def generate_sliding_detection_data_from_image(self, imgpath, scale_pairlist):
from PIL import Image
import iutils as iu
import imgproc
self.init_meta('sliding')
img = Image.open(imgpath)
ndata = 0
newdim = self.savedata_info['newdim']
steps = self.imgdata_info['steps']
# x1 is the size, x1 - 1 is the last pixel index
fx = lambda x1,x2: np.floor((x1 - newdim[1])/x2) + 1
fy = lambda x1,x2: np.floor((x1 - newdim[0])/x2) + 1
valid_idx = 0
for s in scale_pairlist:
ns = np.floor(np.asarray([img.size[0], img.size[1]]) * np.asarray([s[0], s[1]]))
if ns[0] < newdim[1] or ns[1] < newdim[0]:
break
cur_n = fx(ns[0],steps[0]) * fy(ns[1], steps[1])
valid_idx = valid_idx + 1
ndata = ndata + cur_n
ndata = int(ndata)
scale_pairlist = scale_pairlist[:valid_idx]
print( 'Need to generate %d data' % ndata)
filter_size = self.savedata_info['indmap_para']['filter_size']
stride = self.savedata_info['indmap_para']['stride']
mdim = self.get_indmapdim(newdim, filter_size, stride)
joint_filter_size = self.savedata_info['indmap_para']['joint_filter_size']
joint_stride = self.savedata_info['indmap_para']['joint_stride']
jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, ndata, self.meta['nparts'], self.meta['njoints'])
dicjtname = 'joints8' if self.meta['njoints'] == 8 else 'joints'
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
res['slide_location'] = np.zeros((2,ndata),dtype=np.float)
res['scale'] = np.zeros((2,ndata), dtype=np.float)
res['filenames'] = [imgpath for x in range(ndata)]
idx = 0
dimX = iu.prod(newdim)
for s in scale_pairlist:
ns = np.floor(np.asarray([img.size[0], img.size[1]]) * np.asarray([s[0], s[1]]))
ns = (int(ns[0]),int(ns[1]))
nimg = img.resize((ns[0],ns[1]))
arrimg = imgproc.ensure_rgb(np.asarray(nimg))
for x in range(0, ns[0] - newdim[1] + 1, steps[0]):
for y in range(0, ns[1] - newdim[0] + 1, steps[1]):
res['scale'][...,idx] = np.asarray([s[0],s[1]]).reshape((2))
res['slide_location'][...,idx] = np.asarray([x,y]).reshape((2))
res['data'][...,idx] = arrimg[y:y+newdim[0],x:x+newdim[1],:]
idx = idx + 1
if idx != ndata:
raise HMLPEError('Number of data is not consistent')
res['data'] = res['data'].reshape((-1,ndata),order='F')
return res
def init_meta(self, generate_type):
"""
init meta
meta will only have those field
"""
self.meta = {'imgdata_info':self.imgdata_info,'savedata_info':self.savedata_info}
self.meta['num_vis'] = iu.prod(self.savedata_info['newdim'])
self.meta['data_sum'] = 0
self.meta['ndata'] = 0
if 'part_idx' in self.savedata_info:
self.meta['nparts'] = len(self.savedata_info['part_idx'])
else:
self.meta['nparts'] = len(part_idx)
self.meta['savedata_info']['part_idx'] = part_idx
self.meta['njoints'] = len(self.savedata_info['jointnames'])
self.meta['generate_type'] = generate_type
self.meta['matdim'] = None
self.meta['ind_dim'] = dict()
def init_meta(self, generate_type):
"""
init meta
meta will only have those field
"""
self.meta = {
'imgdata_info': self.imgdata_info,
'savedata_info': self.savedata_info
}
self.meta['num_vis'] = iu.prod(self.savedata_info['newdim'])
self.meta['data_sum'] = 0
self.meta['ndata'] = 0
if 'part_idx' in self.savedata_info:
self.meta['nparts'] = len(self.savedata_info['part_idx'])
else:
self.meta['nparts'] = len(part_idx)
self.meta['savedata_info']['part_idx'] = part_idx
