PaddleX/paddlex/ppdet/data/source/keypoint_coco.py

# Copyright (c) 2021 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.

import os
import cv2
import numpy as np
import json
import copy
import pycocotools
from pycocotools.coco import COCO
from .dataset import DetDataset
from paddlex.ppdet.core.workspace import register, serializable


@serializable
class KeypointBottomUpBaseDataset(DetDataset):
    """Base class for bottom-up datasets. Adapted from
        https://github.com/open-mmlab/mmpose

    All datasets should subclass it.
    All subclasses should overwrite:
        Methods:`_get_imganno`

    Args:
        dataset_dir (str): Root path to the dataset.
        anno_path (str): Relative path to the annotation file.
        image_dir (str): Path to a directory where images are held.
            Default: None.
        num_joints (int): keypoint numbers
        transform (composed(operators)): A sequence of data transforms.
        shard (list): [rank, worldsize], the distributed env params
        test_mode (bool): Store True when building test or
            validation dataset. Default: False.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 transform=[],
                 shard=[0, 1],
                 test_mode=False):
        super().__init__(dataset_dir, image_dir, anno_path)
        self.image_info = {}
        self.ann_info = {}

        self.img_prefix = os.path.join(dataset_dir, image_dir)
        self.transform = transform
        self.test_mode = test_mode

        self.ann_info['num_joints'] = num_joints
        self.img_ids = []

    def parse_dataset(self):
        pass

    def __len__(self):
        """Get dataset length."""
        return len(self.img_ids)

    def _get_imganno(self, idx):
        """Get anno for a single image."""
        raise NotImplementedError

    def __getitem__(self, idx):
        """Prepare image for training given the index."""
        records = copy.deepcopy(self._get_imganno(idx))
        records['image'] = cv2.imread(records['image_file'])
        records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
        records['mask'] = (records['mask'] + 0).astype('uint8')
        records = self.transform(records)
        return records

    def parse_dataset(self):
        return


@register
@serializable
class KeypointBottomUpCocoDataset(KeypointBottomUpBaseDataset):
    """COCO dataset for bottom-up pose estimation. Adapted from
        https://github.com/open-mmlab/mmpose

    The dataset loads raw features and apply specified transforms
    to return a dict containing the image tensors and other information.

    COCO keypoint indexes::

        0: 'nose',
        1: 'left_eye',
        2: 'right_eye',
        3: 'left_ear',
        4: 'right_ear',
        5: 'left_shoulder',
        6: 'right_shoulder',
        7: 'left_elbow',
        8: 'right_elbow',
        9: 'left_wrist',
        10: 'right_wrist',
        11: 'left_hip',
        12: 'right_hip',
        13: 'left_knee',
        14: 'right_knee',
        15: 'left_ankle',
        16: 'right_ankle'

    Args:
        dataset_dir (str): Root path to the dataset.
        anno_path (str): Relative path to the annotation file.
        image_dir (str): Path to a directory where images are held.
            Default: None.
        num_joints (int): keypoint numbers
        transform (composed(operators)): A sequence of data transforms.
        shard (list): [rank, worldsize], the distributed env params
        test_mode (bool): Store True when building test or
            validation dataset. Default: False.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 transform=[],
                 shard=[0, 1],
                 test_mode=False):
        super().__init__(dataset_dir, image_dir, anno_path, num_joints,
                         transform, shard, test_mode)

        self.ann_file = os.path.join(dataset_dir, anno_path)
        self.shard = shard
        self.test_mode = test_mode

    def parse_dataset(self):
        self.coco = COCO(self.ann_file)

        self.img_ids = self.coco.getImgIds()
        if not self.test_mode:
            self.img_ids = [
                img_id for img_id in self.img_ids
                if len(self.coco.getAnnIds(
                    imgIds=img_id, iscrowd=None)) > 0
            ]
        blocknum = int(len(self.img_ids) / self.shard[1])
        self.img_ids = self.img_ids[(blocknum * self.shard[0]):(blocknum * (
            self.shard[0] + 1))]
        self.num_images = len(self.img_ids)
        self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)
        self.dataset_name = 'coco'

        cat_ids = self.coco.getCatIds()
        self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
        print('=> num_images: {}'.format(self.num_images))

    @staticmethod
    def _get_mapping_id_name(imgs):
        """
        Args:
            imgs (dict): dict of image info.

        Returns:
            tuple: Image name & id mapping dicts.

            - id2name (dict): Mapping image id to name.
            - name2id (dict): Mapping image name to id.
        """
        id2name = {}
        name2id = {}
        for image_id, image in imgs.items():
            file_name = image['file_name']
            id2name[image_id] = file_name
            name2id[file_name] = image_id

        return id2name, name2id

    def _get_imganno(self, idx):
        """Get anno for a single image.

