utils.py

import numpy as np
import sys
import torch.optim as optim


####################################### IoU ###############################################

CLASS_LABELS = ['wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture']
UNKNOWN_ID = -100
N_CLASSES = len(CLASS_LABELS)

def confusion_matrix(pred_ids, gt_ids):
    
    assert pred_ids.shape == gt_ids.shape, (pred_ids.shape, gt_ids.shape)
    idxs = (gt_ids >= 0)

    return np.bincount(pred_ids[idxs] * 20 + gt_ids[idxs], minlength=400).reshape((20, 20)).astype(np.ulonglong)

def get_iou(label_id, confusion):
    
    # true positives
    tp = np.longlong(confusion[label_id, label_id])
    # false positives
    fp = np.longlong(confusion[label_id, :].sum()) - tp
    # false negatives
    fn = np.longlong(confusion[:, label_id].sum()) - tp

    denom = (tp + fp + fn)
    if denom == 0:
        return float('nan')
    return (float(tp) / denom, tp, denom)

def evaluate(pred_ids, gt_ids):
    print('evaluating', gt_ids.size, 'points...')
    confusion = confusion_matrix(pred_ids, gt_ids)
    class_ious = {}
    mean_iou = 0

    for i in range(N_CLASSES):
        label_name = CLASS_LABELS[i]
        class_ious[label_name] = get_iou(i, confusion)
        mean_iou += class_ious[label_name][0] / 20

    print('classes          IoU')
    print('----------------------------')
    for i in range(N_CLASSES):
        label_name = CLASS_LABELS[i]
        print('{0:<14s}: {1:>5.3f}   ({2:>6d}/{3:<6d})'.format(label_name, class_ious[label_name][0], class_ious[label_name][1], class_ious[label_name][2]))
    print('mean IOU', mean_iou)



####################################### Ply ###############################################

# Define PLY types
ply_dtypes = dict([
    (b'int8', 'i1'),
    (b'char', 'i1'),
    (b'uint8', 'u1'),
    (b'uchar', 'u1'),
    (b'int16', 'i2'),
    (b'short', 'i2'),
    (b'uint16', 'u2'),
    (b'ushort', 'u2'),
    (b'int32', 'i4'),
    (b'int', 'i4'),
    (b'uint32', 'u4'),
    (b'uint', 'u4'),
    (b'float32', 'f4'),
    (b'float', 'f4'),
    (b'float64', 'f8'),
    (b'double', 'f8')
])

# Numpy reader format
valid_formats = {'ascii': '', 'binary_big_endian': '>',
                 'binary_little_endian': '<'}


# ----------------------------------------------------------------------------------------------------------------------
#
#           Functions
#       \***************/
#


def parse_header(plyfile, ext):
    # Variables
    line = []
    properties = []
    num_points = None

    while b'end_header' not in line and line != b'':
        line = plyfile.readline()

        if b'element' in line:
            line = line.split()
            num_points = int(line[2])

        elif b'property' in line:
            line = line.split()
            properties.append((line[2].decode(), ext + ply_dtypes[line[1]]))

    return num_points, properties


def parse_mesh_header(plyfile, ext):
    # Variables
    line = []
    vertex_properties = []
    num_points = None
    num_faces = None
    current_element = None


    while b'end_header' not in line and line != b'':
        line = plyfile.readline()

        # Find point element
        if b'element vertex' in line:
            current_element = 'vertex'
            line = line.split()
            num_points = int(line[2])

        elif b'element face' in line:
            current_element = 'face'
            line = line.split()
            num_faces = int(line[2])

        elif b'property' in line:
            if current_element == 'vertex':
                line = line.split()
                vertex_properties.append((line[2].decode(), ext + ply_dtypes[line[1]]))
            elif current_element == 'vertex':
                if not line.startswith('property list uchar int'):
                    raise ValueError('Unsupported faces property : ' + line)

    return num_points, num_faces, vertex_properties


def read_ply(filename, triangular_mesh=False):
    """
    Read ".ply" files
    Parameters
    ----------
    filename : string
        the name of the file to read.
    Returns
    -------
    result : array
        data stored in the file
    Examples
    --------
    Store data in file
    >>> points = np.random.rand(5, 3)
    >>> values = np.random.randint(2, size=10)
    >>> write_ply('example.ply', [points, values], ['x', 'y', 'z', 'values'])
    Read the file
    >>> data = read_ply('example.ply')
    >>> values = data['values']
    array([0, 0, 1, 1, 0])
    
    >>> points = np.vstack((data['x'], data['y'], data['z'])).T
    array([[ 0.466  0.595  0.324]
           [ 0.538  0.407  0.654]
           [ 0.850  0.018  0.988]
           [ 0.395  0.394  0.363]
           [ 0.873  0.996  0.092]])
    """

    with open(filename, 'rb') as plyfile:


        # Check if the file start with ply
        if b'ply' not in plyfile.readline():
            raise ValueError('The file does not start whith the word ply')

        # get binary_little/big or ascii
        fmt = plyfile.readline().split()[1].decode()
        if fmt == "ascii":
            raise ValueError('The file is not binary')

        # get extension for building the numpy dtypes
        ext = valid_formats[fmt]

        # PointCloud reader vs mesh reader
        if triangular_mesh:

            # Parse header
            num_points, num_faces, properties = parse_mesh_header(plyfile, ext)

            # Get point data
            vertex_data = np.fromfile(plyfile, dtype=properties, count=num_points)

            # Get face data
            face_properties = [('k', ext + 'u1'),
                               ('v1', ext + 'i4'),
                               ('v2', ext + 'i4'),
                               ('v3', ext + 'i4')]
            faces_data = np.fromfile(plyfile, dtype=face_properties, count=num_faces)

