"""
Media Stream API
================

This tutorial shows how to use torchaudio's I/O stream API to
fetch and decode audio/video data and apply preprocessings that
libavfilter provides.

"""

######################################################################
#
# .. note::
#
#    This tutorial requires Streaming API and FFmpeg libraries (>=4.1, <5).
#
#    The Streaming API is available in nightly builds.
#    Please refer to https://pytorch.org/get-started/locally/
#    for instructions.
#
#    There are multiple ways to install FFmpeg libraries.
#    If you are using Anaconda Python distribution,
#    ``conda install -c anaconda 'ffmpeg<5'`` will install
#    the required libraries.
#

######################################################################
# 1. Overview
# -----------
#
# Streaming API leverages the powerful I/O features of ffmpeg.
#
# It can
#  - Load audio/video in variety of formats
#  - Load audio/video from local/remote source
#  - Load audio/video from microphone, camera and screen
#  - Generate synthetic audio/video signals.
#  - Load audio/video chunk by chunk
#  - Change the sample rate / frame rate, image size, on-the-fly
#  - Apply filters and preprocessings
#
# The streaming API works in three steps.
#
# 1. Open media source (file, device, synthetic pattern generator)
# 2. Configure output stream
# 3. Stream the media
#
# At this moment, the features that the ffmpeg integration provides
# are limited to the form of
#
# `<some media source> -> <optional processing> -> <tensor>`
#
# If you have other forms that can be useful to your usecases,
# (such as integration with `torch.Tensor` type and file-like objects)
# please file a feature request.
#

######################################################################
# 2. Preparation
# --------------
#

import IPython
import matplotlib.pyplot as plt
import torch
import torchaudio

try:
    from torchaudio.io import StreamReader
except ModuleNotFoundError:
    try:
        import google.colab

        print(
            """
            To enable running this notebook in Google Colab, install nightly
            torch and torchaudio builds and the requisite third party libraries by
            adding the following code block to the top of the notebook before running it:

            !pip3 uninstall -y torch torchvision torchaudio
            !pip3 install --pre torch torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
            !add-apt-repository -y ppa:savoury1/ffmpeg4
            !apt-get -qq install -y ffmpeg
            """
        )
    except ModuleNotFoundError:
        pass
    raise

print(torch.__version__)
print(torchaudio.__version__)

base_url = "https://download.pytorch.org/torchaudio/tutorial-assets"
AUDIO_URL = f"{base_url}/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
VIDEO_URL = f"{base_url}/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4.mp4"

######################################################################
# 3. Opening the source
# ---------------------
#
# There are mainly three different sources that streaming API can
# handle. Whichever source is used, the remaining processes
# (configuring the output, applying preprocessing) are same.
#
# 1. Common media formats
# 2. Audio / Video devices
# 3. Synthetic audio / video sources
#
# The following section covers how to open common media formats.
# For the other streams, please refer to the
# `Advanced I/O streams` section.
#

######################################################################
#
# To open a media file, you can simply pass the path of the file to
# the constructor of `StreamReader`.
#
# .. code::
#
#    StreamReader(src="audio.wav")
#
#    StreamReader(src="audio.mp3")
#
# This works for image file, video file and video streams.
#
# .. code::
#
#    # Still image
#    StreamReader(src="image.jpeg")
#
#    # Video file
#    StreamReader(src="video.mpeg")
#
#    # Video on remote server
#    StreamReader(src="https://example.com/video.mp4")
#
#    # Playlist format
#    StreamReader(src="https://example.com/playlist.m3u")
#
# If attempting to load headerless raw data, you can use ``format`` and
# ``option`` to specify the format of the data.
#
# Say, you converted an audio file into faw format with ``sox`` command
# as follow;
#
# .. code::
#
#    # Headerless, 16-bit signed integer PCM, resampled at 16k Hz.
#    $ sox original.wav -r 16000 raw.s2
#
# Such audio can be opened like following.
#
# .. code::
#
#    StreamReader(src="raw.s2", format="s16le", option={"sample_rate": "16000"})
#

######################################################################
# 4. Checking the source streams
# ------------------------------
#
# Once the media is opened, we can inspect the streams and configure
# the output streams.
#
# You can check the number of source streams with
# :py:attr:`~torchaudio.io.StreamReader.num_src_streams`.
#
# .. note::
#    The number of streams is NOT the number of channels.
#    Each audio stream can contain an arbitrary number of channels.
#
# To check the metadata of source stream you can use
# :py:meth:`~torchaudio.io.StreamReader.get_src_stream_info`
# method and provide the index of the source stream.
#
# This method returns
# :py:class:`~torchaudio.io.StreamReader.SourceStream`. If a source
# stream is audio type, then the return type is
# :py:class:`~torchaudio.io.StreamReader.SourceAudioStream`, which is
# a subclass of `SourceStream`, with additional audio-specific attributes.
# Similarly, if a source stream is video type, then the return type is
# :py:class:`~torchaudio.io.StreamReader.SourceVideoStream`.

