yolov3学习笔记系列一、计算map

一、使用valid批处理输出检测结果文本

运行指令

./darknet detector valid cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights

darknet中执行以上代码,yolo将把检测结果输出保存darknet/result 目录下。
在这里插入图片描述

二、使用voc_eval.py计算mAP

# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
 
import xml.etree.ElementTree as ET
import os
import cPickle
import numpy as np
 
def parse_rec(filename):    #通过ET解析xml后返回一个obj
    """ Parse a PASCAL VOC xml file """
    tree = ET.parse(filename)
    objects = []
    for obj in tree.findall('object'):
        obj_struct = {}
        obj_struct['name'] = obj.find('name').text
        obj_struct['pose'] = obj.find('pose').text
        obj_struct['truncated'] = int(obj.find('truncated').text)
        obj_struct['difficult'] = int(obj.find('difficult').text)
        bbox = obj.find('bndbox')
        obj_struct['bbox'] = [int(bbox.find('xmin').text),
                              int(bbox.find('ymin').text),
                              int(bbox.find('xmax').text),
                              int(bbox.find('ymax').text)]
        objects.append(obj_struct)  #objects格式为 [{'name':egret,'pose':Unspecifie等},{'name':egret,'pose':Unspecifie等}]
 
    return objects
 
def voc_ap(rec, prec, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default: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(detpath,
             annopath,
             imagesetfile,
             classname,
             cachedir,
             ovthresh=0.5,
             use_07_metric=False):
    """rec, prec, ap = voc_eval(detpath,
                                annopath,
                                imagesetfile,
                                classname,
                                [ovthresh],
                                [use_07_metric])
    Top level function that does the PASCAL VOC evaluation.
    detpath: Path to detections
        detpath.format(classname) should produce the detection results file.
    annopath: Path to annotations
        annopath.format(imagename) should be the xml annotations file.
    imagesetfile: Text file containing the list of images, one image per line.
    classname: Category name (duh)
    cachedir: Directory for caching the annotations
    [ovthresh]: Overlap threshold (default = 0.5)
    [use_07_metric]: Whether to use VOC07's 11 point AP computation
        (default False)
    """
    # assumes detections are in detpath.format(classname)
    # assumes annotations are in annopath.format(imagename)
    # assumes imagesetfile is a text file with each line an image name 默认txt中是无后缀imgName
    # cachedir caches the annotations in a pickle file
 
    # first load gt
    if not os.path.isdir(cachedir):
        os.mkdir(cachedir)  #若无pkl文件的路径,生成cachedir路径
    cachefile = os.path.join(cachedir, 'annots.pkl')    
    # read list of images
    with open(imagesetfile, 'r') as f:
        lines = f.readlines()
    imagenames = [x.strip() for x in lines] #imagenames为所有imgName的list
 
    if not os.path.isfile(cachefile):   #cache路径下无pkl
        # load annots
        recs = {}   #recs是一个dict,以imagename为key,解析xml后的obj为value,详情见下两句
        for i, imagename in enumerate(imagenames):
            recs[imagename] = parse_rec(annopath.format(imagename)) #依次写入format上imagename的xml路径到resc列表
            if i % 100 == 0:
                print ('Reading annotation for {:d}/{:d}'.format(
                    i + 1, len(imagenames))) #   显示进程
        # save
        print ('Saving cached annotations to {:s}'.format(cachefile))
        with open(cachefile, 'w') as f:
            cPickle.dump(recs, f)   #将resc列表中的内容写入pkl
    else:
        # load
        with open(cachefile, 'r') as f:
            recs = cPickle.load(f)  #若存在pkl,直接load到recs
 
    # extract gt objects for this class
    class_recs = {}
    npos = 0
    for imagename in imagenames:
        R = [obj for obj in recs[imagename] if obj['name'] == classname]    #除去recs中其他类别
        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[imagename] = {'bbox': bbox,
                                 'difficult': difficult,
                                 'det': det}
 
    # read dets
    detfile = detpath.format(classname)
    with open(detfile, 'r') as f:   #读批量验证的结果txt文件
        lines = f.readlines()
        
    splitlines = [x.strip().split(' ') for x in lines]  #split对txt每一行的数据做分割
    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 以下为计算对比各参数
    nd = len(image_ids)
    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

新建compute_mAP.py 文件

from voc_eval import voc_eval
print "RSI:",voc_eval('/data/gary/darknet/results/{}.txt', '/data/gary/VOC2007/Annotations/{}.xml', '/data/gary/VOC2007/ImageSets/Main/test.txt', 'RSI', '.')

运行python compute_mAP.py得到检测结果

运行前删除**./annots.pkl**文件,结果如下:
python compute_mAP.py
其中第三个值为AP,因为本例只求单类ap,即得出mAP。
在这里插入图片描述
参考:https://blog.csdn.net/qq_34806812/article/details/81747494


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