yolo3的ap、mAP计算
yolo3的ap、mAP计算
⼀、准备⼯作
继上次，我们成功编译了darknet，后来惊讶发现在darknet\build\darknet\x64⽬录下就有这两个py⽂件⽤来算ap值：reval_voc_py3.py，voc_eval_py3.py
⼆、先在\64\results中⽣成测试结果⽂件
⾸先通过valid命令，遍历⼀遍测试数据集，跑出来训练好的⽹络在这个测试数据集的结果，命令如下
darknet detector valid cfg/voc.data cfg/tiny_yolo_voc.cfg tiny_yolo_voc.weights
⾸先进⼊x64⽬录下，
cmd或者terminal输⼊例如
$ darknet detector valid data/voc.data yolov3.cfg yolo_5141.weights
分为四部分，darknet detector valid；data/voc.data表⽰我要⽤data⽂件夹下的voc.data；yolov3.cfg表⽰训练weights时⽤到cfg；yolo_5141.weights则是你要测试的weights了。
注意：在执⾏该命令的时候，需要你的当前路径下有⼀个results的⽂件夹，不然会报segmentation fault的错误，如果没有可以⼿动新建。
接下来就会在results⽂件夹下看到
对应有⼏个class就会有⼏个这种⽂件
三、开始测试
下⾯两个就是核⼼测试py
有些地⽅我根据⾃⼰的情况做了修改，然运⾏更加⽅便
"""Reval = re-eval. Re-evaluate saved detections.
usage:
input with the command: $python reval_voc.py --voc_dir VOCdevkit --year 2007 --image_set test --class ./data/voc.names
Actually we input $ python reval_voc.py --voc_dir C:\\Users\\Breeze\\Desktop\\
Mask-or-Not\\darknet\\build\\darknet\\x64\\results
will be okay,since I have got the default value changed to my path.
注释⾥⾯的\得换成\\否则会报错
NOTE:this .py has to be opened with the results.file,otherwise the import of voc_eval would break with error
"""
import argparse
import os
import pickle as cPickle
import sys
import numpy as np
from voc_eval import voc_eval
def parse_args():
"""
Parse input arguments
"""
#以下⼏个argument，我在原来基础上都加了default，其实看着我改过的就很容易理解
#voc_dir就是VOCdevkit的路径
#year默认成你⽂件夹对应的，我是2020
#⼏个可选变量都设成了默认，所以在cmd就只需要输⼊必选变量output_dir 即可，也就是⽣成⽂件保存在这个地#⽅ 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='C:\\Users\\Breeze\\Desktop'
'\\keras-yolo3-master\\VOCdevkit', type=str)
parser.add_argument('--year', dest='year', default='2020', type=str)
parser.add_argument('--image_set', dest='image_set', default='test', type=str)
parser.add_argument('--classes', dest='class_file', default='C:\\Users\\Breeze\\Desktop\\darknet\\'
'darknet-master\\build\darknet\\x64\\data\\voc.names',
type=str)
if len(sys.argv) == 1:
parser.print_help()
args = parser.parse_args()
return args
def get_voc_results_file_template(image_set, out_dir='results'):
filename = 'comp4_det_' + image_set + '_{:s}.txt'
path = os.path.join(out_dir, filename)
return path
def do_python_eval(devkit_path, year, image_set, classes, output_dir='results'):
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 False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(classes):
if cls == '__background__':
continue
filename = get_voc_results_file_template(image_set).format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
print("HERE")
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.an(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.an(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
if __name__ == '__main__':
args = parse_args()
output_dir = os.path.abspath(args.output_dir[0])
print(args.class_file)
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, ar, args.image_set, classes, output_dir) ElementTree as ET
import os
import pickle as 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):
""" 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
# cachedir caches the annotations in a pickle file
# first load gt
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):
#print(annopath.format(imagename))
#print(imagenames)
recs[imagename] = parse_rec("C:\\Users\\Breeze\\Desktop\\keras-yolo3-master\\VOCdevkit\\VOC2020" "\\Annotations\\"+annopath.format(imagename))
#print("PY")
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, 'wb') as f:
cPickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
#print(cachefile)
recs = cPickle.load(f)
# 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]
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,
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
#原⽂⽤的相对路径，不是太好控制，所以这⾥直接改成绝对路径
detfile="C:\\Users\\Breeze\\Desktop\\darknet\\darknet-master\\build\\darknet\\x64\\"+detfile #print(detfile)
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 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
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.)
import pickleinters = 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
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