绘制PR曲线[通俗易懂]

一、获取txt文件

运行darknet官方代码中的darknet detector valid data cfg weight指令（例如:
darknet.exe detector valid data/koujian/koujian.data cfg/yolov3-tiny11.cfg backup/yolov3-tiny11_last.weights），可以在result/目录下得到网络检测的输出txt文件：包括检测的图像名字、类别、概率、边界框位置（左上角和右下角）:

二.新建两个文件：

reval_voc_py3.py

#!/usr/bin/env python



import os, sys, argparse

import numpy as np

import _pickle as cPickle

from voc_eval_py3 import voc_eval

import matplotlib.pyplot as plt



def do_python_eval(label_path, valid_file, classes, output_dir = 'results'):

    cachedir = os.path.join('./', 'annotations_cache')

    aps = []

    use_07_metric = False

    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))

    

    if not os.path.isdir(output_dir):

        os.mkdir(output_dir)

    for i, cls in enumerate(classes):

        if cls == '__background__':

            continue

        rec, prec, ap = voc_eval(

            label_path,

            valid_file,  cls, cachedir, ovthresh=0.5,

            use_07_metric=use_07_metric)

        aps += [ap]

        print('AP for {} = {:.4f}'.format(cls, ap))

        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:

            cPickle.dump({ 

   'rec': rec, 'prec': prec, 'ap': ap}, f)

    print('Mean AP = {:.4f}'.format(np.mean(aps)))

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

    print('Results:')

    for ap in aps:

        print('{:.3f}'.format(ap))

    print('{:.3f}'.format(np.mean(aps)))

  



    fr = open(cls + '_pr.pkl','rb')

    inf = cPickle.load(fr)

    fr.close()



    x=inf['rec']

    y=inf['prec']

    fig = plt.figure(1, dpi=160)

    ax = fig.add_subplot(1,1,1)

    ax.plot(x, y, label='PR')

    #ax.plot(result['Avg Recall'].values, label='Avg Recall')

    #plt.grid()

    ax.legend(loc='upper right')

    ax.set_ylim([0.6, 1.04])

    ax.set_xlim([0.0, 1.05])

    ax.set_title('PR curves')

    ax.set_xlabel('recall')

    ax.set_ylabel('precision')

    ax.spines['top'].set_visible(False)     #去掉上边框

    ax.spines['right'].set_visible(False)   #去掉右边框

    fig.savefig('PR')

    plt.savefig("PR.svg", format="svg")

''' x=inf['rec'] y=inf['prec'] plt.figure() plt.xlabel('recall') plt.ylabel('precision') plt.title('PR cruve') plt.ylim([0.6, 1.05]) plt.plot(x,y, label='PR') plt.legend(loc= 'upper right') plt.show() print('AP：',inf['ap']) '''

if __name__ == '__main__':

    label_path = r'D:\darknet\darknet-master\build\darknet\x64\data\koujian\valid'  		#label文件夹（验证集所在位置）

    valid_file = r'comp4_det_test_koujian.txt'						#valid命令生成的txt文件，在result/目录下。 

    name_path = r'D:\darknet\darknet-master\build\darknet\x64\data\koujian\koujian.names' 	#name文件

    output_dir = os.path.abspath('./')			 		#pkl保存路径

    with open(name_path, 'r') as f:	     

        lines = f.readlines()

    classes = [t.strip('\n') for t in lines]

    print('Evaluating detections')

    do_python_eval(label_path, valid_file, classes, output_dir)

其中需要修改：
(1). label_path # label文件夹，标注txt和图像应在同一目录下
(2). valid_file # valid命令生成的txt文件，在result/目录下。
(3). name_path # name文件
(4). output_dir # 生成的pkl保存路径

2 voc_eval_py3.py

import xml.etree.ElementTree as ET

import os

import _pickle as cPickle

import numpy as np

import cv2



def parse_rec(label_path, label_name):

    objects = []

    label_file = os.path.join(label_path, label_name + '.txt')

    img_file = os.path.join(label_path, label_name + '.jpg')

    height, width, _ = cv2.imread(img_file).shape

    with open(label_file) as f:

        for line in f.readlines():

            obj_struct = { 

   }

            obj_struct['name'] = 'koujian'	#需要修改成自己的names

            obj_struct['difficult'] = int(0)

            center_x, center_y, width_b, height_b =[float(x) for x in line.split()[1:]]

            obj_struct['bbox'] = [int(center_x * width - width * width_b / 2.0),

                                  int(center_y * height - height * height_b / 2.0),

                                  int(center_x * width + width * width_b / 2.0),

                                  int(center_y * height + height * height_b / 2.0)]

            objects.append(obj_struct)

    return objects



def voc_ap(rec, prec, use_07_metric=False):

    if use_07_metric:

        # 11 point metric

        ap = 0.

        for t in np.arange(0., 1.1, 0.1):

            if np.sum(rec >= t) == 0:

                p = 0

            else:

                p = np.max(prec[rec >= t])

            ap = ap + p / 11.

    else:

        # correct AP calculation

        # first append sentinel values at the end

        mrec = np.concatenate(([0.], rec, [1.]))

        mpre = np.concatenate(([0.], prec, [0.]))



