根據這個代碼就可以計算到出各類別的AP/MAP值

# -------------------------------------------------------- # YOLOv4 # 2020.11.05 # -------------------------------------------------------- from __future__ import print_function import argparse import xml.etree.ElementTree as ET import os,sys import pickle import numpy as np import pdbdef parse_args():"""Parse input arguments"""parser = argparse.ArgumentParser(description='Re-evaluate results')parser.add_argument('output_dir', nargs=1, help='results directory',type=str)parser.add_argument('--voc_dir', dest='voc_dir', default='/home/sxl/Data/voc/VOCtrainval/', type=str)parser.add_argument('--year', dest='year', default='2017', type=str)parser.add_argument('--image_set', dest='image_set', default='voc_test', type=str)parser.add_argument('--classes', dest='class_file', default='/home/sxl/Module/DarknetAB1022/data/voc.names', type=str)parser.add_argument('--ovthresh', dest='ovthresh', default=0.5, type=float)if len(sys.argv) == 1:parser.print_help()sys.exit(1)args = parser.parse_args()return args def get_voc_results_file_template(image_set, out_dir = 'results'):#filename = 'comp4_det_' + image_set + '_{:s}.txt'#filename = image_set + '_{:s}.txt'filename = '{:s}.txt'path = os.path.join(out_dir, filename)return pathdef 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').textobj_struct['pose'] = obj.find('pose').textobj_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 objectsdef 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 theVOC 07 11 point method (default:False)."""# VOC在2010之后換了評價方法，所以在這里決定是否用07年的方法if use_07_metric:# 11 point metricap = 0.for t in np.arange(0., 1.1, 0.1): #07年采用的是11插值法，平分recall計算得來if np.sum(rec >= t) == 0:p = 0else:p = np.max(prec[rec >= t]) # 取一個recall閾值之后最大的precisionap = ap + p / 11. # 將11 個precision加和平均else: # 使用2010年后的方法，取所有不同的recall對應的點處的精度值做平均# correct AP calculation# first append sentinel values at the endmrec = np.concatenate(([0.], rec, [1.])) # recall和precision前后分別加了一個值，因為recall最后是1，所以mpre = np.concatenate(([0.], prec, [0.])) # 右邊加了1，precision加的是0# compute the precision envelopefor i in range(mpre.size - 1, 0, -1):mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # 從后往前，排除之前局部增加的precison情況# to calculate area under PR curve, look for points# where X axis (recall) changes valuei = np.where(mrec[1:] != mrec[:-1])[0] # 這里巧妙的錯位，返回剛好TP的位置，# and sum (\Delta recall) * prec 用recall 的間隔對精度作加權平均ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])return apdef voc_eval(detpath,annopath,imagesetfile,classname,cachedir,ovthresh,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 detectionsdetpath.format(classname) should produce the detection results file.annopath: Path to annotationsannopath.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 gtif not os.path.isdir(cachedir):os.mkdir(cachedir)cachefile = os.path.join(cachedir, 'annots.pkl')# read list of imageswith open(imagesetfile, 'r') as f:lines = f.readlines()imagenames = [x.strip() for x in lines]#pdb.set_trace()if not os.path.isfile(cachefile):# load annotsrecs = {}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)))# saveprint('Saving cached annotations to {:s}'.format(cachefile))with open(cachefile, 'wb') as f:pickle.dump(recs, f)else:# loadwith open(cachefile, 'rb') as f:recs = pickle.load(f)# extract gt objects for this classclass_recs = {}npos = 0for imagename in imagenames:R = [obj for obj in recs[imagename] 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) # npos=TP+FNclass_recs[imagename] = {'bbox': bbox,'difficult': difficult,'det': det}# read detsdetfile = detpath.format(classname)#pdb.set_trace()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 confidencesorted_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 FPsnd = 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.infBBGT = R['bbox'].astype(float)if BBGT.size > 0:# compute overlaps# intersectionixmin = 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# unionuni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +(BBGT[:, 2] - BBGT[:, 0] + 1.) *(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)overlaps = inters / uniovmax = 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] = 1else:fp[d] = 1.else:fp[d] = 1.# compute precision recallfp = np.cumsum(fp)tp = np.cumsum(tp)rec = tp / float(npos)# avoid divide by zero in case the first detection matches a difficult# ground truthprec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)ap = voc_ap(rec, prec, use_07_metric)return rec, prec, apdef do_python_eval(devkit_path, year, image_set, classes, ovthresh, output_dir = 'results'):"""Parse input arguments"""# parser = argparse.ArgumentParser(description='Re-evaluate results')# parser.add_argument('output_dir', nargs=1, help='results directory',# type=str)# parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)# parser.add_argument('--year', dest='year', default='2017', type=str)# parser.add_argument('--image_set', dest='image_set', default='test', type=str)# parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)annopath = os.path.join(devkit_path,'VOC' + year,'Annotations','{:s}.xml')imagesetfile = os.path.join(devkit_path,'VOC' + year,'ImageSets','Main',image_set+'.txt')cachedir = os.path.join(devkit_path, 'annotations_cache')aps = []# The PASCAL VOC metric changed in 2010# use_07_metric = True if int(_year) < 2010 else Falseuse_07_metric = Falseprint('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))if not os.path.isdir(output_dir):os.mkdir(output_dir)# i -index cls- categoryfor i, cls in enumerate(classes):if cls == '__background__':continuefilename = get_voc_results_file_template(image_set).format(cls)rec, prec, ap = voc_eval(filename, annopath, imagesetfile, cls, cachedir, ovthresh,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:pickle.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)))print('~~~~~~~~')if __name__ == '__main__':args = parse_args()output_dir = os.path.abspath(args.output_dir[0])with open(args.class_file, 'r') as f:lines = f.readlines()classes = [t.strip('\n') for t in lines]print ('Evaluating detections')do_python_eval(args.voc_dir, args.year, args.image_set, classes, args.ovthresh, output_dir)

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