self.meta['njoints'] = len(self.savedata_info['jointnames'])
self.meta['generate_type'] = generate_type
self.meta['matdim'] = None
self.meta['ind_dim'] = dict()
def generate_data(self, generate_type, allfile = None):
"""
generate_type = 'rt' only
"""
if allfile is None:
allfile = iu.getfilelist( self.imgdata_info['imgdata_path'], '\w+\.mat')
print 'imgdatapath=%s, %d files are found' % (self.imgdata_info['imgdata_path'], len(allfile))
iu.ensure_dir(self.savedata_info['savedir'])
self.batch_id = self.savedata_info['start_patch_id']
ndata = 0
self.meta = {'imgdata_info':self.imgdata_info,'savedata_info':self.savedata_info}
self.meta['num_vis'] = iu.prod(self.savedata_info['newdim'])
self.meta['data_sum'] = 0
self.meta['ndata'] = 0
self.meta['nparts'] = len(part_idx)
for fn in allfile:
if generate_type == 'rt':
mpath = iu.fullfile(self.imgdata_info['imgdata_path'], fn)
self.generate_rt_data(iu.fullfile(mpath))
if self.meta['ndata'] > 0:
self.meta['data_mean'] = self.meta['data_sum'] / self.meta['ndata']
del self.meta['data_sum']
myio.pickle(iu.fullfile(self.savedata_info['savedir'], 'batches.meta'), self.meta)
def generate_positive_data_from_mat(self, generate_type, matpath):
"""
in each mat
mat['X'] is image data
mat['Y'] is npart x ndata array
"""
mat = sio.loadmat(matpath)
dim = mat['dim'][0]
newdim = self.savedata_info['newdim']
if newdim[0] > dim[0] or newdim[1] > dim[1]:
raise HMLPEError('Invalid new size ')
if self.meta['matdim'] is None:
self.meta['matdim'] = dim # record the dimension before sampling
else:
if np.any(self.meta['matdim'] != dim):
raise HMLPEError(
'Inconsistent matdim: Previous dim is %s, current mat dim is %s'
% (str(self.meta['matdim']), str(dim)))
ndata = (mat['X'].shape)[1]
if generate_type in {'rt': 1}:
sample_num = self.savedata_info['sample_num']
totaldata = sample_num * ndata * 2
do_mirror = True
elif generate_type == 'ct':
sample_num = 1
totaldata = sample_num * ndata
do_mirror = False
if (dim[0] - newdim[0] + 1) * (dim[1] - newdim[1] + 1) < sample_num:
raise HMLPEError(' Invalid sample_num')
nparts = self.meta['nparts']
self.meta['ndata'] += totaldata
### BEGIN COMMENT
# njoints = self.meta['njoints']
# if njoints == 8:
# dicjtname = 'joints8'
# else:
# #raise HMLPEError('njoints = %d No supported yet' % (njoints))
# dicjtname = 'joints'
# newdim = self.savedata_info['newdim']
# filter_size = self.savedata_info['indmap_para']['filter_size']
# stride = self.savedata_info['indmap_para']['stride']
# rate = self.savedata_info['indmap_para']['rate']
# mdim = self.get_indmapdim(newdim, filter_size, stride)
# if newdim[0] > dim[0] or newdim[1] > dim[1]:
# raise HMLPEError('Invalid new size ')
# if (dim[0] - newdim[0] + 1) * (dim[1] - newdim[1] + 1) < sample_num:
# raise HMLPEError(' Invalid sample_num')
# joint_filter_size = self.savedata_info['indmap_para']['joint_filter_size']
# joint_stride = self.savedata_info['indmap_para']['joint_stride']
# jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
### END COMMENT
fieldpool = self.get_fieldpool_for_positive_mat_data()
fieldpool['mat'] = mat
self.meta['ind_dim']['part_indmap'] = fieldpool['mdim']
self.meta['ind_dim']['joint_indmap'] = fieldpool['jtmdim']
res = {}
per_size = min(totaldata, self.savedata_info['max_batch_size'])
allX = mat['X'].reshape((dim[0], dim[1], dim[2], ndata), order='F')