        Args:
            idx (int): image idx

        Returns:
            dict: info for model training
        """
        coco = self.coco
        img_id = self.img_ids[idx]
        ann_ids = coco.getAnnIds(imgIds=img_id)
        anno = coco.loadAnns(ann_ids)

        mask = self._get_mask(anno, idx)
        anno = [
            obj for obj in anno
            if obj['iscrowd'] == 0 or obj['num_keypoints'] > 0
        ]

        joints, orgsize = self._get_joints(anno, idx)

        db_rec = {}
        db_rec['im_id'] = img_id
        db_rec['image_file'] = os.path.join(self.img_prefix,
                                            self.id2name[img_id])
        db_rec['mask'] = mask
        db_rec['joints'] = joints
        db_rec['im_shape'] = orgsize

        return db_rec

    def _get_joints(self, anno, idx):
        """Get joints for all people in an image."""
        num_people = len(anno)

        joints = np.zeros(
            (num_people, self.ann_info['num_joints'], 3), dtype=np.float32)

        for i, obj in enumerate(anno):
            joints[i, :self.ann_info['num_joints'], :3] = \
                np.array(obj['keypoints']).reshape([-1, 3])

        img_info = self.coco.loadImgs(self.img_ids[idx])[0]
        joints[..., 0] /= img_info['width']
        joints[..., 1] /= img_info['height']
        orgsize = np.array([img_info['height'], img_info['width']])

        return joints, orgsize

    def _get_mask(self, anno, idx):
        """Get ignore masks to mask out losses."""
        coco = self.coco
        img_info = coco.loadImgs(self.img_ids[idx])[0]

        m = np.zeros((img_info['height'], img_info['width']), dtype=np.float32)

        for obj in anno:
            if 'segmentation' in obj:
                if obj['iscrowd']:
                    rle = pycocotools.mask.frPyObjects(obj['segmentation'],
                                                       img_info['height'],
                                                       img_info['width'])
                    m += pycocotools.mask.decode(rle)
                elif obj['num_keypoints'] == 0:
                    rles = pycocotools.mask.frPyObjects(obj['segmentation'],
                                                        img_info['height'],
                                                        img_info['width'])
                    for rle in rles:
                        m += pycocotools.mask.decode(rle)

        return m < 0.5


@register
@serializable
class KeypointBottomUpCrowdPoseDataset(KeypointBottomUpCocoDataset):
    """CrowdPose dataset for bottom-up pose estimation. Adapted from
        https://github.com/open-mmlab/mmpose

    The dataset loads raw features and apply specified transforms
    to return a dict containing the image tensors and other information.

    CrowdPose keypoint indexes::

        0: 'left_shoulder',
        1: 'right_shoulder',
        2: 'left_elbow',
        3: 'right_elbow',
        4: 'left_wrist',
        5: 'right_wrist',
        6: 'left_hip',
        7: 'right_hip',
        8: 'left_knee',
        9: 'right_knee',
        10: 'left_ankle',
        11: 'right_ankle',
        12: 'top_head',
        13: 'neck'

    Args:
        dataset_dir (str): Root path to the dataset.
        anno_path (str): Relative path to the annotation file.
        image_dir (str): Path to a directory where images are held.
            Default: None.
        num_joints (int): keypoint numbers
        transform (composed(operators)): A sequence of data transforms.
        shard (list): [rank, worldsize], the distributed env params
        test_mode (bool): Store True when building test or
            validation dataset. Default: False.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 transform=[],
                 shard=[0, 1],
                 test_mode=False):
        super().__init__(dataset_dir, image_dir, anno_path, num_joints,
                         transform, shard, test_mode)

        self.ann_file = os.path.join(dataset_dir, anno_path)
        self.shard = shard
        self.test_mode = test_mode

    def parse_dataset(self):
        self.coco = COCO(self.ann_file)

        self.img_ids = self.coco.getImgIds()
        if not self.test_mode:
            self.img_ids = [
                img_id for img_id in self.img_ids
                if len(self.coco.getAnnIds(
                    imgIds=img_id, iscrowd=None)) > 0
            ]
        blocknum = int(len(self.img_ids) / self.shard[1])
        self.img_ids = self.img_ids[(blocknum * self.shard[0]):(blocknum * (
            self.shard[0] + 1))]
        self.num_images = len(self.img_ids)
        self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)

        self.dataset_name = 'crowdpose'
        print('=> num_images: {}'.format(self.num_images))


@serializable
class KeypointTopDownBaseDataset(DetDataset):
    """Base class for top_down datasets.

