def compute_ap()

in utils_cv/detection/references/pycocotools_cocoeval.py [0:0]

• 29 lines of code
• 13 McCabe index (conditional complexity)

def compute_ap(v, ap=1, iouThr=None, areaRng='all', maxDets=100, verbose = False):
    p = Params()
    aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
    mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
    if ap == 1:
        # dimension of precision: [TxRxKxAxM]
        s = v.eval['precision']
        # IoU
        if iouThr is not None:
            t = np.where(iouThr == p.iouThrs)[0]
            s = s[t]
        s = s[:,:,:,aind,mind]
    else:
        # dimension of recall: [TxKxAxM]
        s = self.eval['recall']
        if iouThr is not None:
            t = np.where(iouThr == p.iouThrs)[0]
            s = s[t]
        s = s[:,:,aind,mind]
    if len(s[s>-1])==0:
        mean_s = -1
    else:
        mean_s = np.mean(s[s>-1])

    if verbose:
        iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'
        titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
        typeStr = '(AP)' if ap==1 else '(AR)'
        iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
            if iouThr is None else '{:0.2f}'.format(iouThr)
        resStr = iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)
        print(resStr)
    
    return mean_s