0
for i in range(len(query_labels)):
similarity_distance = calculate_similarity_distance(query_features[i], gallery_features)
cmc, ap = evaluate_with_index(
np.argsort(similarity_distance)[::-1],
*get_right_and_junk_index(query_labels[i], gallery_labels, query_camera_labels[i], gallery_camera_labels)
)
if cmc[0] == -1:
continue
total_cmc += cmc
total_average_precision += ap
return total_cmc.astype(np.float64) / len(query_labels), total_average_precision / len(query_labels)
def main(opt):
result = scipy.io.loadmat('pytorch_result.mat')
if opt.GPU:
CMC, mAP = evaluate(torch.FloatTensor(result['query_f']).cuda(), result['query_label'][0], result['query_cam'][0],
torch.FloatTensor(result['gallery_f']).cuda(), result['gallery_label'][0],
result['gallery_cam'][0])
else:
CMC, mAP = evaluate(result['query_f'], result['query_label'][0], result['query_cam'][0],
result['gallery_f'], result['gallery_label'][0], result['gallery_cam'][0])
print('Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f' % (CMC[0], CMC[4], CMC[9], mAP))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Evaluate')
parser.add_argument('-g', '--GPU', action='store_true', help='use GPU')
main(parser.parse_args())
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