<gh_stars>0
# Copyright 2021 The TensorFlow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import base64
import io
from typing import Any, List, Mapping, Sequence, Optional

from distinctipy import distinctipy
import numpy as np
import PIL
import umap
from bokeh.plotting import ColumnDataSource, figure, show, output_notebook
from tqdm.auto import tqdm

from tensorflow_similarity.types import FloatTensor, Tensor


def tensor2images(tensor: Tensor, size: Optional[int] = 64) -> List[str]:
    """Convert tensor images back to in memory images
    encoded in base 64.

    Args:
        tensor: 4D tensor that represent an image list.
        size: Image size to output in pixels. Defaults to 64.

    Returns:
        list of images encoded as base64 strings
    """

    # casting as iterating over a Tensor is slow.
    data = np.array(tensor)

    # if image provided are scaled between [0,1] then rescale
    if np.max(data) <= 1:
        data = data * 255

    # cast as int so PIL accepts its
    data = data.astype(np.uint8)

    imgs_b64 = []
    for a in tqdm(data, desc="generating diplayabe images"):
        # if single channel, treat it as black and white
        if a.shape[-1] == 1:
            a = np.reshape(a, (a.shape[0], a.shape[1]))
            img = PIL.Image.fromarray(a, 'L')
        else:
            img = PIL.Image.fromarray(a)

        img_resized = img.resize((size, size))
        buffer = io.BytesIO()
        img_resized.save(buffer, format='JPEG')
        img_bytes = buffer.getvalue()
        img64 = 'data:image/png;base64,%s' % str(
            base64.b64encode(img_bytes))[2:-1]
        imgs_b64.append(img64)

    return imgs_b64


def projector(embeddings: FloatTensor,
              labels: Optional[Sequence[Any]] = None,
              class_mapping: Optional[Sequence[int]] = None,
              images: Optional[Tensor] = None,
              image_size: int = 64,
              tooltips_info: Optional[Mapping[str, Sequence[str]]] = None,
              pt_size: int = 3,
              colorize: bool = True,
              pastel_factor: float = 0.1,
              plot_size: int = 600,
              active_drag: str = 'box_zoom',
              densmap: bool = True):
    """Visualize the embeddings in 2D or 3D using UMAP projection

    Args:
        embeddings: The embeddings outputed by the model that
        are to be visualized

        labels: Labels associated with the embeddings. If not supplied treat
        each example as its own classes.

        class_mapping: Dictionary or list that maps the class numerical ids
        to their name.

        images: Images to display in tooltip on hover. Usually x_test tensor.

        image_size: size of the images displayed in the tool tip.
        Defaults to 64.

        pt_size: Size of the points displayed on the visualization.
        Defaults to 3.

        tooltips_info: Dictionary of information to display in the tooltips.

        colorize: Colorize the clusters. Defaults to true.

        pastel_factor: Modify the color palette to be more pastel.

        densmap: Use UMAP dense mapper which provides better density
        estimation but is a little slower. Defaults to True.
    """

    print("perfoming projection using UMAP")
    reducer = umap.UMAP(densmap=densmap)
    # FIXME: 2d vs 3d
    cords = reducer.fit_transform(embeddings)

    # sample id
    _idxs = [i for i in range(len(embeddings))]

    # labels?
    if labels is not None:
        # if labels are already names just use them.
        if isinstance(labels[0], str):
            _labels = labels
        else:
            _labels = [int(i) for i in labels]
    else:
        # treat each examples as its own class
        _labels = _idxs

    # class name mapping?
    if class_mapping:
        _labels_txt = [class_mapping[i] for i in _labels]
    else:
        _labels_txt = [str(i) for i in _labels]

    class_list = sorted(set(_labels_txt))
    num_classes = len(class_list)

    # generate data
    data = dict(
        id=_idxs,
        x=[i[0] for i in cords],
        y=[i[1] for i in cords],
        labels=_labels,
        labels_txt=_labels_txt,
    )

    # colors if needed
    if labels is not None and colorize:
        # generate colors
        colors = {}
        for idx, c in enumerate(
                distinctipy.get_colors(num_classes,
                                       pastel_factor=pastel_factor)):
            # this is needed as labels can be strings or int or else
            cls_id = class_list[idx]
            colors[cls_id] = distinctipy.get_hex(c)

