Python core.get_classes方法代码示例

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def re_result(result, dataset='coco', score_thr=0.5):
    '''
    返回human boxes and scores
    '''
    class_names = get_classes(dataset)
    if isinstance(result, tuple):
        bbox_result, segm_result = result
    else:
        bbox_result, segm_result = result, None
    bboxes = np.vstack(bbox_result)
    # bounding boxes
    labels = [
        np.full(bbox.shape[0], i, dtype=np.int32)
        for i, bbox in enumerate(bbox_result)
    ]
    labels = np.concatenate(labels)

    # only for human class
    re_bboxes, re_scores = det_bboxes(bboxes, labels, score_thr=score_thr)

    return re_bboxes, re_scores 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def init_detector(config, checkpoint=None, device='cuda:0'):
    """Initialize a detector from config file.

    Args:
        config (str or :obj:`mmcv.Config`): Config file path or the config
            object.
        checkpoint (str, optional): Checkpoint path. If left as None, the model
            will not load any weights.

    Returns:
        nn.Module: The constructed detector.
    """
    if isinstance(config, str):
        config = mmcv.Config.fromfile(config)
    elif not isinstance(config, mmcv.Config):
        raise TypeError('config must be a filename or Config object, '
                        f'but got {type(config)}')
    config.model.pretrained = None
    model = build_detector(config.model, test_cfg=config.test_cfg)
    if checkpoint is not None:
        checkpoint = load_checkpoint(model, checkpoint)
        if 'CLASSES' in checkpoint['meta']:
            model.CLASSES = checkpoint['meta']['CLASSES']
        else:
            warnings.simplefilter('once')
            warnings.warn('Class names are not saved in the checkpoint\'s '
                          'meta data, use COCO classes by default.')
            model.CLASSES = get_classes('coco')
    model.cfg = config  # save the config in the model for convenience
    model.to(device)
    model.eval()
    return model 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def init_detector(config, checkpoint=None, device='cuda:0'):
    """Initialize a detector from config file.

    Args:
        config (str or :obj:`mmcv.Config`): Config file path or the config
            object.
        checkpoint (str, optional): Checkpoint path. If left as None, the model
            will not load any weights.

    Returns:
        nn.Module: The constructed detector.
    """
    if isinstance(config, str):
        config = mmcv.Config.fromfile(config)
    elif not isinstance(config, mmcv.Config):
        raise TypeError('config must be a filename or Config object, '
                        'but got {}'.format(type(config)))
    config.model.pretrained = None
    model = build_detector(config.model, test_cfg=config.test_cfg)
    if checkpoint is not None:
        checkpoint = load_checkpoint(model, checkpoint)
        if 'CLASSES' in checkpoint['meta']:
            model.CLASSES = checkpoint['meta']['CLASSES']
        else:
            warnings.warn('Class names are not saved in the checkpoint\'s '
                          'meta data, use COCO classes by default.')
            model.CLASSES = get_classes('coco')
    model.cfg = config  # save the config in the model for convenience
    model.to(device)
    model.eval()
    return model 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def init_detector(config, checkpoint=None, device='cuda:0'):
    """Initialize a detector from config file.

    Args:
        config (str or :obj:`mmcv.Config`): Config file path or the config
            object.
        checkpoint (str, optional): Checkpoint path. If left as None, the model
            will not load any weights.

    Returns:
        nn.Module: The constructed detector.
    """
    if isinstance(config, str):
        config = mmcv.Config.fromfile(config)
    elif not isinstance(config, mmcv.Config):
        raise TypeError('config must be a filename or Config object, '
                        'but got {}'.format(type(config)))
    config.model.pretrained = None
    model = build_detector(config.model, test_cfg=config.test_cfg)
    if checkpoint is not None:
        checkpoint = load_checkpoint(model, checkpoint)
        if 'CLASSES' in checkpoint['meta']:
            model.CLASSES = checkpoint['meta']['classes']
        else:
            warnings.warn('Class names are not saved in the checkpoint\'s '
                          'meta data, use COCO classes by default.')
            model.CLASSES = get_classes('coco')
    model.cfg = config  # save the config in the model for convenience
    model.to(device)
    model.eval()
    return model 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def show_result(img, result, dataset='coco', score_thr=0.3):
    class_names = get_classes(dataset)
    labels = [
        np.full(bbox.shape[0], i, dtype=np.int32)
        for i, bbox in enumerate(result)
    ]
    labels = np.concatenate(labels)
    bboxes = np.vstack(result)
    img = mmcv.imread(img)
    mmcv.imshow_det_bboxes(
        img.copy(),
        bboxes,
        labels,
        class_names=class_names,
        score_thr=score_thr) 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def show_result(img, result, dataset='coco', score_thr=0.5, out_file=None, wait_time=0):
    img = mmcv.imread(img)
    class_names = get_classes(dataset)
    if isinstance(result, tuple):
        bbox_result, segm_result = result
    else:
        bbox_result, segm_result = result, None
    bboxes = np.vstack(bbox_result)
    # draw segmentation masks
    if segm_result is not None:
        segms = mmcv.concat_list(segm_result)
        inds = np.where(bboxes[:, -1] > score_thr)[0]
        for i in inds:
            color_mask = np.random.randint(
                0, 256, (1, 3), dtype=np.uint8)
            mask = maskUtils.decode(segms[i]).astype(np.bool)
            img[mask] = img[mask] * 0.5 + color_mask * 0.5
    # draw bounding boxes
    labels = [
        np.full(bbox.shape[0], i, dtype=np.int32)
        for i, bbox in enumerate(bbox_result)
    ]
    labels = np.concatenate(labels)

