import base64
import io
import time

import numpy as np
import param
import PIL
import skimage

from PIL import Image, ImageFilter
from skimage import data, filters
from skimage.color.adapt_rgb import adapt_rgb, each_channel
from skimage.draw import rectangle
from skimage.exposure import rescale_intensity
from skimage.feature import Cascade

import panel as pn
import sys



HEIGHT = 600 # pixels
WIDTH = 600 # pixels
TIMEOUT = 500 # milliseconds

if sys.platform == 'emscripten':
    # Performance is higher when no round trip to server
    HEIGHT = 800
    WIDTH = 800
    TIMEOUT=100

CSS="""
.mdc-drawer {background: var(--light-bg-color) !important;}"""

pn.extension(raw_css=[CSS], sizing_mode="stretch_width")


class ImageModel(pn.viewable.Viewer):
    """Base class for image models."""

    def __init__(self, **params):
        super().__init__(**params)

        with param.edit_constant(self):
            self.name = self.__class__.name.replace("Model", "")
        self.view = self.create_view()

    def __panel__(self):
        return self.view

    def apply(self, image: str, height: int = HEIGHT, width: int = WIDTH) -> str:
        """Transforms a base64 encoded jpg image to a base64 encoded jpg BytesIO object"""
        raise NotImplementedError()

    def create_view(self):
        """Creates a view of the parameters of the transform to enable the user to configure them"""
        return pn.Param(self, name=self.name)

    def transform(self, image):
        """Transforms the image"""
        raise NotImplementedError()


class PILImageModel(ImageModel):
    """Base class for PIL image models"""

    @staticmethod
    def to_pil_img(value: str, height=HEIGHT, width=WIDTH):
        """Converts a base64 jpeg image string to a PIL.Image"""
        encoded_data = value.split(",")[1]
        base64_decoded = base64.b64decode(encoded_data)
        image = Image.open(io.BytesIO(base64_decoded))
        image.draft("RGB", (height, width))
        return image

    @staticmethod
    def from_pil_img(image: Image):
        """Converts a PIL.Image to a base64 encoded JPG BytesIO object"""
        buff = io.BytesIO()
        image.save(buff, format="JPEG")
        return buff

    def apply(self, image: str, height: int = HEIGHT, width: int = WIDTH) -> io.BytesIO:
        pil_img = self.to_pil_img(image, height=height, width=width)

        transformed_image = self.transform(pil_img)

        return self.from_pil_img(transformed_image)

    def transform(self, image: PIL.Image) -> PIL.Image:
        """Transforms the PIL.Image image"""
        raise NotImplementedError()


class NumpyImageModel(ImageModel):
    """Base class for np.ndarray image models"""

    @staticmethod
    def to_np_ndarray(image: str, height=HEIGHT, width=WIDTH) -> np.ndarray:
        """Converts a base64 encoded jpeg string to a np.ndarray"""
        pil_img = PILImageModel.to_pil_img(image, height=height, width=width)
        return np.array(pil_img)

    @staticmethod
    def from_np_ndarray(image: np.ndarray) -> io.BytesIO:
        """Converts np.ndarray jpeg image to a jpeg BytesIO instance"""
        if image.dtype == np.dtype("float64"):
            image = (image * 255).astype(np.uint8)
        pil_img = PIL.Image.fromarray(image)
        return PILImageModel.from_pil_img(pil_img)

    def apply(self, image: str, height: int = HEIGHT, width: int = WIDTH) -> io.BytesIO:
        np_array = self.to_np_ndarray(image, height=height, width=width)

        transformed_image = self.transform(np_array)

        return self.from_np_ndarray(transformed_image)

    def transform(self, image: np.ndarray) -> np.ndarray:
        """Transforms the np.array image"""
        raise NotImplementedError()


class Timer(pn.viewable.Viewer):
    """Helper Component used to show duration trends"""

    _trends = param.Dict()

    def __init__(self, **params):
        super().__init__()

        self.last_updates = {}
        self._trends = {}

        self._layout = pn.Row(**params)

    def time_it(self, name, func, *args, **kwargs):
        """Measures the duration of the execution of the func function and reports it under the
        name specified"""
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        duration = round(end - start, 2)
        self._report(name=name, duration=duration)
        return result

    def inc_it(self, name):
        """Measures the duration since the last time inc_it was called and reports it under the
        specified name"""
        start = self.last_updates.get(name, time.time())
        end = time.time()
        duration = round(end - start, 2)
        self._report(name=name, duration=duration)
        self.last_updates[name] = end

    def _report(self, name, duration):
        if not name in self._trends:
            self._trends[name] = pn.indicators.Trend(
                name=name,
                data={"x": [1], "y": [duration]},
                height=100,
                width=150,
                sizing_mode="fixed",
            )
            self.param.trigger("_trends")
        else:
            trend = self._trends[name]
            next_x = max(trend.data["x"]) + 1
            trend.stream({"x": [next_x], "y": [duration]}, rollover=10)

