def add_part_indicatormap(data_dir, save_dir, mdim, rate, filter_size, stride):
"""
This function is used for generating part indicator map for old data
data_dir is the directory that you put all batch_datayes
"""
allfile = iu.getfilelist(data_dir, 'data_batch_\d+')
meta_path = iu.fullfile(data_dir, 'batches.meta')
iu.ensure_dir(save_dir)
if iu.exists(meta_path, 'file'):
d_meta = myio.unpickle(meta_path)
if 'savedata_info' not in d_meta:
d_meta['savedata_info'] = dict()
d_meta['savedata_info']['indmap_para'] = dict()
d_meta['savedata_info']['indmap_para']['filter_size'] = filter_size
d_meta['savedata_info']['indmap_para']['stride'] = stride
d_meta['savedata_info']['indmap_para']['rate'] = rate
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), d_meta)
for fn in allfile:
print 'Processing %s' % fn
d = myio.unpickle(iu.fullfile(data_dir, fn))
ndata = d['data'].shape[-1]
nparts = 7
d['indmap'] = np.zeros((nparts, mdim[0], mdim[1], ndata), dtype=np.bool)
for i in range(ndata):
jts = d['joints8'][...,i]
d['indmap'][...,i] = HMLPE.create_part_indicatormap(jts, part_idx, mdim, rate, filter_size, stride)
myio.pickle(iu.fullfile(save_dir, fn), d)
def batch_generate_pymeta(data_root_folder, force_to_generate=False):
"""
This function will convert batches into pymeta style which can be loaded by convnet
"""
allfolder = iu.getfolderlist(data_root_folder)
print "Get %d folders" % len(allfolder)
l = []
import sys
for fn in allfolder:
a = DHMLPE()
fp = iu.fullfile(data_root_folder, fn, "matlab_meta.mat")
if iu.exists(fp, "file"):
save_fp = iu.fullfile(data_root_folder, fn, "batches.meta")
print "-----------------------------"
print "Processing ", fp
if iu.exists(save_fp, "file") and not force_to_generate:
print "Ha ha, it exists!"
else:
meta = a.get_convnet_meta(fp)
mio.pickle(save_fp, meta)
print "Saved %s" % save_fp
else:
l = l + [fp]
print "=============\n"
print "Here is what I cannot find (%d in total)" % len(l)
print l
def MakeDataFromImages(imgdir, max_per_batch, save_dir=None, save_name=None):
import iutils as iu
import iconvnet_datacvt as icvt
from PIL import Image
if max_per_batch == 0:
raise CifarError('max_per_batch can' 't not be zero')
allfiles = iu.getfilelist(imgdir, '.*jpg|.*bmp|.*png$')
ndata = len(allfiles)
iu.ensure_dir(save_dir)
d = PrepareData(min(max_per_batch, ndata))
j = 0
if save_name is None:
save_name = 'data_batch'
bid = 1
for i, fn in enumerate(allfiles):
if j == max_per_batch:
j = 0
if not save_dir is None:
icvt.ut.pickle(
iu.fullfile(save_dir, save_name + '_' + str(bid)), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = PrepareData(ndata - i)
fp = iu.fullfile(imgdir, fn)
img = iu.imgproc.ensure_rgb(np.asarray(Image.open(fp)))
img = Image.fromarray(img).resize((img_size[0], img_size[1]))
arr_img = np.asarray(img).reshape((dim_data), order='F')
d['data'][..., j] = arr_img
j = j + 1
if not save_dir is None:
icvt.ut.pickle(iu.fullfile(save_dir, save_name + '_' + str(bid)), d)
def generate_positive_data(self, generate_type, allfile=None):
"""
generate_type = 'rt': random translation
'ct' center block
"""
if allfile is None:
allfile = iu.getfilelist(self.imgdata_info['imgdatapath'],
'\w+\.mat')
print 'imgdatapath=%s, %d files are found' % (
self.imgdata_info['imgdatapath'], len(allfile))
iu.ensure_dir(self.savedata_info['savedir'])
self.batch_id = self.savedata_info['start_patch_id']
self.init_meta(generate_type)
print self.meta
np.random.seed(7)
for fn in allfile:
print 'Processing %s ' % fn
mpath = iu.fullfile(self.imgdata_info['imgdatapath'], fn)
self.generate_positive_data_from_mat(generate_type,
iu.fullfile(mpath))
if self.meta['ndata'] > 0:
self.meta['data_mean'] = self.meta['data_sum'] / self.meta['ndata']
self.meta['data_mean'] = self.meta['data_mean'].reshape((-1, 1))
else:
self.meta['data_mean'] = 0
del self.meta['data_sum']
myio.pickle(iu.fullfile(self.savedata_info['savedir'], 'batches.meta'),
self.meta)
def batch_generate_pymeta(data_root_folder, force_to_generate=False):
"""
This function will convert batches into pymeta style which can be loaded by convnet
"""
allfolder = iu.getfolderlist(data_root_folder)
print 'Get %d folders' % len(allfolder)
l = []
import sys
for fn in allfolder:
a = DHMLPE()
fp = iu.fullfile(data_root_folder, fn, 'matlab_meta.mat')
if iu.exists(fp, 'file'):
save_fp = iu.fullfile(data_root_folder, fn, 'batches.meta')
print '-----------------------------'
print 'Processing ', fp
if iu.exists(save_fp, 'file') and not force_to_generate:
print 'Ha ha, it exists!'
else:
meta = a.get_convnet_meta(fp)
mio.pickle(save_fp, meta)
print 'Saved %s' % save_fp
else:
l = l + [fp]
print '=============\n'
print 'Here is what I cannot find (%d in total)' % len(l)
print l
def MakeDataFromImages(imgdir, max_per_batch , save_dir = None, save_name=None):
import iutils as iu
import iconvnet_datacvt as icvt
from PIL import Image
if max_per_batch == 0:
raise CifarError('max_per_batch can''t not be zero')
allfiles = iu.getfilelist(imgdir, '.*jpg|.*bmp|.*png$')
ndata = len(allfiles)
iu.ensure_dir(save_dir)
d = PrepareData(min(max_per_batch, ndata))
j = 0
if save_name is None:
save_name = 'data_batch'
bid = 1
for i,fn in enumerate(allfiles):
if j == max_per_batch:
j = 0
if not save_dir is None:
icvt.ut.pickle(iu.fullfile(save_dir, save_name + '_' + str(bid)), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = PrepareData(ndata-i)
fp = iu.fullfile(imgdir, fn)
img = iu.imgproc.ensure_rgb(np.asarray(Image.open(fp)))
img = Image.fromarray(img).resize((img_size[0],img_size[1]))
arr_img = np.asarray(img).reshape((dim_data), order='F')
d['data'][...,j] = arr_img
j = j + 1
if not save_dir is None:
icvt.ut.pickle(iu.fullfile(save_dir, save_name + '_' + str(bid)), d)
def add_part_indicatormap(data_dir, save_dir, mdim, rate, filter_size, stride):
"""
This function is used for generating part indicator map for old data
data_dir is the directory that you put all batch_datayes
"""
allfile = iu.getfilelist(data_dir, 'data_batch_\d+')
meta_path = iu.fullfile(data_dir, 'batches.meta')
iu.ensure_dir(save_dir)
if iu.exists(meta_path, 'file'):
d_meta = myio.unpickle(meta_path)
if 'savedata_info' not in d_meta:
d_meta['savedata_info'] = dict()
d_meta['savedata_info']['indmap_para'] = dict()
d_meta['savedata_info']['indmap_para']['filter_size'] = filter_size
d_meta['savedata_info']['indmap_para']['stride'] = stride
d_meta['savedata_info']['indmap_para']['rate'] = rate
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), d_meta)
for fn in allfile:
print 'Processing %s' % fn
d = myio.unpickle(iu.fullfile(data_dir, fn))
ndata = d['data'].shape[-1]
nparts = 7
d['indmap'] = np.zeros((nparts, mdim[0], mdim[1], ndata),
dtype=np.bool)
for i in range(ndata):
jts = d['joints8'][..., i]
d['indmap'][..., i] = HMLPE.create_part_indicatormap(
jts, part_idx, mdim, rate, filter_size, stride)
myio.pickle(iu.fullfile(save_dir, fn), d)
def ReadDataToCifarDic(imgdir, example_path, data_category, max_per_batch,
save_dir):
"""
read all data in 'data_category'
into cifar style dictionary
"""
import scipy.io as sio
import iutils as iu
import cifar
import iconvnet_datacvt as icvt
from iutils import imgproc as imgproc
from PIL import Image
if data_category != 'istest':
print 'I haven' 't implement joints8 part '
#raise ModecError('I haven''t implement joints8 part ')
all_examples = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_examples, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
s_first = min(ndata, max_per_batch)
d = cifar.PrepareData(s_first)
d['oridet'] = np.ndarray((4, s_first), dtype=np.int)
d['filepath'] = [str() for x in range(s_first)]
d['coords'] = np.ndarray((2, 29, s_first), dtype=np.float32)
tdsize = cifar.img_size[0] # make sure img_size[0] == img_size[1]
j = 0
bid = 1
for i in range(ndata):
if j == max_per_batch:
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), \
d)
bid = bid + 1
j = 0
if ndata - i < max_per_batch:
d = cifar.PrepareData(ndata - i)
fn = str(examples[i]['filepath'][0])
fp = iu.fullfile(imgdir, fn)
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filepath'][j] = fp
d['oridet'][..., j] = tbox
d['oribbox'][..., j] = bbox = ExtendBndbox(tbox, img.size)
d['coords'][..., j] = examples[i]['coords']
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape(
(8, 2), order='C')
d['joints8'][..., j] = TransformPoints(orijoints8, bbox,
cifar.img_size).reshape(
(16), order='C')
img = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(img[bbox[1]:bbox[3], bbox[0]:bbox[2], :])
data_img = np.asarray(sub_img.resize((cifar.img_size[0],\
cifar.img_size[1]))).reshape((cifar.dim_data),order='F')
d['data'][..., j] = data_img
j = j + 1
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), d)
def ReadDataToHMLPEDic(imgdir,example_path, data_category, max_per_batch,save_dir):
"""
Read all data in 'data_category'
into HMLPE dictionary
There is no need to generating training data, since they can be generated in
hmlpe.py
"""
import scipy.io as sio
import iutils as iu
import iread.myio as mio
import iread.hmlpe as hmlpe
import imgproc
from PIL import Image
if data_category != 'istest':
print 'Warn: The correctness of data type %s is not guaranteed' % data_category
all_example = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_example, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
buf_size = min(ndata, max_per_batch)
dimdic = {'data':(112,112,3), 'part_indmap':(8,8), 'joint_indmap':(8,8)}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
d['oridet'] = np.zeros((4,buf_size), dtype=np.int)
d['coords'] = np.ndarray((2,29, buf_size), dtype=np.float32)
tdsize = dimdic['data'][0]
dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
d['data'] = d['data'].reshape((dsize, -1),order='F')
d['is_positive'][:] = True
d['is_mirror'][:] = False
bid = 1
j = 0
for i in range(ndata):
if j == max_per_batch:
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filenames'][j] = fp
d['coords'][...,j] = examples[i]['coords']
d['oribbox'][...,j] = bbox = ExtendBndbox(tbox, img.size)
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape((8,2),order='C') - 1 # to python stype 0-idx
d['joints8'][...,j] = TransformPoints(orijoints8, bbox, dimdic['data']).reshape((8,2),order='C')
imgarr = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2],:])
data_img = np.asarray(sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape((dsize),order='F')
d['data'][...,j] = data_img
d['indmap'][...,j] = hmlpe.HMLPE.create_part_indicatormap(d['joints8'][...,j], hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
d['joint_indmap'][...,j] = hmlpe.HMLPE.create_joint_indicatormap(d['joints8'][...,j], dimdic['joint_indmap'], 30.0, 12.0)
d['jointmasks'][...,j] = hmlpe.HMLPE.makejointmask(dimdic['data'], d['joints8'][...,j])
j = j + 1
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
def ReadCropImageToHMLPEDic(dataset_dir, save_dir, istrain = False, isOC = True):
"""
This function will be used for generating testing data
Because training and testing data has different format in oribbox
For generating trainign samples,
please use
create_lsp_regression_data.m
(dataset_dir, type=3, opt)
opt.OC = ?
