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# -                        Open3D: www.open3d.org                            -
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"""Utility functions for the Open3D TensorBoard plugin."""

import os
from copy import deepcopy
from collections import OrderedDict
import logging
import threading

import numpy as np
from tensorboard.backend.event_processing.plugin_event_multiplexer import EventMultiplexer
from tensorboard.backend.event_processing.plugin_asset_util import PluginDirectory
from tensorboard.compat.tensorflow_stub.pywrap_tensorflow import masked_crc32c

import open3d as o3d
from open3d.visualization import rendering
from open3d.ml.vis import Colormap
from open3d.ml.vis import LabelLUT
from . import plugin_data_pb2
from . import metadata

try:
    from tensorflow.io.gfile import GFile as _fileopen
except ImportError:
    _fileopen = open

# Setup Python logger to emulate Open3D C++ logger.
_log = logging.getLogger("Open3D")
_log.propagate = False
_stream_handler = logging.StreamHandler()
_stream_handler.setFormatter(
    logging.Formatter('[%(name)s %(levelname)s T:%(threadName)s] %(message)s'))
_stream_handler.setLevel(logging.WARNING)
_log.setLevel(logging.WARNING)
_log.addHandler(_stream_handler)


class ReadWriteLock:
    """A lock object that allows many simultaneous "read locks", but
    only one "write lock."

    Implmentation from Python Cookbook (O'Reilly)
    https://www.oreilly.com/library/view/python-cookbook/0596001673/ch06s04.html
    Credit: Sami Hangaslammi
    """

    def __init__(self):
        self._read_ready = threading.Condition(threading.Lock())
        self._readers = 0

    def acquire_read(self):
        """Acquire a read lock. Blocks only if a thread has acquired the write
        lock.
        """
        self._read_ready.acquire()
        try:
            self._readers += 1
        finally:
            self._read_ready.release()

    def release_read(self):
        """Release a read lock."""
        self._read_ready.acquire()
        try:
            self._readers -= 1
            if not self._readers:
                self._read_ready.notifyAll()
        finally:
            self._read_ready.release()

    def acquire_write(self):
        """Acquire a write lock. Blocks until there are no acquired read or
        write locks.
        """
        self._read_ready.acquire()
        while self._readers > 0:
            self._read_ready.wait()

    def release_write(self):
        """Release a write lock."""
        self._read_ready.release()


class LRUCache:
    """Cache storing recent items, i.e. least recently used will be ejected when
    a new item needs to be added to a full cache. This is thread safe for
    concurrent access.
    """

    def __init__(self, max_items=128):
        """
        Args:
            max_items (int): Max items in cache.
        """
        self.cache = OrderedDict()
        self.max_items = max_items
        self.rwlock = ReadWriteLock()
        # hits, misses are not protected against concurrent access for
        # performance.
        self.hits = 0
        self.misses = 0

    def get(self, key):
        """Retrieve value corresponding to ``key`` from the cache.

        Return:
            Value if ``key`` is found, else None.
        """
        self.rwlock.acquire_read()
        value = self.cache.get(key)  # None if not found
        self.rwlock.release_read()
        if value is None:
            self.misses += 1
            _log.debug(str(self))
            return None
        self.rwlock.acquire_write()
        self.cache.move_to_end(key)
        self.rwlock.release_write()
        self.hits += 1
        _log.debug(str(self))
        return value

    def put(self, key, value):
        """Add (key, value) pair to the cache. If cache limits are exceeded,
        eject key-value pairs till the cache is within limits.
        """
        self.rwlock.acquire_write()
        self.cache[key] = value
        self.cache.move_to_end(key)
        if len(self.cache) > self.max_items:
            self.cache.popitem(last=False)
        self.rwlock.release_write()
        _log.debug(str(self))

    def clear(self):
        """Invalidate cache."""
        self.rwlock.acquire_write()
        self.cache.clear()
        self.rwlock.release_write()

    def __str__(self):
        return (f"Items: {len(self.cache)}/{self.max_items}, "
                f"Hits: {self.hits}, Misses: {self.misses}")


def _classify_properties(tensormap):
    """Classify custom geometry properties (other than positions, indices,
    colors, normals) into labels and other custom properties.

