import numpy as np
import glob
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(BASE_DIR)

# -----------------------------------------------------------------------------
# CONSTANTS
# -----------------------------------------------------------------------------

DATA_PATH = os.path.join(ROOT_DIR, 'data', 'Stanford3dDataset_v1.2_Aligned_Version')
g_classes = [x.rstrip() for x in open(os.path.join(BASE_DIR, 'meta/class_names.txt'))]
g_class2label = {cls: i for i,cls in enumerate(g_classes)}
g_class2color = {'ceiling':	[0,255,0],
                 'floor':	[0,0,255],
                 'wall':	[0,255,255],
                 'beam':        [255,255,0],
                 'column':      [255,0,255],
                 'window':      [100,100,255],
                 'door':        [200,200,100],
                 'table':       [170,120,200],
                 'chair':       [255,0,0],
                 'sofa':        [200,100,100],
                 'bookcase':    [10,200,100],
                 'board':       [200,200,200],
                 'clutter':     [50,50,50]} 
g_easy_view_labels = [7,8,9,10,11,1]
g_label2color = {g_classes.index(cls): g_class2color[cls] for cls in g_classes}


# -----------------------------------------------------------------------------
# CONVERT ORIGINAL DATA TO OUR DATA_LABEL FILES
# -----------------------------------------------------------------------------

def collect_point_label(anno_path, out_filename, file_format='txt'):
    """ Convert original dataset files to data_label file (each line is XYZRGBL).
        We aggregated all the points from each instance in the room.

    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save collected points and labels (each line is XYZRGBL)
        file_format: txt or numpy, determines what file format to save.
    Returns:
        None
    Note:
        the points are shifted before save, the most negative point is now at origin.
    """
    points_list = []
    
    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f)
        labels = np.ones((points.shape[0],1)) * g_class2label[cls]
        points_list.append(np.concatenate([points, labels], 1)) # Nx7
    
    data_label = np.concatenate(points_list, 0)
    xyz_min = np.amin(data_label, axis=0)[0:3]
    data_label[:, 0:3] -= xyz_min
    
    if file_format=='txt':
        fout = open(out_filename, 'w')
        for i in range(data_label.shape[0]):
            fout.write('%f %f %f %d %d %d %d\n' % \
                          (data_label[i,0], data_label[i,1], data_label[i,2],
                           data_label[i,3], data_label[i,4], data_label[i,5],
                           data_label[i,6]))
        fout.close()
    elif file_format=='numpy':
        np.save(out_filename, data_label)
    else:
        print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \
            (file_format))
        exit()

def point_label_to_obj(input_filename, out_filename, label_color=True, easy_view=False, no_wall=False):
    """ For visualization of a room from data_label file,
	input_filename: each line is X Y Z R G B L
	out_filename: OBJ filename,
            visualize input file by coloring point with label color
        easy_view: only visualize furnitures and floor
    """
    data_label = np.loadtxt(input_filename)
    data = data_label[:, 0:6]
    label = data_label[:, -1].astype(int)
    fout = open(out_filename, 'w')
    for i in range(data.shape[0]):
        color = g_label2color[label[i]]
        if easy_view and (label[i] not in g_easy_view_labels):
            continue
        if no_wall and ((label[i] == 2) or (label[i]==0)):
            continue
        if label_color:
            fout.write('v %f %f %f %d %d %d\n' % \
                (data[i,0], data[i,1], data[i,2], color[0], color[1], color[2]))
        else:
            fout.write('v %f %f %f %d %d %d\n' % \
                (data[i,0], data[i,1], data[i,2], data[i,3], data[i,4], data[i,5]))
    fout.close()
 


# -----------------------------------------------------------------------------
# PREPARE BLOCK DATA FOR DEEPNETS TRAINING/TESTING
# -----------------------------------------------------------------------------

def sample_data(data, num_sample):
    """ data is in N x ...
        we want to keep num_samplexC of them.
        if N > num_sample, we will randomly keep num_sample of them.
        if N < num_sample, we will randomly duplicate samples.
    """
    N = data.shape[0]
    if (N == num_sample):
        return data, range(N)
    elif (N > num_sample):
        sample = np.random.choice(N, num_sample)
        return data[sample, ...], sample
    else:
        sample = np.random.choice(N, num_sample-N)
        dup_data = data[sample, ...]
        return np.concatenate([data, dup_data], 0), range(N)+list(sample)

def sample_data_label(data, label, num_sample):
    new_data, sample_indices = sample_data(data, num_sample)
    new_label = label[sample_indices]
    return new_data, new_label
    
