test.py 文件源码

def im_detect(net, im, boxes):
    """Detect object classes in an image given object proposals.

    Arguments:
        net (caffe.Net): Fast R-CNN network to use
        im (ndarray): color image to test (in BGR order)
        boxes (ndarray): R x 4 array of object proposals

    Returns:
        scores (ndarray): R x K array of object class scores (K includes
            background as object category 0)
        boxes (ndarray): R x (4*K) array of predicted bounding boxes
    """
    blobs, unused_im_scale_factors = _get_blobs(im, boxes)

    # When mapping from image ROIs to feature map ROIs, there's some aliasing
    # (some distinct image ROIs get mapped to the same feature ROI).
    # Here, we identify duplicate feature ROIs, so we only compute features
    # on the unique subset.
    for i in xrange(len(blobs['data'])):
        if cfg.DEDUP_BOXES > 0:
            v = np.array([1, 1e3, 1e6, 1e9, 1e12])
            hashes = np.round(blobs['rois'][i] * cfg.DEDUP_BOXES).dot(v)
            _, index, inv_index = np.unique(hashes, return_index=True,
                                            return_inverse=True)
            blobs['rois'][i] = blobs['rois'][i][index, :]

        # reshape network inputs
        net.blobs['data'].reshape(*(blobs['data'][i].shape))
        net.blobs['rois'].reshape(*(blobs['rois'][i].shape))

        blobs_out = net.forward(data=blobs['data'][i].astype(np.float32, copy=False),
                                rois=blobs['rois'][i].astype(np.float32, copy=False))


        scores_tmp = blobs_out['cls_score_7_1']
        if cfg.DEDUP_BOXES > 0:
            # Map scores and predictions back to the original set of boxes
            scores_tmp = scores_tmp[inv_index, :]
            # pred_boxes = pred_boxes[inv_index, :]

        if i == 0:        
            scores = np.copy(scores_tmp)
        else:
            scores += scores_tmp

    pred_boxes = np.tile(boxes, (1, scores.shape[1]))

    return scores, pred_boxes