def eval_det()

in wypr/utils/eval_det.py [0:0]

• 26 lines of code
• 12 McCabe index (conditional complexity)

def eval_det(pred_all, gt_all, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou):
    """ Generic functions to compute precision/recall for object detection
        for multiple classes.
        Input:
            pred_all: map of {img_id: [(classname, bbox, score)]}
            gt_all: map of {img_id: [(classname, bbox)]}
            ovthresh: scalar, iou threshold
            use_07_metric: bool, if true use VOC07 11 point method
        Output:
            rec: {classname: rec}
            prec: {classname: prec_all}
            ap: {classname: scalar}
    """
    pred = {} # map {classname: pred}
    gt = {} # map {classname: gt}
    for img_id in pred_all.keys():
        for classname, bbox, score in pred_all[img_id]:
            if classname not in pred: pred[classname] = {}
            if img_id not in pred[classname]:
                pred[classname][img_id] = []
            if classname not in gt: gt[classname] = {}
            if img_id not in gt[classname]:
                gt[classname][img_id] = []
            pred[classname][img_id].append((bbox,score))
    for img_id in gt_all.keys():
        for classname, bbox in gt_all[img_id]:
            if classname not in gt: gt[classname] = {}
            if img_id not in gt[classname]:
                gt[classname][img_id] = []
            gt[classname][img_id].append(bbox)

    rec = {}
    prec = {}
    ap = {}
    for classname in gt.keys():
        print('Computing AP for class: ', classname)
        rec[classname], prec[classname], ap[classname] = eval_det_cls(pred[classname], gt[classname], ovthresh, use_07_metric, get_iou_func)
        print(classname, ap[classname])
    
    return rec, prec, ap