    Args:
        tensormap: geometry TensorMap (e.g.: `point` or `vertex`)

    Returns:
        label_prop (List): properties treated as labels (1D Int values).
        custom_prop (Dict): properties treated as custom properties (Float
            values, possibly multidimensional).
    """
    label_prop = {}
    custom_prop = {}
    exp_size = (tensormap['positions'].shape[0]
                if 'positions' in tensormap else tensormap['indices'].shape[0])
    for name, tensor in tensormap.items():
        if name in ('positions', 'colors', 'normals',
                    'indices') or name.startswith('_'):
            continue
        if (tensor.shape == (exp_size, 1) and
                tensor.dtype not in (o3d.core.float32, o3d.core.float64,
                                     o3d.core.undefined)):
            label_prop.update({name: 1})
        else:
            custom_prop.update({name: tensor.shape[1]})
    return label_prop, custom_prop


class Open3DPluginDataReader:
    """Manage TB event data and geometry data for common use by all
    Open3DPluginWindow instances. This is thread safe for simultaneous use by
    multiple browser clients with a multi-threaded web server. Read geometry
    data is cached in memory.

    Args:
        logdir (str): TensorBoard logs directory.
        cache_max_items (int): Max geometry elements to be cached in memory.
    """

    def __init__(self, logdir, cache_max_items=128):
        self.logdir = logdir
        self.event_mux = EventMultiplexer(tensor_size_guidance={
            metadata.PLUGIN_NAME: 0  # Store all metadata in RAM
        })
        self._run_to_tags = {}
        self._event_lock = threading.Lock()  # Protect TB event file data
        # Geometry data reading
        self._tensor_events = dict()
        self.geometry_cache = LRUCache(max_items=cache_max_items)
        self.runtag_prop_shape = dict()
        self._file_handles = {}  # {filename, (open_handle, read_lock)}
        self._file_handles_lock = threading.Lock()
        self.reload_events()

    def reload_events(self):
        """Reload event file"""
        self.event_mux.AddRunsFromDirectory(self.logdir)
        self.event_mux.Reload()
        run_tags = self.event_mux.PluginRunToTagToContent(metadata.PLUGIN_NAME)
        with self._event_lock:
            self._run_to_tags = {
                run: sorted(tagdict.keys())
                for run, tagdict in sorted(run_tags.items())
            }
            self._tensor_events = dict()  # Invalidate index
        # Close all open files
        with self._file_handles_lock:
            while len(self._file_handles) > 0:
                unused_filename, file_handle = self._file_handles.popitem()
                with file_handle[1]:
                    file_handle[0].close()

        _log.debug(f"Event data reloaded: {self._run_to_tags}")

    def is_active(self):
        """Do we have any Open3D data to display?"""
        with self._event_lock:
            return any(len(tags) > 0 for tags in self._run_to_tags.values())

    @property
    def run_to_tags(self):
        """Locked access to the run_to_tags map."""
        with self._event_lock:
            return self._run_to_tags

    def tensor_events(self, run):
        """Locked access to tensor events for a run."""
        with self._event_lock:
            if run not in self._tensor_events:
                self._tensor_events[run] = {
                    tag: self.event_mux.Tensors(run, tag)
                    for tag in self._run_to_tags[run]
                }
            return self._tensor_events[run]

    def get_label_to_names(self, run, tag):
        """Get label (id) to name (category) mapping for a tag."""
        md_proto = self.event_mux.SummaryMetadata(run, tag)
        lab2name = metadata.parse_plugin_metadata(md_proto.plugin_data.content)
        return dict(sorted(lab2name.items()))