Y2dname = fieldpool['Y2dname']
allY = mat[Y2dname].reshape((2, -1, ndata), order='F')
newlen = iu.prod(newdim)
# prepare data buffer
res = self.prepare_savebuffer({'data':fieldpool['newdim'], 'part_indmap':fieldpool['mdim'], 'joint_indmap': fieldpool['jtmdim']},\
per_size, self.meta['nparts'],\
self.meta['njoints'])
tmpres = dict()
pre_nc = 0
nc = 0
res['is_positive'][:] = True
for it in range(ndata):
curX = allX[..., it]
curY = allY[..., it].transpose()
curfilename = str(
mat['imagepathlist'][0,
it][0]) if 'imagepathlist' in mat else ''
mesh = self.create_augumentation_mesh(dim, newdim, generate_type)
l = (np.random.permutation(range(len(mesh))))[:sample_num]
fieldpool['matidx'] = it
fieldpool['curfilename'] = curfilename
for p in l:
r, c = mesh[p]
tmpX = curX
tmpX = np.roll(tmpX, shift=-int(r), axis=0)
tmpX = np.roll(tmpX, shift=-int(c), axis=1)
tmpY = curY - 1 + np.asarray([-c, -r])
fieldpool['r'] = r
fieldpool['c'] = c
####
fieldpool['curX'] = tmpX
fieldpool['Y'] = tmpY
# tmpX = tmpX[:newdim[0], :newdim[1],:]
# res['data'][...,nc - pre_nc] = tmpX
# res[dicjtname][..., nc - pre_nc] = tmpY
# res['jointmasks'][...,nc - pre_nc] = self.makejointmask(newdim, tmpY)
# res['filenames'][nc - pre_nc] = curfilename
# res['oribbox'][...,nc-pre_nc] = mat['oribbox'][...,it]
# res['indmap'][...,nc-pre_nc] = self.create_part_indicatormap(tmpY, self.meta['savedata_info']['part_idx'], mdim, rate, filter_size, stride)
# res['joint_indmap'][...,nc-pre_nc] = self.create_joint_indicatormap(tmpY, jtmdim, joint_filter_size, joint_stride)
# res['joint_sample_offset'][...,nc-pre_nc] = [c, r]
# res['is_mirror'][...,nc-pre_nc] = False
self.fill_in_positive_mat_data_to_dic(res, nc - pre_nc, \
fieldpool, False)
nc = nc + 1
if not do_mirror:
continue
#flip image
tmpX = tmpX[:, ::-1, :]
tmpY = self.flip_joints(newdim, tmpY)
fieldpool['curX'] = tmpX
fieldpool['Y'] = tmpY
self.fill_in_positive_mat_data_to_dic(res, nc - pre_nc, \
fieldpool, True)
# res['data'][...,nc - pre_nc] = tmpX
# res[dicjtname][...,nc -pre_nc] = tmpY
# res['jointmasks'][...,nc - pre_nc] = self.makejointmask(newdim, tmpY)
# res['filenames'][nc - pre_nc] = curfilename
# res['oribbox'][...,nc-pre_nc] = mat['oribbox'][...,it]
# res['indmap'][...,nc-pre_nc] = self.create_part_indicatormap(tmpY, part_idx, mdim, rate, filter_size, stride)
# res['joint_indmap'][...,nc-pre_nc] = self.create_joint_indicatormap(tmpY, jtmdim, joint_filter_size, joint_stride)
# res['joint_sample_offset'][...,nc-pre_nc] = [c, r]
# res['is_mirror'][...,nc-pre_nc] = True
nc = nc + 1
t = 2 if do_mirror else 1
if nc - pre_nc + t * sample_num > per_size or nc == totaldata:
tmpres = self.truncated_copydic(res, nc - pre_nc)
tmpres['data'] = tmpres['data'].reshape((-1, nc - pre_nc),
order='F')
self.meta['data_sum'] = self.meta['data_sum'] + tmpres[
'data'].sum(axis=1, dtype=float)
savepath = iu.fullfile(self.savedata_info['savedir'], \
self.savedata_info['savename'] + \
'_' + str(self.batch_id))
myio.pickle(savepath, tmpres)
self.batch_id = self.batch_id + 1
pre_nc = nc
def generate_sliding_detection_data_from_image(self, imgpath,
scale_pairlist):
from PIL import Image
import iutils as iu
import imgproc
self.init_meta('sliding')
img = Image.open(imgpath)
ndata = 0
newdim = self.savedata_info['newdim']
