PaddleDetection-release-2.6/ppdet/data/source/pose3d_cmb.py

# Copyright (c) 2022 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   
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#     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, 
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import os
import cv2
import numpy as np
import json
import copy
import pycocotools
from pycocotools.coco import COCO
from .dataset import DetDataset
from ppdet.core.workspace import register, serializable
from paddle.io import Dataset


@serializable
class Pose3DDataset(DetDataset):
    """Pose3D Dataset class. 

    Args:
        dataset_dir (str): Root path to the dataset.
        anno_list (list of str): each of the element is a relative path to the annotation file.
        image_dirs (list of str): each of path is a relative path where images are held.
        transform (composed(operators)): A sequence of data transforms.
        test_mode (bool): Store True when building test or
            validation dataset. Default: False.
        24 joints order:
        0-2: 'R_Ankle', 'R_Knee', 'R_Hip', 
        3-5:'L_Hip', 'L_Knee', 'L_Ankle', 
        6-8:'R_Wrist', 'R_Elbow', 'R_Shoulder', 
        9-11:'L_Shoulder','L_Elbow','L_Wrist',
        12-14:'Neck','Top_of_Head','Pelvis',
        15-18:'Thorax','Spine','Jaw','Head',
        19-23:'Nose','L_Eye','R_Eye','L_Ear','R_Ear'
    """

    def __init__(self,
                 dataset_dir,
                 image_dirs,
                 anno_list,
                 transform=[],
                 num_joints=24,
                 test_mode=False):
        super().__init__(dataset_dir, image_dirs, anno_list)
        self.image_info = {}
        self.ann_info = {}
        self.num_joints = num_joints

        self.transform = transform
        self.test_mode = test_mode

        self.img_ids = []
        self.dataset_dir = dataset_dir
        self.image_dirs = image_dirs
        self.anno_list = anno_list

    def get_mask(self, mvm_percent=0.3):
        num_joints = self.num_joints
        mjm_mask = np.ones((num_joints, 1)).astype(np.float32)
        if self.test_mode == False:
            pb = np.random.random_sample()
            masked_num = int(
                pb * mvm_percent *
                num_joints)  # at most x% of the joints could be masked
            indices = np.random.choice(
                np.arange(num_joints), replace=False, size=masked_num)
            mjm_mask[indices, :] = 0.0
        # return mjm_mask

        num_joints = 10
        mvm_mask = np.ones((num_joints, 1)).astype(np.float)
        if self.test_mode == False:
            num_vertices = num_joints
            pb = np.random.random_sample()
            masked_num = int(
                pb * mvm_percent *
                num_vertices)  # at most x% of the vertices could be masked
            indices = np.random.choice(
                np.arange(num_vertices), replace=False, size=masked_num)
            mvm_mask[indices, :] = 0.0

        mjm_mask = np.concatenate([mjm_mask, mvm_mask], axis=0)
        return mjm_mask

    def filterjoints(self, x):
        if self.num_joints == 24:
            return x
        elif self.num_joints == 14:
            return x[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18], :]
        elif self.num_joints == 17:
            return x[
                [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 19], :]
        else:
            raise ValueError(
                "unsupported joint numbers, only [24 or 17 or 14] is supported!")

