def test_net(sess, net, imdb, weights_filename , max_per_image=300, thresh=0.05, vis=False):
    """Test a Fast R-CNN network on an image database."""
    num_images = len(imdb.image_index)
    # all detections are collected into:
    #    all_boxes[cls][image] = N x 5 array of detections in
    #    (x1, y1, x2, y2, score)
    all_boxes = [[[] for _ in xrange(num_images)]
                 for _ in xrange(imdb.num_classes)]

    output_dir = get_output_dir(imdb, weights_filename)
    # timers
    _t = {'im_detect' : Timer(), 'misc' : Timer()}

    if not cfg.TEST.HAS_RPN:
        roidb = imdb.roidb

    for i in xrange(num_images):
        # filter out any ground truth boxes
        if cfg.TEST.HAS_RPN:
            box_proposals = None
        else:
            # The roidb may contain ground-truth rois (for example, if the roidb
            # comes from the training or val split). We only want to evaluate
            # detection on the *non*-ground-truth rois. We select those the rois
            # that have the gt_classes field set to 0, which means there's no
            # ground truth.
            box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]

        im = cv2.imread(imdb.image_path_at(i))
        _t['im_detect'].tic()
        scores, boxes = im_detect(sess, net, im, box_proposals)
        _t['im_detect'].toc()

        _t['misc'].tic()
        if vis:
            image = im[:, :, (2, 1, 0)]
            plt.cla()
            plt.imshow(image)

        # skip j = 0, because it's the background class
        for j in xrange(1, imdb.num_classes):
            inds = np.where(scores[:, j] > thresh)[0]
            cls_scores = scores[inds, j]
            cls_boxes = boxes[inds, j*4:(j+1)*4]
            cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
                .astype(np.float32, copy=False)
            keep = nms(cls_dets, cfg.TEST.NMS)
            cls_dets = cls_dets[keep, :]
            if vis:
                vis_detections(image, imdb.classes[j], cls_dets)
            all_boxes[j][i] = cls_dets
        if vis:
           plt.show()
        # Limit to max_per_image detections *over all classes*
        if max_per_image > 0:
            image_scores = np.hstack([all_boxes[j][i][:, -1]
                                      for j in xrange(1, imdb.num_classes)])
            if len(image_scores) > max_per_image:
                image_thresh = np.sort(image_scores)[-max_per_image]
                for j in xrange(1, imdb.num_classes):
                    keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
                    all_boxes[j][i] = all_boxes[j][i][keep, :]
        _t['misc'].toc()

        print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
              .format(i + 1, num_images, _t['im_detect'].average_time,
                      _t['misc'].average_time)

    det_file = os.path.join(output_dir, 'detections.pkl')
    with open(det_file, 'wb') as f:
        cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)

    print 'Evaluating detections'
    imdb.evaluate_detections(all_boxes, output_dir)
Test a Fast R-CNN network on an image database.
lib/fast_rcnn/test.py