#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from __future__ import print_function

import unittest
import numpy as np
import sys
import math
import paddle
import paddle.fluid as fluid
from op_test import OpTest
from test_anchor_generator_op import anchor_generator_in_python
import copy
from test_generate_proposals_op import clip_tiled_boxes, box_coder, nms


def generate_proposals_v2_in_python(scores, bbox_deltas, im_shape, anchors,
                                    variances, pre_nms_topN, post_nms_topN,
                                    nms_thresh, min_size, eta, pixel_offset):
    all_anchors = anchors.reshape(-1, 4)
    rois = np.empty((0, 5), dtype=np.float32)
    roi_probs = np.empty((0, 1), dtype=np.float32)

    rpn_rois = []
    rpn_roi_probs = []
    rois_num = []
    num_images = scores.shape[0]
    for img_idx in range(num_images):
        img_i_boxes, img_i_probs = proposal_for_one_image(
            im_shape[img_idx, :], all_anchors, variances,
            bbox_deltas[img_idx, :, :, :], scores[img_idx, :, :, :],
            pre_nms_topN, post_nms_topN, nms_thresh, min_size, eta,
            pixel_offset)
        rois_num.append(img_i_probs.shape[0])
        rpn_rois.append(img_i_boxes)
        rpn_roi_probs.append(img_i_probs)

    return rpn_rois, rpn_roi_probs, rois_num


def proposal_for_one_image(im_shape, all_anchors, variances, bbox_deltas,
                           scores, pre_nms_topN, post_nms_topN, nms_thresh,
                           min_size, eta, pixel_offset):
    # Transpose and reshape predicted bbox transformations to get them
    # into the same order as the anchors:
    #   - bbox deltas will be (4 * A, H, W) format from conv output
    #   - transpose to (H, W, 4 * A)
    #   - reshape to (H * W * A, 4) where rows are ordered by (H, W, A)
    #     in slowest to fastest order to match the enumerated anchors
    bbox_deltas = bbox_deltas.transpose((1, 2, 0)).reshape(-1, 4)
    all_anchors = all_anchors.reshape(-1, 4)
    variances = variances.reshape(-1, 4)
    # Same story for the scores:
    #   - scores are (A, H, W) format from conv output
    #   - transpose to (H, W, A)
    #   - reshape to (H * W * A, 1) where rows are ordered by (H, W, A)
    #     to match the order of anchors and bbox_deltas
    scores = scores.transpose((1, 2, 0)).reshape(-1, 1)

    # sort all (proposal, score) pairs by score from highest to lowest
    # take top pre_nms_topN (e.g. 6000)
    if pre_nms_topN <= 0 or pre_nms_topN >= len(scores):
        order = np.argsort(-scores.squeeze())
    else:
        # Avoid sorting possibly large arrays;
        # First partition to get top K unsorted
        # and then sort just those
        inds = np.argpartition(-scores.squeeze(), pre_nms_topN)[:pre_nms_topN]
        order = np.argsort(-scores[inds].squeeze())
        order = inds[order]
    scores = scores[order, :]
    bbox_deltas = bbox_deltas[order, :]
    all_anchors = all_anchors[order, :]
    proposals = box_coder(all_anchors, bbox_deltas, variances, pixel_offset)
    # clip proposals to image (may result in proposals with zero area
    # that will be removed in the next step)
    proposals = clip_tiled_boxes(proposals, im_shape, pixel_offset)
    # remove predicted boxes with height or width < min_size
    keep = filter_boxes(proposals, min_size, im_shape, pixel_offset)
    if len(keep) == 0:
        proposals = np.zeros((1, 4)).astype('float32')
        scores = np.zeros((1, 1)).astype('float32')
        return proposals, scores
    proposals = proposals[keep, :]
    scores = scores[keep, :]

    # apply loose nms (e.g. threshold = 0.7)
    # take post_nms_topN (e.g. 1000)
    # return the top proposals
    if nms_thresh > 0:
        keep = nms(boxes=proposals,
                   scores=scores,
                   nms_threshold=nms_thresh,
                   eta=eta,
                   pixel_offset=pixel_offset)
        if post_nms_topN > 0 and post_nms_topN < len(keep):
            keep = keep[:post_nms_topN]
        proposals = proposals[keep, :]
        scores = scores[keep, :]

    return proposals, scores


def filter_boxes(boxes, min_size, im_shape, pixel_offset=True):
    """Only keep boxes with both sides >= min_size and center within the image.
    """
    # Scale min_size to match image scale
    min_size = max(min_size, 1.0)
    offset = 1 if pixel_offset else 0
    ws = boxes[:, 2] - boxes[:, 0] + offset
    hs = boxes[:, 3] - boxes[:, 1] + offset
    if pixel_offset:
        x_ctr = boxes[:, 0] + ws / 2.
        y_ctr = boxes[:, 1] + hs / 2.
        keep = np.where((ws >= min_size) & (hs >= min_size) & (x_ctr < im_shape[
            1]) & (y_ctr < im_shape[0]))[0]
    else:
        keep = np.where((ws >= min_size) & (hs >= min_size))[0]
    return keep