    def parse_dataset(self):
        print("Loading annotations..., please wait")
        self.annos = []
        im_id = 0
        self.human36m_num = 0
        for idx, annof in enumerate(self.anno_list):
            img_prefix = os.path.join(self.dataset_dir, self.image_dirs[idx])
            dataf = os.path.join(self.dataset_dir, annof)
            with open(dataf, 'r') as rf:
                anno_data = json.load(rf)
                annos = anno_data['data']
                new_annos = []
                print("{} has annos numbers: {}".format(dataf, len(annos)))
                for anno in annos:
                    new_anno = {}
                    new_anno['im_id'] = im_id
                    im_id += 1
                    imagename = anno['imageName']
                    if imagename.startswith("COCO_train2014_"):
                        imagename = imagename[len("COCO_train2014_"):]
                    elif imagename.startswith("COCO_val2014_"):
                        imagename = imagename[len("COCO_val2014_"):]
                    imagename = os.path.join(img_prefix, imagename)
                    if not os.path.exists(imagename):
                        if "train2017" in imagename:
                            imagename = imagename.replace("train2017",
                                                          "val2017")
                            if not os.path.exists(imagename):
                                print("cannot find imagepath:{}".format(
                                    imagename))
                                continue
                        else:
                            print("cannot find imagepath:{}".format(imagename))
                            continue
                    new_anno['imageName'] = imagename
                    if 'human3.6m' in imagename:
                        self.human36m_num += 1
                    new_anno['bbox_center'] = anno['bbox_center']
                    new_anno['bbox_scale'] = anno['bbox_scale']
                    new_anno['joints_2d'] = np.array(anno[
                        'gt_keypoint_2d']).astype(np.float32)
                    if new_anno['joints_2d'].shape[0] == 49:
                        #if the joints_2d is in SPIN format(which generated by eft), choose the last 24 public joints
                        #for detail please refer: https://github.com/nkolot/SPIN/blob/master/constants.py
                        new_anno['joints_2d'] = new_anno['joints_2d'][25:]
                    new_anno['joints_3d'] = np.array(anno[
                        'pose3d'])[:, :3].astype(np.float32)
                    new_anno['mjm_mask'] = self.get_mask()
                    if not 'has_3d_joints' in anno:
                        new_anno['has_3d_joints'] = int(1)
                        new_anno['has_2d_joints'] = int(1)
                    else:
                        new_anno['has_3d_joints'] = int(anno['has_3d_joints'])
                        new_anno['has_2d_joints'] = int(anno['has_2d_joints'])
                    new_anno['joints_2d'] = self.filterjoints(new_anno[
                        'joints_2d'])
                    self.annos.append(new_anno)
                del annos

    def get_temp_num(self):
        """get temporal data number, like human3.6m"""
        return self.human36m_num

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

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

    def __getitem__(self, idx):
        """Prepare image for training given the index."""
        records = copy.deepcopy(self._get_imganno(idx))
        imgpath = records['imageName']
        assert os.path.exists(imgpath), "cannot find image {}".format(imgpath)
        records['image'] = cv2.imread(imgpath)
        records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
        records = self.transform(records)
        return records

    def check_or_download_dataset(self):
        alldatafind = True
        for image_dir in self.image_dirs:
            image_dir = os.path.join(self.dataset_dir, image_dir)
            if not os.path.isdir(image_dir):
                print("dataset [{}] is not found".format(image_dir))
                alldatafind = False
        if not alldatafind:
            raise ValueError(
                "Some dataset is not valid and cannot download automatically now, please prepare the dataset first"
            )


@register
@serializable
class Keypoint3DMultiFramesDataset(Dataset):
    """24 keypoints 3D dataset for pose estimation. 

    each item is a list of images

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

    Args:
        dataset_dir (str): Root path to the dataset.
        image_dir (str): Path to a directory where images are held.
    """

    def __init__(
            self,
            dataset_dir,  # 数据集根目录
            image_dir,  # 图像文件夹
            p3d_dir,  # 3D关键点文件夹
            json_path,
            img_size,  #图像resize大小
            num_frames,  # 帧序列长度
            anno_path=None, ):

        self.dataset_dir = dataset_dir
        self.image_dir = image_dir
        self.p3d_dir = p3d_dir
        self.json_path = json_path
        self.img_size = img_size
        self.num_frames = num_frames
        self.anno_path = anno_path

        self.data_labels, self.mf_inds = self._generate_multi_frames_list()

    def _generate_multi_frames_list(self):
        act_list = os.listdir(self.dataset_dir)  # 动作列表
        count = 0
        mf_list = []
        annos_dict = {'images': [], 'annotations': [], 'act_inds': []}
        for act in act_list:  #对每个动作，生成帧序列
            if '.' in act:
                continue