def room2blocks(data, label, num_point, block_size=1.0, stride=1.0,
                random_sample=False, sample_num=None, sample_aug=1):
    """ Prepare block training data.
    Args:
        data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1]
            assumes the data is shifted (min point is origin) and aligned
            (aligned with XYZ axis)
        label: N size uint8 numpy array from 0-12
        num_point: int, how many points to sample in each block
        block_size: float, physical size of the block in meters
        stride: float, stride for block sweeping
        random_sample: bool, if True, we will randomly sample blocks in the room
        sample_num: int, if random sample, how many blocks to sample
            [default: room area]
        sample_aug: if random sample, how much aug
    Returns:
        block_datas: K x num_point x 6 np array of XYZRGB, RGB is in [0,1]
        block_labels: K x num_point x 1 np array of uint8 labels
        
    TODO: for this version, blocking is in fixed, non-overlapping pattern.
    """
    assert(stride<=block_size)

    limit = np.amax(data, 0)[0:3]
     
    # Get the corner location for our sampling blocks    
    xbeg_list = []
    ybeg_list = []
    if not random_sample:
        num_block_x = int(np.ceil((limit[0] - block_size) / stride)) + 1
        num_block_y = int(np.ceil((limit[1] - block_size) / stride)) + 1
        for i in range(num_block_x):
            for j in range(num_block_y):
                xbeg_list.append(i*stride)
                ybeg_list.append(j*stride)
    else:
        num_block_x = int(np.ceil(limit[0] / block_size))
        num_block_y = int(np.ceil(limit[1] / block_size))
        if sample_num is None:
            sample_num = num_block_x * num_block_y * sample_aug
        for _ in range(sample_num):
            xbeg = np.random.uniform(-block_size, limit[0]) 
            ybeg = np.random.uniform(-block_size, limit[1]) 
            xbeg_list.append(xbeg)
            ybeg_list.append(ybeg)

    # Collect blocks
    block_data_list = []
    block_label_list = []
    idx = 0
    for idx in range(len(xbeg_list)): 
       xbeg = xbeg_list[idx]
       ybeg = ybeg_list[idx]
       xcond = (data[:,0]<=xbeg+block_size) & (data[:,0]>=xbeg)
       ycond = (data[:,1]<=ybeg+block_size) & (data[:,1]>=ybeg)
       cond = xcond & ycond
       if np.sum(cond) < 100: # discard block if there are less than 100 pts.
           continue
       
       block_data = data[cond, :]
       block_label = label[cond]
       
       # randomly subsample data
       block_data_sampled, block_label_sampled = \
           sample_data_label(block_data, block_label, num_point)
       block_data_list.append(np.expand_dims(block_data_sampled, 0))
       block_label_list.append(np.expand_dims(block_label_sampled, 0))
            
    return np.concatenate(block_data_list, 0), \
           np.concatenate(block_label_list, 0)


def room2blocks_plus(data_label, num_point, block_size, stride,
                     random_sample, sample_num, sample_aug):
    """ room2block with input filename and RGB preprocessing.
    """
    data = data_label[:,0:6]
    data[:,3:6] /= 255.0
    label = data_label[:,-1].astype(np.uint8)
    
    return room2blocks(data, label, num_point, block_size, stride,
                       random_sample, sample_num, sample_aug)
   
def room2blocks_wrapper(data_label_filename, num_point, block_size=1.0, stride=1.0,
                        random_sample=False, sample_num=None, sample_aug=1):
    if data_label_filename[-3:] == 'txt':
        data_label = np.loadtxt(data_label_filename)
    elif data_label_filename[-3:] == 'npy':
        data_label = np.load(data_label_filename)
    else:
        print('Unknown file type! exiting.')
        exit()
    return room2blocks_plus(data_label, num_point, block_size, stride,
                            random_sample, sample_num, sample_aug)

def room2blocks_plus_normalized(data_label, num_point, block_size, stride,
                                random_sample, sample_num, sample_aug):
    """ room2block, with input filename and RGB preprocessing.
        for each block centralize XYZ, add normalized XYZ as 678 channels
    """
    data = data_label[:,0:6]
    data[:,3:6] /= 255.0
    label = data_label[:,-1].astype(np.uint8)
    max_room_x = max(data[:,0])
    max_room_y = max(data[:,1])
    max_room_z = max(data[:,2])
    