        # compute the precision envelope

        for i in range(mpre.size - 1, 0, -1):

            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])



        # to calculate area under PR curve, look for points

        # where X axis (recall) changes value

        i = np.where(mrec[1:] != mrec[:-1])[0]



        # and sum (\Delta recall) * prec

        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])

    return ap



def voc_eval(label_path,

             detpath,

             classname,

             cachedir,

             ovthresh=0.5,

             use_07_metric=False):

    # first load gt

    if not os.path.isdir(cachedir):

        os.mkdir(cachedir)

    cachefile = os.path.join(cachedir, 'annots.pkl')



    label_file = []

    for f in os.listdir(label_path):

        file, tmp = f.split('.')

        if  tmp == 'txt':

            label_file.append(file)

    

    if not os.path.isfile(cachefile):

        # load annots

        recs = { 

   }

        for label_name in label_file:

            recs[label_name] = parse_rec(label_path, label_name)

        with open(cachefile, 'wb') as f:

            cPickle.dump(recs, f)

    else:

        # load

        print('!!! cachefile = ',cachefile)

        with open(cachefile, 'rb') as f:

            recs = cPickle.load(f)



    # extract gt objects for this class

    class_recs = { 

   }

    npos = 0   #修改

    for label_name in label_file:

    # for imagename in imagenames:

        R = [obj for obj in recs[label_name] if obj['name'] == classname]

        bbox = np.array([x['bbox'] for x in R])

        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)

        det = [False] * len(R)

        npos = npos + sum(~difficult)

        class_recs[label_name] = { 

   'bbox': bbox,

                                 'difficult': difficult,

                                 'det': det}



    # read dets

    detfile = detpath

    # detfile = detpath.format(classname)

    with open(detfile, 'r') as f:

        lines = f.readlines()



    splitlines = [x.strip().split(' ') for x in lines]

    image_ids = [x[0] for x in splitlines]

    confidence = np.array([float(x[1]) for x in splitlines])

    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])



    # sort by confidence

    sorted_ind = np.argsort(-confidence)

    sorted_scores = np.sort(-confidence)

    BB = BB[sorted_ind, :]

    image_ids = [image_ids[x] for x in sorted_ind]

    # go down dets and mark TPs and FPs

    nd = len(image_ids)

    # print(image_ids)

    # print(nd)

    tp = np.zeros(nd)

    fp = np.zeros(nd)

    for d in range(nd):

        R = class_recs[image_ids[d]]

        bb = BB[d, :].astype(float)

        ovmax = -np.inf

        BBGT = R['bbox'].astype(float)



        if BBGT.size > 0:

            # compute overlaps

            # intersection

            ixmin = np.maximum(BBGT[:, 0], bb[0])

            iymin = np.maximum(BBGT[:, 1], bb[1])

            ixmax = np.minimum(BBGT[:, 2], bb[2])

            iymax = np.minimum(BBGT[:, 3], bb[3])

            iw = np.maximum(ixmax - ixmin + 1., 0.)

            ih = np.maximum(iymax - iymin + 1., 0.)

            inters = iw * ih



            # union

            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +

                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *

                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)



            overlaps = inters / uni

            ovmax = np.max(overlaps)

            jmax = np.argmax(overlaps)



        if ovmax > ovthresh:

            if not R['difficult'][jmax]:

                if not R['det'][jmax]:

                    tp[d] = 1.

                    R['det'][jmax] = 1

                else:

                    fp[d] = 1.

        else:

            fp[d] = 1.



    # compute precision recall

    fp = np.cumsum(fp)

    tp = np.cumsum(tp)

    rec = tp / float(npos)

    # avoid divide by zero in case the first detection matches a difficult

    # ground truth

    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)

    ap = voc_ap(rec, prec, use_07_metric)



    return rec, prec, ap

三、运行

将以上valid命令生成的txt文件和py文件放在同一个文件夹下，在python中终端运行reval_voc_py.py，可在该文件夹下得到一个pkl文件，名字为你检测的物体名字。如果没错的话应该会直接在终端生成一个PR曲线图。
当然，你也可以根据上面生成的这个pkl文件，再新建一个PR_draw.py文件：

import _pickle as cPickle

import matplotlib.pyplot as plt

fr = open('koujian_pr.pkl','rb')#这里open中第一个参数需要修改成自己生产的pkl文件

#fr1= open('quexian_pr.pkl','rb')

inf = cPickle.load(fr)



fr.close()



 

x=inf['rec']

y=inf['prec']



plt.figure()

plt.xlabel('recall')

plt.ylabel('precision')

plt.title('PR cruve')

plt.plot(x,y, label='PR')

plt.legend(loc='upper right')

plt.show()

 

print('AP：',inf['ap'])

运行该文件，同样可以得到PR曲线图。

参考链接

https://blog.csdn.net/Mr_kuilei/article/details/105641774
https://blog.csdn.net/qq_33350808/article/details/83178002

绘制PR曲线[通俗易懂]

一、 获取txt文件

二.新建两个文件：

三、运行

参考链接

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一、获取txt文件