            json_path = os.path.join(self.dataset_dir, act, self.json_path)
            with open(json_path, 'r') as j:
                annos = json.load(j)
            length = len(annos['images'])
            for k, v in annos.items():
                if k in annos_dict:
                    annos_dict[k].extend(v)
            annos_dict['act_inds'].extend([act] * length)

            mf = [[i + j + count for j in range(self.num_frames)]
                  for i in range(0, length - self.num_frames + 1)]
            mf_list.extend(mf)
            count += length

        print("total data number:", len(mf_list))
        return annos_dict, mf_list

    def __call__(self, *args, **kwargs):
        return self

    def __getitem__(self, index):  # 拿一个连续的序列
        inds = self.mf_inds[
            index]  # 如[568, 569, 570, 571, 572, 573]，长度为num_frames

        images = self.data_labels['images']  # all images
        annots = self.data_labels['annotations']  # all annots

        act = self.data_labels['act_inds'][inds[0]]  # 动作名（文件夹名）

        kps3d_list = []
        kps3d_vis_list = []
        names = []

        h, w = 0, 0
        for ind in inds:  # one image
            height = float(images[ind]['height'])
            width = float(images[ind]['width'])
            name = images[ind]['file_name']  # 图像名称，带有后缀

            kps3d_name = name.split('.')[0] + '.obj'
            kps3d_path = os.path.join(self.dataset_dir, act, self.p3d_dir,
                                      kps3d_name)

            joints, joints_vis = self.kps3d_process(kps3d_path)
            joints_vis = np.array(joints_vis, dtype=np.float32)

            kps3d_list.append(joints)
            kps3d_vis_list.append(joints_vis)
            names.append(name)

        kps3d = np.array(kps3d_list)  # (6, 24, 3),(num_frames, joints_num, 3)
        kps3d_vis = np.array(kps3d_vis_list)

        # read image
        imgs = []
        for name in names:
            img_path = os.path.join(self.dataset_dir, act, self.image_dir, name)

            image = cv2.imread(img_path, cv2.IMREAD_COLOR |
                               cv2.IMREAD_IGNORE_ORIENTATION)
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

            imgs.append(np.expand_dims(image, axis=0))

        imgs = np.concatenate(imgs, axis=0)
        imgs = imgs.astype(
            np.float32)  # (6, 1080, 1920, 3),(num_frames, h, w, c)

        # attention: 此时图像和标注是镜像的
        records = {
            'kps3d': kps3d,
            'kps3d_vis': kps3d_vis,
            "image": imgs,
            'act': act,
            'names': names,
            'im_id': index
        }

        return self.transform(records)

    def kps3d_process(self, kps3d_path):
        count = 0
        kps = []
        kps_vis = []

        with open(kps3d_path, 'r') as f:
            lines = f.readlines()
            for line in lines:
                if line[0] == 'v':
                    kps.append([])
                    line = line.strip('\n').split(' ')[1:]
                    for kp in line:
                        kps[-1].append(float(kp))
                    count += 1

                    kps_vis.append([1, 1, 1])

        kps = np.array(kps)  # 52，3
        kps_vis = np.array(kps_vis)

        kps *= 10  # scale points
        kps -= kps[[0], :]  # set root point to zero

        kps = np.concatenate((kps[0:23], kps[[37]]), axis=0)  # 24,3

        kps *= 10

        kps_vis = np.concatenate((kps_vis[0:23], kps_vis[[37]]), axis=0)  # 24,3

        return kps, kps_vis

    def __len__(self):
        return len(self.mf_inds)

    def get_anno(self):
        if self.anno_path is None:
            return
        return os.path.join(self.dataset_dir, self.anno_path)

    def check_or_download_dataset(self):
        return

    def parse_dataset(self, ):
        return

    def set_transform(self, transform):
        self.transform = transform

    def set_epoch(self, epoch_id):
        self._epoch = epoch_id

    def set_kwargs(self, **kwargs):
        self.mixup_epoch = kwargs.get('mixup_epoch', -1)
        self.cutmix_epoch = kwargs.get('cutmix_epoch', -1)
        self.mosaic_epoch = kwargs.get('mosaic_epoch', -1)