    data_batch, label_batch = room2blocks(data, label, num_point, block_size, stride,
                                          random_sample, sample_num, sample_aug)
    new_data_batch = np.zeros((data_batch.shape[0], num_point, 9))
    for b in range(data_batch.shape[0]):
        new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x
        new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y
        new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z
        minx = min(data_batch[b, :, 0])
        miny = min(data_batch[b, :, 1])
        data_batch[b, :, 0] -= (minx+block_size/2)
        data_batch[b, :, 1] -= (miny+block_size/2)
    new_data_batch[:, :, 0:6] = data_batch
    return new_data_batch, label_batch


def room2blocks_wrapper_normalized(data_label_filename, num_point, block_size=1.0, stride=1.0,
                                   random_sample=False, sample_num=None, sample_aug=1):
    if data_label_filename[-3:] == 'txt':
        data_label = np.loadtxt(data_label_filename)
    elif data_label_filename[-3:] == 'npy':
        data_label = np.load(data_label_filename)
    else:
        print('Unknown file type! exiting.')
        exit()
    return room2blocks_plus_normalized(data_label, num_point, block_size, stride,
                                       random_sample, sample_num, sample_aug)

def room2samples(data, label, sample_num_point):
    """ Prepare whole room samples.

    Args:
        data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1]
            assumes the data is shifted (min point is origin) and
            aligned (aligned with XYZ axis)
        label: N size uint8 numpy array from 0-12
        sample_num_point: int, how many points to sample in each sample
    Returns:
        sample_datas: K x sample_num_point x 9
                     numpy array of XYZRGBX'Y'Z', RGB is in [0,1]
        sample_labels: K x sample_num_point x 1 np array of uint8 labels
    """
    N = data.shape[0]
    order = np.arange(N)
    np.random.shuffle(order) 
    data = data[order, :]
    label = label[order]

    batch_num = int(np.ceil(N / float(sample_num_point)))
    sample_datas = np.zeros((batch_num, sample_num_point, 6))
    sample_labels = np.zeros((batch_num, sample_num_point, 1))

    for i in range(batch_num):
        beg_idx = i*sample_num_point
        end_idx = min((i+1)*sample_num_point, N)
        num = end_idx - beg_idx
        sample_datas[i,0:num,:] = data[beg_idx:end_idx, :]
        sample_labels[i,0:num,0] = label[beg_idx:end_idx]
        if num < sample_num_point:
            makeup_indices = np.random.choice(N, sample_num_point - num)
            sample_datas[i,num:,:] = data[makeup_indices, :]
            sample_labels[i,num:,0] = label[makeup_indices]
    return sample_datas, sample_labels

def room2samples_plus_normalized(data_label, num_point):
    """ room2sample, with input filename and RGB preprocessing.
        for each block centralize XYZ, add normalized XYZ as 678 channels
    """
    data = data_label[:,0:6]
    data[:,3:6] /= 255.0
    label = data_label[:,-1].astype(np.uint8)
    max_room_x = max(data[:,0])
    max_room_y = max(data[:,1])
    max_room_z = max(data[:,2])
    #print(max_room_x, max_room_y, max_room_z)
    
    data_batch, label_batch = room2samples(data, label, num_point)
    new_data_batch = np.zeros((data_batch.shape[0], num_point, 9))
    for b in range(data_batch.shape[0]):
        new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x
        new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y
        new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z
        #minx = min(data_batch[b, :, 0])
        #miny = min(data_batch[b, :, 1])
        #data_batch[b, :, 0] -= (minx+block_size/2)
        #data_batch[b, :, 1] -= (miny+block_size/2)
    new_data_batch[:, :, 0:6] = data_batch
    return new_data_batch, label_batch


def room2samples_wrapper_normalized(data_label_filename, num_point):
    if data_label_filename[-3:] == 'txt':
        data_label = np.loadtxt(data_label_filename)
    elif data_label_filename[-3:] == 'npy':
        data_label = np.load(data_label_filename)
    else:
        print('Unknown file type! exiting.')
        exit()
    return room2samples_plus_normalized(data_label, num_point)


# -----------------------------------------------------------------------------
# EXTRACT INSTANCE BBOX FROM ORIGINAL DATA (for detection evaluation)
# -----------------------------------------------------------------------------

def collect_bounding_box(anno_path, out_filename):
    """ Compute bounding boxes from each instance in original dataset files on
        one room. **We assume the bbox is aligned with XYZ coordinate.**
    
    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save instance bounding boxes for that room.
            each line is x1 y1 z1 x2 y2 z2 label,
            where (x1,y1,z1) is the point on the diagonal closer to origin
    Returns:
        None
    Note:
        room points are shifted, the most negative point is now at origin.
    """
    bbox_label_list = []

