//  [python]
def worker(gpu_id, images, det_net, args, result_queue):
    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)

    img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3])  # is RGB. not BGR
    img_batch = tf.cast(img_plac, tf.float32)

    if cfgs.NET_NAME in ['resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d']:
        img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
    else:
        img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)

    img_batch = tf.expand_dims(img_batch, axis=0)

    detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
        input_img_batch=img_batch,
        gtboxes_batch_h=None,
        gtboxes_batch_r=None)

    init_op = tf.group(
        tf.global_variables_initializer(),
        tf.local_variables_initializer()
    )

    restorer, restore_ckpt = det_net.get_restorer()

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True

    with tf.Session(config=config) as sess:
        sess.run(init_op)
        if not restorer is None:
            restorer.restore(sess, restore_ckpt)
            print('restore model %d ...' % gpu_id)

        for img_path in images:
            # if 'P0006.png' not in img_path:
            #     continue
            img = cv2.imread(img_path)

            box_res_rotate = []
            label_res_rotate = []
            score_res_rotate = []

            imgH = img.shape[0]
            imgW = img.shape[1]

            img_short_side_len_list = cfgs.IMG_SHORT_SIDE_LEN if isinstance(cfgs.IMG_SHORT_SIDE_LEN, list) else [
                cfgs.IMG_SHORT_SIDE_LEN]
            img_short_side_len_list = [img_short_side_len_list[0]] if not args.multi_scale else img_short_side_len_list

            if imgH < args.h_len:
                temp = np.zeros([args.h_len, imgW, 3], np.float32)
                temp[0:imgH, :, :] = img
                img = temp
                imgH = args.h_len

            if imgW < args.w_len:
                temp = np.zeros([imgH, args.w_len, 3], np.float32)
                temp[:, 0:imgW, :] = img
                img = temp
                imgW = args.w_len

            for hh in range(0, imgH, args.h_len - args.h_overlap):
                if imgH - hh - 1 < args.h_len:
                    hh_ = imgH - args.h_len
                else:
                    hh_ = hh
                for ww in range(0, imgW, args.w_len - args.w_overlap):
                    if imgW - ww - 1 < args.w_len:
                        ww_ = imgW - args.w_len
                    else:
                        ww_ = ww
                    src_img = img[hh_:(hh_ + args.h_len), ww_:(ww_ + args.w_len), :]

                    for short_size in img_short_side_len_list:
                        max_len = cfgs.IMG_MAX_LENGTH
                        if args.h_len < args.w_len:
                            new_h, new_w = short_size, min(int(short_size * float(args.w_len) / args.h_len), max_len)
                        else:
                            new_h, new_w = min(int(short_size * float(args.h_len) / args.w_len), max_len), short_size
                        img_resize = cv2.resize(src_img, (new_w, new_h))

                        resized_img, det_boxes_r_, det_scores_r_, det_category_r_ = \
                            sess.run(
                                [img_batch, detection_boxes, detection_scores, detection_category],
                                feed_dict={img_plac: img_resize[:, :, ::-1]}
                            )

                        resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
                        src_h, src_w = src_img.shape[0], src_img.shape[1]

                        if len(det_boxes_r_) > 0:
                            det_boxes_r_ = forward_convert(det_boxes_r_, False)
                            det_boxes_r_[:, 0::2] *= (src_w / resized_w)
                            det_boxes_r_[:, 1::2] *= (src_h / resized_h)
                            # det_boxes_r_ = backward_convert(det_boxes_r_, False)

                            for ii in range(len(det_boxes_r_)):
                                box_rotate = det_boxes_r_[ii]
                                box_rotate[0::2] = box_rotate[0::2] + ww_
                                box_rotate[1::2] = box_rotate[1::2] + hh_
                                box_res_rotate.append(box_rotate)
                                label_res_rotate.append(det_category_r_[ii])
                                score_res_rotate.append(det_scores_r_[ii])