and hmlpe.py
"""
import iutils as iu
import iread.hmlpe as hmlpe
import iread.myio as mio
import scipy.io as sio
from PIL import Image
ndata = 1000
if istrain:
s_idx = 0
else:
s_idx = 1000
imgdir = iu.fullfile(dataset_dir, 'images-crop')
if isOC:
dmat = sio.loadmat(iu.fullfile(dataset_dir, 'jointsOC.mat'))
else:
dmat = sio.loadmat(iu.fullfile(dataset_dir, 'joints-crop.mat'))
lsp_jt = dmat['joints']
dimdic = {'data':(112,112,3), 'part_indmap':(8,8), 'joint_indmap': (8,8)}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, ndata, nparts, njoints)
d['data'] = d['data'].reshape((-1,ndata),order='F')
d['is_mirror'][:] = False
d['is_positive'][:] = True
for idx in range(s_idx, s_idx + ndata):
imgpath = iu.fullfile(imgdir, 'im%04d.jpg' % (idx + 1))
img = Image.open(imgpath)
i = idx - s_idx
orijoints8, isvisible = GetJoints8(lsp_jt[...,idx])
bbox = GetUpperBodyBox(img.size)
img_arr = np.asarray(img)[bbox[1]:bbox[3], bbox[0]:bbox[2],:]
s = np.asarray([(dimdic['data'][1]-1.0)/(bbox[2] - bbox[0]),(dimdic['data'][0]-1.0)/(bbox[3]-bbox[1])]).reshape((1,2))
tjoints = (orijoints8 - bbox[0:2,:].reshape((1,2)))*s
masks = hmlpe.HMLPE.makejointmask(dimdic['data'], tjoints)
d['data'][...,i] = np.asarray(Image.fromarray(img_arr).resize((dimdic['data'][0], dimdic['data'][1]))).reshape((-1,1),order='F').flatten()
d['joints8'][...,i] = tjoints
d['jointmasks'][...,i] = np.logical_and(masks, isvisible)
d['filenames'][i] = imgpath
d['oribbox'][...,i] = bbox.flatten()
d['indmap'][...,i] = hmlpe.HMLPE.create_part_indicatormap(tjoints, hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
d['joint_indmap'][...,i] = hmlpe.HMLPE.create_joint_indicatormap(tjoints, dimdic['joint_indmap'], 30.0, 12.0)
mio.pickle(iu.fullfile(save_dir, 'data_batch_1'), d)
def __init__(self, options = None):
self.cv2 = __import__('cv2')
self.igeo = __import__('ipyml').geometry
if options is None:
self.model_dir = '/opt/visal/tmp/for_sijin/Data/opencv_trained_models'
else:
self.model_dir = options['model_dir']
self.face_cascade = dict()
self.face_cascade['frontal'] = self.cv2.CascadeClassifier(iu.fullfile(self.model_dir, \
'haarcascade_frontalface_default.xml'))
self.face_cascade['profile'] = self.cv2.CascadeClassifier(iu.fullfile(self.model_dir, \
'haarcascade_profileface.xml'))
def ReadDataToCifarDic(imgdir,example_path, data_category, max_per_batch,save_dir):
"""
read all data in 'data_category'
into cifar style dictionary
"""
import scipy.io as sio
import iutils as iu
import cifar
import iconvnet_datacvt as icvt
from iutils import imgproc as imgproc
from PIL import Image
if data_category != 'istest':
print 'I haven''t implement joints8 part '
#raise ModecError('I haven''t implement joints8 part ')
all_examples = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_examples, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
s_first = min(ndata, max_per_batch)
d = cifar.PrepareData(s_first)
d['oridet'] = np.ndarray((4,s_first),dtype=np.int)
d['filepath'] = [str() for x in range(s_first)]
d['coords'] = np.ndarray((2,29,s_first),dtype=np.float32)
tdsize= cifar.img_size[0] # make sure img_size[0] == img_size[1]
j = 0
bid = 1
for i in range(ndata):
if j == max_per_batch:
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), \
d)
bid = bid + 1
j = 0
if ndata - i < max_per_batch:
d = cifar.PrepareData(ndata-i)
fn = str(examples[i]['filepath'][0])
fp = iu.fullfile(imgdir, fn)
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filepath'][j] = fp
d['oridet'][...,j] = tbox
d['oribbox'][...,j] = bbox = ExtendBndbox(tbox,img.size)
d['coords'][...,j] = examples[i]['coords']
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape((8,2),order='C')
d['joints8'][...,j] = TransformPoints(orijoints8, bbox,cifar.img_size).reshape((16),order='C')
img = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(img[bbox[1]:bbox[3], bbox[0]:bbox[2],:])
data_img = np.asarray(sub_img.resize((cifar.img_size[0],\
cifar.img_size[1]))).reshape((cifar.dim_data),order='F')
d['data'][...,j] = data_img
j = j + 1
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)),d)
def __init__(self, options=None):
self.cv2 = __import__('cv2')
self.igeo = __import__('ipyml').geometry
if options is None:
self.model_dir = '/opt/visal/tmp/for_sijin/Data/opencv_trained_models'
else:
self.model_dir = options['model_dir']
self.face_cascade = dict()
self.face_cascade['frontal'] = self.cv2.CascadeClassifier(iu.fullfile(self.model_dir, \
'haarcascade_frontalface_default.xml'))
self.face_cascade['profile'] = self.cv2.CascadeClassifier(iu.fullfile(self.model_dir, \
'haarcascade_profileface.xml'))
def test_addmeta(metadir):
metapath = iu.fullfile(metadir, 'batches.meta')
d_meta = myio.unpickle(metapath)
d_meta['ind_dim'] = dict()
d_meta['ind_dim']['part_indmap'] = (8, 8)
d_meta['ind_dim']['joint_indmap'] = (8, 8)
myio.pickle(metapath, d_meta)
def collect_feature_meta(folder, re_exp='batch_feature_\w+$'):
allfile = sorted(iu.getfilelist(folder, re_exp), key=lambda x:extract_batch_num(x))
feature_list_lst = []
feature_dim = None
indexes_lst = []
feature_names = None
if len(allfile) == 0:
return dict()
for f in allfile:
print f
p = iu.fullfile(folder, f)
d = mio.unpickle(p)
feature_list_lst += [d['feature_list']]
if feature_dim:
if feature_dim!= d['feature_dim']:
raise Exception('feature dim inconsistent')
else:
feature_dim = d['feature_dim']
indexes_lst += [d['info']['indexes']]
indexes = np.concatenate(indexes_lst)
n_feature, n_batch = len(feature_dim), len(allfile)
feature_list = [np.concatenate([feature_list_lst[i][k] for i in range(n_batch)],
axis=-1)
for k in range(n_feature)]
return {'feature_list':feature_list, 'feature_dim':feature_dim, 'info':{'indexes':indexes,
'feature_names':d['info']['feature_names']}}
def pack_01():
"""
input: fc_j0 feature, rel_pose
outputs: rel_pose, fc_j0_feature
"""
source_feature_network_path = '/opt/visal/tmp/for_sijin/Data/saved/theano_models/2015_02_02_acm_act_14_exp_2_19_graph_0012/'
source_meta_path = '/opt/visal/tmp/for_sijin/tmp/tmp_saved'
exp_meta_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2/batches.meta'
save_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_14'
feature_name = 'Relative_Y3d_mono_body'
res = dict()
exp_meta = mio.unpickle(exp_meta_path)
source_meta = dutils.collect_feature_meta(source_meta_path)
rel_pose = exp_meta[feature_name]
fc_j0_feature = source_meta['feature_list'][1]
rel_gt = source_meta['feature_list'][0]
diff = rel_gt.reshape((-1, rel_gt.shape[-1]),order='F') * 1200 - rel_pose
print 'diff is {}'.format(diff.flatten().sum())
feature_list = [rel_pose, fc_j0_feature]
feature_dim = [rel_pose.shape[0], fc_j0_feature.shape[0]]
print feature_dim, '<<<feature dim'
res = {'feature_list': feature_list, 'feature_dim':feature_dim,
'info':{'indexes':source_meta['info']['indexes'],
'max_depth': 1200.0}}
indexes = res['info']['indexes']
res['info']['soure_feature_network_path'] = source_feature_network_path
print indexes[:10], min(indexes), max(indexes)
print 'The number of data is {} == {}'.format(indexes.size, feature_list[0].shape[-1])
iu.ensure_dir(save_path)
mio.pickle(iu.fullfile(save_path, 'batches.meta'), res)
def gbns(name,sp, params):
"""
get bias for combining norm and scale layer
params[0] = model_folder
params[1] = norm layer name [source]
params[2] = scale layer name [source]
"""
model_folder, norm_name, scale_name = params[0], params[1], params[2]