        # map point to their color
        _colors = [colors[i] for i in _labels_txt]
        data['colors'] = _colors
    else:
        _colors = []

    # building custom tooltips
    tooltips = '<div style="border:1px solid #ABABAB">'

    if images is not None:
        imgs = tensor2images(images, image_size)
        data['imgs'] = imgs
        # have to write custom tooltip html.
        tooltips += '<center><img src="@imgs"/></center>'  # noqa

    # adding user info
    if tooltips_info:
        for k, v in tooltips_info.items():
            data[k] = v
            tooltips += "%s:@%s <br>" % (k, k)

    tooltips += 'Class:@labels_txt <br>ID:@id </div>'

    # to bokeh format
    source = ColumnDataSource(data=data)
    output_notebook()
    fig = figure(tooltips=tooltips,
                 plot_width=plot_size,
                 plot_height=plot_size,
                 active_drag=active_drag,
                 active_scroll="wheel_zoom")

    # remove grid and axis
    fig.xaxis.visible = False
    fig.yaxis.visible = False
    fig.xgrid.visible = False
    fig.ygrid.visible = False

    # draw points
    if len(_colors):
        fig.circle('x', 'y', size=pt_size, color='colors', source=source)
    else:
        fig.circle('x', 'y', size=pt_size, source=source)

    # render
    output_notebook()
    show(fig, notebook_handle=True)

# Copyright 2021 The TensorFlow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import base64
import io
from typing import Any, List, Mapping, Sequence, Optional

from distinctipy import distinctipy
import numpy as np
import PIL
import umap
from bokeh.plotting import ColumnDataSource, figure, show, output_notebook
from tqdm.auto import tqdm

from tensorflow_similarity.types import FloatTensor, Tensor


def tensor2images(tensor: Tensor, size: Optional[int] = 64) -> List[str]:
    """Convert tensor images back to in memory images
    encoded in base 64.

    Args:
        tensor: 4D tensor that represent an image list.
        size: Image size to output in pixels. Defaults to 64.

    Returns:
        list of images encoded as base64 strings
    """

    # casting as iterating over a Tensor is slow.
    data = np.array(tensor)

    # if image provided are scaled between [0,1] then rescale
    if np.max(data) <= 1:
        data = data * 255

    # cast as int so PIL accepts its
    data = data.astype(np.uint8)

    imgs_b64 = []
    for a in tqdm(data, desc="generating diplayabe images"):
        # if single channel, treat it as black and white
        if a.shape[-1] == 1:
            a = np.reshape(a, (a.shape[0], a.shape[1]))
            img = PIL.Image.fromarray(a, 'L')
        else:
            img = PIL.Image.fromarray(a)

        img_resized = img.resize((size, size))
        buffer = io.BytesIO()
        img_resized.save(buffer, format='JPEG')
        img_bytes = buffer.getvalue()
        img64 = 'data:image/png;base64,%s' % str(
            base64.b64encode(img_bytes))[2:-1]
        imgs_b64.append(img64)

    return imgs_b64


def projector(embeddings: FloatTensor,
              labels: Optional[Sequence[Any]] = None,
              class_mapping: Optional[Sequence[int]] = None,
              images: Optional[Tensor] = None,
              image_size: int = 64,
              tooltips_info: Optional[Mapping[str, Sequence[str]]] = None,
              pt_size: int = 3,
              colorize: bool = True,
              pastel_factor: float = 0.1,
              plot_size: int = 600,
              active_drag: str = 'box_zoom',
              densmap: bool = True):
    """Visualize the embeddings in 2D or 3D using UMAP projection

    Args:
        embeddings: The embeddings outputed by the model that
        are to be visualized

        labels: Labels associated with the embeddings. If not supplied treat
        each example as its own classes.

        class_mapping: Dictionary or list that maps the class numerical ids
        to their name.

        images: Images to display in tooltip on hover. Usually x_test tensor.

        image_size: size of the images displayed in the tool tip.
        Defaults to 64.

        pt_size: Size of the points displayed on the visualization.
        Defaults to 3.

        tooltips_info: Dictionary of information to display in the tooltips.

        colorize: Colorize the clusters. Defaults to true.

        pastel_factor: Modify the color palette to be more pastel.