    def read_from_file(self, filename, read_location, read_size,
                       read_masked_crc32c):
        """Read data from the file ``filename`` from a given offset
        ``read_location`` and size ``read_size``. Data is validated with the provided
        ``masked_crc32c``. This is thread safe and manages a list of open files.
        """
        with self._file_handles_lock:
            if filename not in self._file_handles:
                self._file_handles[filename] = (_fileopen(filename, "rb"),
                                                threading.Lock())
                if not self._file_handles[filename][0].seekable():
                    raise RuntimeError(filename + " does not support seeking."
                                       " This storage is not supported.")
            # lock to seek + read
            file_handle = self._file_handles[filename]
            file_handle[1].acquire()

        file_handle[0].seek(read_location)
        buf = file_handle[0].read(read_size)
        file_handle[1].release()
        if masked_crc32c(buf) == read_masked_crc32c:
            return buf
        else:
            return None

    def update_runtag_prop_shape(self, run, tag, geometry,
                                 inference_data_proto):
        """Update list of custom properties and their shapes for different runs
        and tags.
        """
        tag_prop_shape = self.runtag_prop_shape.setdefault(run, dict())
        prop_shape = tag_prop_shape.setdefault(tag, dict())
        if len(prop_shape) == 0 and not geometry.is_empty():
            for prop_type in ('point', 'vertex'):  # exclude 'line'
                if hasattr(geometry, prop_type):
                    label_props, custom_props = _classify_properties(
                        getattr(geometry, prop_type))
                    prop_shape.update(custom_props)
                    prop_shape.update(label_props)
            if len(inference_data_proto.inference_result) > 0:
                # Only bbox labels can be visualized. Scalars such as
                # 'confidence' from BoundingBox3D requires
                # unlitGradient.GRADIENT shader support for LineSet.
                prop_shape.update({'labels': 1})

    def read_geometry(self, run, tag, step, batch_idx, step_to_idx):
        """Geometry reader from msgpack files."""
        idx = step_to_idx[step]
        metadata_proto = plugin_data_pb2.Open3DPluginData()
        run_tensor_events = self.tensor_events(run)
        metadata_proto.ParseFromString(
            run_tensor_events[tag][idx].tensor_proto.string_val[0])
        data_dir = PluginDirectory(os.path.join(self.logdir, run),
                                   metadata.PLUGIN_NAME)
        filename = os.path.join(data_dir, metadata_proto.batch_index.filename)
        read_location = metadata_proto.batch_index.start_size[batch_idx].start
        read_size = metadata_proto.batch_index.start_size[batch_idx].size
        read_masked_crc32c = metadata_proto.batch_index.start_size[
            batch_idx].masked_crc32c
        cache_key = (filename, read_location, read_size, run, tag, step,
                     batch_idx)
        geometry = self.geometry_cache.get(cache_key)
        if geometry is None:  # Read from storage
            buf = self.read_from_file(filename, read_location, read_size,
                                      read_masked_crc32c)
            if buf is None:
                raise IOError(f"Geometry {cache_key} reading failed! CRC "
                              "mismatch in msgpack data.")
            msg_tag, msg_step, geometry = o3d.io.rpc.data_buffer_to_meta_geometry(
                buf)
            if geometry is None:
                raise IOError(f"Geometry {cache_key} reading failed! Possible "
                              "msgpack or TensorFlow event file corruption.")
            if tag != msg_tag or step != msg_step:
                _log.warning(
                    f"Mismatch between TensorFlow event (tag={tag}, step={step})"
                    f" and msgpack (tag={msg_tag}, step={msg_step}) data. "
                    "Possible data corruption.")
            _log.debug(f"Geometry {cache_key} reading successful!")
            self.geometry_cache.put(cache_key, geometry)