stat_folder = iu.fullfile(model_folder, 'stat')
stat_path = Solver.get_saved_model_path(stat_folder)
stat = mio.unpickle(stat_path)
model = Solver.get_saved_model(model_folder)
layers = get_layers(model)
W= layers[scale_name][2]['weights'][0]
b= layers[scale_name][2]['biases'][0]
print 'W-------------'
iu.print_common_statistics(W)
print 'b'
iu.print_common_statistics(b)
if 'epsilon' in layers[norm_name][2]:
epsilon = layers[norm_name][2]['epsilon']
else:
epsilon = 1e-6
u = stat['layers'][norm_name]['u'].flatten()
var = stat['layers'][norm_name]['var'].flatten()
return [b - W * u / (np.sqrt(var + epsilon))]
def parse_params(self, params):
DataProvider.parse_params(self, params)
if self.data_dic is None:
if 'data_path' in params:
self.data_dic = mio.unpickle(iu.fullfile(params['data_path'], 'batches.meta'))
else:
raise Exception('data-path is missing')
def gbns(name, sp, params):
"""
get bias for combining norm and scale layer
params[0] = model_folder
params[1] = norm layer name [source]
params[2] = scale layer name [source]
"""
model_folder, norm_name, scale_name = params[0], params[1], params[2]
stat_folder = iu.fullfile(model_folder, 'stat')
stat_path = Solver.get_saved_model_path(stat_folder)
stat = mio.unpickle(stat_path)
model = Solver.get_saved_model(model_folder)
layers = get_layers(model)
W = layers[scale_name][2]['weights'][0]
b = layers[scale_name][2]['biases'][0]
print 'W-------------'
iu.print_common_statistics(W)
print 'b'
iu.print_common_statistics(b)
if 'epsilon' in layers[norm_name][2]:
epsilon = layers[norm_name][2]['epsilon']
else:
epsilon = 1e-6
u = stat['layers'][norm_name]['u'].flatten()
var = stat['layers'][norm_name]['var'].flatten()
return [b - W * u / (np.sqrt(var + epsilon))]
def test_CroopedDHMPLEJointDataWarper():
data_dir = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2'
data_path = iu.fullfile(data_dir, 'batches.meta')
params = {'batch_size': 1024, 'data_path': data_dir}
data_dic = None
train = False
data_range = range(0, 132744)
test_range = range(132744, 162008)
dp = CroppedDHMLPEJointDataWarper(data_dic, train, data_range, params)
epoch, batchnum, alldata = dp.get_next_batch()
print ' {}.{} [] of {}'.format(epoch, batchnum, len(alldata))
show_idx = 100
img = np.require(dp.get_plottable_data(alldata[0][..., show_idx].reshape(
(-1, 1), order='F')),
dtype=np.uint8)
sp = img.shape
img = img.reshape((sp[0], sp[1], sp[2]), order='F')
print np.max(img.flatten())
print np.min(img.flatten())
pl.subplot(2, 1, 1)
pl.imshow(img)
pl.subplot(2, 1, 2)
img = dp.cropped_mean_image
pl.imshow(img / 255.0)
pl.show()
def test_addmeta(metadir):
metapath = iu.fullfile(metadir, 'batches.meta')
d_meta = myio.unpickle(metapath)
d_meta['ind_dim'] = dict()
d_meta['ind_dim']['part_indmap'] = (8,8)
d_meta['ind_dim']['joint_indmap'] = (8,8)
myio.pickle(metapath, d_meta)
def parse_params(self, params):
DataProvider.parse_params(self, params)
if self.data_dic is None:
if 'data_path' in params:
self.data_dic = mio.unpickle(
iu.fullfile(params['data_path'], 'batches.meta'))
else:
raise Exception('data-path is missing')
def collect_feature(folder, item, re_exp='batch_feature_\w+$'):
allfile = sorted(iu.getfilelist(folder, re_exp), key=lambda x:extract_batch_num(x))
l = []
for f in allfile:
p = iu.fullfile(folder, f)
d = mio.unpickle(p)
l = l + [d[item]]
return np.concatenate(l, axis=1)
def ExtractAnnotation(annot_dir):
import iutils as iu
eplist = [2, 3, 4, 5, 6]
d = dict()
for e in eplist:
txtname = iu.fullfile(annot_dir, 'buffy_s5e' + str(e) + '_sticks.txt')
ed = ReadBuffyTxt(txtname)
d[e] = ed
return d
def ExtractAnnotation(annot_dir):
import iutils as iu
eplist = [2,3,4,5,6]
d = dict()
for e in eplist:
txtname = iu.fullfile(annot_dir, 'buffy_s5e' + str(e) + '_sticks.txt')
ed = ReadBuffyTxt(txtname)
d[e] = ed
return d
def __init__(self, imgdir):
self.Image = __import__('Image')
self.imgdir = imgdir
self.cur_idx = -1
self.images_path = [iu.fullfile(imgdir, x) for x in \
sorted(iu.getfilelist(imgdir, '.*\.(jpg|png)'))]
if len(self.images_path) == 0:
raise DemoError('I cannot find image uder %s ' % self.images_path)
print 'I got %d images' % len(self.images_path)
ICameraBasic.__init__(self)
def __init__(self, imgdir):
self.Image = __import__('Image')
self.imgdir = imgdir
self.cur_idx = -1
self.images_path = [iu.fullfile(imgdir, x) for x in \
sorted(iu.getfilelist(imgdir, '.*\.(jpg|png)'))]
if len(self.images_path) == 0:
raise DemoError('I cannot find image uder %s ' % self.images_path)
print 'I got %d images' % len(self.images_path)
ICameraBasic.__init__(self)
def cvt1(source_exp_name, target_exp_name):
print '''
SP_t004_act_14:
source meta [rel_gt, img_feature_accv_fc_j0, relskel_feature_t004]
Raw_SP_t004_act_14:
target meta [rel_gt, img_feature_accv_fc_j0, rel_gt]
'''
base_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/'
source_meta = mio.unpickle(
iu.fullfile(base_path, 'folder_%s' % source_exp_name, 'batches.meta'))
target_meta_folder = iu.fullfile(base_path, 'folder_%s' % target_exp_name)
target_meta_path = iu.fullfile(target_meta_folder, 'batches.meta')
d = source_meta.copy()
print d.keys()
d['feature_list'] = [source_meta['feature_list'][k] for k in [0, 1, 0]]
d['feature_dim'] = [source_meta['feature_dim'][k] for k in [0, 1, 0]]
# print d['info']
print 'folder :{}\n path {}'.format(target_meta_folder, target_meta_path)
iu.ensure_dir(target_meta_folder)
mio.pickle(target_meta_path, d)
def cvt1(source_exp_name, target_exp_name):
print '''
SP_t004_act_14:
source meta [rel_gt, img_feature_accv_fc_j0, relskel_feature_t004]
Raw_SP_t004_act_14:
target meta [rel_gt, img_feature_accv_fc_j0, rel_gt]
'''
base_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/'
source_meta = mio.unpickle(iu.fullfile(base_path, 'folder_%s' % source_exp_name,
'batches.meta'))
target_meta_folder = iu.fullfile(base_path, 'folder_%s' % target_exp_name)
target_meta_path = iu.fullfile(target_meta_folder, 'batches.meta')
d = source_meta.copy()
print d.keys()
d['feature_list'] = [source_meta['feature_list'][k] for k in [0, 1, 0]]
d['feature_dim'] = [source_meta['feature_dim'][k] for k in [0, 1, 0]]
# print d['info']
print 'folder :{}\n path {}'.format(target_meta_folder, target_meta_path)
iu.ensure_dir(target_meta_folder)
mio.pickle(target_meta_path, d)
def merge_batch_data(data_dir_list,
save_dir,
is_symbolic=True,
batch_start_num=1):
"""
This function will merge all the data_batches in data_dir into one folder
and rename them accordining.
Of cause, meta data will be updated
"""
import os
import shutil
iu.ensure_dir(save_dir)
meta = None
for ddir in data_dir_list:
cur_meta = myio.unpickle(iu.fullfile(ddir, 'batches.meta'))
meta = HMLPE.merge_meta(meta, cur_meta)
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), meta)
cur_id = batch_start_num
for ddir in data_dir_list:
all_file = iu.getfilelist(ddir, 'data_batch_\d+')
print 'I find %d batches in %s' % (len(all_file), ddir)
if is_symbolic:
for fn in all_file:
sn = iu.fullfile(save_dir, 'data_batch_%d' % cur_id)
if iu.exists(sn, 'file'):
os.remove(sn)
os.symlink(iu.fullfile(ddir, fn), sn)
cur_id = cur_id + 1
else:
for fn in all_file:
shutil.copyfile(
iu.fullfile(ddir, fn),
iu.fullfile(save_dir, 'data_batch_%d' % cur_id))
cur_id = cur_id + 1
def merge_batch_data(data_dir_list, save_dir, is_symbolic = True, batch_start_num = 1):
"""
This function will merge all the data_batches in data_dir into one folder
and rename them accordining.
