class_apy.py

def calc_class_ap(lbls, preds, max_dist=30):
    metrics = {}
    for c in classes:
        metrics[c] = [],[],[]
    


    for img_id,pred_row in preds.items():
        lbl_row = lbls[img_id]
        
        for cls in classes:
            tps, fps, n_gts = metrics[cls]
            
            pred_pts = pred_row[cls] if cls in pred_row else []
            lbl_pts = lbl_row[cls] if cls in lbl_row else []
            
            detected = []
            dists = []
            for pred_pt in pred_pts:

                calc = [(euclidean_dist(lb, pred_pt), lb) for lb in lbl_pts if lb not in detected]
                if len(calc) == 0:
                    dists.append(1e10)
                else:
                    dist, lb = min(calc)
                    detected.append(lb)

                    dists.append(dist)
            tp = (np.array(dists) < max_dist)
            fp = ~tp
            
            n_gts.append(len(lbl_pts))
            tps.extend(tp.astype(np.uint8).tolist())
            fps.extend(fp.astype(np.uint8).tolist())
            
    # Calculate average precision per class
    aps = []
    for idx,c in enumerate(classes):
        tps, fps, n_gts = metrics[c]
        tp = np.array(tps).cumsum(0) if tps else np.array([0])
        fp = np.array(fps).cumsum(0) if fps else np.array([0])
        precision = tp / (tp + fp + 1e-8)
        recall = tp / sum(n_gts)
        
        aps.append({ 
            'class': c, 
            'ap':round(compute_ap(precision, recall), 4), 
            'tp': int(tp[-1]), 
            'fp': int(fp[-1]), 
            'gt': sum(n_gts)
        })
    return aps