如何将tf-faster-rcnn中的框画在一张图内

在运行tf-faster-r cnn代码时，预测框不是画在一张图上

tf-faster-rcnn的demo时图片中预测框的并没有放到一张图片上去，而是按照类别进行的划分，即：每一类一张图片，现在需要把框放到一张图上去，需要修改代码tools/demo.py，修改成如下代码：

def vis_detections(ax, im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]
        print(bbox[0], bbox[1], bbox[2], bbox[3])
        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
        )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14)

def demo(sess, net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the demo image
    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    im = cv2.imread(im_file)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(sess, net, im)
    timer.toc()
    print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(im, aspect='equal')
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1  # because we skipped background
        cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(ax, im, cls, dets, thresh=CONF_THRESH)
        plt.draw()

测试结果：