def get_crop_locs(lblfile, view, height, width):
# everything is in matlab indexing
bodylbl = apt.loadmat(lblfile)
try:
lsz = np.array(bodylbl['labeledpos']['size'])
curpts = np.nan * np.ones(lsz).flatten()
idx = np.array(bodylbl['labeledpos']['idx']) - 1
val = np.array(bodylbl['labeledpos']['val'])
curpts[idx] = val
curpts = np.reshape(curpts, np.flipud(lsz))
except IndexError:
if bodylbl['labeledpos'].ndim == 3:
curpts = np.array(bodylbl['labeledpos'])
curpts = np.transpose(curpts, [2, 1, 0])
else:
if hasattr(bodylbl['labeledpos'][0], 'idx'):
lsz = np.array(bodylbl['labeledpos'][0].size)
curpts = np.nan * np.ones(lsz).flatten()
idx = np.array(bodylbl['labeledpos'][0].idx) - 1
val = np.array(bodylbl['labeledpos'][0].val)
curpts[idx] = val
curpts = np.reshape(curpts, np.flipud(lsz))
else:
curpts = np.array(bodylbl['labeledpos'][0])
curpts = np.transpose(curpts, [2, 1, 0])
neck_locs = curpts[0, :, 5 + 10 * view]
reg_params = apt.loadmat(crop_reg_file)
x_reg = reg_params['reg_view{}_x'.format(view + 1)]
y_reg = reg_params['reg_view{}_y'.format(view + 1)]
x_left = int(round(x_reg[0] + x_reg[1] * neck_locs[0]))
x_left = 1 if x_left < 1 else x_left
x_right = x_left + crop_size[view][0] - 1
if x_right > width:
x_left = width - crop_size[view][0] + 1
x_right = width
y_top = int(round(y_reg[0] + y_reg[1] * neck_locs[1]))
y_top = 1 if y_top < 1 else y_top
y_bottom = y_top + crop_size[view][1] - 1
if y_bottom > height:
y_bottom = height
y_top = height - crop_size[view][1] + 1
return [x_left, x_right, y_top, y_bottom]
def create_tfrecords(args):
data_file = '/groups/branson/bransonlab/mayank/PoseTF/headTracking/trnDataSH_20180503_notable.mat'
split_file = '/groups/branson/bransonlab/apt/experiments/data/trnSplits_20180509.mat'
D = h5py.File(data_file,'r')
S = APT_interface.loadmat(split_file)
nims = D['IMain_crop2'].shape[1]
movid = np.array(D['mov_id']).T - 1
frame_num = np.array(D['frm']).T - 1
if args['split_type'] == 'easy':
split_arr = S['xvMain3Easy']
else:
split_arr = S['xvMain3Hard']
for view in range(2):
for split in range(3):
args['view'] = view
args['split_num'] = split
conf = get_conf(args)
if not os.path.exists(conf.cachedir):
os.mkdir(conf.cachedir)
outdir = conf.cachedir
train_filename = os.path.join(outdir,conf.trainfilename)
val_filename = os.path.join(outdir,conf.valfilename)
env = tf.python_io.TFRecordWriter(train_filename + '.tfrecords')
val_env = tf.python_io.TFRecordWriter(val_filename + '.tfrecords')
splits = [[], []]
all_locs = np.array(D['xyLblMain_crop2'])[view,:,:,:]
for indx in range(nims):
cur_im = np.array(D[D['IMain_crop2'][view,indx]]).T
cur_locs = all_locs[...,indx].T
mov_num = movid[indx]
cur_frame_num = frame_num[indx]
if split_arr[indx,split] == 1:
cur_env = val_env
splits[1].append([indx,cur_frame_num[0],0])
else:
cur_env = env
splits[0].append([indx,cur_frame_num[0],0])
im_raw = cur_im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': int64_feature(cur_im.shape[0]),
'width': int64_feature(cur_im.shape[1]),
'depth': int64_feature(1),
'trx_ndx': int64_feature(0),
'locs': float_feature(cur_locs.flatten()),
'expndx':float_feature(mov_num),
'ts':float_feature(cur_frame_num[0]),
'image_raw':bytes_feature(im_raw)
}))
cur_env.write(example.SerializeToString())
env.close()
val_env.close()
with open(os.path.join(outdir, 'splitdata.json'), 'w') as f:
json.dump(splits, f)
D.close()
def create_tfrecords(args):
data_file = '/groups/branson/bransonlab/mayank/PoseTF/headTracking/trnDataSH_20180503_notable.mat'
split_file = '/groups/branson/bransonlab/apt/experiments/data/trnSplits_20180509.mat'
D = h5py.File(data_file,'r')
S = APT_interface.loadmat(split_file)
nims = D['IMain_crop2'].shape[1]
movid = np.array(D['mov_id']).T - 1
frame_num = np.array(D['frm']).T - 1
if args['split_type'] == 'easy':
split_arr = S['xvMain3Easy']
else:
split_arr = S['xvMain3Hard']
for view in range(2):
for split in range(3):
args['view'] = view
args['split_num'] = split
conf = get_conf(args)
if not os.path.exists(conf.cachedir):
os.mkdir(conf.cachedir)
outdir = conf.cachedir
train_filename = os.path.join(outdir,conf.trainfilename)
val_filename = os.path.join(outdir,conf.valfilename)
env = tf.python_io.TFRecordWriter(train_filename + '.tfrecords')
val_env = tf.python_io.TFRecordWriter(val_filename + '.tfrecords')
splits = [[], []]
all_locs = np.array(D['xyLblMain_crop2'])[view,:,:,:]
for indx in range(nims):
cur_im = np.array(D[D['IMain_crop2'][view,indx]]).T
cur_locs = all_locs[...,indx].T
mov_num = movid[indx]
cur_frame_num = frame_num[indx]
if split_arr[indx,split] == 1:
cur_env = val_env
splits[1].append([indx,cur_frame_num[0],0])
else:
cur_env = env
splits[0].append([indx,cur_frame_num[0],0])
im_raw = cur_im.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': int64_feature(cur_im.shape[0]),
'width': int64_feature(cur_im.shape[1]),
'depth': int64_feature(1),
'trx_ndx': int64_feature(0),
'locs': float_feature(cur_locs.flatten()),
'expndx':float_feature(mov_num),
'ts':float_feature(cur_frame_num[0]),
'image_raw':bytes_feature(im_raw)
}))
cur_env.write(example.SerializeToString())
env.close()
val_env.close()
with open(os.path.join(outdir, 'splitdata.json'), 'w') as f:
json.dump(splits, f)
D.close()