PaddleX/paddlex/cv/datasets/easydata_det.py

# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os.path as osp
import random
import copy
import json
import cv2
import numpy as np
import paddlex.utils.logging as logging
from paddlex.utils import path_normalization
from .voc import VOCDetection
from .dataset import is_pic
from .dataset import get_encoding


class EasyDataDet(VOCDetection):
    """读取EasyDataDet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'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,
                 file_list,
                 label_list,
                 transforms=None,
                 num_workers='auto',
                 buffer_size=100,
                 parallel_method='process',
                 shuffle=False):
        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, 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
            })

        from pycocotools.mask import decode
        ct = 0
        ann_ct = 0
        with open(file_list, encoding=get_encoding(file_list)) as f:
            for line in f:
                img_file, json_file = [osp.join(data_dir, x) \
                        for x in line.strip().split()[:2]]
                img_file = path_normalization(img_file)
                json_file = path_normalization(json_file)
                if not is_pic(img_file):
                    continue
                if not osp.isfile(json_file):
                    continue
                if not osp.exists(img_file):
                    raise IOError('The image file {} is not exist!'.format(
                        img_file))
                with open(json_file, mode='r', \
                          encoding=get_encoding(json_file)) as j:
                    json_info = json.load(j)
                im_id = np.array([ct])
                im = cv2.imread(img_file)
                im_w = im.shape[1]
                im_h = im.shape[0]
                objs = json_info['labels']
                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)
                gt_poly = [None] * len(objs)
                for i, obj in enumerate(objs):
                    cname = obj['name']
                    gt_class[i][0] = cname2cid[cname]
                    x1 = max(0, obj['x1'])
                    y1 = max(0, obj['y1'])
                    x2 = min(im_w - 1, obj['x2'])
                    y2 = min(im_h - 1, obj['y2'])
                    gt_bbox[i] = [x1, y1, x2, y2]
                    is_crowd[i][0] = 0
                    if 'mask' in obj:
                        mask_dict = {}
                        mask_dict['size'] = [im_h, im_w]
                        mask_dict['counts'] = obj['mask'].encode()
                        mask = decode(mask_dict)
                        gt_poly[i] = self.mask2polygon(mask)
                    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)),
                        'segmentation': [[x1, y1, x1, y2, x2, y2, x2, y1]]
                        if gt_poly[i] is None else gt_poly[i],
                        'category_id': cname2cid[cname],
                        'id': ann_ct,
                        'difficult': 0
                    })
                    ann_ct += 1
                im_info = {
                    'im_id': im_id,
                    '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 None not in gt_poly:
                    label_info['gt_poly'] = gt_poly
                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)
        # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
        # or matplotlib.backends is imported for the first time
        # pycocotools import matplotlib
        import matplotlib
        matplotlib.use('Agg')
        from pycocotools.coco import COCO
        self.coco_gt = COCO()
        self.coco_gt.dataset = annotations
        self.coco_gt.createIndex()

    def mask2polygon(self, mask):
        contours, hierarchy = cv2.findContours(
            (mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        segmentation = []
        for contour in contours:
            contour_list = contour.flatten().tolist()
            if len(contour_list) > 4:
                segmentation.append(contour_list)
        return segmentation