CMC

rank-1,rank-5,mAP

第一种，网上的标准计算公式

https://blog.csdn.net/u013698770/article/details/60776102
https://blog.csdn.net/zkp_987/article/details/79969512
https://blog.csdn.net/kaixinjiuxing666/article/details/81272796

import numpy as np

indexxx = np.array([[8,9,4,7,5,6,3,2,1,0],[9,6,7,2,1,4,3,5,8,0],[7,9,5,3,1,2,4,6,8,0]])
good_index = np.array([1,3,5,7,9])
CMC = np.array([0,0,0,0,0,0,0,0,0,0])
mAP = 0.0

for i in indexxx:
    cmc = np.array([0,0,0,0,0,0,0,0,0,0])
    index = i
    ngood = len(good_index)
    mask = np.in1d(index, good_index)
    rows_good = np.argwhere(mask==True)
    rows_good = rows_good.flatten()
    cmc[rows_good[0]:] = 1
    print('cmc:',cmc)
    CMC += cmc

    ap = 0.0
    for i in range(ngood):
        d_recall = 1.0/ngood
        precision = (i+1)*1.0/(rows_good[i]+1)
        ap = ap + d_recall*precision
    print('ap:{:.2f}%:'.format(100*ap))
    mAP += ap

CMC = CMC/3
mAP = mAP/3

print('top1:{:.2f}%  top5:{:.2f}%  mAP:{:.2f}%'.format(100*CMC[0],100*CMC[4],100*mAP))

cmc: [0 1 1 1 1 1 1 1 1 1]
ap:54.54%
cmc: [1 1 1 1 1 1 1 1 1 1]
ap:69.26%
cmc: [1 1 1 1 1 1 1 1 1 1]
ap:100.00%
top1:66.67%  top5:100.00%  mAP:74.60%

第二种：baseline的计算公式

def compute_mAP(index, good_index, junk_index):
    ap = 0
    cmc = torch.IntTensor(len(index)).zero_()
    if good_index.size==0:   # if empty
        cmc[0] = -1
        return ap,cmc

    # remove junk_index
    mask = np.in1d(index, junk_index, invert=True)
    index = index[mask]

    # find good_index index
    ngood = len(good_index)
    mask = np.in1d(index, good_index)
    rows_good = np.argwhere(mask==True)
    rows_good = rows_good.flatten()
    
    cmc[rows_good[0]:] = 1
    for i in range(ngood):
        d_recall = 1.0/ngood
        precision = (i+1)*1.0/(rows_good[i]+1)
        if rows_good[i]!=0:
            old_precision = i*1.0/rows_good[i]
        else:
            old_precision=1.0
        ap = ap + d_recall*(old_precision + precision)/2

    return ap, cmc

CMC = torch.IntTensor(len(gallery_label)).zero_()
ap = 0.0
#print(query_label)
for i in range(len(query_label)):
    ap_tmp, CMC_tmp = evaluate(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
    if CMC_tmp[0]==-1:
        continue
    CMC = CMC + CMC_tmp
    ap += ap_tmp
    print(i, CMC_tmp[0])

CMC = CMC.float()
CMC = CMC/len(query_label) #average CMC
print('top1:%f top5:%f top10:%f mAP:%f'%(CMC[0],CMC[4],CMC[9],ap/len(query_label)))

第二种的简化版本，只计算CMC

def evaluate(qf,ql,qc,gf,gl,gc):
    """
    qf: list [1,2,3]
    ql: 1
    qc: 1
    gf: list [[1,2,3],[1,2,3]]
    gl: [1,2,3]
    gc: [1,2,3]
    len(gf)==len(gl)==len(gc)
    """
    query = qf
    score = np.dot(gf,query)
    # predict index
    index = np.argsort(score)  #from small to large # 表示位置，[4,3,1,0,2]
    index = index[::-1] # 表示
    #index = index[0:2000]
    # good index
    query_index = np.argwhere(gl==ql) # list [[1],[2]] # 表示位置，即galley中的第几个样本是相同的id
    camera_index = np.argwhere(gc==qc) # list [[1],[2]] # 表示位置

    good_index = np.setdiff1d(query_index, camera_index, assume_unique=True) # [2,3]表示同一个id不同摄像头的图片的位置
    junk_index1 = np.argwhere(gl==-1) #  [[1],[2]] 表示id为-1的图片的位置
    junk_index2 = np.intersect1d(query_index, camera_index) #  [1,2] 表示同一个id同一个摄像头的图片的位置
    junk_index = np.append(junk_index2, junk_index1) #.flatten())
    
    CMC_tmp = compute_mAP(index, good_index, junk_index)
    return CMC_tmp

def compute_cmc(index, good_index, junk_index):
    """
    index: list [4,3,1,0,2]，已经排序，数字表示第几张图片
    good_index: [3,1] list 位置 数字表示第几张图片
    junk_index: [4,2]list 位置 数字表示第几张图片
    """
    cmc = torch.IntTensor(len(index)).zero_()
    if good_index.size==0:   # if empty
        cmc[0] = -1
        return ap,cmc

    # remove junk_index
    mask = np.in1d(index, junk_index, invert=True)
    index = index[mask]  # [3,1,0]

    # find good_index index
    ngood = len(good_index)
    mask = np.in1d(index, good_index) # [t,t,f]
    rows_good = np.argwhere(mask==True) # 
    rows_good = rows_good.flatten() # [0,1]
    
    cmc[rows_good[0]:] = 1

    return cmc
def main():
    result = scipy.io.loadmat('pytorch_result.mat')
    query_feature = result['query_f']  # list [[1,2,3],[1,2,3],[1,2,3]]
    query_cam = result['query_cam'][0] # list [1,2,3]
    query_label = result['query_label'][0] # list [1,2,3]
    gallery_feature = result['gallery_f']  # list [[1,2,3],[1,2,3],[1,2,3]]
    gallery_cam = result['gallery_cam'][0] # list [1,2,3]
    gallery_label = result['gallery_label'][0] # list [1,2,3]


    CMC = torch.IntTensor(len(gallery_label)).zero_()
    for i in range(len(query_label)):
        CMC_tmp = evaluate(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
        if CMC_tmp[0]==-1:
            continue
        CMC = CMC + CMC_tmp
        print(i, CMC_tmp[0])
    CMC = CMC.float()
    CMC = CMC/len(query_label) #average CMC
    print('top1:%f top5:%f top10:%f'%(CMC[0],CMC[4],CMC[9])

def get_id(img_path):
    camera_id = []
    labels = []
    for path, v in img_path:
        filename = path.split('/')[-1]
        label = filename[0:4]
        camera = filename.split('c')[1]
        if label[0:2]=='-1':
            labels.append(-1)
        else:
            labels.append(int(label))
        camera_id.append(int(camera[0]))
    return camera_id, labels

gallery_cam,gallery_label = get_id(gallery_path)
query_cam,query_label = get_id(query_path)