    All datasets should subclass it.
    All subclasses should overwrite:
        Methods:`_get_db`

    Args:
        dataset_dir (str): Root path to the dataset.
        image_dir (str): Path to a directory where images are held.
        anno_path (str): Relative path to the annotation file.
        num_joints (int): keypoint numbers
        transform (composed(operators)): A sequence of data transforms.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 transform=[]):
        super().__init__(dataset_dir, image_dir, anno_path)
        self.image_info = {}
        self.ann_info = {}

        self.img_prefix = os.path.join(dataset_dir, image_dir)
        self.transform = transform

        self.ann_info['num_joints'] = num_joints
        self.db = []

    def __len__(self):
        """Get dataset length."""
        return len(self.db)

    def _get_db(self):
        """Get a sample"""
        raise NotImplementedError

    def __getitem__(self, idx):
        """Prepare sample for training given the index."""
        records = copy.deepcopy(self.db[idx])
        records['image'] = cv2.imread(records['image_file'], cv2.IMREAD_COLOR |
                                      cv2.IMREAD_IGNORE_ORIENTATION)
        records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
        records['score'] = records['score'] if 'score' in records else 1
        records = self.transform(records)
        # print('records', records)
        return records


@register
@serializable
class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
    """COCO dataset for top-down pose estimation. Adapted from
        https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
        Copyright (c) Microsoft, under the MIT License.

    The dataset loads raw features and apply specified transforms
    to return a dict containing the image tensors and other information.

    COCO keypoint indexes:

        0: 'nose',
        1: 'left_eye',
        2: 'right_eye',
        3: 'left_ear',
        4: 'right_ear',
        5: 'left_shoulder',
        6: 'right_shoulder',
        7: 'left_elbow',
        8: 'right_elbow',
        9: 'left_wrist',
        10: 'right_wrist',
        11: 'left_hip',
        12: 'right_hip',
        13: 'left_knee',
        14: 'right_knee',
        15: 'left_ankle',
        16: 'right_ankle'

    Args:
        dataset_dir (str): Root path to the dataset.
        image_dir (str): Path to a directory where images are held.
        anno_path (str): Relative path to the annotation file.
        num_joints (int): Keypoint numbers
        trainsize (list):[w, h] Image target size
        transform (composed(operators)): A sequence of data transforms.
        bbox_file (str): Path to a detection bbox file
            Default: None.
        use_gt_bbox (bool): Whether to use ground truth bbox
            Default: True.
        pixel_std (int): The pixel std of the scale
            Default: 200.
        image_thre (float): The threshold to filter the detection box
            Default: 0.0.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 trainsize,
                 transform=[],
                 bbox_file=None,
                 use_gt_bbox=True,
                 pixel_std=200,
                 image_thre=0.0):
        super().__init__(dataset_dir, image_dir, anno_path, num_joints,
                         transform)

        self.bbox_file = bbox_file
        self.use_gt_bbox = use_gt_bbox
        self.trainsize = trainsize
        self.pixel_std = pixel_std
        self.image_thre = image_thre
        self.dataset_name = 'coco'

    def parse_dataset(self):
        if self.use_gt_bbox:
            self.db = self._load_coco_keypoint_annotations()
        else:
            self.db = self._load_coco_person_detection_results()

    def _load_coco_keypoint_annotations(self):
        coco = COCO(self.get_anno())
        img_ids = coco.getImgIds()
        gt_db = []
        for index in img_ids:
            im_ann = coco.loadImgs(index)[0]
            width = im_ann['width']
            height = im_ann['height']
            file_name = im_ann['file_name']
            im_id = int(im_ann["id"])

            annIds = coco.getAnnIds(imgIds=index, iscrowd=False)
            objs = coco.loadAnns(annIds)

            valid_objs = []
            for obj in objs:
                x, y, w, h = obj['bbox']
                x1 = np.max((0, x))
                y1 = np.max((0, y))
                x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
                y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
                if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
                    obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
                    valid_objs.append(obj)
            objs = valid_objs

            rec = []
            for obj in objs:
                if max(obj['keypoints']) == 0:
                    continue

                joints = np.zeros(
                    (self.ann_info['num_joints'], 3), dtype=np.float)
                joints_vis = np.zeros(
                    (self.ann_info['num_joints'], 3), dtype=np.float)
                for ipt in range(self.ann_info['num_joints']):
                    joints[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
                    joints[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
                    joints[ipt, 2] = 0
                    t_vis = obj['keypoints'][ipt * 3 + 2]
                    if t_vis > 1:
                        t_vis = 1
                    joints_vis[ipt, 0] = t_vis
                    joints_vis[ipt, 1] = t_vis
                    joints_vis[ipt, 2] = 0

                center, scale = self._box2cs(obj['clean_bbox'][:4])
                rec.append({
                    'image_file': os.path.join(self.img_prefix, file_name),
                    'center': center,
                    'scale': scale,
                    'joints': joints,
                    'joints_vis': joints_vis,
                    'im_id': im_id,
                })
            gt_db.extend(rec)

