YOLOV5——计算预测数据的精确率（precision）和召回率（recall）

1、预测数据

　　使用 yolov5 预测输出的是带标记框的图片，所以需要先将预测的数据输出为文本格式，如json。

　　可以参考我之前的博客 https://www.cnblogs.com/yxyun/p/14250455.html

json文件格式如下图

[
    {
        "name":"01_05_0002.jpg",
        "category":"1",
        "bbox":[
            3550,
            1813,
            4106,
            2468
        ],
        "score":0.9482421875
    },
    {
        "name":"01_05_0002.jpg",
        "category":"1",
        "bbox":[
            4041,
            1655,
            4570,
            2291
        ],
        "score":0.9521484375
    }
]

2、测试图的标记数据

　　因为使用的是 yolov5 ，所以测试集的标记数据和训练集一样都是 txt 格式的标注数据

<object-class> <x_center> <y_center> <width> <height>

3、将预测数据和标注数据匹配

　　一个测试图中可能存在好几个目标物，那预测数据和标注数据中就会存在好几个标注框，计算两个框的交集面积，交集面积最大的两个框才是互相匹配的。

计算两个矩形框的交集面积

def label_area_detect(label_bbox_list, detect_bbox_list):
    x_label_min, y_label_min, x_label_max, y_label_max = label_bbox_list
    x_detect_min, y_detect_min, x_detect_max, y_detect_max = detect_bbox_list
    if (x_label_max <= x_detect_min or x_detect_max < x_label_min) or ( y_label_max <= y_detect_min or y_detect_max <= y_label_min):
        return 0
    else:
        lens = min(x_label_max, x_detect_max) - max(x_label_min, x_detect_min)
        wide = min(y_label_max, y_detect_max) - max(y_label_min, y_detect_min)
        return lens * wide

 

4、多分类 precision 和 recall 的计算

 　　将预测数据和标注数据匹配算出一个 N*N 的矩阵

4、代码

import os
import json
import  numpy as np
from PIL import Image

# class name
classes = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12']
# 初始化二维0数组
result_list = np.array(np.zeros([len(classes), len(classes)]))

# 获取图片宽高
def get_image_width_high(full_image_name):
    image = Image.open(full_image_name)
    image_width, image_high = image.size[0], image.size[1]
    return image_width, image_high


# 读取原始标注数据
def read_label_txt(full_label_name, full_image_name):
    fp = open(full_label_name, mode="r")
    lines = fp.readlines()
    image_width, image_high = get_image_width_high(full_image_name)
    object_list = []
    for line in lines:
        array = line.split()
        x_label_min = (float(array[1]) - float(array[3]) / 2) * image_width
        x_label_max = (float(array[1]) + float(array[3]) / 2) * image_width
        y_label_min = (float(array[2]) - float(array[4]) / 2) * image_high
        y_label_max = (float(array[2]) + float(array[4]) / 2) * image_high
        bbox = [round(x_label_min, 2), round(y_label_min, 2), round(x_label_max, 2), round(y_label_max, 2)]
        category = int(array[0])
        obj_info = {
            'category' : category,
            'bbox' : bbox
        }
        object_list.append(obj_info)
    return object_list


# 计算交集面积
def label_area_detect(label_bbox_list, detect_bbox_list):
    x_label_min, y_label_min, x_label_max, y_label_max = label_bbox_list
    x_detect_min, y_detect_min, x_detect_max, y_detect_max = detect_bbox_list
    if (x_label_max <= x_detect_min or x_detect_max < x_label_min) or ( y_label_max <= y_detect_min or y_detect_max <= y_label_min):
        return 0
    else:
        lens = min(x_label_max, x_detect_max) - max(x_label_min, x_detect_min)
        wide = min(y_label_max, y_detect_max) - max(y_label_min, y_detect_min)
        return lens * wide

# label 匹配 detect
def label_match_detect(image_name, label_list, detect_list):
    for label in label_list:
        area_max = 0
        area_category = 0
        label_category = label['category']
        label_bbox = label['bbox']
        for detect in detect_list:
            if detect['name'] == image_name:
                detect_bbox = detect['bbox']
                area = label_area_detect(label_bbox, detect_bbox)
                if area > area_max:
                    area_max = area
                    area_category = detect['category']

        result_list[int(label_category)][classes.index(str(area_category))] += 1


def main():
    image_path = '../image_data/seed/test/images/'  # 图片文件路径
    label_path = '../image_data/seed/test/labels/'  # 标注文件路径
    detect_path = 'result.json'    # 预测的数据
    precision = 0     # 精确率
    recall = 0        # 召回率
    # 读取 预测 文件数据
    with open(detect_path, 'r') as load_f:
        detect_list = json.load(load_f)
    # 读取图片文件数据
    all_image = os.listdir(image_path)
    for i in range(len(all_image)):
        full_image_path = os.path.join(image_path, all_image[i])
        # 分离文件名和文件后缀
        image_name, image_extension = os.path.splitext(all_image[i])
        # 拼接标注路径
        full_label_path = os.path.join(label_path, image_name + '.txt')
        # 读取标注数据
        label_list = read_label_txt(full_label_path, full_image_path)
        # 标注数据匹配detect
        label_match_detect(all_image[i], label_list, detect_list)
    # print(result_list)
    for i in range(len(classes)):
        row_sum, col_sum = sum(result_list[i]), sum(result_list[r][i] for r in range(len(classes)))
        precision += result_list[i][i] / float(col_sum)
        recall += result_list[i][i] / float(row_sum)
    print(f'precision: {precision / len(classes) * 100}%  recall: {recall / len(classes) * 100}%')


if __name__ == '__main__':
    main()

　　输出：