Python cfg.PIXEL_MEANS属性代码示例

# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import PIXEL_MEANS [as 别名]
def get_image_blob(im, target_scale, target_max_size):
    """Convert an image into a network input.

    Arguments:
        im (ndarray): a color image in BGR order

    Returns:
        blob (ndarray): a data blob holding an image pyramid
        im_scale (float): image scale (target size) / (original size)
        im_info (ndarray)
    """
    processed_im, im_scale = prep_im_for_blob(
        im, cfg.PIXEL_MEANS, target_scale, target_max_size
    )
    blob = im_list_to_blob(processed_im)
    # NOTE: this height and width may be larger than actual scaled input image
    # due to the FPN.COARSEST_STRIDE related padding in im_list_to_blob. We are
    # maintaining this behavior for now to make existing results exactly
    # reproducible (in practice using the true input image height and width
    # yields nearly the same results, but they are sometimes slightly different
    # because predictions near the edge of the image will be pruned more
    # aggressively).
    height, width = blob.shape[2], blob.shape[3]
    im_info = np.hstack((height, width, im_scale))[np.newaxis, :]
    return blob, im_scale, im_info.astype(np.float32) 

# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import PIXEL_MEANS [as 别名]
def _get_image_blob(roidb):
    """Builds an input blob from the images in the roidb at the specified
    scales.
    """
    num_images = len(roidb)
    # Sample random scales to use for each image in this batch
    scale_inds = np.random.randint(
        0, high=len(cfg.TRAIN.SCALES), size=num_images
    )
    processed_ims = []
    im_scales = []
    for i in range(num_images):
        im = cv2.imread(roidb[i]['image'])
        assert im is not None, \
            'Failed to read image \'{}\''.format(roidb[i]['image'])
        if roidb[i]['flipped']:
            im = im[:, ::-1, :]
        target_size = cfg.TRAIN.SCALES[scale_inds[i]]
        im, im_scale = blob_utils.prep_im_for_blob(
            im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE
        )
        im_scales.append(im_scale)
        processed_ims.append(im)

    # Create a blob to hold the input images
    blob = blob_utils.im_list_to_blob(processed_ims)

    return blob, im_scales 

# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import PIXEL_MEANS [as 别名]
def run_model_pb(args, net, init_net, im, check_blobs):
    workspace.ResetWorkspace()
    workspace.RunNetOnce(init_net)
    mutils.create_input_blobs_for_net(net.Proto())
    workspace.CreateNet(net)

    # input_blobs, _ = core_test._get_blobs(im, None)
    input_blobs = _prepare_blobs(
        im,
        cfg.PIXEL_MEANS,
        cfg.TEST.SCALE, cfg.TEST.MAX_SIZE
    )
    gpu_blobs = []
    if args.device == 'gpu':
        gpu_blobs = ['data']
    for k, v in input_blobs.items():
        workspace.FeedBlob(
            core.ScopedName(k),
            v,
            mutils.get_device_option_cuda() if k in gpu_blobs else
            mutils.get_device_option_cpu()
        )

    try:
        workspace.RunNet(net)
        scores = workspace.FetchBlob('score_nms')
        classids = workspace.FetchBlob('class_nms')
        boxes = workspace.FetchBlob('bbox_nms')
    except Exception as e:
        print('Running pb model failed.\n{}'.format(e))
        # may not detect anything at all
        R = 0
        scores = np.zeros((R,), dtype=np.float32)
        boxes = np.zeros((R, 4), dtype=np.float32)
        classids = np.zeros((R,), dtype=np.float32)

    boxes = np.column_stack((boxes, scores))

    # sort the results based on score for comparision
    boxes, _, _, classids = _sort_results(
        boxes, None, None, classids)

    # write final result back to workspace
    workspace.FeedBlob('result_boxes', boxes)
    workspace.FeedBlob('result_classids', classids)

    ret = _get_result_blobs(check_blobs)

    return ret