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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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| import copy
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| import logging
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| import random
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| import cv2
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| import numpy as np
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| import torch
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| from torch.utils.data import Dataset
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| from utils.transforms import get_affine_transform
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| from utils.transforms import affine_transform
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| from utils.transforms import fliplr_joints
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| logger = logging.getLogger(__name__)
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| class JointsDataset(Dataset):
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| def __init__(self, cfg, root, image_set, is_train, transform=None):
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| self.num_joints = 0
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| self.pixel_std = 200
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| self.flip_pairs = []
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| self.parent_ids = []
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| self.is_train = is_train
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| self.root = root
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| self.image_set = image_set
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| self.output_path = cfg.OUTPUT_DIR
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| self.data_format = cfg.DATASET.DATA_FORMAT
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| self.scale_factor = cfg.DATASET.SCALE_FACTOR
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| self.rotation_factor = cfg.DATASET.ROT_FACTOR
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| self.flip = cfg.DATASET.FLIP
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| self.num_joints_half_body = cfg.DATASET.NUM_JOINTS_HALF_BODY
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| self.prob_half_body = cfg.DATASET.PROB_HALF_BODY
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| self.color_rgb = cfg.DATASET.COLOR_RGB
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| self.target_type = cfg.MODEL.TARGET_TYPE
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| self.image_size = np.array(cfg.MODEL.IMAGE_SIZE)
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| self.heatmap_size = np.array(cfg.MODEL.HEATMAP_SIZE)
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| self.sigma = cfg.MODEL.SIGMA
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| self.use_different_joints_weight = cfg.LOSS.USE_DIFFERENT_JOINTS_WEIGHT
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| self.joints_weight = 1
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| self.transform = transform
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| self.db = []
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| def _get_db(self):
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| raise NotImplementedError
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| def evaluate(self, cfg, preds, output_dir, *args, **kwargs):
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| raise NotImplementedError
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| def half_body_transform(self, joints, joints_vis):
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| upper_joints = []
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| lower_joints = []
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| for joint_id in range(self.num_joints):
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| if joints_vis[joint_id][0] > 0:
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| if joint_id in self.upper_body_ids:
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| upper_joints.append(joints[joint_id])
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| else:
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| lower_joints.append(joints[joint_id])
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|
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| if np.random.randn() < 0.5 and len(upper_joints) > 2:
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| selected_joints = upper_joints
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| else:
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| selected_joints = lower_joints \
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| if len(lower_joints) > 2 else upper_joints
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|
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| if len(selected_joints) < 2:
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| return None, None
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|
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| selected_joints = np.array(selected_joints, dtype=np.float32)
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| center = selected_joints.mean(axis=0)[:2]
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| left_top = np.amin(selected_joints, axis=0)
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| right_bottom = np.amax(selected_joints, axis=0)
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| w = right_bottom[0] - left_top[0]
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| h = right_bottom[1] - left_top[1]
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| if w > self.aspect_ratio * h:
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| h = w * 1.0 / self.aspect_ratio
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| elif w < self.aspect_ratio * h:
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| w = h * self.aspect_ratio
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| scale = np.array(
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| [
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| w * 1.0 / self.pixel_std,
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| h * 1.0 / self.pixel_std
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| ],
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| dtype=np.float32
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| )
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| scale = scale * 1.5
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| return center, scale
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|
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| def __len__(self,):
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| return len(self.db)
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| def __getitem__(self, idx):
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| db_rec = copy.deepcopy(self.db[idx])
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| image_file = db_rec['image']
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| filename = db_rec['filename'] if 'filename' in db_rec else ''
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| imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''
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| category_id = db_rec['category_id'] if 'category_id' in db_rec else ''
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| area = db_rec['area'] if 'area' in db_rec else ''
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| if self.data_format == 'zip':
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| from utils import zipreader
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| data_numpy = zipreader.imread(
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| image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
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| )
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| else:
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| data_numpy = cv2.imread(
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| image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
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| )
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| if self.color_rgb:
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| data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
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|
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| if data_numpy is None:
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| logger.error('=> fail to read {}'.format(image_file))
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| raise ValueError('Fail to read {}'.format(image_file))
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|
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| joints = db_rec['joints_3d']
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| joints_vis = db_rec['joints_3d_vis']
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| c = db_rec['center']
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| s = db_rec['scale']
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| score = db_rec['score'] if 'score' in db_rec else 1
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| r = 0
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| if self.is_train:
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| sf = self.scale_factor
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| rf = self.rotation_factor
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| s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
