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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao ([email protected])
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import os
import json_tricks as json
from collections import OrderedDict
import numpy as np
from scipy.io import loadmat, savemat
from dataset.JointsDataset import JointsDataset
logger = logging.getLogger(__name__)
class MPIIDataset(JointsDataset):
def __init__(self, cfg, root, image_set, is_train, transform=None):
super().__init__(cfg, root, image_set, is_train, transform)
self.num_joints = 16
self.flip_pairs = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
self.parent_ids = [1, 2, 6, 6, 3, 4, 6, 6, 7, 8, 11, 12, 7, 7, 13, 14]
self.upper_body_ids = (7, 8, 9, 10, 11, 12, 13, 14, 15)
self.lower_body_ids = (0, 1, 2, 3, 4, 5, 6)
self.db = self._get_db()
if is_train and cfg.DATASET.SELECT_DATA:
self.db = self.select_data(self.db)
logger.info('=> load {} samples'.format(len(self.db)))
def _get_db(self):
# create train/val split
file_name = os.path.join(
self.root, 'annot', self.image_set+'.json'
)
with open(file_name) as anno_file:
anno = json.load(anno_file)
gt_db = []
for a in anno:
image_name = a['image']
c = np.array(a['center'], dtype=float)
s = np.array([a['scale'], a['scale']], dtype=float)
# Adjust center/scale slightly to avoid cropping limbs
if c[0] != -1:
c[1] = c[1] + 15 * s[1]
s = s * 1.25
# MPII uses matlab format, index is based 1,
# we should first convert to 0-based index
c = c - 1
joints_3d = np.zeros((self.num_joints, 3), dtype=float)
joints_3d_vis = np.zeros((self.num_joints, 3), dtype=float)
if self.image_set != 'test':
joints = np.array(a['joints'])
joints[:, 0:2] = joints[:, 0:2] - 1
joints_vis = np.array(a['joints_vis'])
assert len(joints) == self.num_joints, \
'joint num diff: {} vs {}'.format(len(joints),
self.num_joints)
joints_3d[:, 0:2] = joints[:, 0:2]
joints_3d_vis[:, 0] = joints_vis[:]
joints_3d_vis[:, 1] = joints_vis[:]
image_dir = 'images.zip@' if self.data_format == 'zip' else 'images'
gt_db.append(
{
'image': os.path.join(self.root, image_dir, image_name),
'center': c,
'scale': s,
'joints_3d': joints_3d,
'joints_3d_vis': joints_3d_vis,
'filename': '',
'imgnum': 0,
}
)
return gt_db
def evaluate(self, cfg, preds, output_dir, *args, **kwargs):
# convert 0-based index to 1-based index
preds = preds[:, :, 0:2] + 1.0
if output_dir:
pred_file = os.path.join(output_dir, 'pred.mat')
savemat(pred_file, mdict={'preds': preds})
if 'test' in cfg.DATASET.TEST_SET:
return {'Null': 0.0}, 0.0
SC_BIAS = 0.6
threshold = 0.5
gt_file = os.path.join(cfg.DATASET.ROOT,
'annot',
'gt_{}.mat'.format(cfg.DATASET.TEST_SET))
gt_dict = loadmat(gt_file)
dataset_joints = gt_dict['dataset_joints']
jnt_missing = gt_dict['jnt_missing']
pos_gt_src = gt_dict['pos_gt_src']
# headboxes_src = gt_dict['headboxes_src']
pos_pred_src = np.transpose(preds, [1, 2, 0])
head = np.where(dataset_joints == 'head')[1][0]
lsho = np.where(dataset_joints == 'lsho')[1][0]
lelb = np.where(dataset_joints == 'lelb')[1][0]
lwri = np.where(dataset_joints == 'lwri')[1][0]
lhip = np.where(dataset_joints == 'lhip')[1][0]
lkne = np.where(dataset_joints == 'lkne')[1][0]
lank = np.where(dataset_joints == 'lank')[1][0]
rsho = np.where(dataset_joints == 'rsho')[1][0]
relb = np.where(dataset_joints == 'relb')[1][0]
rwri = np.where(dataset_joints == 'rwri')[1][0]
rkne = np.where(dataset_joints == 'rkne')[1][0]
rank = np.where(dataset_joints == 'rank')[1][0]
rhip = np.where(dataset_joints == 'rhip')[1][0]
jnt_visible = 1 - jnt_missing
uv_error = pos_pred_src - pos_gt_src
uv_err = np.linalg.norm(uv_error, axis=1)
# headsizes = headboxes_src[1, :, :] - headboxes_src[0, :, :]
# headsizes = np.linalg.norm(headsizes, axis=0)
# headsizes *= SC_BIAS
# scale = np.multiply(headsizes, np.ones((len(uv_err), 1)))
scale = gt_dict['scale']
print(scale.shape)
scaled_uv_err = np.divide(uv_err, scale)
scaled_uv_err = np.multiply(scaled_uv_err, jnt_visible)
jnt_count = np.sum(jnt_visible, axis=1)
less_than_threshold = np.multiply((scaled_uv_err <= threshold),
jnt_visible)
PCKh = np.divide(100.*np.sum(less_than_threshold, axis=1), jnt_count)
# save
rng = np.arange(0, 0.5+0.01, 0.01)
pckAll = np.zeros((len(rng), 16))
for r in range(len(rng)):
threshold = rng[r]
less_than_threshold = np.multiply(scaled_uv_err <= threshold,
jnt_visible)
pckAll[r, :] = np.divide(100.*np.sum(less_than_threshold, axis=1),
jnt_count)
PCKh = np.ma.array(PCKh, mask=False)
PCKh.mask[6:8] = True
jnt_count = np.ma.array(jnt_count, mask=False)
jnt_count.mask[6:8] = True
jnt_ratio = jnt_count / np.sum(jnt_count).astype(float)
name_value = [
('Head', PCKh[head]),
('Shoulder', 0.5 * (PCKh[lsho] + PCKh[rsho])),
('Elbow', 0.5 * (PCKh[lelb] + PCKh[relb])),
('Wrist', 0.5 * (PCKh[lwri] + PCKh[rwri])),
('Hip', 0.5 * (PCKh[lhip] + PCKh[rhip])),
('Knee', 0.5 * (PCKh[lkne] + PCKh[rkne])),
('Ankle', 0.5 * (PCKh[lank] + PCKh[rank])),
('Mean', np.sum(PCKh * jnt_ratio)),
('[email protected]', np.sum(pckAll[11, :] * jnt_ratio))
]
name_value = OrderedDict(name_value)
return name_value, name_value['Mean']
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