# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
#
# Based on:
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Construct minibatches for Detectron networks."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import cv2
import logging
import numpy as np

from core.config import cfg
import roi_data.fast_rcnn
import roi_data.retinanet
import roi_data.rpn
import utils.blob as blob_utils

logger = logging.getLogger(__name__)


def get_minibatch_blob_names(is_training=True):
    """Return blob names in the order in which they are read by the data loader.
    """
    # data blob: holds a batch of N images, each with 3 channels
    blob_names = ['data']
    if cfg.RPN.RPN_ON:
        # RPN-only or end-to-end Faster R-CNN
        blob_names += roi_data.rpn.get_rpn_blob_names(is_training=is_training)
    elif cfg.RETINANET.RETINANET_ON:
        blob_names += roi_data.retinanet.get_retinanet_blob_names(
            is_training=is_training
        )
    else:
        # Fast R-CNN like models trained on precomputed proposals
        blob_names += roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=is_training
        )
    return blob_names


def get_minibatch(roidb):
    """Given a roidb, construct a minibatch sampled from it."""
    # We collect blobs from each image onto a list and then concat them into a
    # single tensor, hence we initialize each blob to an empty list
    blobs = {k: [] for k in get_minibatch_blob_names()}
    # Get the input image blob, formatted for caffe2
    im_blob, im_scales = _get_image_blob(roidb)
    blobs['data'] = im_blob
    if cfg.RPN.RPN_ON:
        # RPN-only or end-to-end Faster/Mask R-CNN
        valid = roi_data.rpn.add_rpn_blobs(blobs, im_scales, roidb)
    elif cfg.RETINANET.RETINANET_ON:
        im_width, im_height = im_blob.shape[3], im_blob.shape[2]
        # im_width, im_height corresponds to the network input: padded image
        # (if needed) width and height. We pass it as input and slice the data
        # accordingly so that we don't need to use SampleAsOp
        valid = roi_data.retinanet.add_retinanet_blobs(
            blobs, im_scales, roidb, im_width, im_height
        )
    else:
        # Fast R-CNN like models trained on precomputed proposals
        valid = roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb)
    return blobs, valid


def _get_image_blob(roidb):
    """Builds an input blob from the images in the roidb at the specified
    scales.
    """
    num_images = len(roidb)
    # Sample random scales to use for each image in this batch
    scale_inds = np.random.randint(
        0, high=len(cfg.TRAIN.SCALES), size=num_images
    )
    processed_ims = []
    im_scales = []
    for i in range(num_images):
        im = cv2.imread(roidb[i]['image'])
        assert im is not None, \
            'Failed to read image \'{}\''.format(roidb[i]['image'])
        if roidb[i]['flipped']:
            im = im[:, ::-1, :]
        target_size = cfg.TRAIN.SCALES[scale_inds[i]]
        im, im_scale = blob_utils.prep_im_for_blob(
            im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE
        )
        im_scales.append(im_scale)
        processed_ims.append(im)

    # Create a blob to hold the input images
    blob = blob_utils.im_list_to_blob(processed_ims)

    return blob, im_scales
