# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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 absolute_import
import copy
import os.path as osp
import random
import numpy as np
import paddlex.utils.logging as logging
import paddlex as pst
from .voc import VOCDetection
from .dataset import is_pic


class CocoDetection(VOCDetection):
    """读取MSCOCO格式的检测数据集，并对样本进行相应的处理，该格式的数据集同样可以应用到实例分割模型的训练中。

    Args:
        data_dir (str): 数据集所在的目录路径。
        ann_file (str): 数据集的标注文件，为一个独立的json格式文件。
        transforms (paddlex.det.transforms): 数据集中每个样本的预处理/增强算子。
        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的一半。
        buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
        parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
            线程和'process'进程两种方式。默认为'process'（Windows和Mac下会强制使用thread，该参数无效）。
        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
    """

    def __init__(self,
                 data_dir,
                 ann_file,
                 transforms=None,
                 num_workers='auto',
                 buffer_size=100,
                 parallel_method='process',
                 shuffle=False):
        from pycocotools.coco import COCO

        super(VOCDetection, self).__init__(
            transforms=transforms,
            num_workers=num_workers,
            buffer_size=buffer_size,
            parallel_method=parallel_method,
            shuffle=shuffle)
        self.file_list = list()
        self.labels = list()
        self._epoch = 0

        coco = COCO(ann_file)
        self.coco_gt = coco
        img_ids = coco.getImgIds()
        cat_ids = coco.getCatIds()
        catid2clsid = dict({catid: i + 1 for i, catid in enumerate(cat_ids)})
        cname2cid = dict({
            coco.loadCats(catid)[0]['name']: clsid
            for catid, clsid in catid2clsid.items()
        })
        for label, cid in sorted(cname2cid.items(), key=lambda d: d[1]):
            self.labels.append(label)
        logging.info("Starting to read file list from dataset...")
        for img_id in img_ids:
            img_anno = coco.loadImgs(img_id)[0]
            im_fname = osp.join(data_dir, img_anno['file_name'])
            if not is_pic(im_fname):
                continue
            im_w = float(img_anno['width'])
            im_h = float(img_anno['height'])
            ins_anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=False)
            instances = coco.loadAnns(ins_anno_ids)

            bboxes = []
            for inst in instances:
                x, y, box_w, box_h = inst['bbox']
                x1 = max(0, x)
                y1 = max(0, y)
                x2 = min(im_w - 1, x1 + max(0, box_w - 1))
                y2 = min(im_h - 1, y1 + max(0, box_h - 1))
                if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
                    inst['clean_bbox'] = [x1, y1, x2, y2]
                    bboxes.append(inst)
                else:
                    logging.warning(
                        "Found an invalid bbox in annotations: im_id: {}, area: {} x1: {}, y1: {}, x2: {}, y2: {}."
                        .format(img_id, float(inst['area']), x1, y1, x2, y2))
            num_bbox = len(bboxes)
            gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
            gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
            gt_score = np.ones((num_bbox, 1), dtype=np.float32)
            is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
            difficult = np.zeros((num_bbox, 1), dtype=np.int32)
            gt_poly = None

            for i, box in enumerate(bboxes):
                catid = box['category_id']
                gt_class[i][0] = catid2clsid[catid]
                gt_bbox[i, :] = box['clean_bbox']
                is_crowd[i][0] = box['iscrowd']
                if 'segmentation' in box:
                    if gt_poly is None:
                        gt_poly = [None] * num_bbox
                    gt_poly[i] = box['segmentation']

            im_info = {
                'im_id': np.array([img_id]).astype('int32'),
                'image_shape': np.array([im_h, im_w]).astype('int32'),
            }
            label_info = {
                'is_crowd': is_crowd,
                'gt_class': gt_class,
                'gt_bbox': gt_bbox,
                'gt_score': gt_score,
                'difficult': difficult
            }
            if gt_poly is not None:
                label_info['gt_poly'] = gt_poly

            coco_rec = (im_info, label_info)
            self.file_list.append([im_fname, coco_rec])
        if not len(self.file_list) > 0:
            raise Exception('not found any coco record in %s' % (ann_file))
        logging.info("{} samples in file {}".format(
            len(self.file_list), ann_file))
        self.num_samples = len(self.file_list)