steps = self.imgdata_info['steps']
# x1 is the size, x1 - 1 is the last pixel index
fx = lambda x1, x2: np.floor((x1 - newdim[1]) / x2) + 1
fy = lambda x1, x2: np.floor((x1 - newdim[0]) / x2) + 1
valid_idx = 0
for s in scale_pairlist:
ns = np.floor(
np.asarray([img.size[0], img.size[1]]) *
np.asarray([s[0], s[1]]))
if ns[0] < newdim[1] or ns[1] < newdim[0]:
break
cur_n = fx(ns[0], steps[0]) * fy(ns[1], steps[1])
valid_idx = valid_idx + 1
ndata = ndata + cur_n
ndata = int(ndata)
scale_pairlist = scale_pairlist[:valid_idx]
print('Need to generate %d data' % ndata)
filter_size = self.savedata_info['indmap_para']['filter_size']
stride = self.savedata_info['indmap_para']['stride']
mdim = self.get_indmapdim(newdim, filter_size, stride)
joint_filter_size = self.savedata_info['indmap_para'][
'joint_filter_size']
joint_stride = self.savedata_info['indmap_para']['joint_stride']
jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, ndata, self.meta['nparts'], self.meta['njoints'])
dicjtname = 'joints8' if self.meta['njoints'] == 8 else 'joints'
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
res['slide_location'] = np.zeros((2, ndata), dtype=np.float)
res['scale'] = np.zeros((2, ndata), dtype=np.float)
res['filenames'] = [imgpath for x in range(ndata)]
idx = 0
dimX = iu.prod(newdim)
for s in scale_pairlist:
ns = np.floor(
np.asarray([img.size[0], img.size[1]]) *
np.asarray([s[0], s[1]]))
ns = (int(ns[0]), int(ns[1]))
nimg = img.resize((ns[0], ns[1]))
arrimg = imgproc.ensure_rgb(np.asarray(nimg))
for x in range(0, ns[0] - newdim[1] + 1, steps[0]):
for y in range(0, ns[1] - newdim[0] + 1, steps[1]):
res['scale'][..., idx] = np.asarray([s[0], s[1]]).reshape(
(2))
res['slide_location'][..., idx] = np.asarray([x,
y]).reshape(
(2))
res['data'][..., idx] = arrimg[y:y + newdim[0],
x:x + newdim[1], :]
idx = idx + 1
if idx != ndata:
raise HMLPEError('Number of data is not consistent')
res['data'] = res['data'].reshape((-1, ndata), order='F')
return res
def generate_rt_data(self, matpath):
"""
in each mat
mat['X'] is image data
mat['Y'] is npart x ndata array
"""
mat = sio.loadmat(matpath)
dim = mat['dim'][0]
ndata = (mat['X'].shape)[1]
sample_num = self.savedata_info['sample_num']
totaldata = sample_num * ndata * 2
newdim = self.savedata_info['newdim']
nparts = self.meta['nparts']
filter_size = self.savedata_info['indmap_para']['filter_size']
stride = self.savedata_info['indmap_para']['stride']
rate = self.savedata_info['indmap_para']['rate']
mdim = self.get_indmapdim(newdim, filter_size, stride)
if newdim[0] > dim[0] or newdim[1] > dim[1]:
raise HMLPEError('Invalid new size ')
if (dim[0] - newdim[0] + 1) * (dim[1] - newdim[1] + 1) < sample_num:
raise HMLPEError(' Invalid sample_num')
res = {}
per_size = min(totaldata, self.savedata_info['max_batch_size'])
mesh = iu.cartesian_product2(np.asarray( [range(dim[0] - newdim[0] + 1), \
range(dim[1] - newdim[1] + 1)]))
allX = mat['X'].reshape( (dim[0], dim[1],dim[2], ndata), order='F')
allY = mat['Y'].reshape( (2,-1, ndata), order='F')
newlen = iu.prod( newdim )
res['data'] = np.ndarray([newdim[0], newdim[1], newdim[2], per_size], \
dtype = np.uint8)
res['labels'] = np.ndarray( [allY.shape[1], per_size], dtype=np.int)
res['joints8'] = np.ndarray( [8, 2, per_size], dtype=np.float)