Of cause, meta data will be updated
"""
import os
import shutil
iu.ensure_dir(save_dir)
meta = None
for ddir in data_dir_list:
cur_meta = myio.unpickle(iu.fullfile(ddir, 'batches.meta'))
meta = HMLPE.merge_meta(meta, cur_meta)
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), meta)
cur_id = batch_start_num
for ddir in data_dir_list:
all_file = iu.getfilelist(ddir, 'data_batch_\d+')
print 'I find %d batches in %s' % (len(all_file), ddir)
if is_symbolic:
for fn in all_file:
sn = iu.fullfile(save_dir, 'data_batch_%d' % cur_id)
if iu.exists(sn, 'file'):
os.remove(sn)
os.symlink(iu.fullfile(ddir, fn), sn)
cur_id = cur_id + 1
else:
for fn in all_file:
shutil.copyfile(iu.fullfile(ddir, fn), iu.fullfile(save_dir, 'data_batch_%d' % cur_id))
cur_id = cur_id + 1
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(self, generate_type, allfile = None):
"""
generate_type = 'rt': random translation
'ct' center block
"""
if allfile is None:
allfile = iu.getfilelist( self.imgdata_info['imgdatapath'], '\w+\.mat')
print 'imgdatapath=%s, %d files are found' % (self.imgdata_info['imgdatapath'], len(allfile))
iu.ensure_dir(self.savedata_info['savedir'])
self.batch_id = self.savedata_info['start_patch_id']
self.init_meta(generate_type)
print self.meta
np.random.seed(7)
for fn in allfile:
print 'Processing %s ' % fn
mpath = iu.fullfile(self.imgdata_info['imgdatapath'], fn)
self.generate_positive_data_from_mat(generate_type ,iu.fullfile(mpath))
if self.meta['ndata'] > 0:
self.meta['data_mean'] = self.meta['data_sum'] / self.meta['ndata']
self.meta['data_mean'] = self.meta['data_mean'].reshape((-1,1))
else:
self.meta['data_mean'] = 0
del self.meta['data_sum']
myio.pickle(iu.fullfile(self.savedata_info['savedir'], 'batches.meta'), self.meta)
def do_accveval(self):
images_folder = self.op.get_value('images_folder')
# get all jpg file in images_folder
allfiles = iu.getfilelist(images_folder, '.*\.jpg')
images_path = [iu.fullfile(images_folder, p) for p in allfiles]
n_image = len(images_path)
images = self.load_images(images_path)
mean_image_path = self.op.get_value('mean_image_path')
mean_image = sio.loadmat(mean_image_path)['cropped_mean_image']
mean_image_arr = mean_image.reshape((-1, 1), order='F')
input_images = images - mean_image_arr
# pack input images into batch data
data = [
input_images,
np.zeros((51, n_image), dtype=np.single),
np.zeros((1700, n_image), dtype=np.single)
]
# allocate the buffer for prediction
pred_buffer = np.zeros((n_image, 51), dtype=np.single)
data.append(pred_buffer)
ext_data = [
np.require(elem, dtype=np.single, requirements='C')
for elem in data
]
# run the model
## get the joint prediction layer indexes
self.pred_layer_idx = self.get_layer_idx('fc_j2', check_type='fc')
self.libmodel.startFeatureWriter(ext_data, self.pred_layer_idx)
self.finish_batch()
raw_pred = ext_data[-1].T
pred = dhmlpe_features.convert_relskel2rel(raw_pred) * 1200.0
# show the first prediction
show_idx = 0
img = np.array(Image.open(images_path[show_idx]))
fig = pl.figure(0)
ax1 = fig.add_subplot(121)
ax1.imshow(img)
ax2 = fig.add_subplot(122, projection='3d')
cur_pred = pred[..., show_idx].reshape((3, -1), order='F')
part_idx = iread.h36m_hmlpe.part_idx
params = {'elev': -94, 'azim': -86, 'linewidth': 6, 'order': 'z'}
dutils.show_3d_skeleton(cur_pred.T, part_idx, params)
def gwns(name, sp_list, params):
"""
get weights for combining norm and scale layer
params[0] = model_folder
params[1] = norm layer name [source]
params[2] = scale layer name [source]
"""
model_folder, norm_name, scale_name = params[0], params[1], params[2]
stat_folder = iu.fullfile(model_folder, 'stat')
stat_path = Solver.get_saved_model_path(stat_folder)
stat = mio.unpickle(stat_path)
print 'stat keys = {}'.format(stat['layers'].keys())
model = Solver.get_saved_model(model_folder)
layers = get_layers(model)
W = layers[scale_name][2]['weights']
if 'epsilon' in layers[norm_name][2]:
epsilon = layers[norm_name][2]['epsilon']
else:
epsilon = 1e-6
# u = stat['layers'][norm_name]['u'].flatten()
var = stat['layers'][norm_name]['var'].flatten()
return [W[0] / np.sqrt(var + epsilon)]
def gwns(name, sp_list, params):
"""
get weights for combining norm and scale layer
params[0] = model_folder
params[1] = norm layer name [source]
params[2] = scale layer name [source]
"""
model_folder, norm_name, scale_name = params[0], params[1], params[2]
stat_folder = iu.fullfile(model_folder, 'stat')
stat_path = Solver.get_saved_model_path(stat_folder)
stat = mio.unpickle(stat_path)
print 'stat keys = {}'.format(stat['layers'].keys())
model = Solver.get_saved_model(model_folder)
layers = get_layers(model)
W= layers[scale_name][2]['weights']
if 'epsilon' in layers[norm_name][2]:
epsilon = layers[norm_name][2]['epsilon']
else:
epsilon = 1e-6
# u = stat['layers'][norm_name]['u'].flatten()
var = stat['layers'][norm_name]['var'].flatten()
return [W[0] / np.sqrt(var + epsilon)]
def do_accveval(self):
images_folder = self.op.get_value('images_folder')
# get all jpg file in images_folder
allfiles = iu.getfilelist(images_folder, '.*\.jpg')
images_path = [iu.fullfile(images_folder, p) for p in allfiles]
n_image = len(images_path)
images = self.load_images(images_path)
mean_image_path = self.op.get_value('mean_image_path')
mean_image = sio.loadmat(mean_image_path)['cropped_mean_image']
mean_image_arr = mean_image.reshape((-1,1),order='F')
input_images = images - mean_image_arr
# pack input images into batch data
data = [input_images, np.zeros((51,n_image),dtype=np.single),
np.zeros((1700,n_image), dtype=np.single)]
# allocate the buffer for prediction
pred_buffer = np.zeros((n_image, 51),dtype=np.single)
data.append(pred_buffer)
ext_data = [np.require(elem,dtype=np.single, requirements='C') for elem in data]
# run the model
## get the joint prediction layer indexes
self.pred_layer_idx = self.get_layer_idx('fc_j2',check_type='fc')
self.libmodel.startFeatureWriter(ext_data, self.pred_layer_idx)
self.finish_batch()
raw_pred = ext_data[-1].T
pred = dhmlpe_features.convert_relskel2rel(raw_pred) * 1200.0
# show the first prediction
show_idx = 0
img = np.array(Image.open(images_path[show_idx]))
fig = pl.figure(0)
ax1 = fig.add_subplot(121)
ax1.imshow(img)
ax2 = fig.add_subplot(122,projection='3d')
cur_pred = pred[..., show_idx].reshape((3,-1),order='F')
part_idx = iread.h36m_hmlpe.part_idx
params = {'elev':-94, 'azim':-86, 'linewidth':6, 'order':'z'}
dutils.show_3d_skeleton(cur_pred.T, part_idx, params)
def pack_01():
"""
input: fc_j0 feature, rel_pose
outputs: rel_pose, fc_j0_feature
"""
source_feature_network_path = '/opt/visal/tmp/for_sijin/Data/saved/theano_models/2015_02_02_acm_act_14_exp_2_19_graph_0012/'
source_meta_path = '/opt/visal/tmp/for_sijin/tmp/tmp_saved'
exp_meta_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2/batches.meta'
save_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_14'
feature_name = 'Relative_Y3d_mono_body'
res = dict()
exp_meta = mio.unpickle(exp_meta_path)
source_meta = dutils.collect_feature_meta(source_meta_path)
rel_pose = exp_meta[feature_name]
fc_j0_feature = source_meta['feature_list'][1]
rel_gt = source_meta['feature_list'][0]
diff = rel_gt.reshape((-1, rel_gt.shape[-1]), order='F') * 1200 - rel_pose
print 'diff is {}'.format(diff.flatten().sum())
feature_list = [rel_pose, fc_j0_feature]
feature_dim = [rel_pose.shape[0], fc_j0_feature.shape[0]]
print feature_dim, '<<<feature dim'
res = {
'feature_list': feature_list,
'feature_dim': feature_dim,
'info': {
'indexes': source_meta['info']['indexes'],
'max_depth': 1200.0
}
}
indexes = res['info']['indexes']
res['info']['soure_feature_network_path'] = source_feature_network_path
print indexes[:10], min(indexes), max(indexes)
print 'The number of data is {} == {}'.format(indexes.size,
feature_list[0].shape[-1])
iu.ensure_dir(save_path)
mio.pickle(iu.fullfile(save_path, 'batches.meta'), res)
def process(op):
data_folder = op.get_value('load_file')
save_path = op.get_value('save_path')
# data_folder = '/public/sijinli2/ibuffer/2015-01-16/net2_test_for_stat_2000'
all_files = iu.getfilelist(data_folder, '\d+@\d+$')
print all_files
d = mio.unpickle(iu.fullfile(data_folder, all_files[0]))
