Faster R-CNN 绘制 precision-recall曲线

本教程主要参考下面链接博主内容：

点我

1.修改pascal_voc.py文件

• 文档开始添加

• 
  import matplotlib.pyplot as plt
  import pylab as pl
  from sklearn.metrics import precision_recall_curve
  from itertools import cycle

  修改_do_python_eval函数：

    def _do_python_eval(self, output_dir = 'output'):
        annopath = os.path.join(
            self._devkit_path,
            'VOC' + self._year,
            'Annotations',
            '{:s}.xml')
        imagesetfile = os.path.join(
            self._devkit_path,
            'VOC' + self._year,
            'ImageSets',
            'Main',
            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 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)
            rec, prec, ap = voc_eval(
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
                use_07_metric=use_07_metric)
            aps += [ap]
            pl.plot(rec, prec, lw=2, 
                    label='Precision-recall curve of class {} (area = {:.4f})'
                          ''.format(cls, 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)

        pl.xlabel('Recall')
        pl.ylabel('Precision')
        plt.grid(True)
        pl.ylim([0.0, 1.05])
        pl.xlim([0.0, 1.0])
        pl.title('Precision-Recall')
        pl.legend(loc="upper right")     
        plt.show()

        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('--------------------------------------------------------------')

2.执行test_net.py文件

解释：这段代码不是根据日志信息输出的，而是在测试过程中输出的

当你之前已经训练好模型，那么需要重新执行一下测试命令已输出曲线，命令如下：

./tools/test_net.py --gpu 0 --def models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt --net output/faster_rcnn_end2end/voc_2007_trainval/vgg16_faster_rcnn_iter_70000.caffemodel --cfg experiments/cfgs/faster_rcnn_end2end.yml

这里需要修改你的模型位置和名称，以及yml的位置，我上述命令为默认地址。

 

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