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使用voc
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voc_eval.py地址为https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py mAP计算过程输入： 1、各类检测到的目标框txt文件
txt文件内容如下，第一列是图像名字（不带后缀），第二列是置信度，剩下依次是xmin、ymin、xmax、ymax
2、Annotations文件 3、验证图像名字列表 4、compute_mAP.py + voc_eval.py （代码见下） 输出： 计算结果 compute_mAP.py代码及解析 #-*- coding: utf-8 -*- import os import numpy as np from voc_eval import voc_eval # 注意将voc_eval.py和compute_mAP.py放在同一级目录下 detpath = 'Path to dets txt' # 各类txt文件路径 detfiles = os.listdir(detpath) classes = ('__background__', # always index 0 数据集类别 'class1', 'class2', 'class3', 'class4', 'class5', 'class6') aps = [] # 保存各类ap recs = [] # 保存recall precs = [] # 保存精度 annopath = 'Path to Annotations' + '{: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 = 'Path to VOC2007/ImageSets/Main/test.txt' # 读取图像名字列表文件 cachedir = 'Path to annotations_cache/' for i, cls in enumerate(classes): if cls == '__background__': continue for f in detfiles: # 读取cls类对应的txt文件 if f.find(cls) != -1: filename = detpath + f rec, prec, ap = voc_eval( # 调用voc_eval.py计算cls类的recall precision ap filename, annopath, imagesetfile, cls, cachedir, ovthresh=0, use_07_metric=False) aps += [ap] print('AP for {} = {:.4f}'.format(cls, ap)) print('recall for {} = {:.4f}'.format(cls, rec[-1])) print('precision for {} = {:.4f}'.format(cls, prec[-1])) 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))) print('~~~~~~~~') voc_eval.py源代码及解析 # -*- coding: utf-8 -*- # -------------------------------------------------------- # 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): """ 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) return objects def voc_ap(rec, prec, use_07_metric=False): # voc2007的计算方式和voc2012的计算方式不同，目前一般采用第二种 """ 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 ## 程序入口 def voc_eval(detpath, # 保存检测到的目标框的文件路径，每一类的目标框单独保存在一个文件 annopath, # Annotations的路径 imagesetfile, # 测试图片名字列表 classname, # 类别名称 cachedir, # 缓存文件夹 ovthresh=0.5, # IoU阈值 use_07_metric=False): # mAP计算方法 """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 # cachedir caches the annotations in a pickle file # first load gt 获取真实目标框 # 当程序第一次运行时，会读取Annotations下的xml文件获取每张图片中真实的目标框 # 然后把获取的结果保存在annotations_cache文件夹中 # 以后再次运行时直接从缓存文件夹中读取真实目标 if not os.path.isdir(cachedir): os.mkdir(cachedir) 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] if not os.path.isfile(cachefile): # load annots recs = {} for i, imagename in enumerate(imagenames): recs[imagename] = parse_rec(annopath.format(imagename)) if i % 100 == 0: print 'Reading annotation for {:d}/{:d}'.format( i + 1, len(imagenames)) # save print 'Saving cached annotations to {:s}'.format(cachefile) with open(cachefile, 'w') as f: cPickle.dump(recs, f) else: # load with open(cachefile, 'r') as f: recs = cPickle.load(f) # extract gt objects for this class 提取该类的真实目标 class_recs = {} npos = 0 #保存该类一共有多少真实目标 for imagename in imagenames: R = [obj for obj in recs[imagename] if obj['name'] == classname] # 保存名字为imagename的图片中，类别为classname的目标框的信息 bbox = np.array([x['bbox'] for x in R]) #目标框的坐标 difficult = np.array([x['difficult'] for x in R]).astype(np.bool) #是否是难以识别的目标 det = [False] * len(R) #每一个目标框对应一个det[i]，用来判断该目标框是否已经处理过 npos = npos + sum(~difficult) #计算总的目标个数 class_recs[imagename] = {'bbox': bbox, # 把每一张图像中的目标框信息放到class_recs中 'difficult': difficult, 'det': det} # read dets detfile = detpath.format(classname) # 打开classname类别检测到的目标框文件 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] # 图像名字 confidence = np.array([float(x[1]) for x in splitlines]) # 置信度 BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) # 目标框坐标 # sort by confidence 按照置信度排序 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) # 统计检测到的目标框个数 tp = np.zeros(nd) # 创建tp列表，列表长度为目标框个数 fp = np.zeros(nd) # 创建fp列表，列表长度为目标框个数 for d in range(nd): R = class_recs[image_ids[d]] # 得到图像名字为image_ids[d]真实的目标框信息 bb = BB[d, :].astype(float) # 得到图像名字为image_ids[d]检测的目标框坐标 ovmax = -np.inf BBGT = R['bbox'].astype(float) # 得到图像名字为image_ids[d]真实的目标框坐标 if BBGT.size > 0: # compute overlaps 计算IoU # 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) # 检测到的目标框可能预若干个真实目标框都有交集，选择其中交集最大的 jmax = np.argmax(overlaps) if ovmax > ovthresh: # IoU是否大于阈值 if not R['difficult'][jmax]: # 真实目标框是否难以识别 if not R['det'][jmax]: # 该真实目标框是否已经统计过 tp[d] = 1. # 将tp对应第d个位置变成1 R['det'][jmax] = 1 # 将该真实目标框做标记 else: fp[d] = 1. # 否则将fp对应的位置变为1 else: fp[d] = 1. # 否则将fp对应的位置变为1 # compute precision recall fp = np.cumsum(fp) # 按列累加，最大值即为tp数量 tp = np.cumsum(tp) # 按列累加，最大值即为fp数量 rec = tp / float(npos) # 计算recall # 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) # 计算精度 ap = voc_ap(rec, prec, use_07_metric) # 计算ap return rec, prec, ap
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