                        if args.flip_img:
                            det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \
                                sess.run(
                                    [detection_boxes, detection_scores, detection_category],
                                    feed_dict={img_plac: cv2.flip(img_resize, flipCode=1)[:, :, ::-1]}
                                )
                            if len(det_boxes_r_flip) > 0:
                                det_boxes_r_flip = forward_convert(det_boxes_r_flip, False)
                                det_boxes_r_flip[:, 0::2] *= (src_w / resized_w)
                                det_boxes_r_flip[:, 1::2] *= (src_h / resized_h)

                                for ii in range(len(det_boxes_r_flip)):
                                    box_rotate = det_boxes_r_flip[ii]
                                    box_rotate[0::2] = (src_w - box_rotate[0::2]) + ww_
                                    box_rotate[1::2] = box_rotate[1::2] + hh_
                                    box_res_rotate.append(box_rotate)
                                    label_res_rotate.append(det_category_r_flip[ii])
                                    score_res_rotate.append(det_scores_r_flip[ii])

                            det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \
                                sess.run(
                                    [detection_boxes, detection_scores, detection_category],
                                    feed_dict={img_plac: cv2.flip(img_resize, flipCode=0)[:, :, ::-1]}
                                )
                            if len(det_boxes_r_flip) > 0:
                                det_boxes_r_flip = forward_convert(det_boxes_r_flip, False)
                                det_boxes_r_flip[:, 0::2] *= (src_w / resized_w)
                                det_boxes_r_flip[:, 1::2] *= (src_h / resized_h)

                                for ii in range(len(det_boxes_r_flip)):
                                    box_rotate = det_boxes_r_flip[ii]
                                    box_rotate[0::2] = box_rotate[0::2] + ww_
                                    box_rotate[1::2] = (src_h - box_rotate[1::2]) + hh_
                                    box_res_rotate.append(box_rotate)
                                    label_res_rotate.append(det_category_r_flip[ii])
                                    score_res_rotate.append(det_scores_r_flip[ii])

            box_res_rotate = np.array(box_res_rotate)
            label_res_rotate = np.array(label_res_rotate)
            score_res_rotate = np.array(score_res_rotate)

            box_res_rotate_ = []
            label_res_rotate_ = []
            score_res_rotate_ = []
            threshold = {'roundabout': 0.1, 'tennis-court': 0.3, 'swimming-pool': 0.1, 'storage-tank': 0.2,
                         'soccer-ball-field': 0.3, 'small-vehicle': 0.2, 'ship': 0.2, 'plane': 0.3,
                         'large-vehicle': 0.1, 'helicopter': 0.2, 'harbor': 0.0001, 'ground-track-field': 0.3,
                         'bridge': 0.0001, 'basketball-court': 0.3, 'baseball-diamond': 0.3}

            for sub_class in range(1, cfgs.CLASS_NUM + 1):
                index = np.where(label_res_rotate == sub_class)[0]
                if len(index) == 0:
                    continue
                tmp_boxes_r = box_res_rotate[index]
                tmp_label_r = label_res_rotate[index]
                tmp_score_r = score_res_rotate[index]

                tmp_boxes_r_ = backward_convert(tmp_boxes_r, False)

                try:
                    inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_),
                                                    scores=np.array(tmp_score_r),
                                                    iou_threshold=threshold[LABEL_NAME_MAP[sub_class]],
                                                    max_output_size=5000)
                except:
                    tmp_boxes_r_ = np.array(tmp_boxes_r_)
                    tmp = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
                    tmp[:, 0:-1] = tmp_boxes_r_
                    tmp[:, -1] = np.array(tmp_score_r)
                    # Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
                    jitter = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
                    jitter[:, 0] += np.random.rand(tmp_boxes_r_.shape[0], ) / 1000
                    inx = rotate_gpu_nms(np.array(tmp, np.float32) + np.array(jitter, np.float32),
                                         float(threshold[LABEL_NAME_MAP[sub_class]]), 0)

                box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
                score_res_rotate_.extend(np.array(tmp_score_r)[inx])
                label_res_rotate_.extend(np.array(tmp_label_r)[inx])

            result_dict = {'boxes': np.array(box_res_rotate_), 'scores': np.array(score_res_rotate_),
                           'labels': np.array(label_res_rotate_), 'image_id': img_path}
            result_queue.put_nowait(result_dict)