# 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.
##############################################################################

"""Test a RetinaNet network on an image database"""

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

import numpy as np
import logging
from collections import defaultdict

from caffe2.python import core, workspace

from core.config import cfg
from modeling.generate_anchors import generate_anchors
from utils.timer import Timer

import utils.blob as blob_utils
import utils.boxes as box_utils

logger = logging.getLogger(__name__)


def _create_cell_anchors():
    """
    Generate all types of anchors for all fpn levels/scales/aspect ratios.
    This function is called only once at the beginning of inference.
    """
    k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
    scales_per_octave = cfg.RETINANET.SCALES_PER_OCTAVE
    aspect_ratios = cfg.RETINANET.ASPECT_RATIOS
    anchor_scale = cfg.RETINANET.ANCHOR_SCALE
    A = scales_per_octave * len(aspect_ratios)
    anchors = {}
    for lvl in range(k_min, k_max + 1):
        # create cell anchors array
        stride = 2. ** lvl
        cell_anchors = np.zeros((A, 4))
        a = 0
        for octave in range(scales_per_octave):
            octave_scale = 2 ** (octave / float(scales_per_octave))
            for aspect in aspect_ratios:
                anchor_sizes = (stride * octave_scale * anchor_scale, )
                anchor_aspect_ratios = (aspect, )
                cell_anchors[a, :] = generate_anchors(
                    stride=stride, sizes=anchor_sizes,
                    aspect_ratios=anchor_aspect_ratios)
                a += 1
        anchors[lvl] = cell_anchors
    return anchors


def im_detect_bbox(model, im, timers=None):
    """Generate RetinaNet detections on a single image."""
    if timers is None:
        timers = defaultdict(Timer)
    # Although anchors are input independent and could be precomputed,
    # recomputing them per image only brings a small overhead
    anchors = _create_cell_anchors()
    timers['im_detect_bbox'].tic()
    k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
    A = cfg.RETINANET.SCALES_PER_OCTAVE * len(cfg.RETINANET.ASPECT_RATIOS)
    inputs = {}
    inputs['data'], im_scale, inputs['im_info'] = \
        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
    cls_probs, box_preds = [], []
    for lvl in range(k_min, k_max + 1):
        suffix = 'fpn{}'.format(lvl)
        cls_probs.append(core.ScopedName('retnet_cls_prob_{}'.format(suffix)))
        box_preds.append(core.ScopedName('retnet_bbox_pred_{}'.format(suffix)))
    for k, v in inputs.items():
        workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))

    workspace.RunNet(model.net.Proto().name)
    cls_probs = workspace.FetchBlobs(cls_probs)
    box_preds = workspace.FetchBlobs(box_preds)

    # here the boxes_all are [x0, y0, x1, y1, score]
    boxes_all = defaultdict(list)

    cnt = 0
    for lvl in range(k_min, k_max + 1):
        # create cell anchors array
        stride = 2. ** lvl
        cell_anchors = anchors[lvl]

        # fetch per level probability
        cls_prob = cls_probs[cnt]
        box_pred = box_preds[cnt]
        cls_prob = cls_prob.reshape((
            cls_prob.shape[0], A, int(cls_prob.shape[1] / A),
            cls_prob.shape[2], cls_prob.shape[3]))
        box_pred = box_pred.reshape((
            box_pred.shape[0], A, 4, box_pred.shape[2], box_pred.shape[3]))
        cnt += 1

        if cfg.RETINANET.SOFTMAX:
            cls_prob = cls_prob[:, :, 1::, :, :]

        cls_prob_ravel = cls_prob.ravel()
        # In some cases [especially for very small img sizes], it's possible that
        # candidate_ind is empty if we impose threshold 0.05 at all levels. This
        # will lead to errors since no detections are found for this image. Hence,
        # for lvl 7 which has small spatial resolution, we take the threshold 0.0
        th = cfg.RETINANET.INFERENCE_TH if lvl < k_max else 0.0
        candidate_inds = np.where(cls_prob_ravel > th)[0]
        if (len(candidate_inds) == 0):
            continue

        pre_nms_topn = min(cfg.RETINANET.PRE_NMS_TOP_N, len(candidate_inds))
        inds = np.argpartition(
            cls_prob_ravel[candidate_inds], -pre_nms_topn)[-pre_nms_topn:]
        inds = candidate_inds[inds]

        inds_5d = np.array(np.unravel_index(inds, cls_prob.shape)).transpose()
        classes = inds_5d[:, 2]
        anchor_ids, y, x = inds_5d[:, 1], inds_5d[:, 3], inds_5d[:, 4]
        scores = cls_prob[:, anchor_ids, classes, y, x]

        boxes = np.column_stack((x, y, x, y)).astype(dtype=np.float32)
        boxes *= stride
        boxes += cell_anchors[anchor_ids, :]

        if not cfg.RETINANET.CLASS_SPECIFIC_BBOX:
            box_deltas = box_pred[0, anchor_ids, :, y, x]
        else:
            box_cls_inds = classes * 4
            box_deltas = np.vstack(
                [box_pred[0, ind:ind + 4, yi, xi]
                 for ind, yi, xi in zip(box_cls_inds, y, x)]
            )
        pred_boxes = (
            box_utils.bbox_transform(boxes, box_deltas)
            if cfg.TEST.BBOX_REG else boxes)
        pred_boxes /= im_scale
        pred_boxes = box_utils.clip_tiled_boxes(pred_boxes, im.shape)
        box_scores = np.zeros((pred_boxes.shape[0], 5))
        box_scores[:, 0:4] = pred_boxes
        box_scores[:, 4] = scores

        for cls in range(1, cfg.MODEL.NUM_CLASSES):
            inds = np.where(classes == cls - 1)[0]
            if len(inds) > 0:
                boxes_all[cls].extend(box_scores[inds, :])
    timers['im_detect_bbox'].toc()

    # Combine predictions across all levels and retain the top scoring by class
    timers['misc_bbox'].tic()
    detections = []
    for cls, boxes in boxes_all.items():
        cls_dets = np.vstack(boxes).astype(dtype=np.float32)
        # do class specific nms here
        keep = box_utils.nms(cls_dets, cfg.TEST.NMS)
        cls_dets = cls_dets[keep, :]
        out = np.zeros((len(keep), 6))
        out[:, 0:5] = cls_dets
        out[:, 5].fill(cls)
        detections.append(out)

    # detections (N, 6) format:
    #   detections[:, :4] - boxes
    #   detections[:, 4] - scores
    #   detections[:, 5] - classes
    detections = np.vstack(detections)
    # sort all again
    inds = np.argsort(-detections[:, 4])
    detections = detections[inds[0:cfg.TEST.DETECTIONS_PER_IM], :]

    # Convert the detections to image cls_ format (see core/test_engine.py)
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    for c in range(1, num_classes):
        inds = np.where(detections[:, 5] == c)[0]
        cls_boxes[c] = detections[inds, :5]
    timers['misc_bbox'].toc()

    return cls_boxes
