def do_python_eval(self):
"""
python evaluation wrapper
:return: info_str
"""
info_str = ''
annopath = os.path.join(self.data_path, 'Annotations', '{0!s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets',
self.image_set + '.txt')
annocache = os.path.join(self.cache_path,
self.name + '_annotations.pkl')
filename = self.get_result_file_template().format('all')
ap = vid_eval(filename,
annopath,
imageset_file,
self.classes_map,
annocache,
ovthresh=0.5)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('AP for {} = {:.4f}'.format(cls, ap[cls_ind - 1]))
info_str += 'AP for {} = {:.4f}\n'.format(cls, ap[cls_ind - 1])
print('Mean [email protected] = {:.4f}'.format(np.mean(ap)))
info_str += 'Mean [email protected] = {:.4f}\n\n'.format(np.mean(ap))
return info_str
def do_python_eval_gen(self):
"""
python evaluation wrapper
:return: info_str
"""
info_str = ''
annopath = os.path.join(self.data_path, 'Annotations', '{0!s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets',
self.image_set + '_eval.txt')
annocache = os.path.join(self.cache_path,
self.name + '_annotations.pkl')
with open(imageset_file, 'w') as f:
for i in range(len(self.pattern)):
for j in range(self.frame_seg_len[i]):
f.write((self.pattern[i] % (self.frame_seg_id[i] + j)) +
' ' + str(self.frame_id[i] + j) + '\n')
filename = self.get_result_file_template().format('all')
ap = vid_eval(filename,
annopath,
imageset_file,
self.classes_map,
annocache,
ovthresh=0.5)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('AP for {} = {:.4f}'.format(cls, ap[cls_ind - 1]))
info_str += 'AP for {} = {:.4f}\n'.format(cls, ap[cls_ind - 1])
print('Mean [email protected] = {:.4f}'.format(np.mean(ap)))
info_str += 'Mean [email protected] = {:.4f}\n\n'.format(np.mean(ap))
return info_str
def do_python_eval_gen(self):
"""
python evaluation wrapper
:return: info_str
"""
info_str = ''
annopath = os.path.join(self.data_path, 'Annotations', '{0!s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', self.image_set + '_eval.txt')
annocache = os.path.join(self.cache_path, self.name + '_annotations.pkl')
with open(imageset_file, 'w') as f:
for i in range(len(self.pattern)):
for j in range(self.frame_seg_len[i]):
f.write((self.pattern[i] % (self.frame_seg_id[i] + j)) + ' ' + str(self.frame_id[i] + j) + '\n')
filename = self.get_result_file_template().format('all')
ap = vid_eval(filename, annopath, imageset_file, self.classes_map, annocache, ovthresh=0.5)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('AP for {} = {:.4f}'.format(cls, ap[cls_ind-1]))
info_str += 'AP for {} = {:.4f}\n'.format(cls, ap[cls_ind-1])
print('Mean [email protected] = {:.4f}'.format(np.mean(ap)))
info_str += 'Mean [email protected] = {:.4f}\n\n'.format(np.mean(ap))
return info_str
def do_python_eval(self):
"""
python evaluation wrapper
:return: info_str
"""
info_str = ''
annopath = os.path.join(self.data_path, 'Annotations', '{0!s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', self.image_set + '.txt')
annocache = os.path.join(self.cache_path, self.name + '_annotations.pkl')
filename = self.get_result_file_template().format('all')
ap = vid_eval(filename, annopath, imageset_file, self.classes_map, annocache, ovthresh=0.5)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('AP for {} = {:.4f}'.format(cls, ap[cls_ind-1]))
info_str += 'AP for {} = {:.4f}\n'.format(cls, ap[cls_ind-1])
print('Mean [email protected] = {:.4f}'.format(np.mean(ap)))
info_str += 'Mean [email protected] = {:.4f}\n\n'.format(np.mean(ap))
return info_str
def do_python_eval_gen(self, gpu_number=None):
"""
python evaluation wrapper
:return: info_str
"""
info_str = ''
annopath = os.path.join(self.data_path, 'Annotations', '{0!s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets',
self.image_set + '_eval.txt')
annocache = os.path.join(self.cache_path,
self.name + '_annotations.pkl')
with open(imageset_file, 'w') as f:
for i in range(len(self.pattern)):
for j in range(self.frame_seg_len[i]):
f.write((self.pattern[i] % (self.frame_seg_id[i] + j)) +
' ' + str(self.frame_id[i] + j) + '\n')
if gpu_number != None:
filenames = []
for i in range(gpu_number):
filename = self.get_result_file_template(i).format('all')
filenames.append(filename)
multifiles = True # contains multi cache results of all boxes
else:
filenames = self.get_result_file_template().format('all')
multifiles = False
############ eval by classes ################
ap, recall, precision = vid_eval(multifiles,
filenames,
annopath,
imageset_file,
self.classes_map,
annocache,
ovthresh=0.5)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('AP for {} = {:.4f}'.format(cls, ap[cls_ind - 1]))
info_str += 'AP for {} = {:.4f}\n'.format(cls, ap[cls_ind - 1])
print('Recall for {} = {:.4f}'.format(cls, recall[cls_ind - 1]))
info_str += 'Recall for {} = {:.4f}\n'.format(
cls, recall[cls_ind - 1])
print('Precision for {} = {:.4f}'.format(cls,
precision[cls_ind - 1]))
info_str += 'Precision for {} = {:.4f}\n'.format(
cls, precision[cls_ind - 1])
# print('Mean [email protected] = {:.4f}'.format(np.mean(ap)))
# info_str += 'Mean [email protected] = {:.4f}\n\n'.format(np.mean(ap))
############# eval by motion ################
# if self.enable_detailed_eval:
# # init motion areas and area ranges
# motion_ranges = [[0.0, 1.0], [0.0, 0.7], [0.7, 0.9], [0.9, 1.0]]
# # area_ranges = [[0, 1e5 * 1e5], [0, 50 * 50], [50 * 50, 150 * 150], [150 * 150, 1e5 * 1e5]]
# area_ranges = [[0, 1e5 * 1e5]]
# else:
# motion_ranges = [[0.0, 1.0]]
# area_ranges = [[0, 1e5 * 1e5]]
#
# ap = vid_eval_motion(multifiles, filenames, annopath, imageset_file, self.classes_map, annocache, self.motion_iou_path,
# motion_ranges, area_ranges, ovthresh=0.5)
#
# for motion_index, motion_range in enumerate(motion_ranges):
# for area_index, area_range in enumerate(area_ranges):
# print '================================================='
# print 'motion [{0:.1f} {1:.1f}], area [{2} {3} {4} {5}]'.format(
# motion_range[0], motion_range[1], np.sqrt(area_range[0]), np.sqrt(area_range[0]),
# np.sqrt(area_range[1]), np.sqrt(area_range[1]))
# info_str += 'motion [{0:.1f} {1:.1f}], area [{2} {3} {4} {5}]'.format(
# motion_range[0], motion_range[1], np.sqrt(area_range[0]), np.sqrt(area_range[0]),
# np.sqrt(area_range[1]), np.sqrt(area_range[1]))
# print('Mean [email protected] = {:.4f}'.format(np.mean(
# [ap[motion_index][area_index][i] for i in range(len(ap[motion_index][area_index])) if
# ap[motion_index][area_index][i] >= 0])))
# info_str += 'Mean [email protected] = {:.4f}'.format(np.mean(
# [ap[motion_index][area_index][i] for i in range(len(ap[motion_index][area_index])) if
# ap[motion_index][area_index][i] >= 0]))
return info_str
