"""
 Copyright (C) 2018-2020 Intel Corporation

 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.
"""

import logging as log

from extensions.ops.DetectionOutput import DetectionOutput
from mo.front.caffe.collect_attributes import merge_attrs
from mo.front.common.partial_infer.multi_box_detection import multi_box_detection_infer
from mo.front.extractor import FrontExtractorOp


class DetectionOutputFrontExtractor(FrontExtractorOp):
    op = 'DetectionOutput'
    enabled = True

    @classmethod
    def extract(cls, node):
        pl = node.pb
        assert pl, 'Protobuf layer can not be empty'

        param = pl.detection_output_param

        # TODO rewrite params as complex structures
        if hasattr(param, 'nms_param'):
            nms_threshold = param.nms_param.nms_threshold
            eta = param.nms_param.eta
            if param.nms_param.top_k == 0:
                top_k = -1
            else:
                top_k = param.nms_param.top_k

        code_type_values = [
            "",
            "caffe.PriorBoxParameter.CORNER",
            "caffe.PriorBoxParameter.CENTER_SIZE",
            "caffe.PriorBoxParameter.CORNER_SIZE"
        ]

        code_type = code_type_values[1]
        if hasattr(param, 'code_type'):
            if param.code_type < 1 or param.code_type > 3:
                log.error("Incorrect value of code_type parameter")
                return
            code_type = code_type_values[param.code_type]

        visualize_threshold = param.visualize_threshold if param.visualize_threshold else 0.6

        resize_mode_values = [
            "",
            "caffe.ResizeParameter.WARP",
            "caffe.ResizeParameter.FIT_SMALL_SIZE",
            "caffe.ResizeParameter.FIT_LARGE_SIZE_AND_PAD"
        ]

        if param.save_output_param.resize_param.resize_mode < 1 or param.save_output_param.resize_param.resize_mode > 3:
            log.error("Incorrect value of resize_mode parameter")
            return
        resize_mode = resize_mode_values[param.save_output_param.resize_param.resize_mode]

        pad_mode_values = [
            "",
            "caffe.ResizeParameter.CONSTANT",
            "caffe.ResizeParameter.MIRRORED",
            "caffe.ResizeParameter.REPEAT_NEAREST"
        ]

        if param.save_output_param.resize_param.pad_mode < 1 or param.save_output_param.resize_param.pad_mode > 3:
            log.error("Incorrect value of pad_mode parameter")
        else:
            pad_mode = pad_mode_values[param.save_output_param.resize_param.pad_mode]

        interp_mode_values = [
            "",
            "caffe.ResizeParameter.LINEAR",
            "caffe.ResizeParameter.AREA",
            "caffe.ResizeParameter.NEAREST",
            "caffe.ResizeParameter.CUBIC",
            "caffe.ResizeParameter.LANCZOS4"
        ]
        interp_mode = ""
        for x in param.save_output_param.resize_param.interp_mode:
            if x < 1 or x > 5:
                log.error("Incorrect value of interp_mode parameter")
                return
            interp_mode += interp_mode_values[x]

        attrs = {
            'num_classes': param.num_classes,
            'share_location': int(param.share_location),
            'background_label_id': param.background_label_id,
            'code_type': code_type,
            'variance_encoded_in_target': int(param.variance_encoded_in_target),
            'keep_top_k': param.keep_top_k,
            'confidence_threshold': param.confidence_threshold,
            'visualize': param.visualize,
            'visualize_threshold': visualize_threshold,
            'save_file': param.save_file,
            # nms_param
            'nms_threshold': nms_threshold,
            'top_k': top_k,
            'eta': eta,
            # save_output_param
            'output_directory': param.save_output_param.output_directory,
            'output_name_prefix': param.save_output_param.output_name_prefix,
            'output_format': param.save_output_param.output_format,
            'label_map_file': param.save_output_param.label_map_file,
            'name_size_file': param.save_output_param.name_size_file,
            'num_test_image': param.save_output_param.num_test_image,
            # save_output_param.resize_param
            'prob': param.save_output_param.resize_param.prob,
            'resize_mode': resize_mode,
            'height': param.save_output_param.resize_param.height,
            'width': param.save_output_param.resize_param.width,
            'height_scale': param.save_output_param.resize_param.height_scale,
            'width_scale': param.save_output_param.resize_param.width_scale,
            'pad_mode': pad_mode,
            'pad_value': ','.join(str(x) for x in param.save_output_param.resize_param.pad_value),
            'interp_mode': interp_mode,
        }

        # these params can be omitted in caffe.proto and in param as consequence,
        # so check if it is set or set to default
        fields = [field[0].name for field in param.ListFields()]
        if 'input_width' in fields:
            attrs['input_width'] = param.input_width
        if 'input_height' in fields:
            attrs['input_height'] = param.input_height
        if 'normalized' in fields:
            attrs['normalized'] = int(param.normalized)
        if 'objectness_score' in fields:
            attrs['objectness_score'] = param.objectness_score

        mapping_rule = merge_attrs(param, attrs)

        # force setting infer function because it doesn't exist in proto so merge_attrs will not set it
        mapping_rule.update({'infer': multi_box_detection_infer})

        # update the attributes of the node
        DetectionOutput.update_node_stat(node, mapping_rule)
        return cls.enabled
