def __init__(self, config_file='config.cfg', model='hg_refined_tiny_200'):
""" Initilize the Predictor
Args:
config_file : *.cfg file with model's parameters
model : *.index file's name. (weights to load)
"""
t = time()
params = process_config(config_file)
datatest1 = DataGenerator(joints_name=params['joint_list'],
img_dir_test=params['img_directory_test1'],
test_data_file=params['test_txt_file1'],
remove_joints=params['remove_joints'])
datatest1._create_test_table()
datatest2 = DataGenerator(joints_name=params['joint_list'],
img_dir_test=params['img_directory_test2'],
test_data_file=params['test_txt_file2'],
remove_joints=params['remove_joints'])
datatest2._create_test_table()
datatest3 = DataGenerator(joints_name=params['joint_list'],
img_dir_test=params['img_directory_test3'],
test_data_file=params['test_txt_file3'],
remove_joints=params['remove_joints'])
datatest3._create_test_table()
datatest4 = DataGenerator(joints_name=params['joint_list'],
img_dir_test=params['img_directory_test4'],
test_data_file=params['test_txt_file4'],
remove_joints=params['remove_joints'])
datatest4._create_test_table()
self.predict = PredictProcessor(params)
self.predict.color_palette()
self.predict.LINKS_JOINTS()
self.predict.model_init()
self.predict.load_model(load=model)
self.predict._create_prediction_tensor()
# self.predict.compute_pck(datagen=datatest,idlh=9,idrs=2)
# self.predict.save_output_as_mat(datagen=datatest,idlh=9,idrs=2)
self.predict.save_multioutput_as_mat(datagen1=datatest1,
datagen2=datatest2,
datagen3=datatest3,
datagen4=datatest4,
idlh=9,
idrs=2)
print('Done: ', time() - t, ' sec.')
params = process_config('config.cfg')
print('--Creating Dataset')
dataset = DataGenerator(params['img_directory'],
params['training_txt_file'],
params['num_joints'],
params['val_directory'],
params['val_txt_file'],
params['test_directory'],
params['test_txt_file'],
params['resolutions'],
params['headed'],
head_train=params['head_train'],
head_test=params['head_test'],
head_val=params['head_val'])
dataset._create_test_table(
) #creates the lists with dicts of the coord. of boxes, joints and the corresp. weights
model = HourglassModel(nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
attention=params['mcam'],
training=False,
drop_rate=params['dropout_rate'],
lear_rate=params['learning_rate'],
decay=params['learning_rate_decay'],
decay_step=params['decay_step'],
dataset=dataset,
name=params['name'],
