写出yolov3算法评价指标的代码

以下是使用Python和OpenCV实现的YOLOv3算法评价指标代码：

import cv2
import numpy as np

# 加载类标签文件
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# 加载标注文件
with open('val2017.txt', 'r') as f:
    annotations = [line.strip().split() for line in f.readlines()]

# 读取预测结果文件
results = np.loadtxt('yolo_result.txt', delimiter=',', dtype=np.float32)

# 定义评价指标
tp = 0  # 正确检测到的目标数
fp = 0  # 错误检测到的目标数
fn = 0  # 未检测到的目标数
total_iou = 0  # 所有检测框的IoU之和
num_boxes = 0  # 检测框总数

# 遍历所有标注
for ann in annotations:
    # 读取图像
    img = cv2.imread(ann[0])
    # 获取标注框
    true_box = np.array([float(x) for x in ann[1:]], dtype=np.int32)
    true_class = true_box[-1]
    # 遍历所有预测结果
    for result in results:
        pred_box = result[1:5].astype(np.int32)
        pred_class = int(result[6])
        # 判断是否为同一类别
        if pred_class == true_class:
            # 计算IoU
            iou = calculate_iou(pred_box, true_box)
            # 如果IoU大于阈值，则判定为正确检测
            if iou > 0.5:
                tp += 1
                total_iou += iou
                break
    else:
        # 如果未检测到目标，则判定为未检测到
        fn += 1

# 计算误检率
fp = len(results) - tp
false_positive_rate = fp / len(annotations)

# 计算平均IoU
mean_iou = total_iou / tp

# 输出结果
print('True Positive: ', tp)
print('False Positive: ', fp)
print('False Negative: ', fn)
print('False Positive Rate: ', false_positive_rate)
print('Mean IoU: ', mean_iou)

# 计算IoU的函数
def calculate_iou(box1, box2):
    x1, y1, w1, h1 = box1
    x2, y2, w2, h2 = box2
    area1 = w1 * h1
    area2 = w2 * h2
    x_left = max(x1, x2)
    y_top = max(y1, y2)
    x_right = min(x1 + w1, x2 + w2)
    y_bottom = min(y1 + h1, y2 + h2)
    if x_right <= x_left or y_bottom <= y_top:
        return 0.0
    intersect = (x_right - x_left) * (y_bottom - y_top)
    iou = intersect / (area1 + area2 - intersect)
    return iou

注意，以上代码中使用了一个名为`yolo_result.txt`的文件来存储YOLOv3的预测结果。文件的格式为：

image_path,x1,y1,x2,y2,confidence,class

其中`x1,y1,x2,y2`分别表示检测框的左上角和右下角坐标，`confidence`表示置信度，`class`表示类别标签。