how to use your cmc.py about dgd_person_reid

[Cysu]

Suppose you have 3 gallery samples with ID 0,1,1 and 4 probe samples with ID 0,0,1,1, then you need to

• Compute 3x4 pairwise distance matrix D, where D[i, j] = distance between gallery-i and probe-j
• Create a gallery ID list gids = [0,1,1]
• Create a probe ID list pids = [0,0,1,1]

Then you can call cmc(D, gids, pids) to get CMC.

[Phoebe-star]

sorry I don't know
if I have the cuhk03 person id ,like : 000_1 ,000_2 , 001_1 , 001_2 , 002_1 , 002_2
000_1 ,000_2 is the same person ; 001_1 , 001_2 is the same person ; 002_1 , 002_2 is the same person

A network can compute the person is the same or not , output is true or false
how to know the CMC?

and the CMC is relationship with rank-1 , rank-2 ...... ?
thanks you

[Cysu]

@Phoebe-star Our network does not compute whether two persons are the same or not. It receives only one image as input, and outputs the feature vector. After getting the features of all the gallery and probe images, we can compute pairwise Euclidean distances. Based on which we can compute CMC.

A brief introduction of CMC can be found here.

[Phoebe-star]

where can I find rank-m?
thanks

[Cysu]

@Phoebe-star CMC top-1 is rank-1. They are the same, literally.

[Phoebe-star]

################################
import numpy as np

def _cmc_core(D, G, P):
m, n = D.shape
order = np.argsort(D, axis=0)
match = (G[order] == P)
return (match.sum(axis=1) * 1.0 / n).cumsum()

def cmc(distmat, glabels=None, plabels=None, ds=None, repeat=None):
"""Compute the Cumulative Match Characteristic (CMC)
This function assumes that gallery labels have no duplication. If there are
duplications, random downsampling will be performed on gallery labels, and
the computation will be repeated to get an average result.
Parameters
----------
distmat : numpy.ndarray
The distance matrix. distmat[i, j] is the distance between i-th
gallery sample and j-th probe sample.
glabels : numpy.ndarray or None, optional
plabels : numpy.ndarray or None, optional
If None, then gallery and probe labels are assumed to have no
duplications. Otherwise, they represent the vector of gallery and probe
labels. Default is None.
ds : int or None, optional
If None, then no downsampling on gallery labels will be performed.
Otherwise, it represents the number of gallery labels to be randomly
selected. Default is None.
repeat : int or None, optional
If None, then the function will repeat the computation for 100 times
when downsampling is performed. Otherwise, it specifies the number of
repetition. Default is None.
Returns
-------
out : numpy.ndarray
The rank-1 to rank-m accuracy, where m is the number of (downsampled)
gallery labels.
"""
m, n = distmat.shape
if glabels is None and plabels is None:
glabels = np.arange(0, m)
plabels = np.arange(0, n)
if isinstance(glabels, list):
glabels = np.asarray(glabels)
if isinstance(plabels, list):
plabels = np.asarray(plabels)
ug = np.unique(glabels)
if ds is None:
ds = ug.size
if repeat is None:
if ds == ug.size and ug.size == len(glabels):
repeat = 1
else:
repeat = 100

ret = 0
for __ in xrange(repeat):
    # Randomly select gallery labels
    G = np.random.choice(ug, ds, replace=False)
    # Select corresponding probe samples
    p_inds = [i for i in xrange(len(plabels)) if plabels[i] in G]
    P = plabels[p_inds]
    # Randomly select one gallery sample per label selected
    D = np.zeros((ds, P.size))
    for i, g in enumerate(G):
        samples = np.where(glabels == g)[0]
        j = np.random.choice(samples)
        D[i, :] = distmat[j, p_inds]
    # Compute CMC
    ret += _cmc_core(D, G, P)
return ret / repeat

gids = [0,1,1]
pids = [0,0,1,1]
D =np.matrix("0.8 0.9 0.3 0.5;0.3 0.5 0.6 0.1;0.9 0.1 0.1 0.1")
print ( cmc(D, gids, pids) )
###################################

I run the cmc.py code
and the result is [ 0.52 1. ] [ from the code print ( cmc(D, gids, pids) ) ]
what dose it mean?
thanks thanks

[Cysu]

It means the top-1 accuracy is 52% and the top-2 accuracy is already 100%.