        return gt_db

    def _box2cs(self, box):
        x, y, w, h = box[:4]
        center = np.zeros((2), dtype=np.float32)
        center[0] = x + w * 0.5
        center[1] = y + h * 0.5
        aspect_ratio = self.trainsize[0] * 1.0 / self.trainsize[1]

        if w > aspect_ratio * h:
            h = w * 1.0 / aspect_ratio
        elif w < aspect_ratio * h:
            w = h * aspect_ratio
        scale = np.array(
            [w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std],
            dtype=np.float32)
        if center[0] != -1:
            scale = scale * 1.25

        return center, scale

    def _load_coco_person_detection_results(self):
        all_boxes = None
        bbox_file_path = os.path.join(self.dataset_dir, self.bbox_file)
        with open(bbox_file_path, 'r') as f:
            all_boxes = json.load(f)

        if not all_boxes:
            print('=> Load %s fail!' % bbox_file_path)
            return None

        kpt_db = []
        for n_img in range(0, len(all_boxes)):
            det_res = all_boxes[n_img]
            if det_res['category_id'] != 1:
                continue
            file_name = det_res[
                'filename'] if 'filename' in det_res else '%012d.jpg' % det_res[
                    'image_id']
            img_name = os.path.join(self.img_prefix, file_name)
            box = det_res['bbox']
            score = det_res['score']
            im_id = int(det_res['image_id'])

            if score < self.image_thre:
                continue

            center, scale = self._box2cs(box)
            joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
            joints_vis = np.ones(
                (self.ann_info['num_joints'], 3), dtype=np.float)
            kpt_db.append({
                'image_file': img_name,
                'im_id': im_id,
                'center': center,
                'scale': scale,
                'score': score,
                'joints': joints,
                'joints_vis': joints_vis,
            })

        return kpt_db


@register
@serializable
class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
    """MPII dataset for topdown pose estimation. Adapted from
        https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
        Copyright (c) Microsoft, under the MIT License.

    The dataset loads raw features and apply specified transforms
    to return a dict containing the image tensors and other information.

    MPII keypoint indexes::

        0: 'right_ankle',
        1: 'right_knee',
        2: 'right_hip',
        3: 'left_hip',
        4: 'left_knee',
        5: 'left_ankle',
        6: 'pelvis',
        7: 'thorax',
        8: 'upper_neck',
        9: 'head_top',
        10: 'right_wrist',
        11: 'right_elbow',
        12: 'right_shoulder',
        13: 'left_shoulder',
        14: 'left_elbow',
        15: 'left_wrist',

    Args:
        dataset_dir (str): Root path to the dataset.
        image_dir (str): Path to a directory where images are held.
        anno_path (str): Relative path to the annotation file.
        num_joints (int): Keypoint numbers
        trainsize (list):[w, h] Image target size
        transform (composed(operators)): A sequence of data transforms.
    """

    def __init__(self,
                 dataset_dir,
                 image_dir,
                 anno_path,
                 num_joints,
                 transform=[]):
        super().__init__(dataset_dir, image_dir, anno_path, num_joints,
                         transform)

        self.dataset_name = 'mpii'

    def parse_dataset(self):
        with open(self.get_anno()) as anno_file:
            anno = json.load(anno_file)

        gt_db = []
        for a in anno:
            image_name = a['image']
            im_id = a['image_id'] if 'image_id' in a else int(
                os.path.splitext(image_name)[0])

            c = np.array(a['center'], dtype=np.float)
            s = np.array([a['scale'], a['scale']], dtype=np.float)

            # Adjust center/scale slightly to avoid cropping limbs
            if c[0] != -1:
                c[1] = c[1] + 15 * s[1]
                s = s * 1.25
            c = c - 1

            joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
            joints_vis = np.zeros(
                (self.ann_info['num_joints'], 3), dtype=np.float)
            if 'joints' in a:
                joints_ = np.array(a['joints'])
                joints_[:, 0:2] = joints_[:, 0:2] - 1
                joints_vis_ = np.array(a['joints_vis'])
                assert len(joints_) == self.ann_info[
                    'num_joints'], 'joint num diff: {} vs {}'.format(
                        len(joints_), self.ann_info['num_joints'])

                joints[:, 0:2] = joints_[:, 0:2]
                joints_vis[:, 0] = joints_vis_[:]
                joints_vis[:, 1] = joints_vis_[:]

            gt_db.append({
                'image_file': os.path.join(self.img_prefix, image_name),
                'im_id': im_id,
                'center': c,
                'scale': s,
                'joints': joints,
                'joints_vis': joints_vis
            })
        print("number length: {}".format(len(gt_db)))
        self.db = gt_db