        # Fill in properties by reference
        for prop_ref in metadata_proto.property_references:
            prop = plugin_data_pb2.Open3DPluginData.GeometryProperty.Name(
                prop_ref.geometry_property)
            if prop_ref.step_ref >= step:
                _log.warning(
                    f"Incorrect future step reference {prop_ref.step_ref} for"
                    f" property {prop} of geometry at step {step}. Ignoring.")
                continue
            geometry_ref = self.read_geometry(run, tag, prop_ref.step_ref,
                                              batch_idx, step_to_idx)[0]
            # "vertex_normals" -> ["vertex", "normals"]
            prop_map, prop_attribute = prop.split("_")
            if prop_map == "vertex" and not isinstance(
                    geometry, o3d.t.geometry.TriangleMesh):
                prop_map = "point"
            # geometry.vertex["normals"] = geometry_ref.vertex["normals"]
            getattr(geometry, prop_map)[prop_attribute] = getattr(
                geometry_ref, prop_map)[prop_attribute]

        # Aux data (e.g. labels, confidences) -> not cached
        aux_read_location = metadata_proto.batch_index.start_size[
            batch_idx].aux_start
        aux_read_size = metadata_proto.batch_index.start_size[
            batch_idx].aux_size
        aux_read_masked_crc32c = metadata_proto.batch_index.start_size[
            batch_idx].aux_masked_crc32c
        data_bbox_proto = plugin_data_pb2.InferenceData()
        if aux_read_size > 0:
            data_bbox_serial = self.read_from_file(filename, aux_read_location,
                                                   aux_read_size,
                                                   aux_read_masked_crc32c)
            if data_bbox_serial is None:
                raise IOError(f"Aux data for {cache_key} reading failed! CRC "
                              "mismatch in protobuf data.")
            data_bbox_proto.ParseFromString(data_bbox_serial)

        self.update_runtag_prop_shape(run, tag, geometry, data_bbox_proto)
        return geometry, data_bbox_proto


def _normalize(tensor):
    """Normalize tensor by scaling and shifting to range [0, 1].

    Args:
        tensor: Open3D / Numpy float tensor. Int tensors are returned unchanged.

    Return:
        tuple: (Normalized tensor, min value of tensor, max value of tensor) min
        and max are stretched to include 0 or 1 for degenerate tensors.
    """
    if tensor.dtype in (o3d.core.float32, o3d.core.float64, np.float32,
                        np.float64):
        m, M = tensor.min().item(), tensor.max().item()
        if M <= m + 1e-6:  # stretch degenerate range to include [0, 1]
            m, M = min(m, 0), max(M, 1)
        return (tensor - m) * (1. / (M - m)), m, M
    return tensor, 0, 1


def _u8_to_float(color):
    return tuple(float(c) / 255 for c in color)


def _float_to_u8(color):
    return tuple(round(255 * c) for c in color)


class RenderUpdate:
    """Update rendering for a geometry, based on an incoming
    "tensorboard/update_rendering" message. Default rendering is used with an
    empty message.

    We use uint8 colors everywhere, since they are more compact to store during
    serialization. Colormaps are converted to float here, since rendering uses
    float colormaps.
    """

    _LINES_PER_BBOX = 17  # BoundingBox3D
    ALL_UPDATED = ["property", "shader", "colormap"]
    _CMAPS = {
        "RAINBOW": Colormap.make_rainbow(),
        "GREYSCALE": Colormap.make_greyscale(),
    }
    DICT_COLORMAPS = {
        name: {
            # float -> uint8, and RGB -> RGBA
            point.value: _float_to_u8(point.color) + (255,)
            for point in cmap.points
        } for name, cmap in _CMAPS.items()
    }
    LABELLUT_COLORS = tuple(
        _float_to_u8(color + [1.0])
        for color in LabelLUT.get_colors(mode='lightbg'))

    def __init__(self, window_scaling, message, label_to_names):
        from open3d.visualization.async_event_loop import async_event_loop
        self._gui = async_event_loop
        self._window_scaling = window_scaling
        self._label_to_names = label_to_names
        render_state = message.get("render_state", {
            "property": "",
            "index": 0,
            "shader": "",
            "colormap": None,
        })
        self._updated = message.get("updated", deepcopy(self.ALL_UPDATED))
        self._property = render_state["property"]
        if int(render_state["index"]) >= 0:
            self._index = int(render_state["index"])
        # For custom scalar / 3-vector visualization
        self.data_range = None
        self._shader = render_state["shader"]