    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f)
        label = g_class2label[cls]
        # Compute tightest axis aligned bounding box
        xyz_min = np.amin(points[:, 0:3], axis=0)
        xyz_max = np.amax(points[:, 0:3], axis=0)
        ins_bbox_label = np.expand_dims(
            np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0)
        bbox_label_list.append(ins_bbox_label)

    bbox_label = np.concatenate(bbox_label_list, 0)
    room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0)
    bbox_label[:, 0:3] -= room_xyz_min 
    bbox_label[:, 3:6] -= room_xyz_min 

    fout = open(out_filename, 'w')
    for i in range(bbox_label.shape[0]):
        fout.write('%f %f %f %f %f %f %d\n' % \
                      (bbox_label[i,0], bbox_label[i,1], bbox_label[i,2],
                       bbox_label[i,3], bbox_label[i,4], bbox_label[i,5],
                       bbox_label[i,6]))
    fout.close()

def bbox_label_to_obj(input_filename, out_filename_prefix, easy_view=False):
    """ Visualization of bounding boxes.
    
    Args:
        input_filename: each line is x1 y1 z1 x2 y2 z2 label
        out_filename_prefix: OBJ filename prefix,
            visualize object by g_label2color
        easy_view: if True, only visualize furniture and floor
    Returns:
        output a list of OBJ file and MTL files with the same prefix
    """
    bbox_label = np.loadtxt(input_filename)
    bbox = bbox_label[:, 0:6]
    label = bbox_label[:, -1].astype(int)
    v_cnt = 0 # count vertex
    ins_cnt = 0 # count instance
    for i in range(bbox.shape[0]):
        if easy_view and (label[i] not in g_easy_view_labels):
            continue
        obj_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.obj'
        mtl_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.mtl'
        fout_obj = open(obj_filename, 'w')
        fout_mtl = open(mtl_filename, 'w')
        fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename)))

        length = bbox[i, 3:6] - bbox[i, 0:3]
        a = length[0]
        b = length[1]
        c = length[2]
        x = bbox[i, 0]
        y = bbox[i, 1]
        z = bbox[i, 2]
        color = np.array(g_label2color[label[i]], dtype=float) / 255.0

        material = 'material%d' % (ins_cnt)
        fout_obj.write('usemtl %s\n' % (material))
        fout_obj.write('v %f %f %f\n' % (x,y,z+c))
        fout_obj.write('v %f %f %f\n' % (x,y+b,z+c))
        fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c))
        fout_obj.write('v %f %f %f\n' % (x+a,y,z+c))
        fout_obj.write('v %f %f %f\n' % (x,y,z))
        fout_obj.write('v %f %f %f\n' % (x,y+b,z))
        fout_obj.write('v %f %f %f\n' % (x+a,y+b,z))
        fout_obj.write('v %f %f %f\n' % (x+a,y,z))
        fout_obj.write('g default\n')
        v_cnt = 0 # for individual box
        fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt))
        fout_obj.write('\n')

        fout_mtl.write('newmtl %s\n' % (material))
        fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2]))
        fout_mtl.write('\n')
        fout_obj.close()
        fout_mtl.close() 

        v_cnt += 8
        ins_cnt += 1

def bbox_label_to_obj_room(input_filename, out_filename_prefix, easy_view=False, permute=None, center=False, exclude_table=False):
    """ Visualization of bounding boxes.
    
    Args:
        input_filename: each line is x1 y1 z1 x2 y2 z2 label
        out_filename_prefix: OBJ filename prefix,
            visualize object by g_label2color
        easy_view: if True, only visualize furniture and floor
        permute: if not None, permute XYZ for rendering, e.g. [0 2 1]
        center: if True, move obj to have zero origin
    Returns:
        output a list of OBJ file and MTL files with the same prefix
    """
    bbox_label = np.loadtxt(input_filename)
    bbox = bbox_label[:, 0:6]
    if permute is not None:
        assert(len(permute)==3)
        permute = np.array(permute)
        bbox[:,0:3] = bbox[:,permute]
        bbox[:,3:6] = bbox[:,permute+3]
    if center:
        xyz_max = np.amax(bbox[:,3:6], 0)
        bbox[:,0:3] -= (xyz_max/2.0)
        bbox[:,3:6] -= (xyz_max/2.0)
        bbox /= np.max(xyz_max/2.0)
    label = bbox_label[:, -1].astype(int)
    obj_filename = out_filename_prefix+'.obj' 
    mtl_filename = out_filename_prefix+'.mtl'

