freq = hist.sum(1) / hist.sum()
print 'fwavacc', (freq[freq > 0] * iu[freq > 0]).sum()
filename = os.path.join(output_dir, 'segmentation.txt')
with open(filename, 'wt') as f:
for i in range(n_cl):
f.write('{:f}\n'.format(iu[i]))
filename = os.path.join(output_dir, 'confusion_matrix.txt')
with open(filename, 'wt') as f:
for i in range(n_cl):
for j in range(n_cl):
f.write('{:f} '.format(hist[i, j]))
f.write('\n')
# pose accuracy
if cfg.TEST.POSE_REG:
for i in xrange(1, self.num_classes):
print '{} correct poses: {}, all poses: {}, accuracy: {}'.format(self.classes[i], count_correct[i], count_all[i], float(count_correct[i]) / float(count_all[i]))
if cfg.TEST.POSE_REFINE:
print '{} correct poses after refinement: {}, all poses: {}, accuracy: {}'.format( \
self.classes[i], count_correct_refined[i], count_all[i], float(count_correct_refined[i]) / float(count_all[i]))
print '{} correct poses after ICP: {}, all poses: {}, accuracy: {}'.format( \
self.classes[i], count_correct_icp[i], count_all[i], float(count_correct_icp[i]) / float(count_all[i]))
if __name__ == '__main__':
d = datasets.ycb('train')
res = d.roidb
from IPython import embed; embed()
with open(filename, 'wt') as f:
for i in range(n_cl):
f.write('{:f}\n'.format(iu[i]))
filename = os.path.join(output_dir, 'confusion_matrix.txt')
with open(filename, 'wt') as f:
for i in range(n_cl):
for j in range(n_cl):
f.write('{:f} '.format(hist[i, j]))
f.write('\n')
# pose accuracy
if cfg.TEST.POSE_REG:
for i in range(1, self.num_classes):
print(
'{} correct poses: {}, all poses: {}, accuracy: {}'.format(
self.classes[i], count_correct[i], count_all[i],
float(count_correct[i]) / float(count_all[i])))
if cfg.TEST.POSE_REFINE:
print('{} correct poses after refinement: {}, all poses: {}, accuracy: {}'.format( \
self.classes[i], count_correct_refined[i], count_all[i], float(count_correct_refined[i]) / float(count_all[i])))
print('{} correct poses after ICP: {}, all poses: {}, accuracy: {}'.format( \
self.classes[i], count_correct_icp[i], count_all[i], float(count_correct_icp[i]) / float(count_all[i])))
if __name__ == '__main__':
d = datasets.ycb('trainval')
res = d.roidb
from IPython import embed
embed()