    mmcv.imshow_det_bboxes(
        img.copy(),
        bboxes,
        labels,
        class_names=class_names,
        score_thr=score_thr,
        show=out_file is None,
        wait_time=wait_time)

    return re_bboxes, re_scores 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def show_result(img, result, dataset='coco', score_thr=0.3, out_file=None):
    img = mmcv.imread(img)
    class_names = get_classes(dataset)
    if isinstance(result, tuple):
        bbox_result, segm_result = result
    else:
        bbox_result, segm_result = result, None
    bboxes = np.vstack(bbox_result)
    # draw segmentation masks
    if segm_result is not None:
        segms = mmcv.concat_list(segm_result)
        inds = np.where(bboxes[:, -1] > score_thr)[0]
        for i in inds:
            color_mask = np.random.randint(
                0, 256, (1, 3), dtype=np.uint8)
            mask = maskUtils.decode(segms[i]).astype(np.bool)
            img[mask] = img[mask] * 0.5 + color_mask * 0.5
    # draw bounding boxes
    labels = [
        np.full(bbox.shape[0], i, dtype=np.int32)
        for i, bbox in enumerate(bbox_result)
    ]
    labels = np.concatenate(labels)
    mmcv.imshow_det_bboxes(
        img.copy(),
        bboxes,
        labels,
        class_names=class_names,
        score_thr=score_thr,
        show=out_file is None) 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def show_result(self,
                    data,
                    result,
                    img_norm_cfg,
                    dataset=None,
                    score_thr=0.3):
        if isinstance(result, tuple):
            bbox_result, segm_result = result
        else:
            bbox_result, segm_result = result, None

        img_tensor = data['img'][0]
        img_metas = data['img_meta'][0].data[0]
        imgs = tensor2imgs(img_tensor, **img_norm_cfg)
        assert len(imgs) == len(img_metas)

        if dataset is None:
            class_names = self.CLASSES
        elif isinstance(dataset, str):
            class_names = get_classes(dataset)
        elif isinstance(dataset, (list, tuple)):
            class_names = dataset
        else:
            raise TypeError(
                'dataset must be a valid dataset name or a sequence'
                ' of class names, not {}'.format(type(dataset)))

        for img, img_meta in zip(imgs, img_metas):
            h, w, _ = img_meta['img_shape']
            img_show = img[:h, :w, :]

            bboxes = np.vstack(bbox_result)
            # draw segmentation masks
            if segm_result is not None:
                segms = mmcv.concat_list(segm_result)
                inds = np.where(bboxes[:, -1] > score_thr)[0]
                for i in inds:
                    color_mask = np.random.randint(
                        0, 256, (1, 3), dtype=np.uint8)
                    mask = maskUtils.decode(segms[i]).astype(np.bool)
                    img_show[mask] = img_show[mask] * 0.5 + color_mask * 0.5
            # draw bounding boxes
            labels = [
                np.full(bbox.shape[0], i, dtype=np.int32)
                for i, bbox in enumerate(bbox_result)
            ]
            labels = np.concatenate(labels)
            mmcv.imshow_det_bboxes(
                img_show,
                bboxes,
                labels,
                class_names=class_names,
                score_thr=score_thr) 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import get_classes [as 别名]
def show_result(self, data, result, dataset=None, score_thr=0.3):
        if isinstance(result, tuple):
            bbox_result, segm_result = result
        else:
            bbox_result, segm_result = result, None

        img_tensor = data['img'][0]
        img_metas = data['img_meta'][0].data[0]
        imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
        assert len(imgs) == len(img_metas)

        if dataset is None:
            class_names = self.CLASSES
        elif isinstance(dataset, str):
            class_names = get_classes(dataset)
        elif isinstance(dataset, (list, tuple)):
            class_names = dataset
        else:
            raise TypeError(
                'dataset must be a valid dataset name or a sequence'
                ' of class names, not {}'.format(type(dataset)))

        for img, img_meta in zip(imgs, img_metas):
            h, w, _ = img_meta['img_shape']
            img_show = img[:h, :w, :]

            bboxes = np.vstack(bbox_result)
            # draw segmentation masks
            if segm_result is not None:
                segms = mmcv.concat_list(segm_result)
                inds = np.where(bboxes[:, -1] > score_thr)[0]
                for i in inds:
                    color_mask = np.random.randint(
                        0, 256, (1, 3), dtype=np.uint8)
                    mask = maskUtils.decode(segms[i]).astype(np.bool)
                    img_show[mask] = img_show[mask] * 0.5 + color_mask * 0.5
            # draw bounding boxes
            labels = [
                np.full(bbox.shape[0], i, dtype=np.int32)
                for i, bbox in enumerate(bbox_result)
            ]
            labels = np.concatenate(labels)
            mmcv.imshow_det_bboxes(
                img_show,
                bboxes,
                labels,
                class_names=class_names,
                score_thr=score_thr)