ms = d['model_state']
if op.get_value('cost_name') is not None:
cost_names = op.get_value('cost_name').split(',')
n_cost = len(cost_name)
else:
n_cost = len(d['solver_params']['train_error'][0])
cost_names = d['solver_params']['train_error'][0].keys()
print 'Start to plot'
start_time = time()
for i in range(n_cost):
pl.subplot(n_cost, 1, i + 1)
plot_cost(op, d, cost_names[i])
print 'Cost {} seconds '.format(time()- start_time)
if save_path:
imgproc.imsave_tight(save_path)
pl.show()
def process(op):
data_folder = op.get_value('load_file')
save_path = op.get_value('save_path')
# data_folder = '/public/sijinli2/ibuffer/2015-01-16/net2_test_for_stat_2000'
all_files = iu.getfilelist(data_folder, '\d+@\d+$')
print all_files
d = mio.unpickle(iu.fullfile(data_folder, all_files[0]))
ms = d['model_state']
if op.get_value('cost_name') is not None:
cost_names = op.get_value('cost_name').split(',')
n_cost = len(cost_name)
else:
n_cost = len(d['solver_params']['train_error'][0])
cost_names = d['solver_params']['train_error'][0].keys()
print 'Start to plot'
start_time = time()
for i in range(n_cost):
pl.subplot(n_cost, 1, i + 1)
plot_cost(op, d, cost_names[i])
print 'Cost {} seconds '.format(time() - start_time)
if save_path:
imgproc.imsave_tight(save_path)
pl.show()
def test_CroopedDHMPLEJointDataWarper():
data_dir = "/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2"
data_path = iu.fullfile(data_dir, "batches.meta")
params = {"batch_size": 1024, "data_path": data_dir}
data_dic = None
train = False
data_range = range(0, 132744)
test_range = range(132744, 162008)
dp = CroppedDHMLPEJointDataWarper(data_dic, train, data_range, params)
epoch, batchnum, alldata = dp.get_next_batch()
print " {}.{} [] of {}".format(epoch, batchnum, len(alldata))
show_idx = 100
img = np.require(dp.get_plottable_data(alldata[0][..., show_idx].reshape((-1, 1), order="F")), dtype=np.uint8)
sp = img.shape
img = img.reshape((sp[0], sp[1], sp[2]), order="F")
print np.max(img.flatten())
print np.min(img.flatten())
pl.subplot(2, 1, 1)
pl.imshow(img)
pl.subplot(2, 1, 2)
img = dp.cropped_mean_image
pl.imshow(img / 255.0)
pl.show()
def shuffle_data(source_dir, target_dir, max_per_file = 4000):
"""
This function will shuflle all the data in source_dir
and save it to target_dir
"""
if source_dir == target_dir:
raise HMLPEError('source dir can not be the same as target dir')
import shutil
import sys
iu.ensure_dir( target_dir)
shutil.copy(iu.fullfile(source_dir, 'batches.meta'), \
iu.fullfile(target_dir, 'batches.meta'))
meta = myio.unpickle(iu.fullfile(source_dir, 'batches.meta'))
ndata = meta['ndata']
nbatch = (ndata - 1) / max_per_file + 1
nparts = meta['nparts']
njoints = meta['njoints']
newdim = meta['savedata_info']['newdim']
filter_size = meta['savedata_info']['indmap_para']['filter_size']
stride = meta['savedata_info']['indmap_para']['stride']
joint_filter_size = meta['savedata_info']['indmap_para']['joint_filter_size']
joint_stride = meta['savedata_info']['indmap_para']['joint_stride']
mdim = HMLPE.get_indmapdim(newdim, filter_size, stride)
jtmdim = HMLPE.get_indmapdim(newdim, joint_filter_size, joint_stride)
print('There are %d data in total, I need %d batch to hold it' %(ndata, nbatch))
print 'Begin creating empty files'
rest = ndata
d = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, max_per_file, nparts, njoints)
HMLPE.adjust_savebuffer_shape(d)
for b in range(nbatch):
cur_n = min(max_per_file, rest)
if b != nbatch - 1:
saved = d
else:
saved = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, cur_n, nparts, njoints)
HMLPE.adjust_savebuffer_shape(saved)
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)), saved)
rest = rest - cur_n
print 'End creating'
allbatchfn = iu.getfilelist(source_dir, 'data_batch_\d+')
np.random.seed(7)
perm = range(ndata)
np.random.shuffle(perm)
buf_cap = 12 # store six batch at most
nround = (nbatch - 1)/buf_cap + 1
for rd in range(nround):
print ('Round %d of %d' % (rd,nround))
buf = dict()
offset = 0
for fn in allbatchfn:
print( 'Processing %s' % fn )
d = myio.unpickle(iu.fullfile(source_dir, fn))
cur_n = d['data'].shape[-1]
for b in range(rd * buf_cap, min(nbatch, (rd+1)*buf_cap)):
sys.stdout.write('\rpadding %d of %d' % (b + 1, nbatch))
sys.stdout.flush()
sidx = b * max_per_file
eidx = min(ndata, sidx + max_per_file)
cur_idx_list = [i for i in range(cur_n) if perm[offset + i] >= sidx and perm[offset + i] < eidx]
if len(cur_idx_list) == 0:
continue
if not b in buf:
dsave = myio.unpickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)))
buf[b] = dsave
else:
dsave = buf[b]
save_idx_list = [perm[ x + offset] - sidx for x in cur_idx_list]
HMLPE.selective_copydic(d, dsave, cur_idx_list, save_idx_list)
# myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), dsave)
print 'Finished %s' % fn
offset = offset + cur_n
for b in range(rd * buf_cap, min(nbatch, (rd+1)*buf_cap)):
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), buf[b])
def generate_negative_data_from_image(self, generate_type, allfile=None):
"""
generate_type = 'neg_sample'
savedata_info should have 'neg_sample_num':
indicating sampling how many negative window per image
If some image is small, then it will try to generate as much as possible
"""
import Image
if allfile is None:
allfile = iu.getfilelist(self.imgdata_info['imgdatapath'], \
'\w+(\.png|\.jpg|\.pgm|.jpeg)')
print 'imgdatapath=%s, %d images are found' % (
self.imgdata_info['imgdatapath'], len(allfile))
iu.ensure_dir(self.savedata_info['savedir'])
savedir = self.savedata_info['savedir']
self.batch_id = self.savedata_info['start_patch_id']
self.init_meta(generate_type)
print(self.meta)
sample_num = self.savedata_info['neg_sample_num']
totaldata = len(allfile) * sample_num
self.meta['ndata'] = 0
newdim = self.savedata_info['newdim']
nparts = self.meta['nparts']
njoints = self.meta['njoints']
if njoints == 8:
dicjtname = 'joints8'
else:
dicjtname = 'joints'
#raise HMLPEError('njoints = %d are not supported yet' % njoints)
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)
self.meta['ind_dim']['part_indmap'] = mdim
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)
self.meta['ind_dim']['joint_indmap'] = jtmdim
per_size = min(totaldata, self.savedata_info['max_batch_size'])
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, per_size, nparts, njoints)
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
pre_nc = 0
nc = 0
np.random.seed(7)
for it, fn in enumerate(allfile):
print('Processing %s' % fn)
curimgpath = iu.fullfile(self.imgdata_info['imgdatapath'], fn)
img = np.asarray(Image.open(curimgpath), dtype=np.uint8)
imgdim = img.shape
if imgdim[0] < newdim[0] or imgdim[1] < newdim[1]:
print('small image, ignored')
continue
mesh = self.create_augumentation_mesh(imgdim, newdim,
generate_type)
ts = min(len(mesh), sample_num)
l = (np.random.permutation(range(len(mesh))))[:ts]
for p in l:
r, c = mesh[p]
timg = img[r:r + newdim[0], c:c + newdim[0], :]
res['data'][..., nc - pre_nc] = timg
res['joint_sample_offset'][..., nc - pre_nc] = [c, r]
res['filenames'][nc - pre_nc] = curimgpath
res['oribbox'][..., nc - pre_nc] = [
c, r, c + newdim[1] - 1, r + newdim[0] - 1
]
nc = nc + 1
if sample_num + nc - pre_nc > per_size or it == len(allfile) - 1:
tmpres = self.truncated_copydic(res, nc - pre_nc)
tmpres['data'] = tmpres['data'].reshape((-1, nc - pre_nc),
order='F')
self.meta['data_sum'] += tmpres['data'].sum(axis=1,
dtype=float)
self.meta['ndata'] += nc - pre_nc
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
if self.meta['ndata'] > 0:
self.meta['data_mean'] = self.meta['data_sum'] / self.meta['ndata']
self.meta['data_mean'] = self.meta['data_mean'].reshape((-1, 1),
order='F')
else:
self.meta['data_mean'] = 0
del self.meta['data_sum']
myio.pickle(iu.fullfile(self.savedata_info['savedir'], 'batches.meta'),
self.meta)
def ReadTestDataToCifarDic(imgdir, annot_dir, tech_report_path, save_path=None):
"""
This program will use the detection window by the author
image data are extracted from the extended detection window
return type: dictionary with the following keys
ep
fr
annotation
oriimg
bbox
joints8 ! (coordinate w.r.t bounding box)
data !
