<reponame>jinfagang/mmdetection<filename>mmdet/datasets/custom_coco.py
# Copyright (C) 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 numpy as np

from .coco import CocoDataset
from .registry import DATASETS


@DATASETS.register_module
class CustomCocoDataset(CocoDataset):

    def __init__(self, classes, min_size=None, *args, **kwargs):
        self.CLASSES = classes
        super().__init__(*args, **kwargs)

        self.min_size = min_size

    def _parse_ann_info(self, img_info, ann_info):
        """Parse bbox and mask annotation.

        Args:
            ann_info (list[dict]): Annotation info of an image.
            with_mask (bool): Whether to parse mask annotations.

        Returns:
            dict: A dict containing the following keys: bboxes, bboxes_ignore,
                labels, masks, seg_map. "masks" are raw annotations and not
                decoded into binary masks.
        """
        gt_bboxes = []
        gt_labels = []
        gt_bboxes_ignore = []
        gt_masks_ann = []

        for i, ann in enumerate(ann_info):
            if ann.get('ignore', False):
                continue
            x1, y1, w, h = ann['bbox']
            if ann['area'] <= 0 or w < 1 or h < 1:
                continue
            if self.min_size is not None:
                if w < self.min_size or h < self.min_size:
                    continue
            bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
            if ann.get('iscrowd', False):
                gt_bboxes_ignore.append(bbox)
            else:
                gt_bboxes.append(bbox)
                gt_labels.append(self.cat2label[ann['category_id']])
                gt_masks_ann.append(ann['segmentation'])

        if gt_bboxes:
            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
            gt_labels = np.array(gt_labels, dtype=np.int64)
        else:
            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
            gt_labels = np.array([], dtype=np.int64)

        if gt_bboxes_ignore:
            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
        else:
            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)

        seg_map = img_info['filename'].replace('jpg', 'png')

        ann = dict(
            bboxes=gt_bboxes,
            labels=gt_labels,
            bboxes_ignore=gt_bboxes_ignore,
            masks=gt_masks_ann,
            seg_map=seg_map)

        return ann


# Copyright (C) 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 numpy as np

from .coco import CocoDataset
from .registry import DATASETS


@DATASETS.register_module
class CustomCocoDataset(CocoDataset):

    def __init__(self, classes, min_size=None, *args, **kwargs):
        self.CLASSES = classes
        super().__init__(*args, **kwargs)

        self.min_size = min_size

    def _parse_ann_info(self, img_info, ann_info):
        """Parse bbox and mask annotation.

        Args:
            ann_info (list[dict]): Annotation info of an image.
            with_mask (bool): Whether to parse mask annotations.

        Returns:
            dict: A dict containing the following keys: bboxes, bboxes_ignore,
                labels, masks, seg_map. "masks" are raw annotations and not
                decoded into binary masks.
        """
        gt_bboxes = []
        gt_labels = []
        gt_bboxes_ignore = []
        gt_masks_ann = []

        for i, ann in enumerate(ann_info):
            if ann.get('ignore', False):
                continue
            x1, y1, w, h = ann['bbox']
            if ann['area'] <= 0 or w < 1 or h < 1:
                continue
            if self.min_size is not None:
                if w < self.min_size or h < self.min_size:
                    continue
            bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
            if ann.get('iscrowd', False):
                gt_bboxes_ignore.append(bbox)
            else:
                gt_bboxes.append(bbox)
                gt_labels.append(self.cat2label[ann['category_id']])
                gt_masks_ann.append(ann['segmentation'])

        if gt_bboxes:
            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
            gt_labels = np.array(gt_labels, dtype=np.int64)
        else:
            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
            gt_labels = np.array([], dtype=np.int64)

        if gt_bboxes_ignore:
            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
        else:
            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)

        seg_map = img_info['filename'].replace('jpg', 'png')

        ann = dict(
            bboxes=gt_bboxes,
            labels=gt_labels,
            bboxes_ignore=gt_bboxes_ignore,
            masks=gt_masks_ann,
            seg_map=seg_map)

        return ann
# Copyright (C) 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. Parse bbox and mask annotation.          Args:             ann_info (list[dict]): Annotation info of an image.             with_mask (bool): Whether to parse mask annotations.          Returns:             dict: A dict containing the following keys: bboxes, bboxes_ignore,                 labels, masks, seg_map. "masks" are raw annotations and not                 decoded into binary masks.