# Copyright (C) 2020 Intel Corporation
#
# SPDX-License-Identifier: MIT

# Implements MOTS format https://www.vision.rwth-aachen.de/page/mots

from enum import Enum
from glob import glob
import logging as log
import numpy as np
import os
import os.path as osp

from datumaro.components.extractor import (SourceExtractor, Importer,
    DatasetItem, AnnotationType, Mask, LabelCategories
)
from datumaro.components.converter import Converter
from datumaro.util.image import load_image, save_image
from datumaro.util.mask_tools import merge_masks


class MotsPath:
    MASKS_DIR = 'instances'
    IMAGE_DIR = 'images'
    IMAGE_EXT = '.jpg'
    LABELS_FILE = 'labels.txt'
    MAX_INSTANCES = 1000

MotsLabels = Enum('MotsLabels', [
    ('background', 0),
    ('car', 1),
    ('pedestrian', 2),
    ('ignored', 10),
])

class MotsPngExtractor(SourceExtractor):
    @staticmethod
    def detect_dataset(path):
        if osp.isdir(osp.join(path, MotsPath.MASKS_DIR)):
            return [{'url': path, 'format': 'mots_png'}]
        return []

    def __init__(self, path, subset_name=None):
        assert osp.isdir(path), path
        super().__init__(subset=subset_name)
        self._images_dir = osp.join(path, 'images')
        self._anno_dir = osp.join(path, MotsPath.MASKS_DIR)
        self._categories = self._parse_categories(
            osp.join(self._anno_dir, MotsPath.LABELS_FILE))
        self._items = self._parse_items()

    def _parse_categories(self, path):
        if osp.isfile(path):
            with open(path) as f:
                labels = [l.strip() for l in f]
        else:
            labels = [l.name for l in MotsLabels]
        return { AnnotationType.label: LabelCategories.from_iterable(labels) }

    def _parse_items(self):
        items = []
        for p in sorted(p for p in
                glob(self._anno_dir + '/**/*.png', recursive=True)):
            item_id = osp.splitext(osp.relpath(p, self._anno_dir))[0]
            items.append(DatasetItem(id=item_id, subset=self._subset,
                image=osp.join(self._images_dir, item_id + MotsPath.IMAGE_EXT),
                annotations=self._parse_annotations(p)))
        return items

    @staticmethod
    def _lazy_extract_mask(mask, v):
        return lambda: mask == v

    def _parse_annotations(self, path):
        combined_mask = load_image(path, dtype=np.uint16)
        masks = []
        for obj_id in np.unique(combined_mask):
            class_id, instance_id = divmod(obj_id, MotsPath.MAX_INSTANCES)
            z_order = 0
            if class_id == 0:
                continue # background
            if class_id == 10 and \
                    len(self._categories[AnnotationType.label]) < 10:
                z_order = 1
                class_id = self._categories[AnnotationType.label].find(
                    MotsLabels.ignored.name)[0]
            else:
                class_id -= 1
            masks.append(Mask(self._lazy_extract_mask(combined_mask, obj_id),
                label=class_id, z_order=z_order,
                attributes={'track_id': instance_id}))
        return masks


class MotsImporter(Importer):
    @classmethod
    def find_sources(cls, path):
        subsets = MotsPngExtractor.detect_dataset(path)
        if not subsets:
            for p in os.listdir(path):
                detected = MotsPngExtractor.detect_dataset(osp.join(path, p))
                for s in detected:
                    s.setdefault('options', {})['subset_name'] = p
                subsets.extend(detected)
        return subsets


class MotsPngConverter(Converter):
    DEFAULT_IMAGE_EXT = MotsPath.IMAGE_EXT

    def apply(self):
        for subset_name, subset in self._extractor.subsets().items():
            subset_dir = osp.join(self._save_dir, subset_name)
            images_dir = osp.join(subset_dir, MotsPath.IMAGE_DIR)
            anno_dir = osp.join(subset_dir, MotsPath.MASKS_DIR)
            os.makedirs(anno_dir, exist_ok=True)

            for item in subset:
                log.debug("Converting item '%s'", item.id)

                if self._save_images:
                    if item.has_image and item.image.has_data:
                        self._save_image(item,
                            osp.join(images_dir, self._make_image_filename(item)))
                    else:
                        log.debug("Item '%s' has no image", item.id)

                self._save_annotations(item, anno_dir)

            with open(osp.join(anno_dir, MotsPath.LABELS_FILE), 'w') as f:
                f.write('\n'.join(l.name
                    for l in subset.categories()[AnnotationType.label].items))

    def _save_annotations(self, item, anno_dir):
        masks = [a for a in item.annotations if a.type == AnnotationType.mask]
        if not masks:
            return

        instance_ids = [int(a.attributes['track_id']) for a in masks]
        masks = sorted(zip(masks, instance_ids), key=lambda e: e[0].z_order)
        mask = merge_masks([
            m.image * (MotsPath.MAX_INSTANCES * (1 + m.label) + id)
            for m, id in masks])
        save_image(osp.join(anno_dir, item.id + '.png'), mask,
            create_dir=True, dtype=np.uint16)