        # { label or value : float tuple (r,g,b,a) in range [0., 1.] }
        self._colormap = None  # Initialize in first apply()
        if render_state["colormap"] is not None:
            to_number = int if self._shader == "unlitGradient.LUT" else float
            self._colormap = {
                to_number(label_value): tuple(int(c) for c in rgba) for
                label_value, rgba in sorted(render_state["colormap"].items(),
                                            key=lambda l_c: to_number(l_c[0]))
            }

    def get_render_state(self):
        """Return current render state."""
        return {
            "property": self._property,
            "index": self._index,
            "shader": self._shader,
            "colormap": self._colormap,
            "range": self.data_range or [0, 1]
        }

    def _update_range(self, min_val, max_val):
        if self.data_range is None:
            self.data_range = [min_val, max_val]
        else:
            self.data_range = [
                min(min_val, self.data_range[0]),
                max(max_val, self.data_range[1])
            ]

    def _set_vis_minmax(self, geometry_vertex, material):
        m, M = 0., 1.
        if "__visualization_scalar" in geometry_vertex:
            m = geometry_vertex["__visualization_scalar"].min().item()
            M = geometry_vertex["__visualization_scalar"].max().item()
            if M <= m + 1e-6:  # stretch degenerate range to include [0, 1]
                m, M = min(m, 0), max(M, 1)
            self._update_range(m, M)
        material.scalar_min, material.scalar_max = m, M
        _log.debug("material colormap range range set to "
                   f"{material.scalar_min, material.scalar_max}")

    def _set_render_defaults(self, geometry, inference_result, label_props,
                             custom_props):
        """Set default options for rendering (shader and property), based on
        data type and properties.
        """
        geometry_vertex = (geometry.point
                           if hasattr(geometry, 'point') else geometry.vertex)
        if self._shader == "":
            if (len(inference_result) > 0  # Have BB labels
                    or len(label_props) > 0):  # Have vertex labels
                self._shader = "unlitGradient.LUT"
                if self._property == "":
                    self._property = (next(iter(label_props))
                                      if len(label_props) > 0 else "labels")
                    self._index = 0
            elif len(custom_props) > 0:
                self._shader = "unlitGradient.GRADIENT.RAINBOW"
                if self._property == "":
                    self._property = next(iter(custom_props))
                    self._index = 0
            elif (isinstance(geometry, o3d.t.geometry.LineSet) or
                  "normals" in geometry_vertex):
                self._shader = "defaultLit"  # also proxy for unlitLine
            elif "colors" in geometry_vertex:
                self._shader = "defaultUnlit"
            else:  # Only XYZ
                self._shader = "unlitSolidColor"

        # Fix incompatible option. TODO(Sameer): Move this logic to JS
        if (self._property in custom_props and
                self._shader == "unlitGradient.LUT"):
            self._shader = "unlitGradient.GRADIENT.RAINBOW"
            self._colormap = None
        if (self._property in label_props and
                self._shader.startswith("unlitGradient.GRADIENT.")):
            self._shader = "unlitGradient.LUT"
            self._colormap = None

    class BackupRestore():
        """Manages backup and restore for tensormap[property] tensors. Call
        backup(tensormap, property) before modifying the property. This will
        save the data to a backup tensor (with key __property) and ensure a
        tensor is available for modification / assignment. A list of tensormap
        and property tuples is maintained so that a single call to restore()
        will switch the data back to the earlier state. The implementation takes
        care to only allocate a single backup copy of the tensor data which will
        be reused on subsequent calls to backup().
        """

        def __init__(self):
            self.swap_list = []  # swap these during restore
            self.backup_list = []  # backup these during restore

        def backup(self, tm, prop, clone=False, shape=None, dtype=None):
            """Args:

                    tm (TensorMap): tensormap, such as PointCloud.point
                    prop (str): property key, such as 'indices'
                    clone (bool): Backup should be a clone, else empty tensor.
                    shape, dtype: sape, dtype if property and backup are absent
                        and a new empty tensor should be created.
            """
            if prop in tm:
                if "__" + prop in tm:  # swap
                    tm[prop], tm["__" + prop] = tm["__" + prop], tm[prop]
                elif clone:
                    tm["__" + prop] = tm[prop].clone()  # backup
                else:
                    tm["__" + prop] = tm[prop]  # backup
                    tm[prop] = o3d.core.Tensor.empty(tm["__" + prop].shape,
                                                     tm["__" + prop].dtype)
                self.swap_list.append((tm, prop))
                return
            if "__" + prop in tm:  # __prop -> prop
                tm[prop] = tm["__" + prop]
                del tm["__" + prop]
            elif shape is not None and dtype is not None:
                tm[prop] = o3d.core.Tensor.empty(shape, dtype)
            self.backup_list.append((tm, prop))

        def restore(self):
            show = "tensormap props: (swap_list)"
            for tm, prop in self.swap_list:  # swap
                tm[prop], tm["__" + prop] = tm["__" + prop], tm[prop]
                show += prop + repr(tm[prop][:0]) + '\t' + "__" + prop + repr(
                    tm["__" + prop][:0])

            self.swap_list = []
            show += "\n (backup_list) "
            for tm, prop in self.backup_list:  # backup
                tm["__" + prop] = tm[prop]
                del tm[prop]
                show += "__" + prop + repr(tm["__" + prop][:0])
            self.backup_list = []

    def apply(self, o3dvis, geometry_name, geometry, inference_data_proto=None):
        """Apply the RenderUpdate to a geometry.

        Args:
            o3dvis (O3DVisualizer): Window containing the geometry.
            geometry_name (str): Geometry name in the window.
            geometry (o3d.t.geometry): Geometry whose rendering is to be
                updated.
            inference_data_proto : BoundingBox labels and confidences.
        """
        if (len(self._updated) == 0 or geometry.is_empty()):
            _log.debug("No updates, or empty geometry.")
            return

        def get_labelLUT():
            """Create {label: color} mapping from list of colors."""
            return {
                label: self.LABELLUT_COLORS[k]
                for k, label in enumerate(self._label_to_names)
            }

        geometry_vertex = (geometry.point
                           if hasattr(geometry, 'point') else geometry.vertex)
        have_colors = ("colors" in geometry.line if hasattr(
            geometry, 'line') else ("colors" in geometry.triangle if hasattr(
                geometry, 'triangle') else "colors" in geometry_vertex))

        if inference_data_proto is not None:
            inference_result = inference_data_proto.inference_result
        else:
            inference_result = []
        label_props, custom_props = _classify_properties(geometry_vertex)
        self._set_render_defaults(geometry, inference_result, label_props,
                                  custom_props)

        if o3dvis.scene.has_geometry(geometry_name):
            updated = self._updated
            # update_geometry() only accepts tPointCloud
            if not isinstance(geometry, o3d.t.geometry.PointCloud):
                o3dvis.remove_geometry(geometry_name)
        else:
            updated = deepcopy(self.ALL_UPDATED)

        geometry_update_flag = 0
        material_update_flag = 0
        swap = RenderUpdate.BackupRestore()
        material = o3dvis.get_geometry_material(geometry_name)

        # Visualize scalar / 3-vector property with color map
        if "property" in updated or "shader" in updated:
            # Float Scalar with colormap
            if ((self._property in custom_props or
                 self._property in label_props) and
                    self._shader.startswith("unlitGradient") and
                    geometry_vertex[self._property].shape[1] > self._index):
                geometry_vertex["__visualization_scalar"] = geometry_vertex[
                    self._property][:, self._index].to(
                        o3d.core.float32).contiguous()
                geometry_update_flag |= rendering.Scene.UPDATE_UV0_FLAG
            # 3-vector as RGB
            elif (self._property in custom_props and
                  self._shader == "defaultUnlit" and
                  geometry_vertex[self._property].shape[1] >= 3):
                swap.backup(geometry_vertex, "colors")
                geometry_vertex["colors"], min_val, max_val = _normalize(
                    geometry_vertex[self._property][:, :3])
                self._update_range(min_val, max_val)
                geometry_update_flag |= rendering.Scene.UPDATE_COLORS_FLAG

