def __calc_APs(self, iou_thresh=0.5, use_07_metric=False):
"""
Calculate ap values for each category
:param iou_thresh:
:param use_07_metric:
:return:dict{cls:ap}
"""
filename = os.path.join(self.pred_result_path,
"comp4_det_test_{:s}.txt")
cachedir = os.path.join(self.pred_result_path, "cache")
# annopath = os.path.join(self.val_data_path, 'Annotations', '{:s}.xml')
annopath = os.path.join(self.val_data_path,
"Annotations\\" + "{:s}.xml")
imagesetfile = os.path.join(self.val_data_path, "ImageSets", "Main",
"test.txt")
APs = {}
Recalls = {}
Precisions = {}
for i, cls in enumerate(self.classes):
R, P, AP = voc_eval.voc_eval(
filename,
annopath,
imagesetfile,
cls,
cachedir,
iou_thresh,
use_07_metric,
)
Recalls[cls] = R
Precisions[cls] = P
APs[cls] = AP
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
def __calc_APs(self, iou_thresh=0.5, use_07_metric=False):
"""
计算每个类别的ap值
:param iou_thresh:
:param use_07_metric:
:return:dict{cls:ap}
"""
filename = os.path.join(self.pred_result_path, 'voc',
'comp4_det_test_{:s}.txt')
cachedir = os.path.join(self.pred_result_path, 'voc', 'cache')
annopath = os.path.join(self.val_data_path, 'Annotations/{:s}.xml')
imagesetfile = os.path.join(self.val_data_path, 'ImageSets',
cfg.TEST["EVAL_NAME"] + '.txt')
APs = {}
Recalls = {}
Precisions = {}
for i, cls in enumerate(self.classes):
R, P, AP = voc_eval.voc_eval(filename, annopath, imagesetfile, cls,
cachedir, iou_thresh,
use_07_metric) #调用voc_eval.py的函数进行计算
APs[cls] = AP
Recalls[cls] = R
Precisions[cls] = P
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
def evalMap(save_result_root, xmlRoot, savePath):
"""
save_result_root: root dir should only contain labels.txt. Such as: person.txt, car.txt, mobilephone.txt ...
xmlRoot: should contain ground truth xml files.
savePath: image list file, and the file contains only image name per line.
"""
print("***************************")
print("***************************")
mAP = []
for class_name_file in os.listdir(save_result_root):
class_name = class_name_file[:-4]
rec, prec, ap = voc_eval(save_result_root + '/{}.txt',
xmlRoot + '/{}.xml',
savePath,
class_name,
'.')
print("{} :\t {} ".format(class_name, ap))
mAP.append(ap)
mAP = tuple(mAP)
print("***************************")
print("mAP :\t {}".format( float( sum(mAP)/len(mAP)) ))
print("***************************")
print("***************************")
os.remove('annots.pkl')
def __calc_APs(self, iou_thresh=0.5, use_07_metric=False):
"""
Calculate ap values for each category
:param iou_thresh:
:param use_07_metric:
:return:dict{cls:ap}
"""
filename = os.path.join(self.pred_result_path,
"comp4_det_test_{:s}.txt")
cachedir = os.path.join(self.pred_result_path, "cache")
annopath = os.path.join(self.val_data_path, 'label\\' + '{:s}.xml')
val_data_expr = os.path.join(self.val_data_path, 'val', "*.jpg")
imagesetfile = glob.glob(val_data_expr)
imagesetfile = [path.split("\\")[-1][:-4] for path in imagesetfile]
APs = {}
Recalls = {}
Precisions = {}
for i, cls in enumerate(self.classes):
R, P, AP = voc_eval.voc_eval(
filename,
annopath,
imagesetfile,
cls,
cachedir,
iou_thresh,
use_07_metric,
)
Recalls[cls] = R
Precisions[cls] = P
APs[cls] = AP
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
def __APs_calc(self, iou_thresh=0.5, use_07_metric=False):
"""
:param iou_thresh: 计算AP时用的iou阈值
:param use_07_metric: 是否使用07年的11点计算方式
:return: 所有类别的APs，元素是字典{cls:ap}
"""
filename = os.path.join(self._project_path, 'eval', 'results', 'VOC2007', 'Main', 'comp3_det_test_{:s}.txt')
cachedir = os.path.join(self._project_path, 'eval', 'cache')
annopath = os.path.join(self._dataset_path, 'VOC2007', 'Annotations', '{:s}.xml')
imagesetfile = os.path.join(self._dataset_path, 'VOC2007', 'ImageSets', 'Main', 'test.txt')
APs = {}
for i, cls in enumerate(cfg.CLASSES):
rec, prec, ap = voc_eval.voc_eval(filename, annopath, imagesetfile, cls, cachedir, iou_thresh, use_07_metric)
APs[cls] = ap
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
def __APs_calc(self, iou_thresh=0.5, use_07_metric=False):
filename = os.path.join(self._project_path, 'eval', 'results',
'VOC2007', 'Main', 'comp3_det_test_{:s}.txt')
cachedir = os.path.join(self._project_path, 'eval', 'cache')
#annopath = os.path.join(self._dataset_path, '2007_test', 'Annotations', '{:s}.xml')
annopath = self._dataset_path + '/2007_test' + '/Annotations/' + '{:s}.xml'
imagesetfile = os.path.join(self._dataset_path, '2007_test',
'ImageSets', 'Main', 'test.txt')
APs = {}
for i, cls in enumerate(cfg.CLASSES):
rec, prec, ap = voc_eval.voc_eval(filename, annopath, imagesetfile,
cls, cachedir, iou_thresh,
use_07_metric)
APs[cls] = ap
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
def __calc_APs(self, iou_thresh=0.5, use_07_metric=False):
"""
计算每个类别的ap值
:param iou_thresh:
:param use_07_metric:
:return:dict{cls:ap}
"""
filename = os.path.join(self.pred_result_path, 'comp4_det_test_{:s}.txt')
cachedir = os.path.join(self.pred_result_path, 'cache')
annopath = os.path.join(self.val_data_path, 'Annotations', '{:s}.xml')
imagesetfile = os.path.join(self.val_data_path, 'ImageSets', 'Main', 'test.txt')
APs = {}
for i, cls in enumerate(self.classes):
R, P, AP = voc_eval.voc_eval(filename, annopath, imagesetfile, cls, cachedir, iou_thresh, use_07_metric)
APs[cls] = AP
if os.path.exists(cachedir):
shutil.rmtree(cachedir)
return APs
cachedir = './cachedir'
imageset = ('2007', 'test')
if not os.path.exists(cachedir):
os.mkdir(cachedir)
det_list = [
os.path.join(cachedir_predict, file)
for file in os.listdir(cachedir_predict)
]
det_classes = list()
for file in det_list:
classes = os.path.splitext(os.path.basename(file))[0].split('_')[-1]
det_classes.append(classes)
detpath = file.replace(classes, '%s')
annopath = os.path.join(cfg.VOCroot, 'VOC' + imageset[0], 'Annotations',
'%s.xml')
imagesetfile = os.path.join(cfg.VOCroot, 'VOC' + imageset[0], 'ImageSets',
'Main', imageset[1] + '.txt')
MAPList = list()
for classname in det_classes:
rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname,
cachedir)
print '%s\t AP:%.4f' % (classname, ap)
MAPList.append(ap)
Map = np.array(MAPList)
mean_Map = np.mean(Map)
print '------ Map: %.4f' % (mean_Map)