res['oribbox'] = np.ndarray( [4, per_size], dtype = np.float)
res['indmap'] = np.zeros((nparts, mdim[0], mdim[1], per_size), np.bool)
res['filenames'] =[str() for x in range(per_size)]
tmpres = dict()
cur_id = self.batch_id
pre_nc = 0
nc = 0
for it in range(ndata):
curX = allX[...,it]
curY = allY[...,it].transpose()
curfilename = str(mat['imagepathlist'][0,it][0])
l = (np.random.permutation(range(len(mesh))))[:sample_num]
for p in l:
r,c = mesh[p]
tmpX = curX
tmpX = np.roll(tmpX, shift=-int(r), axis = 0)
tmpX = np.roll(tmpX, shift=-int(c), axis = 1)
tmpX = tmpX[:newdim[0], :newdim[1],:]
res['data'][...,nc - pre_nc] = tmpX
tmpY = curY - 1 + np.asarray([-c,-r])
res['joints8'][..., nc - pre_nc] = tmpY;
res['filenames'][nc - pre_nc] = curfilename
res['oribbox'][...,nc-pre_nc] = res['oribbox'][...,it]
res['indmap'][...,nc-pre_nc] = self.create_indicatormap(tmpY, part_idx, mdim, rate, filter_size, stride)
res['jointmasks'] = self.makejointmask(newdim, tmpY)
nc = nc + 1
#flip image
tmpX = tmpX[:,::-1,:]
res['data'][...,nc - pre_nc] = tmpX
res['filenames'][nc - pre_nc] = curfilename
tmmY = flip_pose8_joint(newdim, tmpY)
res['joints8'][...,nc -pre_nc] = tmpY
res['oribbox'][...,nc-pre_nc] = res['oribbox'][...,it]
res['indmap'][...,nc-pre_nc] = self.create_indicatormap(tmpY, part_idx, mdim, rate, filter_size, stride)
res['jointmasks'] = self.makejointmask(newdim, tmpY)
def generate_positive_data_from_mat(self, generate_type, matpath):
"""
in each mat
mat['X'] is image data
mat['Y'] is npart x ndata array
"""
mat = sio.loadmat(matpath)
dim = mat['dim'][0]
newdim = self.savedata_info['newdim']
if newdim[0] > dim[0] or newdim[1] > dim[1]:
raise HMLPEError('Invalid new size ')
if self.meta['matdim'] is None:
self.meta['matdim'] = dim # record the dimension before sampling
else:
if np.any(self.meta['matdim'] != dim):
raise HMLPEError('Inconsistent matdim: Previous dim is %s, current mat dim is %s' % (str(self.meta['matdim']), str(dim)))
ndata = (mat['X'].shape)[1]
if generate_type in {'rt':1}:
sample_num = self.savedata_info['sample_num']
totaldata = sample_num * ndata * 2
do_mirror = True
elif generate_type == 'ct':
sample_num = 1
totaldata = sample_num * ndata
do_mirror = False
if (dim[0] - newdim[0] + 1) * (dim[1] - newdim[1] + 1) < sample_num:
raise HMLPEError(' Invalid sample_num')
nparts = self.meta['nparts']
self.meta['ndata'] += totaldata
### BEGIN COMMENT
# njoints = self.meta['njoints']
# if njoints == 8:
# dicjtname = 'joints8'
# else:
# #raise HMLPEError('njoints = %d No supported yet' % (njoints))
# dicjtname = 'joints'
# newdim = self.savedata_info['newdim']
# filter_size = self.savedata_info['indmap_para']['filter_size']
# stride = self.savedata_info['indmap_para']['stride']
# rate = self.savedata_info['indmap_para']['rate']
# mdim = self.get_indmapdim(newdim, filter_size, stride)
# if newdim[0] > dim[0] or newdim[1] > dim[1]:
# raise HMLPEError('Invalid new size ')
# if (dim[0] - newdim[0] + 1) * (dim[1] - newdim[1] + 1) < sample_num:
# raise HMLPEError(' Invalid sample_num')
# joint_filter_size = self.savedata_info['indmap_para']['joint_filter_size']
# joint_stride = self.savedata_info['indmap_para']['joint_stride']
# jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
### END COMMENT
fieldpool = self.get_fieldpool_for_positive_mat_data()