    @param.depends("_trends")
    def _panel(self):
        self._layout[:] = list(self._trends.values())
        return self._layout

    def __panel__(self):
        return pn.panel(self._panel)


def to_instance(value, **params):
    """Converts the value to an instance

    Args:
        value: A param.Parameterized class or instance

    Returns:
        An instance of the param.Parameterized class
    """
    if isinstance(value, param.Parameterized):
        value.param.update(**params)
        return value
    return value(**params)


class VideoStreamInterface(pn.viewable.Viewer):
    """An easy to use interface for a VideoStream and a set of transforms"""

    video_stream = param.ClassSelector(
        class_=pn.widgets.VideoStream, constant=True, doc="The source VideoStream", allow_refs=False,
    )

    height = param.Integer(
        default=HEIGHT,
        bounds=(10, 2000),
        step=10,
        doc="""The height of the image converted and shown""",
    )
    width = param.Integer(
        default=WIDTH,
        bounds=(10, 2000),
        step=10,
        doc="""The width of the image converted and shown""",
    )

    model = param.Selector(doc="The currently selected model")

    def __init__(
        self,
        models,
        timeout=TIMEOUT,
        paused=False,
        **params,
    ):
        super().__init__(
            video_stream=pn.widgets.VideoStream(
                name="Video Stream",
                timeout=timeout,
                paused=paused,
                height=0,
                width=0,
                visible=False,
                format="jpeg",
            ),
            **params,
        )
        self.image = pn.pane.JPG(
            height=self.height, width=self.width, sizing_mode="fixed"
        )
        self._updating = False
        models = [to_instance(model) for model in models]
        self.param.model.objects = models
        self.model = models[0]
        self.timer = Timer(sizing_mode="stretch_width")
        self.settings = self._create_settings()
        self._panel = self._create_panel()

    def _create_settings(self):
        return pn.Column(
            pn.Param(
                self.video_stream,
                parameters=["timeout", "paused"],
                widgets={
                    "timeout": {
                        "widget_type": pn.widgets.IntSlider,
                        "start": 10,
                        "end": 2000,
                        "step": 10,
                    }
                },
            ),
            self.timer,
            pn.Param(self, parameters=["height", "width"], name="Image"),
            pn.Param(
                self,
                parameters=["model"],
                expand_button=False,
                expand=False,
                widgets={
                    "model": {
                        "widget_type": pn.widgets.RadioButtonGroup,
                        "orientation": "vertical",
                        "button_type": "primary",
                        "button_style": "outline"
                    }
                },
                name="Model",
            ),
            self._get_transform,
        )

    def _create_panel(self):
        return pn.Row(
            self.video_stream,
            pn.layout.HSpacer(),
            self.image,
            pn.layout.HSpacer(),
            sizing_mode="stretch_width",
            align="center",
        )

    @param.depends("height", "width", watch=True)
    def _update_height_width(self):
        self.image.height = self.height
        self.image.width = self.width

    @param.depends("model")
    def _get_transform(self):
        # Hack: returning self.transform stops working after browsing the transforms for a while
        return self.model.view

    def __panel__(self):
        return self._panel

    @param.depends("video_stream.value", watch=True)
    def _handle_stream(self):
        if self._updating:
            return

        self._updating = True
        if self.model and self.video_stream.value:
            value = self.video_stream.value
            try:
                image = self.timer.time_it(
                    name="Model",
                    func=self.model.apply,
                    image=value,
                    height=self.height,
                    width=self.width,
                )
                self.image.object = image
            except PIL.UnidentifiedImageError:
                print("unidentified image")

            self.timer.inc_it("Last Update")
        self._updating = False


class GaussianBlurModel(PILImageModel):
    """Gaussian Blur Model

    https://pillow.readthedocs.io/en/stable/reference/ImageFilter.html#PIL.ImageFilter.GaussianBlur
    """

    radius = param.Integer(default=0, bounds=(0, 10))

    def transform(self, image: Image):
        return image.filter(ImageFilter.GaussianBlur(radius=self.radius))


class GrayscaleModel(NumpyImageModel):
    """GrayScale Model

    https://scikit-image.org/docs/0.15.x/auto_examples/color_exposure/plot_rgb_to_gray.html
    """

    def transform(self, image: np.ndarray):
        grayscale = skimage.color.rgb2gray(image[:, :, :3])
        return skimage.color.gray2rgb(grayscale)


class SobelModel(NumpyImageModel):
    """Sobel Model

    https://scikit-image.org/docs/0.15.x/auto_examples/color_exposure/plot_adapt_rgb.html
    """
    def transform(self, image):


        @adapt_rgb(each_channel)
        def sobel_each(image):
            return filters.sobel(image)

        return rescale_intensity(1 - sobel_each(image))


@pn.cache()
def get_detector():
    """Returns the Cascade detector"""
    trained_file = data.lbp_frontal_face_cascade_filename()
    return Cascade(trained_file)


class FaceDetectionModel(NumpyImageModel):
    """Face detection using a cascade classifier.

    https://scikit-image.org/docs/0.15.x/auto_examples/applications/plot_face_detection.html
    """

    scale_factor = param.Number(default=1.4, bounds=(1.0, 2.0), step=0.1)
    step_ratio = param.Integer(default=1, bounds=(1, 10))
    size_x = param.Range(default=(60, 322), bounds=(10, 500))
    size_y = param.Range(default=(60, 322), bounds=(10, 500))

    def transform(self, image):
        detector = get_detector()
        detected = detector.detect_multi_scale(
            img=image,
            scale_factor=self.scale_factor,
            step_ratio=self.step_ratio,
            min_size=(self.size_x[0], self.size_y[0]),
            max_size=(self.size_x[1], self.size_y[1]),
        )

        for patch in detected:
            rrr, ccc = rectangle(
                start=(patch["r"], patch["c"]),
                extent=(patch["height"], patch["width"]),
                shape=image.shape[:2],
            )
            image[rrr, ccc, 0] = 200

        return image


component = VideoStreamInterface(
    models=[
        GaussianBlurModel,
        GrayscaleModel,
        SobelModel,
        FaceDetectionModel,
    ]
)
pn.Row(pn.Row(component.settings, max_width=400), component)


pn.template.MaterialTemplate(
    site="Awesome Panel",
    title="VideoStream with ScikitImage",
    sidebar=[component.settings],
    main=[component],
).servable(); # We add ; to not show the template in the notebook as it does not display well.