######################################################################
# For regular audio formats and still image formats, such as `WAV`
# and `JPEG`, the number of souorce streams is 1.
#

streamer = StreamReader(AUDIO_URL)
print("The number of source streams:", streamer.num_src_streams)
print(streamer.get_src_stream_info(0))

######################################################################
# Container formats and playlist formats may contain multiple streams
# of different media type.
#

src = "https://devstreaming-cdn.apple.com/videos/streaming/examples/img_bipbop_adv_example_fmp4/master.m3u8"
streamer = StreamReader(src)
print("The number of source streams:", streamer.num_src_streams)
for i in range(streamer.num_src_streams):
    print(streamer.get_src_stream_info(i))

######################################################################
# 5. Configuring output streams
# -------------------------------
#
# The stream API lets you stream data from an arbitrary combination of
# the input streams. If your application does not need audio or video,
# you can omit them. Or if you want to apply different preprocessing
# to the same source stream, you can duplicate the source stream.
#

######################################################################
# 5.1. Default streams
# --------------------
#
# When there are multiple streams in the source, it is not immediately
# clear which stream should be used.
#
# FFmpeg implements some heuristics to determine the default stream.
# The resulting stream index is exposed via
#
# :py:attr:`~torchaudio.io.StreamReader.default_audio_stream` and
# :py:attr:`~torchaudio.io.StreamReader.default_video_stream`.
#

######################################################################
# 5.2. Configuring output streams
# -------------------------------
#
# Once you know which source stream you want to use, then you can
# configure output streams with
# :py:meth:`~torchaudio.io.StreamReader.add_basic_audio_stream` and
# :py:meth:`~torchaudio.io.StreamReader.add_basic_video_stream`.
#
# These methods provide a simple way to change the basic property of
# media to match the application's requirements.
#
# The arguments common to both methods are;
#
# - ``frames_per_chunk``: How many frames at maximum should be
#   returned at each iteration.
#   For audio, the resulting tensor will be the shape of
#   `(frames_per_chunk, num_channels)`.
#   For video, it will be
#   `(frames_per_chunk, num_channels, height, width)`.
# - ``buffer_chunk_size``: The maximum number of chunks to be buffered internally.
#   When the StreamReader buffered this number of chunks and is asked to pull
#   more frames, StreamReader drops the old frames/chunks.
# - ``stream_index``: The index of the source stream.
#
# For audio output stream, you can provide the following additional
# parameters to change the audio properties.
#
# - ``sample_rate``: When provided, StreamReader resamples the audio on-the-fly.
# - ``dtype``: By default the StreamReader returns tensor of `float32` dtype,
#   with sample values ranging `[-1, 1]`. By providing ``dtype`` argument
#   the resulting dtype and value range is changed.
#
# For video output stream, the following parameters are available.
#
# - ``frame_rate``: Change the frame rate by dropping or duplicating
#   frames. No interpolation is performed.
# - ``width``, ``height``: Change the image size.
# - ``format``: Change the image format.
#

######################################################################
#
# .. code::
#
#    streamer = StreamReader(...)
#
#    # Stream audio from default audio source stream
#    # 256 frames at a time, keeping the original sampling rate.
#    streamer.add_basic_audio_stream(
#        frames_per_chunk=256,
#    )
#
#    # Stream audio from source stream `i`.
#    # Resample audio to 8k Hz, stream 256 frames at each
#    streamer.add_basic_audio_stream(
#        frames_per_chunk=256,
#        stream_index=i,
#        sample_rate=8000,
#    )
#