class TestGenerateProposalsV2Op(OpTest):
    def set_data(self):
        self.init_test_params()
        self.init_test_input()
        self.init_test_output()
        self.inputs = {
            'Scores': self.scores,
            'BboxDeltas': self.bbox_deltas,
            'ImShape': self.im_shape.astype(np.float32),
            'Anchors': self.anchors,
            'Variances': self.variances
        }

        self.attrs = {
            'pre_nms_topN': self.pre_nms_topN,
            'post_nms_topN': self.post_nms_topN,
            'nms_thresh': self.nms_thresh,
            'min_size': self.min_size,
            'eta': self.eta,
            'pixel_offset': self.pixel_offset,
        }

        self.outputs = {
            'RpnRois': (self.rpn_rois[0], [self.rois_num]),
            'RpnRoiProbs': (self.rpn_roi_probs[0], [self.rois_num]),
        }

    def test_check_output(self):
        self.check_output()

    def setUp(self):
        self.op_type = "generate_proposals_v2"
        self.set_data()

    def init_test_params(self):
        self.pre_nms_topN = 12000  # train 12000, test 2000
        self.post_nms_topN = 5000  # train 6000, test 1000
        self.nms_thresh = 0.7
        self.min_size = 3.0
        self.eta = 1.
        self.pixel_offset = True

    def init_test_input(self):
        batch_size = 1
        input_channels = 20
        layer_h = 16
        layer_w = 16
        input_feat = np.random.random(
            (batch_size, input_channels, layer_h, layer_w)).astype('float32')
        self.anchors, self.variances = anchor_generator_in_python(
            input_feat=input_feat,
            anchor_sizes=[16., 32.],
            aspect_ratios=[0.5, 1.0],
            variances=[1.0, 1.0, 1.0, 1.0],
            stride=[16.0, 16.0],
            offset=0.5)
        self.im_shape = np.array([[64, 64]]).astype('float32')
        num_anchors = self.anchors.shape[2]
        self.scores = np.random.random(
            (batch_size, num_anchors, layer_h, layer_w)).astype('float32')
        self.bbox_deltas = np.random.random(
            (batch_size, num_anchors * 4, layer_h, layer_w)).astype('float32')

    def init_test_output(self):
        self.rpn_rois, self.rpn_roi_probs, self.rois_num = generate_proposals_v2_in_python(
            self.scores, self.bbox_deltas, self.im_shape, self.anchors,
            self.variances, self.pre_nms_topN, self.post_nms_topN,
            self.nms_thresh, self.min_size, self.eta, self.pixel_offset)


class TestGenerateProposalsV2OutLodOp(TestGenerateProposalsV2Op):
    def set_data(self):
        self.init_test_params()
        self.init_test_input()
        self.init_test_output()
        self.inputs = {
            'Scores': self.scores,
            'BboxDeltas': self.bbox_deltas,
            'ImShape': self.im_shape.astype(np.float32),
            'Anchors': self.anchors,
            'Variances': self.variances
        }

        self.attrs = {
            'pre_nms_topN': self.pre_nms_topN,
            'post_nms_topN': self.post_nms_topN,
            'nms_thresh': self.nms_thresh,
            'min_size': self.min_size,
            'eta': self.eta,
            'return_rois_num': True
        }

        self.outputs = {
            'RpnRois': (self.rpn_rois[0], [self.rois_num]),
            'RpnRoiProbs': (self.rpn_roi_probs[0], [self.rois_num]),
            'RpnRoisNum': (np.asarray(
                self.rois_num, dtype=np.int32))
        }


class TestGenerateProposalsV2OpNoBoxLeft(TestGenerateProposalsV2Op):
    def init_test_params(self):
        self.pre_nms_topN = 12000  # train 12000, test 2000
        self.post_nms_topN = 5000  # train 6000, test 1000
        self.nms_thresh = 0.7
        self.min_size = 1000.0
        self.eta = 1.
        self.pixel_offset = True


class TestGenerateProposalsV2OpNoOffset(TestGenerateProposalsV2Op):
    def init_test_params(self):
        self.pre_nms_topN = 12000  # train 12000, test 2000
        self.post_nms_topN = 5000  # train 6000, test 1000
        self.nms_thresh = 0.7
        self.min_size = 3.0
        self.eta = 1.
        self.pixel_offset = False


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()
