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voc_eval.py
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voc_eval.py
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# coding:utf-8
from __future__ import division
import numpy as np
import os
import xml.etree.ElementTree as ET
import cPickle
import tensorflow as tf
def parse_rec(filename):
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):
if use_07_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:
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
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):
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
imageset = os.path.splitext(os.path.basename(imagesetfile))[0]
cachefile = os.path.join(cachedir,imageset+"_annots.pkl")
with open(imagesetfile,'r') as f:
lines = f.readlines()
imagenames= [x.strip() for x in lines]
if not os.path.isfile(cachefile):
recs = {}
for i,imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath%imagename)
with open(cachefile,'w') as f:
cPickle.dump(recs,f)
else:
with open(cachefile,'r') as f:
recs = cPickle.load(f)
class_recs = {}
npos = 0
for imagename in imagenames:
if recs[imagename] is not None:
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)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
detfile = detpath%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])
sorted_ind = np.argsort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
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:
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
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
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec,prec,ap
if __name__=='__main__':
detpath='./cachedir/predict_dir/det_test_%s.txt'
annopath='/home/lj/data/VOCdevkit/VOC2007/Annotations/%s.xml'
imagesetfile = '/home/lj/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt'
classname = 'car'
cachedir = './cachedir'
rec,prec,ap = voc_eval(detpath,annopath,imagesetfile,classname,cachedir)
print rec,prec,ap