            # Return vertex data and concatenated faces
            faces = np.vstack((faces_data['v1'], faces_data['v2'], faces_data['v3'])).T
            data = [vertex_data, faces]

        else:

            # Parse header
            num_points, properties = parse_header(plyfile, ext)

            # Get data
            data = np.fromfile(plyfile, dtype=properties, count=num_points)

    return data


def header_properties(field_list, field_names):

    # List of lines to write
    lines = []

    # First line describing element vertex
    lines.append('element vertex %d' % field_list[0].shape[0])

    # Properties lines
    i = 0
    for fields in field_list:
        for field in fields.T:
            lines.append('property %s %s' % (field.dtype.name, field_names[i]))
            i += 1

    return lines


def write_ply(filename, field_list, field_names, triangular_faces=None):
    """
    Write ".ply" files
    Parameters
    ----------
    filename : string
        the name of the file to which the data is saved. A '.ply' extension will be appended to the 
        file name if it does no already have one.
    field_list : list, tuple, numpy array
        the fields to be saved in the ply file. Either a numpy array, a list of numpy arrays or a 
        tuple of numpy arrays. Each 1D numpy array and each column of 2D numpy arrays are considered 
        as one field. 
    field_names : list
        the name of each fields as a list of strings. Has to be the same length as the number of 
        fields.
    Examples
    --------
    >>> points = np.random.rand(10, 3)
    >>> write_ply('example1.ply', points, ['x', 'y', 'z'])
    >>> values = np.random.randint(2, size=10)
    >>> write_ply('example2.ply', [points, values], ['x', 'y', 'z', 'values'])
    >>> colors = np.random.randint(255, size=(10,3), dtype=np.uint8)
    >>> field_names = ['x', 'y', 'z', 'red', 'green', 'blue', values']
    >>> write_ply('example3.ply', [points, colors, values], field_names)
    """

    # Format list input to the right form
    field_list = list(field_list) if (type(field_list) == list or type(field_list) == tuple) else list((field_list,))
    for i, field in enumerate(field_list):
        if field.ndim < 2:
            field_list[i] = field.reshape(-1, 1)
        if field.ndim > 2:
            print('fields have more than 2 dimensions')
            return False    

    # check all fields have the same number of data
    n_points = [field.shape[0] for field in field_list]
    if not np.all(np.equal(n_points, n_points[0])):
        print('wrong field dimensions')
        return False    

    # Check if field_names and field_list have same nb of column
    n_fields = np.sum([field.shape[1] for field in field_list])
    if (n_fields != len(field_names)):
        print('wrong number of field names')
        return False

    # Add extension if not there
    if not filename.endswith('.ply'):
        filename += '.ply'

    # open in text mode to write the header
    with open(filename, 'w') as plyfile:

        # First magical word
        header = ['ply']

        # Encoding format
        header.append('format binary_' + sys.byteorder + '_endian 1.0')

        # Points properties description
        header.extend(header_properties(field_list, field_names))

        # Add faces if needded
        if triangular_faces is not None:
            header.append('element face {:d}'.format(triangular_faces.shape[0]))
            header.append('property list uchar int vertex_indices')

        # End of header
        header.append('end_header')

        # Write all lines
        for line in header:
            plyfile.write("%s\n" % line)

    # open in binary/append to use tofile
    with open(filename, 'ab') as plyfile:

        # Create a structured array
        i = 0
        type_list = []
        for fields in field_list:
            for field in fields.T:
                type_list += [(field_names[i], field.dtype.str)]
                i += 1
        data = np.empty(field_list[0].shape[0], dtype=type_list)
        i = 0
        for fields in field_list:
            for field in fields.T:
                data[field_names[i]] = field
                i += 1

        data.tofile(plyfile)

        if triangular_faces is not None:
            triangular_faces = triangular_faces.astype(np.int32)
            type_list = [('k', 'uint8')] + [(str(ind), 'int32') for ind in range(3)]
            data = np.empty(triangular_faces.shape[0], dtype=type_list)
            data['k'] = np.full((triangular_faces.shape[0],), 3, dtype=np.uint8)
            data['0'] = triangular_faces[:, 0]
            data['1'] = triangular_faces[:, 1]
            data['2'] = triangular_faces[:, 2]
            data.tofile(plyfile)

    return True


def describe_element(name, df):
    """ Takes the columns of the dataframe and builds a ply-like description
    Parameters
    ----------
    name: str
    df: pandas DataFrame
    Returns
    -------
    element: list[str]
    """
    property_formats = {'f': 'float', 'u': 'uchar', 'i': 'int'}
    element = ['element ' + name + ' ' + str(len(df))]

    if name == 'face':
        element.append("property list uchar int points_indices")

    else:
        for i in range(len(df.columns)):
            # get first letter of dtype to infer format
            f = property_formats[str(df.dtypes[i])[0]]
            element.append('property ' + f + ' ' + df.columns.values[i])

    return element



####################################### Scheduler ###############################################

class LambdaStepLR(optim.lr_scheduler.LambdaLR):

    def __init__(self, optimizer, lr_lambda, last_step=-1):
        super(LambdaStepLR, self).__init__(optimizer, lr_lambda, last_step)

    @property
    def last_step(self):
        """Use last_epoch for the step counter"""
        return self.last_epoch

    @last_step.setter
    def last_step(self, v):
        self.last_epoch = v


class PolyLR(LambdaStepLR):
    """DeepLab learning rate policy"""

    def __init__(self, optimizer, max_iter, power=0.9, last_step=-1):
        super(PolyLR, self).__init__(optimizer, lambda s: (1 - s / (max_iter + 1))**power, last_step)