目标检测性能指标代码

目标检测的性能指标包括精度和速度两个方面。常用的精度指标有mAP（mean average precision）、PR曲线、ROC曲线等；常用的速度指标有FPS（frames per second，每秒处理帧数）、推理时间等。以下是使用Python和OpenCV计算mAP的示例代码：

import numpy as np

def calculate_iou(box1, box2):
    x1, y1, w1, h1 = box1
    x2, y2, w2, h2 = box2
    xi1 = max(x1, x2)
    yi1 = max(y1, y2)
    xi2 = min(x1 + w1, x2 + w2)
    yi2 = min(y1 + h1, y2 + h2)
    iw = max(xi2 - xi1, 0)
    ih = max(yi2 - yi1, 0)
    ua = (w1 * h1 + w2 * h2 - iw * ih)
    iou = iw * ih / ua
    return iou

def calculate_ap(gt_boxes, pred_boxes, iou_threshold=0.5):
    tp = [0] * len(pred_boxes)
    fp = [0] * len(pred_boxes)
    gt_detected = [False] * len(gt_boxes)
    pred_scores = [box[0] for box in pred_boxes]
    order = np.argsort(pred_scores)[::-1]
    for i in order:
        _, pred_box, pred_class = pred_boxes[i]
        max_iou = -float('inf')
        max_idx = -1
        for j, gt_box in enumerate(gt_boxes):
            if pred_class != gt_box[0]:
                continue
            iou = calculate_iou(pred_box, gt_box[1:])
            if iou > max_iou:
                max_iou = iou
                max_idx = j
        if max_iou >= iou_threshold:
            if not gt_detected[max_idx]:
                tp[i] = 1
                gt_detected[max_idx] = True
            else:
                fp[i] = 1
        else:
            fp[i] = 1
    tp_acc = np.cumsum(tp)
    fp_acc = np.cumsum(fp)
    recall = tp_acc / len(gt_boxes)
    precision = tp_acc / (tp_acc + fp_acc)
    ap = 0
    for t in np.arange(0, 1.1, 0.1):
        if np.sum(recall >= t) == 0:
            p = 0
        else:
            p = np.max(precision[recall >= t])
        ap += p / 11
    return ap

def calculate_map(gt_boxes_list, pred_boxes_list, iou_threshold=0.5):
    aps = []
    for c in range(len(gt_boxes_list)):
        gt_boxes = gt_boxes_list[c]
        pred_boxes = pred_boxes_list[c]
        ap = calculate_ap(gt_boxes, pred_boxes, iou_threshold)
        aps.append(ap)
    map = sum(aps) / len(aps)
    return map