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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 numpy as np
import copy
from collections import defaultdict
import sys
def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, N=100):
"""Evaluation with cuhk03 metric
Key: one image for each gallery identity is randomly sampled for each query identity.
Random sampling is performed N times (default: N=100).
"""
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
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
orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
if not np.any(orig_cmc):
# this condition is true when query identity does not appear in gallery
continue
kept_g_pids = g_pids[order][keep]
g_pids_dict = defaultdict(list)
for idx, pid in enumerate(kept_g_pids):
g_pids_dict[pid].append(idx)
cmc, AP = 0., 0.
for repeat_idx in range(N):
mask = np.zeros(len(orig_cmc), dtype=np.bool)
for _, idxs in g_pids_dict.items():
# randomly sample one image for each gallery person
rnd_idx = np.random.choice(idxs)
mask[rnd_idx] = True
masked_orig_cmc = orig_cmc[mask]
_cmc = masked_orig_cmc.cumsum()
_cmc[_cmc > 1] = 1
cmc += _cmc[:max_rank].astype(np.float32)
# compute AP
num_rel = masked_orig_cmc.sum()
tmp_cmc = masked_orig_cmc.cumsum()
tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * masked_orig_cmc
AP += tmp_cmc.sum() / num_rel
cmc /= N
AP /= N
all_cmc.append(cmc)
all_AP.append(AP)
num_valid_q += 1.
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
def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
"""Evaluation with market1501 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)
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
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
orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
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
mAP = np.mean(all_AP)
return all_cmc, mAP
def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False):
if use_metric_cuhk03:
return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)