Python test_engine.im_detect_all方法代码示例

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret 

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps
    )

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)

    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName("result_boxes"), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName("result_segms"), _ornone(segms))
        workspace.FeedBlob(core.ScopedName("result_keypoints"), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName("result_classids"), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret 

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=args.thresh,
            kp_thresh=args.kp_thresh,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(envu.yaml_dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=0.7,
            kp_thresh=2,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

# 需要导入模块: from detectron.core import test_engine [as 别名]
# 或者: from detectron.core.test_engine import im_detect_all [as 别名]
def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(yaml.dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    )