indmap ! (all zeros)
This is the previous version, latest version is
ReadTestDataToHMLPEDic
"""
import Stickmen
import scipy.io as sio
import PIL
import iutils as iu
from PIL import Image
import matplotlib.pyplot as plt
import iconvnet_datacvt as icvt
rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
d_annot = ExtractAnnotation(annot_dir)
ndata = rp.shape[1]
data = []
tsize = 112 # the size of test image
indsize = 8
data = dict()
ndet = sum([ (1 if rp[0,i][0].size==0 else rp[0,i][0].size) for i in range(ndata)])
data['ep'] = np.ndarray([ndet], dtype=np.int)
data['fr'] = np.ndarray([ndet], dtype=np.int)
data['annotation'] = np.ndarray([4,6,ndet], dtype=np.float32)
data['indmap'] = np.zeros([7,indsize,indsize, ndet], dtype=np.bool,order='F')
data['indmap_para'] = (30,12)
data['oribbox'] = np.ndarray([4, ndet], dtype=np.float32)
data['data'] = np.ndarray([tsize, tsize, 3, ndet], order='F', dtype=np.uint8)
data['joints8'] = np.ndarray([8,2,ndet], dtype=np.float32)
data['oridet'] = np.ndarray([4,ndet],dtype=np.float32)
data['imgdir'] = imgdir
idx = 0
for i in range(ndata):
ep = rp[0,i][1][0][0]
fr = rp[0,i][2][0][0]
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
img = Image.open(imgpath)
if rp[0,i][0].size==0: # Not detection found by detector
data['ep'][...,idx] = ep
data['fr'][...,idx] = fr
data['annotation'][...,idx] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['oribbox'][...,idx] = np.zeros([4],dtype=np.float32)
data['joints8'][...,idx] = np.ones([8,2], dtype=np.float32) * tsize * 2
data['data'][...,idx] = np.zeros([tsize,tsize,3], dtype=np.uint8)
data['oridet'][...,idx] = np.zeros([4], dtype=np.float32)
idx = idx + 1
continue
for j in range(rp[0,i][0].size):
det = rp[0,i][0][0,j]['det'][0] # det = (x,y,x1,y1)
data['oridet'][...,idx] = det
det = ExtendBndbox(det, img.size)
arr_img = np.asarray(img)[det[1]:det[3]+1, det[0]:det[2]+1,:]
res_img = Image.fromarray(arr_img).resize((tsize,tsize))
data['ep'][...,idx] = ep
data['fr'][...,idx] = fr
data['annotation'][...,idx] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['data'][...,idx] = np.asarray(res_img)
tmppoints = icvt.convert_AHEcoor_to_joints8(data['annotation'][...,idx])
data['joints8'][...,idx] = TransformPoints(tmppoints, det, np.asarray(res_img.size) - 1)
data['oribbox'][...,idx] = det
idx = idx + 1
data['data'] = data['data'].reshape((-1, ndet), order='F')
if save_path is not None:
icvt.ut.pickle(save_path, data)
return data
def generate_negative_data_from_image(self, generate_type, allfile=None):
"""
generate_type = 'neg_sample'
savedata_info should have 'neg_sample_num':
indicating sampling how many negative window per image
If some image is small, then it will try to generate as much as possible
"""
import Image
if allfile is None:
allfile = iu.getfilelist(self.imgdata_info['imgdatapath'], \
'\w+(\.png|\.jpg|\.pgm|.jpeg)')
print 'imgdatapath=%s, %d images are found' % (self.imgdata_info['imgdatapath'], len(allfile))
iu.ensure_dir(self.savedata_info['savedir'])
savedir = self.savedata_info['savedir']
self.batch_id = self.savedata_info['start_patch_id']
self.init_meta(generate_type)
print(self.meta)
sample_num = self.savedata_info['neg_sample_num']
totaldata = len(allfile) * sample_num
self.meta['ndata'] = 0
newdim = self.savedata_info['newdim']
nparts = self.meta['nparts']
njoints = self.meta['njoints']
if njoints == 8:
dicjtname = 'joints8'
else:
dicjtname = 'joints'
#raise HMLPEError('njoints = %d are not supported yet' % njoints)
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)
self.meta['ind_dim']['part_indmap'] = mdim
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)
self.meta['ind_dim']['joint_indmap'] = jtmdim
per_size = min(totaldata, self.savedata_info['max_batch_size'])
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, per_size, nparts, njoints)
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
pre_nc = 0
nc = 0
np.random.seed(7)
for it, fn in enumerate(allfile):
print('Processing %s' % fn)
curimgpath= iu.fullfile(self.imgdata_info['imgdatapath'], fn)
img = np.asarray(Image.open(curimgpath), dtype=np.uint8)
imgdim = img.shape
if imgdim[0] < newdim[0] or imgdim[1] < newdim[1]:
print('small image, ignored')
continue
mesh = self.create_augumentation_mesh(imgdim, newdim, generate_type)
ts = min(len(mesh), sample_num)
l = (np.random.permutation(range(len(mesh))))[:ts]
for p in l:
r, c = mesh[p]
timg = img[r:r+newdim[0],c:c+newdim[0],:]
res['data'][...,nc-pre_nc] = timg
res['joint_sample_offset'][...,nc-pre_nc] = [c,r]
res['filenames'][nc-pre_nc] = curimgpath
res['oribbox'][...,nc-pre_nc] = [c,r,c+newdim[1]-1,r+newdim[0]-1]
nc = nc + 1
if sample_num + nc-pre_nc > per_size or it == len(allfile)-1:
tmpres = self.truncated_copydic(res, nc-pre_nc)
tmpres['data'] = tmpres['data'].reshape((-1,nc-pre_nc),order='F')
self.meta['data_sum'] += tmpres['data'].sum(axis=1,dtype=float)
self.meta['ndata'] += nc - pre_nc
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
if self.meta['ndata'] > 0:
self.meta['data_mean'] = self.meta['data_sum'] / self.meta['ndata']
self.meta['data_mean'] = self.meta['data_mean'].reshape((-1,1),order='F')
else:
self.meta['data_mean'] = 0
del self.meta['data_sum']
myio.pickle(iu.fullfile(self.savedata_info['savedir'], 'batches.meta'), self.meta)
def GetImagePath(imgdir, ep, fr):
import iutils as iu
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
return imgpath
def ReadTestDataToHMLPEDic(imgdir, annot_dir, tech_report_path, save_path=None, iou_thresh=0.5):
"""
This function will use detection window provided by the author
Also, erro detection window are not included
For image without detection, all the joint points will be zero
"""
import Stickmen
import scipy.io as sio
import PIL
import iutils as iu
from PIL import Image
import matplotlib.pyplot as plt
import iread.hmlpe as hp
import iread.myio as mio
import ipyml.geometry as igeo
rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
d_annot = ExtractAnnotation(annot_dir)
ndata = rp.shape[1]
data = []
tsize = 112 # the size of test image
part_ind_dim = (8,8)
joint_ind_dim = (8,8)
filter_size = 32.0
stride = 12.0
indrate = 0.3
joint_filter_size = 32.0
joint_stride = 12.0
njoints = 8
nparts = 7
ndet = ndata # One test point for one image
newdim = (tsize,tsize,3)
data = hp.HMLPE.prepare_savebuffer({'data':newdim,\
'part_indmap': part_ind_dim,\
'joint_indmap':joint_ind_dim },\
ndet, nparts, njoints)
# add Buffy specific field
add_buffy_specific_field(data, ndet, imgdir)
idx = 0
f_calc_area = lambda rec: (rec[1][1]-rec[0][1]) * (rec[1][0]-rec[0][0]) if len(rec)==2 else 0
f_mk_rec = lambda det: ((det[0],det[1]), (det[2], det[3]))
for i in range(ndata):
ep = rp[0,i][1][0][0]
fr = rp[0,i][2][0][0]
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
img = Image.open(imgpath)
data['ep'][...,i] = ep
data['fr'][...,i] = fr
data['filenames'][i] = imgpath
data['is_positive'][...,i] = True
data['annotation'][...,i] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['gt_det'][...,i] = np.asarray(Stickmen.EstDetFromStickmen( data['annotation'][...,i]))
gt_rec = f_mk_rec(data['gt_det'][...,i]) #
gt_area = f_calc_area(gt_rec)
if rp[0,i][0].size == 0: # No detection are found in this image
# imgdata will also be all zero
# oridet will be all zero
# oribbox will be all zero
# joints8 will be all zero
# jointmasks wiil be all zero
# indmap will be all zero
# joint_indmap will be all zero
# nothing need to be done, since they are default value
pass
