def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(self._val_det_bbox, '{:s}' + self._anno_ext)
print "Anno path {}".format(annopath)
imagesetfile = os.path.join(self._devkit_path, "data/det_lists",
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True
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(self._classes):
if cls == '__background__':
continue
filename = self._get_imagenet_results_file_template().format(cls)
print "File name {}".format(filename)
rec, prec, ap = voc_eval(self._wnid_to_ind,
self._class_to_ind,
filename,
annopath,
imagesetfile,
cls,
cachedir,
ovthresh=0.5,
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'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
i = 1
for ap in aps:
print('{}: {:.3f}'.format(self._classes[i], ap))
i += 1
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_python_eval(self, output_dir = 'output'):
annopath = os.path.join(self._val_det_bbox, '{:s}.xml')
print "Anno path {}".format(annopath)
imagesetfile = os.path.join(
self._devkit_path, "data/det_lists",
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True
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(self._classes):
if cls == '__background__':
continue
filename = self._get_imagenet_results_file_template().format(cls)
print "File name {}".format(filename)
rec, prec, ap = voc_eval(self._wnid_to_ind, self._class_to_ind,
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
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'), 'w') as f:
cPickle.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('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(self._devkit_path, 'Annotations', 'DET',
self._image_set_dir, '{:s}.xml')
imagesetfile = os.path.join(self._devkit_path, 'ImageSets', 'DET',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache',
self._anno_set_dir)
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._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(self._classes):
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
# pass in the class id corresponding to the synset
n_cls = self._wnid[i]
rec, prec, ap, ovthresh = voc_eval(filename,
annopath,
imagesetfile,
n_cls,
cachedir,
ovthresh=0.5,
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'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
#print('Mean AP = {:.4f}'.format(np.mean(aps)))
print(aps)
aps = np.array(aps)
results_fd = open("./results_imagenet.txt", "w")
for kdx in range(len(ovthresh)):
#print('{0:.3f}@{1:.2f}'.format(ap[kdx],ovthresh[kdx]))
print('Mean AP = {:.4f} @ {:.2f}'.format(np.mean(aps[:, kdx]),
ovthresh[kdx]))
print('~~~~~~~~')
print('Results:')
sys.stdout.write('{0:>15} (#):'.format("class AP"))
results_fd.write('{0:>15} (#):'.format("class AP"))
for thsh in ovthresh:
sys.stdout.write("\t{:>5}{:.3f}".format("@", thsh))
results_fd.write("\t{:>5}{:.3f}".format("@", thsh))
sys.stdout.write("\n")
results_fd.write("\n")
count_ = 1
for ap in aps:
sys.stdout.write('{:>15} ({}):'.format(self._classes[count_],
count_))
results_fd.write('{:>15} ({}):'.format(self._classes[count_],
count_))
for kdx in range(len(ovthresh)):
sys.stdout.write('\t{0:>10.5f}'.format(ap[kdx], ovthresh[kdx]))
results_fd.write('\t{0:>10.5f}'.format(ap[kdx], ovthresh[kdx]))
sys.stdout.write('\n')
results_fd.write('\n')
count_ += 1
sys.stdout.write('{:>15}:'.format("mAP"))
results_fd.write('{:>15}:'.format("mAP"))
for kdx in range(len(ovthresh)):
sys.stdout.write('\t{:10.5f}'.format(np.mean(aps[:, kdx])))
results_fd.write('\t{:10.5f}'.format(np.mean(aps[:, kdx])))
#print('{0:.3f}@{1:.2f}'.format(ap[kdx],ovthresh[kdx]))
#print('mAP @ {:.2f}: {:.5f} '.format(ovthresh[kdx],np.mean(aps[:,kdx])))
sys.stdout.write('\n')
results_fd.write('\n')
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')