    fout_obj = open(obj_filename, 'w')
    fout_mtl = open(mtl_filename, 'w')
    fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename)))
    v_cnt = 0 # count vertex
    ins_cnt = 0 # count instance
    for i in range(bbox.shape[0]):
        if easy_view and (label[i] not in g_easy_view_labels):
            continue
        if exclude_table and label[i] == g_classes.index('table'):
            continue

        length = bbox[i, 3:6] - bbox[i, 0:3]
        a = length[0]
        b = length[1]
        c = length[2]
        x = bbox[i, 0]
        y = bbox[i, 1]
        z = bbox[i, 2]
        color = np.array(g_label2color[label[i]], dtype=float) / 255.0

        material = 'material%d' % (ins_cnt)
        fout_obj.write('usemtl %s\n' % (material))
        fout_obj.write('v %f %f %f\n' % (x,y,z+c))
        fout_obj.write('v %f %f %f\n' % (x,y+b,z+c))
        fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c))
        fout_obj.write('v %f %f %f\n' % (x+a,y,z+c))
        fout_obj.write('v %f %f %f\n' % (x,y,z))
        fout_obj.write('v %f %f %f\n' % (x,y+b,z))
        fout_obj.write('v %f %f %f\n' % (x+a,y+b,z))
        fout_obj.write('v %f %f %f\n' % (x+a,y,z))
        fout_obj.write('g default\n')
        fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt))
        fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt))
        fout_obj.write('\n')

        fout_mtl.write('newmtl %s\n' % (material))
        fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2]))
        fout_mtl.write('\n')

        v_cnt += 8
        ins_cnt += 1

    fout_obj.close()
    fout_mtl.close() 


def collect_point_bounding_box(anno_path, out_filename, file_format):
    """ Compute bounding boxes from each instance in original dataset files on
        one room. **We assume the bbox is aligned with XYZ coordinate.**
        Save both the point XYZRGB and the bounding box for the point's
        parent element.
 
    Args:
        anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
        out_filename: path to save instance bounding boxes for each point,
            plus the point's XYZRGBL
            each line is XYZRGBL offsetX offsetY offsetZ a b c,
            where cx = X+offsetX, cy=X+offsetY, cz=Z+offsetZ
            where (cx,cy,cz) is center of the box, a,b,c are distances from center
            to the surfaces of the box, i.e. x1 = cx-a, x2 = cx+a, y1=cy-b etc.
        file_format: output file format, txt or numpy
    Returns:
        None

    Note:
        room points are shifted, the most negative point is now at origin.
    """
    point_bbox_list = []

    for f in glob.glob(os.path.join(anno_path, '*.txt')):
        cls = os.path.basename(f).split('_')[0]
        if cls not in g_classes: # note: in some room there is 'staris' class..
            cls = 'clutter'
        points = np.loadtxt(f) # Nx6
        label = g_class2label[cls] # N,
        # Compute tightest axis aligned bounding box
        xyz_min = np.amin(points[:, 0:3], axis=0) # 3,
        xyz_max = np.amax(points[:, 0:3], axis=0) # 3,
        xyz_center = (xyz_min + xyz_max) / 2
        dimension = (xyz_max - xyz_min) / 2

        xyz_offsets = xyz_center - points[:,0:3] # Nx3
        dimensions = np.ones((points.shape[0],3)) * dimension # Nx3
        labels = np.ones((points.shape[0],1)) * label # N
        point_bbox_list.append(np.concatenate([points, labels,
                                           xyz_offsets, dimensions], 1)) # Nx13

    point_bbox = np.concatenate(point_bbox_list, 0) # KxNx13
    room_xyz_min = np.amin(point_bbox[:, 0:3], axis=0)
    point_bbox[:, 0:3] -= room_xyz_min 

    if file_format == 'txt':
        fout = open(out_filename, 'w')
        for i in range(point_bbox.shape[0]):
            fout.write('%f %f %f %d %d %d %d %f %f %f %f %f %f\n' % \
                          (point_bbox[i,0], point_bbox[i,1], point_bbox[i,2],
                           point_bbox[i,3], point_bbox[i,4], point_bbox[i,5],
                           point_bbox[i,6],
                           point_bbox[i,7], point_bbox[i,8], point_bbox[i,9],
                           point_bbox[i,10], point_bbox[i,11], point_bbox[i,12]))
        
        fout.close()
    elif file_format == 'numpy':
        np.save(out_filename, point_bbox)
    else:
        print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \
            (file_format))
        exit()