else:
m = -1
for j in range(rp[0,i][0].size):
det = rp[0,i][0][0,j]['det'][0] # det = (x,y,x1,y1)
cur_rec = f_mk_rec(det)
int_area = f_calc_area(igeo.RectIntersectRect(cur_rec, gt_rec))
if int_area > ( gt_area - int_area ) * iou_thresh:
m = j
break
if m != -1:
oribbox = ExtendBndbox(det, img.size)
arr_img = np.asarray(img)[oribbox[1]:oribbox[3]+1, \
oribbox[0]:oribbox[2]+1,:]
res_img = Image.fromarray(arr_img).resize((tsize,tsize))
data['data'][...,i] =np.asarray(res_img)
tmppoints = convert_AHEcoor_to_joints8(data['annotation'][...,i])
data['joints8'][...,i] = TransformPoints(tmppoints, oribbox, np.asarray(res_img.size) -1)
data['jointmasks'][...,i] = hp.HMLPE.makejointmask(newdim, data['joints8'][...,i])
data['indmap'][...,i] = hp.HMLPE.create_part_indicatormap(data['joints8'][...,i], hp.part_idx, part_ind_dim, indrate, filter_size, stride)
data['joint_indmap'][...,i] = hp.HMLPE.create_joint_indicatormap(data['joints8'][...,i], joint_ind_dim, joint_filter_size, joint_stride)
data['oribbox'][...,i] = oribbox
data['data'] = data['data'].reshape((-1,ndet),order='F')
if save_path is not None:
mio.pickle(save_path, data)
return data
def estimate_pose_main_process(self, input_dic, output_dic):
import imgproc
load_next = True
data, raw_img = input_dic['next_data'], input_dic['raw_img']
ndata = data[0].shape[-1]
# output_dic['num_cases'] += [data[0].shape[-1]]
buf = np.require(np.zeros((data[0].shape[-1], input_dic['data_dim']),\
dtype=n.single), \
requirements='C')
data += [buf]
start_t = time()
self.libmodel.startFeatureWriter(data, input_dic['output_layer_idx'])
if load_next:
input_dic['next_data'], input_dic['raw_img'], input_dic['bnd'] = \
self.get_hmlpe_posedata_from_camera(input_dic['camera'], self.test_data_provider)
self.finish_batch()
print '[%.6f seconds]' % (time() - start_t)
if input_dic['target_type'] in input_dic['convert_dic']:
output_dic['est'] = input_dic['convert_dic'][
input_dic['target_type']](buf.T)
else:
output_dic['est'] = buf.T
if not load_next:
return
sys.stdout.flush()
tmp = input_dic['raw_img']
input_dic['camera_im'].set_data(tmp)
input_dic['camera_fig'].canvas.draw()
s = int(np.sqrt(data[0].shape[0]))
if input_dic['target_type'] == 'hmlpe_2d':
img = Image.fromarray(
np.require(input_dic['raw_img'], dtype=np.uint8))
sx, sy = data[0].shape[1], data[0].shape[0]
output_dic['est'] = output_dic['est'].reshape((2, -1, ndata),
order='F')
njoints = output_dic['est'].shape[1]
cur_bnd = input_dic['bnd']
bnd_sx = np.tile(np.asarray([(v[2]+1.0) \
for v in cur_bnd]).reshape((1,ndata)),\
(njoints, 1)).reshape((1,njoints,ndata),order='F')
bnd_sy = np.tile(np.asarray([(v[3]+1.0) \
for v in cur_bnd]).reshape((1,ndata)),\
(njoints, 1)).reshape((1,njoints,ndata),order='F')
bnd_s = np.concatenate((bnd_sx, bnd_sy), axis=0)
bnd_ax = np.tile(np.asarray([v[0] \
for v in cur_bnd]).reshape((1,ndata)),\
(njoints, 1)).reshape((1,njoints,ndata),order='F')
bnd_ay = np.tile(np.asarray([v[1] \
for v in cur_bnd]).reshape((1,ndata)),\
(njoints, 1)).reshape((1,njoints,ndata),order='F')
bnd_a = np.concatenate((bnd_ax, bnd_ay), axis=0)
output_dic['est'] = output_dic['est'] * bnd_s + bnd_a
draw = ImageDraw.Draw(img)
# draw bnd
for b in cur_bnd:
draw.rectangle((b[0], b[1], b[0] + b[2], b[1] + b[3]))
# draw center rectangle
hx, hy = img.size
draw.rectangle((hx / 2 - hy / 2, 0, hx / 2 + hy / 2, hy),
outline=(255, 0, 0))
self.draw2d_skelenton(output_dic['est'], hmlpe.part_idx, draw)
input_dic['pose_ax'].set_data(np.asarray(img))
input_dic['pose_fig'].canvas.draw()
else:
## Plot pose
input_dic['pose_ax'].cla()
#input_dic['pose_ax'].view_init(azim=-92, elev=-46)
vlim = 0.4
input_dic['pose_ax'].set_xlim([-vlim, vlim])
input_dic['pose_ax'].set_ylim([-vlim, vlim])
input_dic['pose_ax'].set_zlim([-vlim, vlim])
self.plot_skelenton(output_dic['est'], h36m.part_idx,
input_dic['pose_ax'])
imgproc.turn_off_axis(input_dic['pose_ax'])
input_dic['pose_fig'].canvas.draw()
if 'outputdir' in input_dic:
outputdir = input_dic['outputdir']
savecnt = input_dic['savecnt']
print outputdir
for i in range(2):
plt.figure(i)
plt.savefig(
iu.fullfile(outputdir, 'fig_%02d_%06d.jpg' % (i, savecnt)))
input_dic['savecnt'] = savecnt + 1
return input_dic['camera_im'], input_dic['pose_ax']
def ReadTestDataToCifarDic(imgdir,
annot_dir,
tech_report_path,
save_path=None):
"""
This program will use the detection window by the author
image data are extracted from the extended detection window
return type: dictionary with the following keys
ep
fr
annotation
oriimg
bbox
joints8 ! (coordinate w.r.t bounding box)
data !
indmap ! (all zeros)
This is the previous version, latest version is
ReadTestDataToHMLPEDic
"""
import Stickmen
import scipy.io as sio
import PIL
import iutils as iu
from PIL import Image
import matplotlib.pyplot as plt
import iconvnet_datacvt as icvt
rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
d_annot = ExtractAnnotation(annot_dir)
ndata = rp.shape[1]
data = []
tsize = 112 # the size of test image
indsize = 8
data = dict()
ndet = sum([(1 if rp[0, i][0].size == 0 else rp[0, i][0].size)
for i in range(ndata)])
data['ep'] = np.ndarray([ndet], dtype=np.int)
data['fr'] = np.ndarray([ndet], dtype=np.int)
data['annotation'] = np.ndarray([4, 6, ndet], dtype=np.float32)
data['indmap'] = np.zeros([7, indsize, indsize, ndet],
dtype=np.bool,
order='F')
data['indmap_para'] = (30, 12)
data['oribbox'] = np.ndarray([4, ndet], dtype=np.float32)
data['data'] = np.ndarray([tsize, tsize, 3, ndet],
order='F',
dtype=np.uint8)
data['joints8'] = np.ndarray([8, 2, ndet], dtype=np.float32)
data['oridet'] = np.ndarray([4, ndet], dtype=np.float32)
data['imgdir'] = imgdir
idx = 0
for i in range(ndata):
ep = rp[0, i][1][0][0]
fr = rp[0, i][2][0][0]
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
img = Image.open(imgpath)
if rp[0, i][0].size == 0: # Not detection found by detector
data['ep'][..., idx] = ep
data['fr'][..., idx] = fr
data['annotation'][...,
idx] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['oribbox'][..., idx] = np.zeros([4], dtype=np.float32)
data['joints8'][..., idx] = np.ones([8, 2],
dtype=np.float32) * tsize * 2
data['data'][..., idx] = np.zeros([tsize, tsize, 3],
dtype=np.uint8)
data['oridet'][..., idx] = np.zeros([4], dtype=np.float32)
idx = idx + 1
continue
for j in range(rp[0, i][0].size):
det = rp[0, i][0][0, j]['det'][0] # det = (x,y,x1,y1)
data['oridet'][..., idx] = det
det = ExtendBndbox(det, img.size)
arr_img = np.asarray(img)[det[1]:det[3] + 1, det[0]:det[2] + 1, :]
res_img = Image.fromarray(arr_img).resize((tsize, tsize))
data['ep'][..., idx] = ep
data['fr'][..., idx] = fr
data['annotation'][...,
idx] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['data'][..., idx] = np.asarray(res_img)
tmppoints = icvt.convert_AHEcoor_to_joints8(
data['annotation'][..., idx])
data['joints8'][..., idx] = TransformPoints(
tmppoints, det,
np.asarray(res_img.size) - 1)
data['oribbox'][..., idx] = det
idx = idx + 1
data['data'] = data['data'].reshape((-1, ndet), order='F')
if save_path is not None:
icvt.ut.pickle(save_path, data)
return data
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_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 ReadCropImageToHMLPEDic(dataset_dir, save_dir, istrain=False, isOC=True):
"""
This function will be used for generating testing data
Because training and testing data has different format in oribbox
For generating trainign samples,
please use
create_lsp_regression_data.m
(dataset_dir, type=3, opt)
opt.OC = ?
