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'],