实现yolov5漏检率与虚警次数指标计算并显示

项目场景：

某场景下，输出目标漏检率和虚警次数

本项目包含两类红外目标UAV_S与UAV_L，分别对两类目标求漏检率和虚警次数并显示，最后求平均值后显示（实际上两类目标为对数据集进行分析后进行判断得到，实际只有一类目标UAV。以10×10像素为分界分类，有助于提升网络对红外大目标与小目标特征的学习）

可以看到在这张图像中有两个无人机目标，但二者特征差距巨大。通过数据分析，10×10像素以下的无人机目标没有轮廓信息，10×10以上的无人机目标可以看出旋翼等轮廓信息。

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相关原理

基础概念

(1)P=Positive:

目标检测中的类别m，设其为正样本；

(2)N=negative:

目标检测中的类别background，设其为负样本；

(3)TP=True Positive:

把m正确检测为m框的数量(正确的m框)；

(4)FP=False Positive:

把background错误检测为m框的数量(错误的m框)；

(5)TN=True Negative:

把background正确检测为background框的数量(正确的background框)，识别为背景的框(非目标)一般在算法结束时，统一清除不显示；

(6)FN=False Negative:

把m错误检测为background框的数量(错误的background框)。

四个常用指标

(1)精确率(Precision):TP/(TP+FP)

所有判断为正例的例子中，真正为正例的所占的比例

(2)召回率(Recall):TP/(TP+FN)

所有正例中，被判断为正例的比例

(3)漏检率:FN/(TP+FN)=1-Recall

(4)虚警率:FP/(TP+FP)=1-Precision

(5)虚警次数:FP

---

代码：

yolov5_eval.py

import argparse
import os
import platform
import shutil
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (
    check_img_size, non_max_suppression, apply_classifier, scale_coords,
    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.torch_utils import select_device, load_classifier, time_synchronized
from cfg_mAP import Cfg

os.environ["CUDA_VISIBLE_DEVICES"] = "4"

cfg = Cfg


def detect(save_img=False):
    out, source, weights, view_img, save_txt, imgsz = \
        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')

    # Initialize
    set_logging()
    device = select_device(opt.device)
    if os.path.exists(out):
        shutil.rmtree(out)  # delete output folder
    os.makedirs(out)  # make new output folder
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
        modelc.to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
    test_time=[]
    for path, img, im0s, vid_cap in dataset:

        # Inference
        t1 = time_synchronized()

        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # # Inference
        # t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
            else:
                p, s, im0 = path, '', im0s

            img_name = Path(p).name

            txt = open(opt.eval_imgs_name_txt, 'a')
            txt.write(img_name[:-4])
            txt.write('\n')
            txt.close()

            save_path = str(Path(out) / Path(p).name)
            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += '%g %ss, ' % (n, names[int(c)])  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    txt = open(opt.eval_classtxt_path + '/%s' % names[int(cls)], 'a')
                    obj_conf = conf.cpu().numpy()

                    xyxy = torch.tensor(xyxy).numpy()
                    x1 = xyxy[0]
                    y1 = xyxy[1]
                    x2 = xyxy[2]
                    y2 = xyxy[3]

                    new_box = [img_name[:-4], obj_conf, x1, y1, x2, y2]

                    txt.write(" ".join([str(a) for a in new_box]))
                    txt.write('\n')
                    txt.close()

                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

                    if save_img or view_img:  # Add bbox to image
                        label = '%s %.2f' % (cfg.textnames[int(cls)], conf)
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)

            test_time.append(t2 - t1)

            # Print time (inference + NMS)
            print('%sDone. (%.3fs)' % (s, t2 - t1))


            # Stream results
            if view_img:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'images':
                    cv2.imwrite(save_path, im0)
                else:
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = 'mp4v'  # output video codec
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        print('Results saved to %s' % Path(out))
        if platform.system() == 'Darwin' and not opt.update:  # MacOS
            os.system('open ' + save_path)

    print('Done. (%.3fs)' % (time.time() - t0))
    mean_time=sum(test_time)/len(test_time)
    print('mean time:', mean_time)
    print('frame: ', 1/mean_time)


if __name__ == '__main__':

    dir = 'imgs_name_manual.txt'
    if os.path.exists(dir):
        os.remove(dir)
    else:
        open(dir, 'w')

    predictions_manual='predictions_manual'
    class_txt_manual='class_txt_manual'
    cachedir_manual='cachedir_manual'

    if os.path.exists(predictions_manual):
        shutil.rmtree(predictions_manual)  # delete output folder
    os.makedirs(predictions_manual)  # make new output folder

    if os.path.exists(class_txt_manual):
        shutil.rmtree(class_txt_manual)  # delete output folder
    os.makedirs(class_txt_manual)  # make new output folder

    if os.path.exists(cachedir_manual):
        shutil.rmtree(cachedir_manual)  # delete output folder
    os.makedirs(cachedir_manual)  # make new output folder

