image.py 文件源码

def get_segmentation_image(segdb, config):
    """
    propocess image and return segdb
    :param segdb: a list of segdb
    :return: list of img as mxnet format
    """
    num_images = len(segdb)
    assert num_images > 0, 'No images'
    processed_ims = []
    processed_segdb = []
    processed_seg_cls_gt = []
    for i in range(num_images):
        seg_rec = segdb[i]
        assert os.path.exists(seg_rec['image']), '%s does not exist'.format(seg_rec['image'])
        im = np.array(cv2.imread(seg_rec['image']))

        new_rec = seg_rec.copy()

        scale_ind = random.randrange(len(config.SCALES))
        target_size = config.SCALES[scale_ind][0]
        max_size = config.SCALES[scale_ind][1]
        im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
        im_tensor = transform(im, config.network.PIXEL_MEANS)
        im_info = [im_tensor.shape[2], im_tensor.shape[3], im_scale]
        new_rec['im_info'] = im_info

        seg_cls_gt = np.array(Image.open(seg_rec['seg_cls_path']))
        seg_cls_gt, seg_cls_gt_scale = resize(
            seg_cls_gt, target_size, max_size, stride=config.network.IMAGE_STRIDE, interpolation=cv2.INTER_NEAREST)
        seg_cls_gt_tensor = transform_seg_gt(seg_cls_gt)

        processed_ims.append(im_tensor)
        processed_segdb.append(new_rec)
        processed_seg_cls_gt.append(seg_cls_gt_tensor)

    return processed_ims, processed_seg_cls_gt, processed_segdb