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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import logging
import random
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
from utils.transforms import get_affine_transform
from utils.transforms import affine_transform
from utils.transforms import fliplr_joints
logger = logging.getLogger(__name__)
class JointsDataset(Dataset):
def __init__(self, cfg, root, image_set, is_train, transform=None):
self.num_joints = 0
self.pixel_std = 200
self.flip_pairs = []
self.parent_ids = []
self.is_train = is_train
self.root = root
self.image_set = image_set
self.output_path = cfg.OUTPUT_DIR
self.data_format = cfg.DATASET.DATA_FORMAT
self.scale_factor = cfg.DATASET.SCALE_FACTOR
self.rotation_factor = cfg.DATASET.ROT_FACTOR
self.flip = cfg.DATASET.FLIP
self.num_joints_half_body = cfg.DATASET.NUM_JOINTS_HALF_BODY
self.prob_half_body = cfg.DATASET.PROB_HALF_BODY
self.color_rgb = cfg.DATASET.COLOR_RGB
self.target_type = cfg.MODEL.TARGET_TYPE
self.image_size = np.array(cfg.MODEL.IMAGE_SIZE)
self.heatmap_size = np.array(cfg.MODEL.HEATMAP_SIZE)
self.sigma = cfg.MODEL.SIGMA
self.use_different_joints_weight = cfg.LOSS.USE_DIFFERENT_JOINTS_WEIGHT
self.joints_weight = 1
self.transform = transform
self.db = []
def _get_db(self):
raise NotImplementedError
def evaluate(self, cfg, preds, output_dir, *args, **kwargs):
raise NotImplementedError
def half_body_transform(self, joints, joints_vis):
upper_joints = []
lower_joints = []
for joint_id in range(self.num_joints):
if joints_vis[joint_id][0] > 0:
if joint_id in self.upper_body_ids:
upper_joints.append(joints[joint_id])
else:
lower_joints.append(joints[joint_id])
if np.random.randn() < 0.5 and len(upper_joints) > 2:
selected_joints = upper_joints
else:
selected_joints = lower_joints \
if len(lower_joints) > 2 else upper_joints
if len(selected_joints) < 2:
return None, None
selected_joints = np.array(selected_joints, dtype=np.float32)
center = selected_joints.mean(axis=0)[:2]
left_top = np.amin(selected_joints, axis=0)
right_bottom = np.amax(selected_joints, axis=0)
w = right_bottom[0] - left_top[0]
h = right_bottom[1] - left_top[1]
if w > self.aspect_ratio * h:
h = w * 1.0 / self.aspect_ratio
elif w < self.aspect_ratio * h:
w = h * self.aspect_ratio
scale = np.array(
[
w * 1.0 / self.pixel_std,
h * 1.0 / self.pixel_std
],
dtype=np.float32
)
scale = scale * 1.5
return center, scale
def __len__(self,):
return len(self.db)
def __getitem__(self, idx):
db_rec = copy.deepcopy(self.db[idx])
image_file = db_rec['image']
filename = db_rec['filename'] if 'filename' in db_rec else ''
imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''
if self.data_format == 'zip':
from utils import zipreader
data_numpy = zipreader.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
)
else:
data_numpy = cv2.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
)
if self.color_rgb:
data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
if data_numpy is None:
logger.error('=> fail to read {}'.format(image_file))
raise ValueError('Fail to read {}'.format(image_file))
joints = db_rec['joints_3d']
joints_vis = db_rec['joints_3d_vis']
c = db_rec['center']
s = db_rec['scale']
score = db_rec['score'] if 'score' in db_rec else 1
r = 0
if self.is_train:
if (np.sum(joints_vis[:, 0]) > self.num_joints_half_body
and np.random.rand() < self.prob_half_body):
c_half_body, s_half_body = self.half_body_transform(
joints, joints_vis
)
if c_half_body is not None and s_half_body is not None:
c, s = c_half_body, s_half_body
sf = self.scale_factor
rf = self.rotation_factor
s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \
if random.random() <= 0.6 else 0
if self.flip and random.random() <= 0.5:
data_numpy = data_numpy[:, ::-1, :]
joints, joints_vis = fliplr_joints(
joints, joints_vis, data_numpy.shape[1], self.flip_pairs)
c[0] = data_numpy.shape[1] - c[0] - 1
trans = get_affine_transform(c, s, r, self.image_size)
input = cv2.warpAffine(
data_numpy,
trans,
(int(self.image_size[0]), int(self.image_size[1])),
flags=cv2.INTER_LINEAR)
if self.transform:
input = self.transform(input)
for i in range(self.num_joints):
if joints_vis[i, 0] > 0.0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
target, target_weight = self.generate_target(joints, joints_vis)
target = torch.from_numpy(target)
target_weight = torch.from_numpy(target_weight)
meta = {
'image': image_file,
'filename': filename,
'imgnum': imgnum,
'joints': joints,
'joints_vis': joints_vis,
'center': c,
'scale': s,
'rotation': r,
'score': score
}
return input, target, target_weight, meta
def select_data(self, db):
db_selected = []
for rec in db:
num_vis = 0
joints_x = 0.0
joints_y = 0.0
for joint, joint_vis in zip(
rec['joints_3d'], rec['joints_3d_vis']):
if joint_vis[0] <= 0:
continue
num_vis += 1
joints_x += joint[0]
joints_y += joint[1]
if num_vis == 0:
continue
joints_x, joints_y = joints_x / num_vis, joints_y / num_vis
area = rec['scale'][0] * rec['scale'][1] * (self.pixel_std**2)
joints_center = np.array([joints_x, joints_y])
bbox_center = np.array(rec['center'])
diff_norm2 = np.linalg.norm((joints_center-bbox_center), 2)
ks = np.exp(-1.0*(diff_norm2**2) / ((0.2)**2*2.0*area))
metric = (0.2 / 16) * num_vis + 0.45 - 0.2 / 16
if ks > metric:
db_selected.append(rec)
logger.info('=> num db: {}'.format(len(db)))
logger.info('=> num selected db: {}'.format(len(db_selected)))
return db_selected
def generate_target(self, joints, joints_vis):
'''
:param joints: [num_joints, 3]
:param joints_vis: [num_joints, 3]
:return: target, target_weight(1: visible, 0: invisible)
'''
target_weight = np.ones((self.num_joints, 1), dtype=np.float32)
target_weight[:, 0] = joints_vis[:, 0]
assert self.target_type == 'gaussian', \
'Only support gaussian map now!'
if self.target_type == 'gaussian':
target = np.zeros((self.num_joints,
self.heatmap_size[1],
self.heatmap_size[0]),
dtype=np.float32)
tmp_size = self.sigma * 3
for joint_id in range(self.num_joints):
feat_stride = self.image_size / self.heatmap_size
mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)
mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)
# Check that any part of the gaussian is in-bounds
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \
or br[0] < 0 or br[1] < 0:
# If not, just return the image as is
target_weight[joint_id] = 0
continue
# # Generate gaussian
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2))
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])
img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])
v = target_weight[joint_id]
if v > 0.5:
target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
if self.use_different_joints_weight:
target_weight = np.multiply(target_weight, self.joints_weight)
return target, target_weight
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