        # Bounding boxes / LineSet
        if isinstance(geometry, o3d.t.geometry.LineSet):
            if self._property == "" and self._shader == "unlitSolidColor":
                swap.backup(geometry.line,
                            "colors",
                            shape=(len(geometry.line["indices"]), 3),
                            dtype=o3d.core.uint8)
                geometry.line["colors"][:] = next(iter(
                    self._colormap.values()))[:3]
            elif self._property != "" and self._shader == "unlitGradient.LUT":
                if self._colormap is None:
                    self._colormap = get_labelLUT()
                if "colormap" in updated:
                    swap.backup(geometry.line, "indices", clone=True)
                    swap.backup(geometry.line,
                                "colors",
                                shape=(len(geometry.line["indices"]), 3),
                                dtype=o3d.core.uint8)
                    t_lines = geometry.line["indices"]
                    t_colors = geometry.line["colors"]
                    idx = 0
                    for bbir in inference_result:
                        col = self._colormap.setdefault(bbir.label,
                                                        (128, 128, 128, 255))
                        t_colors[idx:idx + self._LINES_PER_BBOX] = col[:3]
                        if col[3] == 0:  # alpha
                            t_lines[idx:idx + self._LINES_PER_BBOX] = 0
                        idx += self._LINES_PER_BBOX

        # PointCloud, Mesh, LineSet with colors
        elif (("shader" in updated or "colormap" in updated) and
              not geometry.has_valid_material()):
            material_update_flag = 1
            material.base_color = ((1.0, 1.0, 1.0, 1.0) if have_colors else
                                   (0.5, 0.5, 0.5, 1.0))
            if self._shader == "defaultLit":
                material.shader = "defaultLit"
            elif self._shader == "defaultUnlit":
                material.shader = "defaultUnlit"
            elif self._shader == "unlitSolidColor":
                material.shader = "unlitSolidColor"
                if self._colormap is None:
                    self._colormap = {0.0: [128, 128, 128, 255]}
                material.base_color = _u8_to_float(
                    next(iter(self._colormap.values())))
            elif self._shader == "unlitGradient.LUT":  # Label Colormap
                if self._colormap is None:
                    self._colormap = get_labelLUT()
                material.shader = "unlitGradient"
                material.gradient = rendering.Gradient()
                material.gradient.mode = rendering.Gradient.LUT
                lmin = min(self._label_to_names.keys())
                lmax = max(self._label_to_names.keys())
                material.scalar_min, material.scalar_max = lmin, lmax
                if len(self._colormap) > 1:
                    norm_cmap = list((float(label - lmin) / (lmax - lmin),
                                      _u8_to_float(color))
                                     for label, color in self._colormap.items())
                    material.gradient.points = list(
                        rendering.Gradient.Point(*lc) for lc in norm_cmap)
                else:
                    material.gradient.points = [
                        rendering.Gradient.Point(0.0, [1.0, 0.0, 1.0, 1.0])
                    ]
            # Colormap (RAINBOW / GREYSCALE): continuous data
            elif self._shader.startswith("unlitGradient.GRADIENT."):
                if self._colormap is None:
                    self._colormap = deepcopy(
                        self.DICT_COLORMAPS[self._shader[23:]])
                material.shader = "unlitGradient"
                material.gradient = rendering.Gradient()
                material.gradient.points = list(
                    rendering.Gradient.Point(value,
                                             _u8_to_float(color[:3]) + (1.,))
                    for value, color in self._colormap.items())
                material.gradient.mode = rendering.Gradient.GRADIENT
                self._set_vis_minmax(geometry_vertex, material)