        densmap: Use UMAP dense mapper which provides better density
        estimation but is a little slower. Defaults to True.
    """

    print("perfoming projection using UMAP")
    reducer = umap.UMAP(densmap=densmap)
    # FIXME: 2d vs 3d
    cords = reducer.fit_transform(embeddings)

    # sample id
    _idxs = [i for i in range(len(embeddings))]

    # labels?
    if labels is not None:
        # if labels are already names just use them.
        if isinstance(labels[0], str):
            _labels = labels
        else:
            _labels = [int(i) for i in labels]
    else:
        # treat each examples as its own class
        _labels = _idxs

    # class name mapping?
    if class_mapping:
        _labels_txt = [class_mapping[i] for i in _labels]
    else:
        _labels_txt = [str(i) for i in _labels]

    class_list = sorted(set(_labels_txt))
    num_classes = len(class_list)

    # generate data
    data = dict(
        id=_idxs,
        x=[i[0] for i in cords],
        y=[i[1] for i in cords],
        labels=_labels,
        labels_txt=_labels_txt,
    )

    # colors if needed
    if labels is not None and colorize:
        # generate colors
        colors = {}
        for idx, c in enumerate(
                distinctipy.get_colors(num_classes,
                                       pastel_factor=pastel_factor)):
            # this is needed as labels can be strings or int or else
            cls_id = class_list[idx]
            colors[cls_id] = distinctipy.get_hex(c)

        # map point to their color
        _colors = [colors[i] for i in _labels_txt]
        data['colors'] = _colors
    else:
        _colors = []

    # building custom tooltips
    tooltips = '<div style="border:1px solid #ABABAB">'

    if images is not None:
        imgs = tensor2images(images, image_size)
        data['imgs'] = imgs
        # have to write custom tooltip html.
        tooltips += '<center><img src="@imgs"/></center>'  # noqa

    # adding user info
    if tooltips_info:
        for k, v in tooltips_info.items():
            data[k] = v
            tooltips += "%s:@%s <br>" % (k, k)

    tooltips += 'Class:@labels_txt <br>ID:@id </div>'

    # to bokeh format
    source = ColumnDataSource(data=data)
    output_notebook()
    fig = figure(tooltips=tooltips,
                 plot_width=plot_size,
                 plot_height=plot_size,
                 active_drag=active_drag,
                 active_scroll="wheel_zoom")

    # remove grid and axis
    fig.xaxis.visible = False
    fig.yaxis.visible = False
    fig.xgrid.visible = False
    fig.ygrid.visible = False

    # draw points
    if len(_colors):
        fig.circle('x', 'y', size=pt_size, color='colors', source=source)
    else:
        fig.circle('x', 'y', size=pt_size, source=source)

    # render
    output_notebook()
    show(fig, notebook_handle=True)
# Copyright 2021 The TensorFlow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #      http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Convert tensor images back to in memory images     encoded in base 64.      Args:         tensor: 4D tensor that represent an image list.         size: Image size to output in pixels. Defaults to 64.      Returns:         list of images encoded as base64 strings # casting as iterating over a Tensor is slow. # if image provided are scaled between [0,1] then rescale # cast as int so PIL accepts its # if single channel, treat it as black and white Visualize the embeddings in 2D or 3D using UMAP projection      Args:         embeddings: The embeddings outputed by the model that         are to be visualized          labels: Labels associated with the embeddings. If not supplied treat         each example as its own classes.          class_mapping: Dictionary or list that maps the class numerical ids         to their name.          images: Images to display in tooltip on hover. Usually x_test tensor.          image_size: size of the images displayed in the tool tip.         Defaults to 64.          pt_size: Size of the points displayed on the visualization.         Defaults to 3.          tooltips_info: Dictionary of information to display in the tooltips.          colorize: Colorize the clusters. Defaults to true.          pastel_factor: Modify the color palette to be more pastel.          densmap: Use UMAP dense mapper which provides better density         estimation but is a little slower. Defaults to True. # FIXME: 2d vs 3d # sample id # labels? # if labels are already names just use them. # treat each examples as its own class # class name mapping? # generate data # colors if needed # generate colors # this is needed as labels can be strings or int or else # map point to their color # building custom tooltips #ABABAB">' # have to write custom tooltip html. # noqa # adding user info # to bokeh format # remove grid and axis # draw points # render