and hmlpe.py
"""
import iutils as iu
import iread.hmlpe as hmlpe
import iread.myio as mio
import scipy.io as sio
from PIL import Image
ndata = 1000
if istrain:
s_idx = 0
else:
s_idx = 1000
imgdir = iu.fullfile(dataset_dir, 'images-crop')
if isOC:
dmat = sio.loadmat(iu.fullfile(dataset_dir, 'jointsOC.mat'))
else:
dmat = sio.loadmat(iu.fullfile(dataset_dir, 'joints-crop.mat'))
lsp_jt = dmat['joints']
dimdic = {
'data': (112, 112, 3),
'part_indmap': (8, 8),
'joint_indmap': (8, 8)
}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, ndata, nparts, njoints)
d['data'] = d['data'].reshape((-1, ndata), order='F')
d['is_mirror'][:] = False
d['is_positive'][:] = True
for idx in range(s_idx, s_idx + ndata):
imgpath = iu.fullfile(imgdir, 'im%04d.jpg' % (idx + 1))
img = Image.open(imgpath)
i = idx - s_idx
orijoints8, isvisible = GetJoints8(lsp_jt[..., idx])
bbox = GetUpperBodyBox(img.size)
img_arr = np.asarray(img)[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
s = np.asarray([(dimdic['data'][1] - 1.0) / (bbox[2] - bbox[0]),
(dimdic['data'][0] - 1.0) / (bbox[3] - bbox[1])
]).reshape((1, 2))
tjoints = (orijoints8 - bbox[0:2, :].reshape((1, 2))) * s
masks = hmlpe.HMLPE.makejointmask(dimdic['data'], tjoints)
d['data'][..., i] = np.asarray(
Image.fromarray(img_arr).resize(
(dimdic['data'][0], dimdic['data'][1]))).reshape(
(-1, 1), order='F').flatten()
d['joints8'][..., i] = tjoints
d['jointmasks'][..., i] = np.logical_and(masks, isvisible)
d['filenames'][i] = imgpath
d['oribbox'][..., i] = bbox.flatten()
d['indmap'][..., i] = hmlpe.HMLPE.create_part_indicatormap(
tjoints, hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
d['joint_indmap'][..., i] = hmlpe.HMLPE.create_joint_indicatormap(
tjoints, dimdic['joint_indmap'], 30.0, 12.0)
mio.pickle(iu.fullfile(save_dir, 'data_batch_1'), d)
def ReadDataToHMLPEDic(imgdir, example_path, data_category, max_per_batch,
save_dir):
"""
Read all data in 'data_category'
into HMLPE dictionary
There is no need to generating training data, since they can be generated in
hmlpe.py
"""
import scipy.io as sio
import iutils as iu
import iread.myio as mio
import iread.hmlpe as hmlpe
import imgproc
from PIL import Image
if data_category != 'istest':
print 'Warn: The correctness of data type %s is not guaranteed' % data_category
all_example = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_example, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
buf_size = min(ndata, max_per_batch)
dimdic = {
'data': (112, 112, 3),
'part_indmap': (8, 8),
'joint_indmap': (8, 8)
}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
d['oridet'] = np.zeros((4, buf_size), dtype=np.int)
d['coords'] = np.ndarray((2, 29, buf_size), dtype=np.float32)
tdsize = dimdic['data'][0]
dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
d['data'] = d['data'].reshape((dsize, -1), order='F')
d['is_positive'][:] = True
d['is_mirror'][:] = False
bid = 1
j = 0
for i in range(ndata):
if j == max_per_batch:
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts,
njoints)
fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filenames'][j] = fp
d['coords'][..., j] = examples[i]['coords']
d['oribbox'][..., j] = bbox = ExtendBndbox(tbox, img.size)
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape(
(8, 2), order='C') - 1 # to python stype 0-idx
d['joints8'][..., j] = TransformPoints(orijoints8, bbox,
dimdic['data']).reshape(
(8, 2), order='C')
imgarr = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2], :])
data_img = np.asarray(
sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape(
(dsize), order='F')
d['data'][..., j] = data_img
d['indmap'][..., j] = hmlpe.HMLPE.create_part_indicatormap(
d['joints8'][..., j], hmlpe.part_idx, dimdic['part_indmap'], 0.3,
30.0, 12.0)
d['joint_indmap'][..., j] = hmlpe.HMLPE.create_joint_indicatormap(
d['joints8'][..., j], dimdic['joint_indmap'], 30.0, 12.0)
d['jointmasks'][...,
j] = hmlpe.HMLPE.makejointmask(dimdic['data'],
d['joints8'][..., j])
j = j + 1
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
def GetImagePath(imgdir, ep, fr):
import iutils as iu
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
return imgpath
import sys
sys.path.append('/home/grads/sijinli2/Projects/DHMLPE/Python/src')
sys.path.append('/home/grads/sijinli2/I_ProgramFile/I_Python/Project/I_utils')
sys.path.append('/home/grads/sijinli2/I_ProgramFile/I_Python/Project')
import iutils as iu
import os
ppath = iu.getparentpath(os.path.realpath(__file__), 2)
# sys.path.append(ppath)
# sys.path.append(iu.fullfile(ppath, 'task'))
sys.path.append(iu.fullfile(ppath, 'src'))
from options import *
def shuffle_data(source_dir, target_dir, max_per_file=4000):
"""
This function will shuflle all the data in source_dir
and save it to target_dir
"""
if source_dir == target_dir:
raise HMLPEError('source dir can not be the same as target dir')
import shutil
import sys
iu.ensure_dir(target_dir)
shutil.copy(iu.fullfile(source_dir, 'batches.meta'), \
iu.fullfile(target_dir, 'batches.meta'))
meta = myio.unpickle(iu.fullfile(source_dir, 'batches.meta'))
ndata = meta['ndata']
nbatch = (ndata - 1) / max_per_file + 1
nparts = meta['nparts']
njoints = meta['njoints']
newdim = meta['savedata_info']['newdim']
filter_size = meta['savedata_info']['indmap_para']['filter_size']
stride = meta['savedata_info']['indmap_para']['stride']
joint_filter_size = meta['savedata_info']['indmap_para'][
'joint_filter_size']
joint_stride = meta['savedata_info']['indmap_para']['joint_stride']
mdim = HMLPE.get_indmapdim(newdim, filter_size, stride)
jtmdim = HMLPE.get_indmapdim(newdim, joint_filter_size, joint_stride)
print('There are %d data in total, I need %d batch to hold it' %
(ndata, nbatch))
print 'Begin creating empty files'
rest = ndata
d = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, max_per_file, nparts, njoints)
HMLPE.adjust_savebuffer_shape(d)
for b in range(nbatch):
cur_n = min(max_per_file, rest)
if b != nbatch - 1:
saved = d
else:
saved = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, cur_n, nparts, njoints)
HMLPE.adjust_savebuffer_shape(saved)
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)), saved)
rest = rest - cur_n
print 'End creating'
allbatchfn = iu.getfilelist(source_dir, 'data_batch_\d+')
np.random.seed(7)
perm = range(ndata)
np.random.shuffle(perm)
buf_cap = 12 # store six batch at most
nround = (nbatch - 1) / buf_cap + 1
for rd in range(nround):
print('Round %d of %d' % (rd, nround))
buf = dict()
offset = 0
for fn in allbatchfn:
print('Processing %s' % fn)
d = myio.unpickle(iu.fullfile(source_dir, fn))
cur_n = d['data'].shape[-1]
for b in range(rd * buf_cap, min(nbatch, (rd + 1) * buf_cap)):
sys.stdout.write('\rpadding %d of %d' % (b + 1, nbatch))
sys.stdout.flush()
sidx = b * max_per_file
eidx = min(ndata, sidx + max_per_file)
cur_idx_list = [
i for i in range(cur_n)
if perm[offset + i] >= sidx and perm[offset + i] < eidx
]
if len(cur_idx_list) == 0:
continue
if not b in buf:
dsave = myio.unpickle(
iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)))
buf[b] = dsave
else:
dsave = buf[b]
save_idx_list = [perm[x + offset] - sidx for x in cur_idx_list]
HMLPE.selective_copydic(d, dsave, cur_idx_list, save_idx_list)
# myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), dsave)
print 'Finished %s' % fn
offset = offset + cur_n
for b in range(rd * buf_cap, min(nbatch, (rd + 1) * buf_cap)):
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)),
buf[b])
def ReadTestDataToHMLPEDic(imgdir,
annot_dir,
tech_report_path,
save_path=None,
iou_thresh=0.5):
"""
This function will use detection window provided by the author
Also, erro detection window are not included
For image without detection, all the joint points will be zero
"""
import Stickmen
import scipy.io as sio
import PIL
import iutils as iu
from PIL import Image
import matplotlib.pyplot as plt
import iread.hmlpe as hp
import iread.myio as mio
import ipyml.geometry as igeo
rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
d_annot = ExtractAnnotation(annot_dir)
ndata = rp.shape[1]
data = []
tsize = 112 # the size of test image
part_ind_dim = (8, 8)
joint_ind_dim = (8, 8)
filter_size = 32.0
stride = 12.0
indrate = 0.3
joint_filter_size = 32.0
joint_stride = 12.0
njoints = 8
nparts = 7
ndet = ndata # One test point for one image
newdim = (tsize, tsize, 3)
data = hp.HMLPE.prepare_savebuffer({'data':newdim,\
'part_indmap': part_ind_dim,\
'joint_indmap':joint_ind_dim },\
ndet, nparts, njoints)
# add Buffy specific field
add_buffy_specific_field(data, ndet, imgdir)
idx = 0
f_calc_area = lambda rec: (rec[1][1] - rec[0][1]) * (rec[1][0] - rec[0][
0]) if len(rec) == 2 else 0
f_mk_rec = lambda det: ((det[0], det[1]), (det[2], det[3]))
for i in range(ndata):
ep = rp[0, i][1][0][0]
fr = rp[0, i][2][0][0]
imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
('%06d' % fr) + '.jpg')
img = Image.open(imgpath)
data['ep'][..., i] = ep
data['fr'][..., i] = fr
data['filenames'][i] = imgpath
data['is_positive'][..., i] = True
data['annotation'][..., i] = Stickmen.ReorderStickmen(d_annot[ep][fr])
data['gt_det'][..., i] = np.asarray(
Stickmen.EstDetFromStickmen(data['annotation'][..., i]))
gt_rec = f_mk_rec(data['gt_det'][..., i]) #
gt_area = f_calc_area(gt_rec)
if rp[0, i][0].size == 0: # No detection are found in this image
# imgdata will also be all zero
# oridet will be all zero
# oribbox will be all zero
# joints8 will be all zero
# jointmasks wiil be all zero
# indmap will be all zero
# joint_indmap will be all zero
# nothing need to be done, since they are default value
pass
else:
m = -1
for j in range(rp[0, i][0].size):
det = rp[0, i][0][0, j]['det'][0] # det = (x,y,x1,y1)
cur_rec = f_mk_rec(det)
int_area = f_calc_area(igeo.RectIntersectRect(cur_rec, gt_rec))
if int_area > (gt_area - int_area) * iou_thresh:
m = j
break
if m != -1:
oribbox = ExtendBndbox(det, img.size)
arr_img = np.asarray(img)[oribbox[1]:oribbox[3]+1, \
oribbox[0]:oribbox[2]+1,:]
res_img = Image.fromarray(arr_img).resize((tsize, tsize))
data['data'][..., i] = np.asarray(res_img)
tmppoints = convert_AHEcoor_to_joints8(data['annotation'][...,
i])
data['joints8'][..., i] = TransformPoints(
tmppoints, oribbox,
np.asarray(res_img.size) - 1)
data['jointmasks'][..., i] = hp.HMLPE.makejointmask(
newdim, data['joints8'][..., i])
data['indmap'][..., i] = hp.HMLPE.create_part_indicatormap(
data['joints8'][..., i], hp.part_idx, part_ind_dim,
indrate, filter_size, stride)
data['joint_indmap'][...,
i] = hp.HMLPE.create_joint_indicatormap(
data['joints8'][...,
i], joint_ind_dim,
joint_filter_size, joint_stride)
data['oribbox'][..., i] = oribbox
data['data'] = data['data'].reshape((-1, ndet), order='F')
if save_path is not None:
mio.pickle(save_path, data)
return data