        if o3dvis.scene.has_geometry(geometry_name):
            if material_update_flag > 0:
                self._gui.run_sync(
                    o3dvis.modify_geometry_material, geometry_name, material
                )  # does not do force_redraw(), so also need update_geometry()
            self._gui.run_sync(o3dvis.update_geometry, geometry_name, geometry,
                               geometry_update_flag)
            _log.debug(
                f"Geometry {geometry_name} updated with flags "
                f"Geo:{geometry_update_flag:b}, Mat:{material_update_flag:b}")
        else:
            self._gui.run_sync(o3dvis.add_geometry, geometry_name, geometry,
                               material if material_update_flag else None)
        self._gui.run_sync(o3dvis.post_redraw)

        swap.restore()
        _log.debug(f"apply complete: {geometry_name} with "
                   f"{self._property}[{self._index}]/{self._shader}")


def to_dict_batch(o3d_geometry_list):
    """Convert sequence of identical Open3D Eigen geometry types to
    attribute-tensor dictionary. The geometry sequence forms a batch of data.
    Custom attributes are not supported.

    Args:
        o3d_geometry_list (Iterable): Iterable (list / tuple / sequence
            generator) of Open3D Eigen geometry types.

    Returns:
        Dict[str: numpy.array]: Dictionary of property names and corresponding
        Numpy arrays.
    """
    if len(o3d_geometry_list) == 0:
        return {}
    vertex_positions = []
    vertex_colors = []
    vertex_normals = []
    triangle_indices = []
    triangle_uvs = []
    line_colors = []
    line_indices = []
    if isinstance(o3d_geometry_list[0], o3d.geometry.PointCloud):
        for geometry in o3d_geometry_list:
            vertex_positions.append(np.asarray(geometry.points))
            vertex_colors.append(np.asarray(geometry.colors))
            vertex_normals.append(np.asarray(geometry.normals))

        geo_dict = {
            'vertex_positions': np.stack(vertex_positions, axis=0),
            'vertex_colors': np.stack(vertex_colors, axis=0),
            'vertex_normals': np.stack(vertex_normals, axis=0),
        }

    elif isinstance(o3d_geometry_list[0], o3d.geometry.TriangleMesh):
        for geometry in o3d_geometry_list:
            vertex_positions.append(np.asarray(geometry.vertices))
            vertex_colors.append(np.asarray(geometry.vertex_colors))
            vertex_normals.append(np.asarray(geometry.vertex_normals))
            triangle_indices.append(np.asarray(geometry.triangles))
            if geometry.has_triangle_uvs():
                triangle_uvs.append(
                    np.asarray(geometry.triangle_uvs).reshape((-1, 3, 2)))

        geo_dict = {
            'vertex_positions': np.stack(vertex_positions, axis=0),
            'vertex_colors': np.stack(vertex_colors, axis=0),
            'vertex_normals': np.stack(vertex_normals, axis=0),
            'triangle_indices': np.stack(triangle_indices, axis=0),
        }
        if len(triangle_uvs) > 0:
            geo_dict.update(triangle_texture_uvs=np.stack(triangle_uvs, axis=0))

    elif isinstance(o3d_geometry_list[0], o3d.geometry.LineSet):
        for geometry in o3d_geometry_list:
            vertex_positions.append(np.asarray(geometry.points))
            line_colors.append(np.asarray(geometry.colors))
            line_indices.append(np.asarray(geometry.lines))

        geo_dict = {
            'vertex_positions': np.stack(vertex_positions, axis=0),
            'line_colors': np.stack(line_colors, axis=0),
            'line_indices': np.stack(line_indices, axis=0),
        }

    else:
        raise NotImplementedError(
            f"Geometry type {type(o3d_geometry_list[0])} is not supported yet.")

    # remove empty arrays
    for prop in tuple(geo_dict.keys()):
        if geo_dict[prop].size == 0:
            del geo_dict[prop]
    return geo_dict