######################################################################
#
# .. code::
#
#    # Stream video from default video source stream.
#    # 10 frames at a time, at 30 FPS
#    # RGB color channels.
#    streamer.add_basic_video_stream(
#        frames_per_chunk=10,
#        frame_rate=30,
#        format="RGB"
#    )
#
#    # Stream video from source stream `j`,
#    # 10 frames at a time, at 30 FPS
#    # BGR color channels with rescaling to 128x128
#    streamer.add_basic_video_stream(
#        frames_per_chunk=10,
#        stream_index=j,
#        frame_rate=30,
#        width=128,
#        height=128,
#        format="BGR"
#    )
#

######################################################################
#
# You can check the resulting output streams in a similar manner as
# checking the source streams.
# :py:attr:`~torchaudio.io.StreamReader.num_out_streams` reports
# the number of configured output streams, and
# :py:meth:`~torchaudio.io.StreamReader.get_out_stream_info`
# fetches the information about the output streams.
#
# .. code::
#
#    for i in range(streamer.num_out_streams):
#        print(streamer.get_out_stream_info(i))
#

######################################################################
#
# If you want to remove an output stream, you can do so with
# :py:meth:`~torchaudio.io.StreamReader.remove_stream` method.
#
# .. code::
#
#    # Removes the first output stream.
#    streamer.remove_stream(0)
#

######################################################################
# 5.3. Streaming
# --------------
#
# To stream media data, the streamer alternates the process of
# fetching and decoding the source data, and passing the resulting
# audio / video data to client code.
#
# There are low-level methods that performs these operations.
# :py:meth:`~torchaudio.io.StreamReader.is_buffer_ready`,
# :py:meth:`~torchaudio.io.StreamReader.process_packet` and
# :py:meth:`~torchaudio.io.StreamReader.pop_chunks`.
#
# In this tutorial, we will use the high-level API, iterator protocol.
# It is as simple as a ``for`` loop.
#
# .. code::
#
#    streamer = StreamReader(...)
#    streamer.add_basic_audio_stream(...)
#    streamer.add_basic_video_stream(...)
#
#    for chunks in streamer.stream():
#        audio_chunk, video_chunk = chunks
#        ...
#

######################################################################
# 6. Example
# ----------
#
# Let's take an example video to configure the output streams.
# We will use the following video.
#
# .. raw:: html
#
#    <iframe width="560" height="315"
#     src="https://www.youtube.com/embed/6zNsc0e3Zns"
#     title="YouTube video player"
#     frameborder="0"
#     allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
#     allowfullscreen></iframe>
#
# Source: https://svs.gsfc.nasa.gov/13013 (This video is in public domain)
#
# Credit: NASA's Goddard Space Flight Center.
#
# NASA's Media Usage Guidelines: https://www.nasa.gov/multimedia/guidelines/index.html
#
#

######################################################################
#
# 6.1. Opening the source media
# ------------------------------
# Firstly, let's list the available streams and its properties.
#

streamer = StreamReader(VIDEO_URL)
for i in range(streamer.num_src_streams):
    print(streamer.get_src_stream_info(i))

######################################################################
#
# Now we configure the output stream.
#
# 6.2. Configuring ouptut streams
# -------------------------------

# fmt: off
# Audio stream with 8k Hz
streamer.add_basic_audio_stream(
    frames_per_chunk=8000,
    sample_rate=8000,
)

# Audio stream with 16k Hz
streamer.add_basic_audio_stream(
    frames_per_chunk=16000,
    sample_rate=16000,
)

# Video stream with 960x540 at 1 FPS.
streamer.add_basic_video_stream(
    frames_per_chunk=1,
    frame_rate=1,
    width=960,
    height=540,
    format="RGB",
)

# Video stream with 320x320 (stretched) at 3 FPS, grayscale
streamer.add_basic_video_stream(
    frames_per_chunk=3,
    frame_rate=3,
    width=320,
    height=320,
    format="GRAY",
)
# fmt: on

######################################################################
# .. note::
#
#    When configuring multiple output streams, in order to keep all
#    streams synced, set parameters so that the ratio between
#    ``frames_per_chunk`` and ``sample_rate`` or ``frame_rate`` is
#    consistent across output streams.
#

######################################################################
# Checking the output streams.
#

for i in range(streamer.num_out_streams):
    print(streamer.get_out_stream_info(i))

######################################################################
# Remove the second audio stream.
#

streamer.remove_stream(1)
for i in range(streamer.num_out_streams):
    print(streamer.get_out_stream_info(i))

######################################################################
# 6.3. Streaming
# --------------
#

######################################################################
# Jump to the 10 second point.
#
streamer.seek(10.0)