    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='runs/exp4_exp/weights/last_exp.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='JPEGImages_manual',
                        help='source')  # file/folder, 0 for webcam
    parser.add_argument('--output', type=str, default='predictions_manual',
                        help='output folder')  # output folder
    parser.add_argument('--eval_imgs_name_txt', type=str, default='imgs_name_manual.txt',
                        help='output folder')  # output folder
    parser.add_argument('--eval_classtxt_path', type=str, default='class_txt_manual',
                        help='output folder')  # output folder
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
    parser.add_argument('--device', default='4', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    opt = parser.parse_args()
    print(opt)

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()

compute_mAP.py

# -*- coding: utf-8 -*-
import os
import numpy as np
from yolov5_eval import yolov5_eval  # 注意将yolov4_eval.py和compute_mAP.py放在同一级目录下
from cfg_mAP import Cfg
import pickle
import shutil

cfg = Cfg
eval_classtxt_path = cfg.eval_classtxt_path  # 各类txt文件路径
eval_classtxt_files = os.listdir(eval_classtxt_path)

classes = cfg.names  # ['combustion_lining', 'fan', 'fan_stator_casing_and_support', 'hp_core_casing', 'hpc_spool', 'hpc_stage_5','mixer', 'nozzle', 'nozzle_cone', 'stand']

aps = []  # 保存各类ap
cls_rec = {}  # 保存recall
cls_prec = {}  # 保存精度
cls_ap = {}
fns = []
FNS = 0

annopath = cfg.eval_Annotations_path + '/{:s}.xml'  # annotations的路径，{:s}.xml方便后面根据图像名字读取对应的xml文件
imagesetfile = cfg.eval_imgs_name_txt  # 读取图像名字列表文件
cachedir = cfg.cachedir

if os.path.exists(cachedir):
    shutil.rmtree(cachedir)  # delete output folder
os.makedirs(cachedir)  # make new output folder

for cls in eval_classtxt_files:  # 读取cls类对应的txt文件
    filename = eval_classtxt_path + cls

    rec, prec, ap, tp, fp, FN = yolov5_eval(  # yolov4_eval.py计算cls类的recall precision ap
        filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.01,
        use_07_metric=False)

    aps += [ap]
    cls_ap[cls] = ap
    cls_rec[cls] = rec[-1]
    cls_prec[cls] = prec[-1]
    fn = 1 - rec[-1]
    fns += [fn]
    FNS += FN
    # print("aaaaa:",FNS,FN)

    # print('AP for {} = {:.4f}'.format(cls, ap))
    # print('recall for {} = {:.4f}'.format(cls, rec[-1]))
    # print('precision for {} = {:.4f}'.format(cls, prec[-1]))
    # print('FN for {} = {:.4f}'.format(cls, fn))

with open(os.path.join(cfg.cachedir, 'cls_ap.pkl'), 'wb') as in_data:
    pickle.dump(cls_ap, in_data, pickle.HIGHEST_PROTOCOL)

with open(os.path.join(cfg.cachedir, 'cls_rec.pkl'), 'wb') as in_data:
    pickle.dump(cls_rec, in_data, pickle.HIGHEST_PROTOCOL)

with open(os.path.join(cfg.cachedir, 'cls_prec.pkl'), 'wb') as in_data:
    pickle.dump(cls_prec, in_data, pickle.HIGHEST_PROTOCOL)

# print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('Mean FP = {:.4f}'.format(np.mean(fns)))
print('All number of FN = {:d}'.format(int(FNS)))

# print('~~~~~~~~')

# print('Results:')
# for ap in aps:
#     print('{:.3f}'.format(ap))
# print('~~~~~~~~')
# print('{:.3f}'.format(np.mean(aps)))
# print('~~~~~~~~')

输出结果：

参考：

https://blog.csdn.net/qq_29007291/article/details/86080456
https://blog.csdn.net/tpz789/article/details/110675268