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| r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \
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| if random.random() <= 0.6 else 0
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| if self.flip and random.random() <= 0.5:
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| data_numpy = data_numpy[:, ::-1, :]
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| joints, joints_vis = fliplr_joints(
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| joints, joints_vis, data_numpy.shape[1], self.flip_pairs[category_id-1])
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| c[0] = data_numpy.shape[1] - c[0] - 1
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| trans = get_affine_transform(c, s, r, self.image_size)
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| input = cv2.warpAffine(
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| data_numpy,
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| trans,
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| (int(self.image_size[0]), int(self.image_size[1])),
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| flags=cv2.INTER_LINEAR)
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| if self.transform:
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| input = self.transform(input)
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| for i in range(self.num_joints):
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| if joints_vis[i, 0] > 0.0:
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| joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
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| target, target_weight = self.generate_target(joints, joints_vis)
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| target = torch.from_numpy(target)
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| target_weight = torch.from_numpy(target_weight)
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| meta = {
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| 'image': image_file,
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| 'filename': filename,
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| 'imgnum': imgnum,
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| 'joints': joints,
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| 'joints_vis': joints_vis,
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| 'center': c,
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| 'scale': s,
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| 'rotation': r,
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| 'score': score,
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| 'category_id': category_id,
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| 'area': area
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| }
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| return input, target, target_weight, meta
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|
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| def select_data(self, db):
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| db_selected = []
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| for rec in db:
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| num_vis = 0
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| joints_x = 0.0
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| joints_y = 0.0
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| for joint, joint_vis in zip(
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| rec['joints_3d'], rec['joints_3d_vis']):
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| if joint_vis[0] <= 0:
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| continue
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| num_vis += 1
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| joints_x += joint[0]
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| joints_y += joint[1]
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| if num_vis == 0:
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| continue
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| joints_x, joints_y = joints_x / num_vis, joints_y / num_vis
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| area = rec['scale'][0] * rec['scale'][1] * (self.pixel_std**2)
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| joints_center = np.array([joints_x, joints_y])
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| bbox_center = np.array(rec['center'])
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| diff_norm2 = np.linalg.norm((joints_center-bbox_center), 2)
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| ks = np.exp(-1.0*(diff_norm2**2) / ((0.2)**2*2.0*area))
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|
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| metric = (0.2 / 16) * num_vis + 0.45 - 0.2 / 16
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| if ks > metric:
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| db_selected.append(rec)
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|
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| logger.info('=> num db: {}'.format(len(db)))
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| logger.info('=> num selected db: {}'.format(len(db_selected)))
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| return db_selected
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|
|
| def generate_target(self, joints, joints_vis):
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| '''
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| :param joints: [num_joints, 3]
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| :param joints_vis: [num_joints, 3]
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| :return: target, target_weight(1: visible, 0: invisible)
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| '''
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| target_weight = np.ones((self.num_joints, 1), dtype=np.float32)
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| target_weight[:, 0] = joints_vis[:, 0]
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|
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| assert self.target_type == 'gaussian', \
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| 'Only support gaussian map now!'
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|
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| if self.target_type == 'gaussian':
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| target = np.zeros((self.num_joints,
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| self.heatmap_size[1],
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| self.heatmap_size[0]),
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| dtype=np.float32)
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| tmp_size = self.sigma * 3
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| for joint_id in range(self.num_joints):
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| feat_stride = self.image_size / self.heatmap_size
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| mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)
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| mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)
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| ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
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| br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
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| if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \
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| or br[0] < 0 or br[1] < 0:
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|
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| target_weight[joint_id] = 0
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| continue
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|
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| size = 2 * tmp_size + 1
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| x = np.arange(0, size, 1, np.float32)
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| y = x[:, np.newaxis]
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| x0 = y0 = size // 2
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| g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2))
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| g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]
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| g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]
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| img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])
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| img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])
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|
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| v = target_weight[joint_id]
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| if v > 0.5:
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| target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
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| g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
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|
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| if self.use_different_joints_weight:
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| target_weight = np.multiply(target_weight, self.joints_weight)
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|
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| return target, target_weight
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|
