我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用fast_rcnn.config.cfg.MATLAB。
def _do_matlab_eval(self, output_dir='output'): print '-----------------------------------------------------' print 'Computing results with the official MATLAB eval code.' print '-----------------------------------------------------' path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', 'VOCdevkit-matlab-wrapper') cmd = 'cd {} && '.format(path) cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) cmd += '-r "dbstop if error; ' cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ .format(self._devkit_path, self._get_comp_id(), self._image_set, output_dir) print('Running:\n{}'.format(cmd)) status = subprocess.call(cmd, shell=True)
def _do_python_eval(self, output_dir = 'output'): annopath = os.path.join(self._data_path, 'Annotations', 'DET', '{}/{}.xml') imagesetfile = os.path.join(self._data_path, 'ImageSets', 'DET', self._image_set + '.txt') cachedir = os.path.join(self._data_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_imagenet_results_file_template().format(cls) rec, prec, ap = imagenet_eval( filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric) aps += [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) 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('--------------------------------------------------------------')
def _do_python_eval(self, output_dir='output'): annopath = os.path.join( ####### self._building_path, 'a_Results', '{:s}.xml') imagesetfile = os.path.join( ####### self._building_path, 'a_Results', self._image_set + '.txt') cachedir = os.path.join(self._building_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=False) #use_07_metric=use_07_metric aps += [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) 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('--------------------------------------------------------------')
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] 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) 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('--------------------------------------------------------------')
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] 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) 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('--------------------------------------------------------------')
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) #''' RFM rec, prec, ap = (0, 0, 0) try: rec, prec, ap = voc_eval( filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric) aps += [ap] except: print ('pascal_voc: error catched') #''' 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) 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('--------------------------------------------------------------')
def _do_python_eval(self, output_dir = 'output'): annopath = os.path.join( self._devkit_path, 'Annotations', '{:s}.xml') imagesetfile = os.path.join( self._devkit_path, 'ImageSets', 'Main', self._image_set + '.txt') cachedir = os.path.join(self._devkit_path, 'annotations_cache') aps = [] recs = [] # 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] recs += [rec[-1]] 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) print('Mean AP = {:.4f}'.format(np.mean(aps))) print('~~~~~~~~') print('Results:') for ap in aps: print('{:.3f}'.format(ap)) print('Mean Recall= {:.4f}'.format(np.mean(recs))) print('~~~~~~~~') print('Results:') for rec in recs: print('{:.3f}'.format(rec)) 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('--------------------------------------------------------------')
def _do_python_eval(self, output_dir = 'output'): annopath = os.path.join( self._devkit_path, 'KI', 'Annotations', '{:s}.xml') imagesetfile = os.path.join( self._devkit_path, 'KI', '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 = 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] 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) 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('--------------------------------------------------------------')