######################################################################
#
# Now, let's finally iterate over the output streams.
#

n_ite = 3
waveforms, vids1, vids2 = [], [], []
for i, (waveform, vid1, vid2) in enumerate(streamer.stream()):
    waveforms.append(waveform)
    vids1.append(vid1)
    vids2.append(vid2)
    if i + 1 == n_ite:
        break

######################################################################
# For audio stream, the chunk Tensor will be the shape of
# `(frames_per_chunk, num_channels)`, and for video stream,
# it is `(frames_per_chunk, num_color_channels, height, width)`.
#

print(waveforms[0].shape)
print(vids1[0].shape)
print(vids2[0].shape)

######################################################################
# Let's visualize what we received.

k = 3
fig = plt.figure()
gs = fig.add_gridspec(3, k * n_ite)
for i, waveform in enumerate(waveforms):
    ax = fig.add_subplot(gs[0, k * i : k * (i + 1)])
    ax.specgram(waveform[:, 0], Fs=8000)
    ax.set_yticks([])
    ax.set_xticks([])
    ax.set_title(f"Iteration {i}")
    if i == 0:
        ax.set_ylabel("Stream 0")
for i, vid in enumerate(vids1):
    ax = fig.add_subplot(gs[1, k * i : k * (i + 1)])
    ax.imshow(vid[0].permute(1, 2, 0))  # NCHW->HWC
    ax.set_yticks([])
    ax.set_xticks([])
    if i == 0:
        ax.set_ylabel("Stream 1")
for i, vid in enumerate(vids2):
    for j in range(3):
        ax = fig.add_subplot(gs[2, k * i + j : k * i + j + 1])
        ax.imshow(vid[j].permute(1, 2, 0), cmap="gray")
        ax.set_yticks([])
        ax.set_xticks([])
        if i == 0 and j == 0:
            ax.set_ylabel("Stream 2")
plt.tight_layout()
plt.show(block=False)

######################################################################
# [Advanced I/O streams]
# ----------------------
#

######################################################################
# 1. Audio / Video device input
# -----------------------------
#
# .. seealso::
#
#    `Device ASR with Emformer RNN-T <./device_asr.html>`__.
#
# Given that the system has proper media devices and libavdevice is
# configured to use the devices, the streaming API can
# pull media streams from these devices.
#
# To do this, we pass additional parameters ``format`` and ``option``
# to the constructor. ``format`` specifies the device component and
# ``option`` dictionary is specific to the specified component.
#
# The exact arguments to be passed depend on the system configuration.
# Please refer to https://ffmpeg.org/ffmpeg-devices.html for the detail.
#
# The following example illustrates how one can do this on MacBook Pro.
#
# First, we need to check the available devices.
#
# .. code::
#
#    $ ffmpeg -f avfoundation -list_devices true -i ""
#    [AVFoundation indev @ 0x143f04e50] AVFoundation video devices:
#    [AVFoundation indev @ 0x143f04e50] [0] FaceTime HD Camera
#    [AVFoundation indev @ 0x143f04e50] [1] Capture screen 0
#    [AVFoundation indev @ 0x143f04e50] AVFoundation audio devices:
#    [AVFoundation indev @ 0x143f04e50] [0] MacBook Pro Microphone
#
# We use `FaceTime HD Camera` as video device (index 0) and
# `MacBook Pro Microphone` as audio device (index 0).
#
# If we do not pass any ``option``, the device uses its default
# configuration. The decoder might not support the configuration.
#
# .. code::
#
#    >>> StreamReader(
#    ...     src="0:0",  # The first 0 means `FaceTime HD Camera`, and
#    ...                 # the second 0 indicates `MacBook Pro Microphone`.
#    ...     format="avfoundation",
#    ... )
#    [avfoundation @ 0x125d4fe00] Selected framerate (29.970030) is not supported by the device.
#    [avfoundation @ 0x125d4fe00] Supported modes:
#    [avfoundation @ 0x125d4fe00]   1280x720@[1.000000 30.000000]fps
#    [avfoundation @ 0x125d4fe00]   640x480@[1.000000 30.000000]fps
#    Traceback (most recent call last):
#      File "<stdin>", line 1, in <module>
#      ...
#    RuntimeError: Failed to open the input: 0:0
#
# By providing ``option``, we can change the format that the device
# streams to a format supported by decoder.
#
# .. code::
#
#    >>> streamer = StreamReader(
#    ...     src="0:0",
#    ...     format="avfoundation",
#    ...     option={"framerate": "30", "pixel_format": "bgr0"},
#    ... )
#    >>> for i in range(streamer.num_src_streams):
#    ...     print(streamer.get_src_stream_info(i))
#    SourceVideoStream(media_type='video', codec='rawvideo', codec_long_name='raw video', format='bgr0', bit_rate=0, width=640, height=480, frame_rate=30.0)
#    SourceAudioStream(media_type='audio', codec='pcm_f32le', codec_long_name='PCM 32-bit floating point little-endian', format='flt', bit_rate=3072000, sample_rate=48000.0, num_channels=2)
#

