Python voc_eval.voc_eval方法代码示例

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
def evaluate(self, path_to_results_dir: str, image_ids: List[str], bboxes: List[List[float]], classes: List[int], probs: List[float]) -> Tuple[float, str]:
        self._write_results(path_to_results_dir, image_ids, bboxes, classes, probs)

        path_to_voc2007_dir = os.path.join(self._path_to_data_dir, 'VOCdevkit', 'VOC2007')
        path_to_main_dir = os.path.join(path_to_voc2007_dir, 'ImageSets', 'Main')
        path_to_annotations_dir = os.path.join(path_to_voc2007_dir, 'Annotations')

        class_to_ap_dict = {}
        for c in range(1, VOC2007.num_classes()):
            category = VOC2007.LABEL_TO_CATEGORY_DICT[c]
            try:
                path_to_cache_dir = os.path.join('caches', 'voc2007')
                os.makedirs(path_to_cache_dir, exist_ok=True)
                _, _, ap = voc_eval(detpath=os.path.join(path_to_results_dir, 'comp3_det_test_{:s}.txt'.format(category)),
                                    annopath=os.path.join(path_to_annotations_dir, '{:s}.xml'),
                                    imagesetfile=os.path.join(path_to_main_dir, 'test.txt'),
                                    classname=category,
                                    cachedir=path_to_cache_dir,
                                    ovthresh=0.5,
                                    use_07_metric=True)
            except IndexError:
                ap = 0

            class_to_ap_dict[c] = ap

        mean_ap = np.mean([v for k, v in class_to_ap_dict.items()]).item()

        detail = ''
        for c in range(1, VOC2007.num_classes()):
            detail += '{:d}: {:s} AP = {:.4f}\n'.format(c, VOC2007.LABEL_TO_CATEGORY_DICT[c], class_to_ap_dict[c])

        return mean_ap, detail 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
def evaluate(self, path_to_results_dir: str, image_ids: List[str], bboxes: List[List[float]], classes: List[int], probs: List[float]) -> Tuple[float, str]:
        self._write_results(path_to_results_dir, image_ids, bboxes, classes, probs)

        path_to_voc2007_dir = os.path.join(self._path_to_data_dir, 'VOCdevkit', 'VOC2007')
        path_to_main_dir = os.path.join(path_to_voc2007_dir, 'ImageSets', 'Main')
        path_to_annotations_dir = os.path.join(path_to_voc2007_dir, 'Annotations')

        class_to_ap_dict = {}
        for c in range(1, VOC2007CatDog.num_classes()):
            category = VOC2007CatDog.LABEL_TO_CATEGORY_DICT[c]
            try:
                path_to_cache_dir = os.path.join('caches', 'voc2007-cat-dog')
                os.makedirs(path_to_cache_dir, exist_ok=True)
                _, _, ap = voc_eval(detpath=os.path.join(path_to_results_dir, 'comp3_det_test_{:s}.txt'.format(category)),
                                    annopath=os.path.join(path_to_annotations_dir, '{:s}.xml'),
                                    imagesetfile=os.path.join(path_to_main_dir, 'test.txt'),
                                    classname=category,
                                    cachedir=path_to_cache_dir,
                                    ovthresh=0.5,
                                    use_07_metric=True)
            except IndexError:
                ap = 0

            class_to_ap_dict[c] = ap

        mean_ap = np.mean([v for k, v in class_to_ap_dict.items()]).item()

        detail = ''
        for c in range(1, VOC2007CatDog.num_classes()):
            detail += '{:d}: {:s} AP = {:.4f}\n'.format(c, VOC2007CatDog.LABEL_TO_CATEGORY_DICT[c], class_to_ap_dict[c])

        return mean_ap, detail 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
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) 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
def _do_matlab_eval(self, comp_id, output_dir='output'):
        rm_results = self.config['cleanup']

        path = os.path.join(os.path.dirname(__file__),
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'setenv(\'LC_ALL\',\'C\'); voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir, int(rm_results))
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True) 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
def _do_python_eval(self, output_dir='output'):
        annopath = os.path.join(
            self._devkit_path,
            'RGZ' + self._year,
            'Annotations',
            '{:s}.xml')
        imagesetfile = os.path.join(
            self._devkit_path,
            'RGZ' + 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('') 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
def _do_matlab_eval(self, comp_id, output_dir='output'):
        rm_results = self.config['cleanup']

        path = os.path.join(os.path.dirname(__file__),
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir, int(rm_results))
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True) 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
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('--------------------------------------------------------------') 

# 需要导入模块: import voc_eval [as 别名]
# 或者: from voc_eval import voc_eval [as 别名]
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_{}'.format(self._year))
        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('--------------------------------------------------------------')