Python provider.getDataFiles方法代码示例

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def load_config(filename):
    global globalConfig
    assert filename.endswith('.json')
    name = os.path.basename(filename)[:-5]
    with open(filename, 'r') as handle:
        dump_to_namespace(configGlobal, json.load(handle))
    configGlobal.__dict__["name"] = name
    configGlobal.data.__dict__["basename"] = os.path.basename(configGlobal.data.basepath)
    configGlobal.logging.__dict__["logdir"] = configGlobal.logging.basedir + f'/{name}'
    if configGlobal.evaluation.has('special'):
        if configGlobal.evaluation.special.mode == 'icp':
            configGlobal.logging.__dict__["logdir"] = configGlobal.logging.basedir + f'/icp_{configGlobal.data.basename}/{name}'

    TRAIN_INDICES = provider.getDataFiles(f'{configGlobal.data.basepath}/split/train.txt')
    VAL_INDICES = provider.getDataFiles(f'{configGlobal.data.basepath}/split/val.txt')
    configGlobal.data.__dict__["ntrain"] = len(TRAIN_INDICES)
    configGlobal.data.__dict__["nval"] = len(VAL_INDICES) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def getDataFiles(self, list_filename):
		return [line.rstrip() for line in open(list_filename)] 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def estimate(area):
    LOG_DIR = 'log{}'.format(area)
    num_classes = 13
    file_path = "data/train_hdf5_file_list_woArea{}.txt".format(area)

    train_file_list = provider.getDataFiles(file_path) 

    mean_ins_size = np.zeros(num_classes)
    ptsnum_in_gt = [[] for itmp in range(num_classes)]

    train_data = []
    train_group = []
    train_sem = []
    for h5_filename in train_file_list:
        cur_data, cur_group, _, cur_sem = provider.loadDataFile_with_groupseglabel_stanfordindoor(h5_filename)
        cur_data = np.reshape(cur_data, [-1, cur_data.shape[-1]])
        cur_group = np.reshape(cur_group, [-1])
        cur_sem = np.reshape(cur_sem, [-1])

        un = np.unique(cur_group)
        for ig, g in enumerate(un):
            tmp = (cur_group == g)
            sem_seg_g = int(stats.mode(cur_sem[tmp])[0])
            ptsnum_in_gt[sem_seg_g].append(np.sum(tmp))

    for idx in range(num_classes):
        mean_ins_size[idx] = np.mean(ptsnum_in_gt[idx]).astype(np.int)

    print(mean_ins_size)
    np.savetxt(os.path.join(LOG_DIR, 'mean_ins_size.txt'),mean_ins_size) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def get_train_dataset(num_point=1024):
    print('get train num_point ', num_point)
    train_files = provider.getDataFiles(
        os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
    return ConcatenatedDataset(
        *(PlyDataset(filepath, num_point=num_point, augment=True) for filepath in train_files)) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def get_test_dataset(num_point=1024):
    print('get test num_point ', num_point)
    test_files = provider.getDataFiles(
        os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'))
    return ConcatenatedDataset(
        *(PlyDataset(filepath, num_point=num_point, augment=False) for filepath in test_files)) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import getDataFiles [as 别名]
def visualize_fv_pc_clas():
    num_points = 1024
    n_classes = 40
    clas = 'person'
    #Create new gaussian
    subdev = 5
    variance = 0.04
    export = False
    display = True
    exp_path = '/home/itzikbs/PycharmProjects/fisherpointnet/paper_images/'

    shape_names = provider.getDataFiles( \
        os.path.join(BASE_DIR, 'data/modelnet' + str(n_classes) + '_ply_hdf5_2048/shape_names.txt'))
    shape_dict = {shape_names[i]: i for i in range(len(shape_names))}

    gmm = utils.get_grid_gmm(subdivisions=[subdev, subdev, subdev], variance=variance)
    # compute fv
    w = tf.constant(gmm.weights_, dtype=tf.float32)
    mu = tf.constant(gmm.means_, dtype=tf.float32)
    sigma = tf.constant(gmm.covariances_, dtype=tf.float32)

    for clas in shape_dict:
        points = provider.load_single_model_class(clas=clas, ind=0, test_train='train', file_idxs=0, num_points=1024,
                                                  n_classes=n_classes)
        points = np.expand_dims(points,0)

        points_tensor = tf.constant(points, dtype=tf.float32)  # convert points into a tensor
        fv_tensor = tf_util.get_fv_minmax(points_tensor, w, mu, sigma, flatten=False)

        sess = tf_util.get_session(2)
        with sess:
            fv = fv_tensor.eval()
        #
        # visualize_single_fv_with_pc(fv_train, points, label_title=clas,
        #                      fig_title='fv_pc', type='paper', pos=[750, 800, 0, 0], export=export,
        #                      filename=BASE_DIR + '/paper_images/fv_pc_' + clas)

        visualize_fv(fv, gmm, label_title=[clas], max_n_images=5, normalization=True, export=export, display=display,
                     filename=exp_path + clas+'_fv', n_scales=1, type='none', fig_title='Figure')
        visualize_pc(points, label_title=clas, fig_title='figure', export=export, filename=exp_path +clas+'_pc')
        plt.close('all')

    #plt.show()