版权声明：本文为博主原创文章，转载请加入原文链接，谢谢。。 https://blog.csdn.net/shawncheer/article/details/78317711
参考：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py
代码块
# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET #读取xml。
import os
import cPickle #序列化存储模块。
import numpy as np
def parse_rec(filename):#解析读取xml函数。
""" 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): #单个测量AP的函数。
""" 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, ######主函数
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. #产生的txt文件，里面是一张图片的各个detection。
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file. #xml 文件与对应的图像相呼应。
imagesetfile: Text file containing the list of images, one image per line. #一个txt文件，里面是每个图片的地址，每行一个地址。
classname: Category name (duh) #种类的名字，即类别。
cachedir: Directory for caching the annotations #缓存标注的目录。
[ovthresh]: Overlap threshold (default = 0.5) #重叠的多少大小。
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False) #是否使用VOC07的11点AP计算。
"""
# 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 加载ground truth。
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl') #即将新建文件的路径。
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines() #读取文本里的所以文本行，作为众多文图片的路径。
imagenames = [x.strip() for x in lines] #所有文件名字。
if not os.path.isfile(cachefile): #如果cachefile文件不存在，则
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename)) #这里的format不知道啥意思
if i % 100 == 0:
print 'Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames)) #进度条。
# save
print 'Saving cached annotations to {:s}'.format(cachefile)
with open(cachefile, 'w') as f:
cPickle.dump(recs, f) #写入cPickle文件里面。写入的是一个字典，左侧为xml文件名，右侧为文件里面个各个参数。
else:
# load
with open(cachefile, 'r') as f:
recs = cPickle.load(f) #如果已经有了这个cPickle文件，则加载一下。
# extract gt objects for this class #对每张图片的xml获取函数指定类的bbox等。
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname] #获取每个文件中某种类别的物体。
bbox = np.array([x['bbox'] for x in R]) #抽取bbox
difficult = np.array([x['difficult'] for x in R]).astype(np.bool) #different基本都为0.
det = [False] * len(R) #list中形参len(R)个False。
npos = npos + sum(~difficult) #自增，sum求得的值基本都为0。
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
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] #图片index。
confidence = np.array([float(x[1]) for x in splitlines]) #类别置信度
BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) #变为浮点型的bbox。
# sort by confidence
sorted_ind = np.argsort(-confidence) #对confidence的index根据值大小进行降序排列。
sorted_scores = np.sort(-confidence) #降序排列。
BB = BB[sorted_ind, :] #重排bbox，由大概率到小概率。
image_ids = [image_ids[x] for x in sorted_ind] 对图片进行重排。
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd) #归零。
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