mirror of https://github.com/AlexeyAB/darknet.git
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
#!/usr/bin/env python
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# Adapt from ->
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# --------------------------------------------------------
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# Fast R-CNN
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# Copyright (c) 2015 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ross Girshick
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# --------------------------------------------------------
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# <- Written by Yaping Sun
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"""Reval = re-eval. Re-evaluate saved detections."""
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import os, sys, argparse
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import numpy as np
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import _pickle as cPickle
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#import cPickle
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from voc_eval_py3 import voc_eval
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def parse_args():
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"""
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Parse input arguments
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"""
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parser = argparse.ArgumentParser(description='Re-evaluate results')
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parser.add_argument('output_dir', nargs=1, help='results directory',
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type=str)
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parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
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parser.add_argument('--year', dest='year', default='2017', type=str)
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parser.add_argument('--image_set', dest='image_set', default='test', type=str)
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parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
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if len(sys.argv) == 1:
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parser.print_help()
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sys.exit(1)
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args = parser.parse_args()
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return args
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def get_voc_results_file_template(image_set, out_dir = 'results'):
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filename = 'comp4_det_' + image_set + '_{:s}.txt'
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path = os.path.join(out_dir, filename)
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return path
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def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
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annopath = os.path.join(
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devkit_path,
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'VOC' + year,
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'Annotations',
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'{}.xml')
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imagesetfile = os.path.join(
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devkit_path,
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'VOC' + year,
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'ImageSets',
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'Main',
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image_set + '.txt')
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cachedir = os.path.join(devkit_path, 'annotations_cache')
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aps = []
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# The PASCAL VOC metric changed in 2010
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use_07_metric = True if int(year) < 2010 else False
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print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
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print('devkit_path=',devkit_path,', year = ',year)
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if not os.path.isdir(output_dir):
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os.mkdir(output_dir)
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for i, cls in enumerate(classes):
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if cls == '__background__':
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continue
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filename = get_voc_results_file_template(image_set).format(cls)
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rec, prec, ap = voc_eval(
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filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
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use_07_metric=use_07_metric)
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aps += [ap]
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print('AP for {} = {:.4f}'.format(cls, ap))
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with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
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cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
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print('Mean AP = {:.4f}'.format(np.mean(aps)))
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print('~~~~~~~~')
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print('Results:')
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for ap in aps:
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print('{:.3f}'.format(ap))
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print('{:.3f}'.format(np.mean(aps)))
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print('~~~~~~~~')
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print('')
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print('--------------------------------------------------------------')
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print('Results computed with the **unofficial** Python eval code.')
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print('Results should be very close to the official MATLAB eval code.')
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print('-- Thanks, The Management')
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print('--------------------------------------------------------------')
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if __name__ == '__main__':
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args = parse_args()
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output_dir = os.path.abspath(args.output_dir[0])
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with open(args.class_file, 'r') as f:
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lines = f.readlines()
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classes = [t.strip('\n') for t in lines]
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print('Evaluating detections')
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do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
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