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reval.py
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reval.py
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#!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Reval = re-eval. Re-evaluate saved detections."""
import _init_paths
from fast_rcnn.test import apply_nms
from fast_rcnn.config import cfg
from datasets.factory import get_imdb
import cPickle
import os, sys, argparse
import numpy as np
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Re-evaluate results')
parser.add_argument('output_dir', nargs=1, help='results directory',
type=str)
parser.add_argument('--imdb', dest='imdb_name',
help='dataset to re-evaluate',
default='voc_2007_test', type=str)
parser.add_argument('--matlab', dest='matlab_eval',
help='use matlab for evaluation',
action='store_true')
parser.add_argument('--comp', dest='comp_mode', help='competition mode',
action='store_true')
parser.add_argument('--nms', dest='apply_nms', help='apply nms',
action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
def from_dets(imdb_name, output_dir, args):
imdb = get_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
imdb.config['matlab_eval'] = args.matlab_eval
with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
dets = cPickle.load(f)
if args.apply_nms:
print 'Applying NMS to all detections'
nms_dets = apply_nms(dets, cfg.TEST.NMS)
else:
nms_dets = dets
print 'Evaluating detections'
imdb.evaluate_detections(nms_dets, output_dir)
if __name__ == '__main__':
args = parse_args()
output_dir = os.path.abspath(args.output_dir[0])
imdb_name = args.imdb_name
from_dets(imdb_name, output_dir, args)