# 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 xml.etree.ElementTree as ET
import paddlex.utils.logging as logging
from .dataset import Dataset
from .dataset import is_pic
from .dataset import get_encoding


class VOCDetection(Dataset):
    """读取PascalVOC格式的检测数据集，并对样本进行相应的处理。

    Args:
        data_dir (str): 数据集所在的目录路径。
        file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
        label_list (str): 描述数据集包含的类别信息文件路径。
        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'thread'（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,
                 file_list,
                 label_list,
                 transforms=None,
                 num_workers='auto',
                 buffer_size=100,
                 parallel_method='thread',
                 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

        annotations = {}
        annotations['images'] = []
        annotations['categories'] = []
        annotations['annotations'] = []

        cname2cid = {}
        label_id = 1
        with open(label_list, 'r', encoding=get_encoding(label_list)) as fr:
            for line in fr.readlines():
                cname2cid[line.strip()] = label_id
                label_id += 1
                self.labels.append(line.strip())
        logging.info("Starting to read file list from dataset...")
        for k, v in cname2cid.items():
            annotations['categories'].append({
                'supercategory': 'component',
                'id': v,
                'name': k
            })
        ct = 0
        ann_ct = 0
        with open(file_list, 'r', encoding=get_encoding(file_list)) as fr:
            while True:
                line = fr.readline()
                if not line:
                    break
                img_file, xml_file = [osp.join(data_dir, x) \
                        for x in line.strip().split()[:2]]
                if not is_pic(img_file):
                    continue
                if not osp.isfile(xml_file):
                    continue
                if not osp.exists(img_file):
                    raise IOError(
                        'The image file {} is not exist!'.format(img_file))
                tree = ET.parse(xml_file)
                if tree.find('id') is None:
                    im_id = np.array([ct])
                else:
                    ct = int(tree.find('id').text)
                    im_id = np.array([int(tree.find('id').text)])

                objs = tree.findall('object')
                im_w = float(tree.find('size').find('width').text)
                im_h = float(tree.find('size').find('height').text)
                gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
                gt_class = np.zeros((len(objs), 1), dtype=np.int32)
                gt_score = np.ones((len(objs), 1), dtype=np.float32)
                is_crowd = np.zeros((len(objs), 1), dtype=np.int32)
                difficult = np.zeros((len(objs), 1), dtype=np.int32)
                for i, obj in enumerate(objs):
                    cname = obj.find('name').text
                    gt_class[i][0] = cname2cid[cname]
                    _difficult = int(obj.find('difficult').text)
                    x1 = float(obj.find('bndbox').find('xmin').text)
                    y1 = float(obj.find('bndbox').find('ymin').text)
                    x2 = float(obj.find('bndbox').find('xmax').text)
                    y2 = float(obj.find('bndbox').find('ymax').text)
                    x1 = max(0, x1)
                    y1 = max(0, y1)
                    x2 = min(im_w - 1, x2)
                    y2 = min(im_h - 1, y2)
                    gt_bbox[i] = [x1, y1, x2, y2]
                    is_crowd[i][0] = 0
                    difficult[i][0] = _difficult
                    annotations['annotations'].append({
                        'iscrowd':
                        0,
                        'image_id':
                        int(im_id[0]),
                        'bbox': [x1, y1, x2 - x1 + 1, y2 - y1 + 1],
                        'area':
                        float((x2 - x1 + 1) * (y2 - y1 + 1)),
                        'category_id':
                        cname2cid[cname],
                        'id':
                        ann_ct,
                        'difficult':
                        _difficult
                    })
                    ann_ct += 1

                im_info = {
                    'im_id': im_id,
                    'origin_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,
                    'gt_poly': [],
                    'difficult': difficult
                }
                voc_rec = (im_info, label_info)
                if len(objs) != 0:
                    self.file_list.append([img_file, voc_rec])
                    ct += 1
                    annotations['images'].append({
                        'height':
                        im_h,
                        'width':
                        im_w,
                        'id':
                        int(im_id[0]),
                        'file_name':
                        osp.split(img_file)[1]
                    })

        if not len(self.file_list) > 0:
            raise Exception('not found any voc record in %s' % (file_list))
        logging.info("{} samples in file {}".format(
            len(self.file_list), file_list))
        self.num_samples = len(self.file_list)
        self.coco_gt = COCO()
        self.coco_gt.dataset = annotations
        self.coco_gt.createIndex()

    def iterator(self):
        self._epoch += 1
        self._pos = 0
        files = copy.deepcopy(self.file_list)
        if self.shuffle:
            random.shuffle(files)
        files = files[:self.num_samples]
        self.num_samples = len(files)
        for f in files:
            records = f[1]
            im_info = copy.deepcopy(records[0])
            label_info = copy.deepcopy(records[1])
            im_info['epoch'] = self._epoch
            if self.num_samples > 1:
                mix_idx = random.randint(1, self.num_samples - 1)
                mix_pos = (mix_idx + self._pos) % self.num_samples
            else:
                mix_pos = 0
            im_info['mixup'] = [
                files[mix_pos][0],
                copy.deepcopy(files[mix_pos][1][0]),
                copy.deepcopy(files[mix_pos][1][1])
            ]
            self._pos += 1
            sample = [f[0], im_info, label_info]
            yield sample