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eval_metrics.py
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eval_metrics.py
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from __future__ import print_function, absolute_import
import os
import glob
import re
import sys
import os.path as osp
import numpy as np
import random
from time import time
"""Cross-Modality ReID"""
def eval_sysu(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=20):
"""Evaluation with sysu metric
Key: for each query identity, its gallery images from the same camera view are discarded.
"""
num_q, num_g = distmat.shape
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
indices = np.argsort(distmat, axis=1)
pred_label = g_pids[indices]
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
# compute cmc curve for each query
new_all_cmc = []
all_cmc = []
all_AP = []
num_valid_q = 0. # number of valid query
for q_idx in range(num_q):
# get query pid and camid
q_pid = q_pids[q_idx]
q_camid = q_camids[q_idx]
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (q_camid == 3) & (g_camids[order] == 2)
keep = np.invert(remove)
# compute cmc curve
# the cmc calculation is different from standard protocol
# we follow the protocol of the author's released code
new_cmc = pred_label[q_idx][keep]
new_index = np.unique(new_cmc, return_index=True)[1]
new_cmc = [new_cmc[index] for index in sorted(new_index)]
new_match = (new_cmc == q_pid).astype(np.int32)
new_cmc = new_match.cumsum()
new_all_cmc.append(new_cmc[:max_rank])
# binary vector, positions with value 1 are correct matches
orig_cmc = matches[q_idx][keep]
if not np.any(orig_cmc):
# this condition is true when query identity does not appear in gallery
continue
cmc = orig_cmc.cumsum()
cmc[cmc > 1] = 1
all_cmc.append(cmc[:max_rank])
num_valid_q += 1.
# compute average precision
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
num_rel = orig_cmc.sum()
tmp_cmc = orig_cmc.cumsum()
tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
AP = tmp_cmc.sum() / num_rel
all_AP.append(AP)
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
all_cmc = np.asarray(all_cmc).astype(np.float32)
all_cmc = all_cmc.sum(0) / num_valid_q
new_all_cmc = np.asarray(new_all_cmc).astype(np.float32)
new_all_cmc = new_all_cmc.sum(0) / num_valid_q
mAP = np.mean(all_AP)
return new_all_cmc, mAP
def eval_regdb(distmat, q_pids, g_pids, max_rank=20):
num_q, num_g = distmat.shape
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
indices = np.argsort(distmat, axis=1)
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
# compute cmc curve for each query
all_cmc = []
all_AP = []
num_valid_q = 0. # number of valid query
# only two cameras
q_camids = np.ones(num_q).astype(np.int32)
g_camids = 2 * np.ones(num_g).astype(np.int32)
for q_idx in range(num_q):
# get query pid and camid
q_pid = q_pids[q_idx]
q_camid = q_camids[q_idx]
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
keep = np.invert(remove)
# compute cmc curve
# binary vector, positions with value 1 are correct matches
raw_cmc = matches[q_idx][keep]
if not np.any(raw_cmc):
# this condition is true when query identity does not appear in gallery
continue
cmc = raw_cmc.cumsum()
cmc[cmc > 1] = 1
all_cmc.append(cmc[:max_rank])
num_valid_q += 1.
# compute average precision
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
num_rel = raw_cmc.sum()
tmp_cmc = raw_cmc.cumsum()
tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
AP = tmp_cmc.sum() / num_rel
all_AP.append(AP)
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
all_cmc = np.asarray(all_cmc).astype(np.float32)
all_cmc = all_cmc.sum(0) / num_valid_q
mAP = np.mean(all_AP)
return all_cmc, mAP