fieldpool['mat'] = mat
self.meta['ind_dim']['part_indmap'] = fieldpool['mdim']
self.meta['ind_dim']['joint_indmap'] = fieldpool['jtmdim']
res = {}
per_size = min(totaldata, self.savedata_info['max_batch_size'])
allX = mat['X'].reshape( (dim[0], dim[1],dim[2], ndata), order='F')
Y2dname = fieldpool['Y2dname']
allY = mat[Y2dname].reshape( (2,-1, ndata), order='F')
newlen = iu.prod( newdim )
# prepare data buffer
res = self.prepare_savebuffer({'data':fieldpool['newdim'], 'part_indmap':fieldpool['mdim'], 'joint_indmap': fieldpool['jtmdim']},\
per_size, self.meta['nparts'],\
self.meta['njoints'])
tmpres = dict()
pre_nc = 0
nc = 0
res['is_positive'][:] = True
for it in range(ndata):
curX = allX[...,it]
curY = allY[...,it].transpose()
curfilename = str(mat['imagepathlist'][0,it][0]) if 'imagepathlist' in mat else ''
mesh = self.create_augumentation_mesh(dim, newdim, generate_type)
l = (np.random.permutation(range(len(mesh))))[:sample_num]
fieldpool['matidx'] = it
fieldpool['curfilename'] = curfilename
for p in l:
r,c = mesh[p]
tmpX = curX
tmpX = np.roll(tmpX, shift=-int(r), axis = 0)
tmpX = np.roll(tmpX, shift=-int(c), axis = 1)
tmpY = curY - 1 + np.asarray([-c,-r])
fieldpool['r'] = r
fieldpool['c'] = c
####
fieldpool['curX'] = tmpX
fieldpool['Y'] = tmpY
# tmpX = tmpX[:newdim[0], :newdim[1],:]
# res['data'][...,nc - pre_nc] = tmpX
# res[dicjtname][..., nc - pre_nc] = tmpY
# res['jointmasks'][...,nc - pre_nc] = self.makejointmask(newdim, tmpY)
# res['filenames'][nc - pre_nc] = curfilename
# res['oribbox'][...,nc-pre_nc] = mat['oribbox'][...,it]
# res['indmap'][...,nc-pre_nc] = self.create_part_indicatormap(tmpY, self.meta['savedata_info']['part_idx'], mdim, rate, filter_size, stride)
# res['joint_indmap'][...,nc-pre_nc] = self.create_joint_indicatormap(tmpY, jtmdim, joint_filter_size, joint_stride)
# res['joint_sample_offset'][...,nc-pre_nc] = [c, r]
# res['is_mirror'][...,nc-pre_nc] = False
self.fill_in_positive_mat_data_to_dic(res, nc - pre_nc, \
fieldpool, False)
nc = nc + 1
if not do_mirror:
continue
#flip image
tmpX = tmpX[:,::-1,:]
tmpY = self.flip_joints(newdim, tmpY)
fieldpool['curX'] = tmpX
fieldpool['Y'] = tmpY
self.fill_in_positive_mat_data_to_dic(res, nc - pre_nc, \
fieldpool, True)
# res['data'][...,nc - pre_nc] = tmpX
# res[dicjtname][...,nc -pre_nc] = tmpY
# res['jointmasks'][...,nc - pre_nc] = self.makejointmask(newdim, tmpY)
# res['filenames'][nc - pre_nc] = curfilename
# res['oribbox'][...,nc-pre_nc] = mat['oribbox'][...,it]
# res['indmap'][...,nc-pre_nc] = self.create_part_indicatormap(tmpY, part_idx, mdim, rate, filter_size, stride)
# res['joint_indmap'][...,nc-pre_nc] = self.create_joint_indicatormap(tmpY, jtmdim, joint_filter_size, joint_stride)
# res['joint_sample_offset'][...,nc-pre_nc] = [c, r]
# res['is_mirror'][...,nc-pre_nc] = True
nc = nc + 1
t = 2 if do_mirror else 1
if nc - pre_nc + t * sample_num > per_size or nc == totaldata:
tmpres = self.truncated_copydic(res, nc-pre_nc)
tmpres['data'] = tmpres['data'].reshape((-1,nc-pre_nc),order='F')
self.meta['data_sum'] = self.meta['data_sum'] + tmpres['data'].sum(axis=1,dtype=float)
savepath = iu.fullfile(self.savedata_info['savedir'], \
self.savedata_info['savename'] + \
'_' + str(self.batch_id))
myio.pickle( savepath, tmpres)
self.batch_id = self.batch_id + 1
pre_nc = nc