######################################################################
# 2. Synthetic source streams
# ---------------------------
#
# As a part of device integration, ffmpeg provides a "virtual device"
# interface. This interface provides synthetic audio / video data
# generation using libavfilter.
#
# To use this, we set ``format=lavfi`` and provide a filter description
# to ``src``.
#
# The detail of filter description can be found at
# https://ffmpeg.org/ffmpeg-filters.html
#

######################################################################
# 2.1. Synthetic audio examples
# -----------------------------
#

######################################################################
# Sine wave with
# ~~~~~~~~~~~~~~
#
# https://ffmpeg.org/ffmpeg-filters.html#sine
#
# .. code::
#
#    StreamReader(src="sine=sample_rate=8000:frequency=360", format="lavfi")
#
# .. raw:: html
#
#    <audio controls>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/sine.wav">
#    </audio>
#    <img
#     src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/sine.png"
#     class="sphx-glr-single-img" style="width:80%">
#

######################################################################
# Generate an audio signal specified by an expression
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# https://ffmpeg.org/ffmpeg-filters.html#aevalsrc
#
# .. code::
#
#    # 5 Hz binaural beats on a 360 Hz carrier
#    StreamReader(
#        src=(
#            'aevalsrc='
#            'sample_rate=8000:'
#            'exprs=0.1*sin(2*PI*(360-5/2)*t)|0.1*sin(2*PI*(360+5/2)*t)'
#        ),
#        format='lavfi',
#     )
#
# .. raw:: html
#
#    <audio controls>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/aevalsrc.wav">
#    </audio>
#    <img
#     src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/aevalsrc.png"
#     class="sphx-glr-single-img" style="width:80%">
#

######################################################################
# Generate noise with
# ~~~~~~~~~~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#anoisesrc
#
# .. code::
#
#    StreamReader(src="anoisesrc=color=pink:sample_rate=8000:amplitude=0.5", format="lavfi")
#
# .. raw:: html
#
#    <audio controls>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/anoisesrc.wav">
#    </audio>
#    <img
#     src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/anoisesrc.png"
#     class="sphx-glr-single-img" style="width:80%">
#

######################################################################
# 2.2. Synthetic video examples
# -----------------------------
#

######################################################################
# Cellular automaton
# ~~~~~~~~~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#cellauto
#
# .. code::
#
#    StreamReader(src=f"cellauto", format="lavfi")
#
# .. raw:: html
#
#    <video controls autoplay loop muted>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/cellauto.mp4">
#    </video>
#

######################################################################
# Mandelbrot
# ~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#cellauto
#
# .. code::
#
#    StreamReader(src=f"mandelbrot", format="lavfi")
#
# .. raw:: html
#
#    <video controls autoplay loop muted>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/mandelbrot.mp4">
#    </video>
#

######################################################################
# MPlayer Test patterns
# ~~~~~~~~~~~~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#mptestsrc
#
# .. code::
#
#    StreamReader(src=f"mptestsrc", format="lavfi")
#
# .. raw:: html
#
#    <video controls autoplay loop muted width=192 height=192>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/mptestsrc.mp4">
#    </video>
#

######################################################################
# John Conway's life game
# ~~~~~~~~~~~~~~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#life
#
# .. code::
#
#    StreamReader(src=f"life", format="lavfi")
#
# .. raw:: html
#
#    <video controls autoplay loop muted>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/life.mp4">
#    </video>
#

######################################################################
# Sierpinski carpet/triangle fractal
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# https://ffmpeg.org/ffmpeg-filters.html#sierpinski
#
# .. code::
#
#    StreamReader(src=f"sierpinski", format="lavfi")
#
# .. raw:: html
#
#    <video controls autoplay loop muted>
#        <source src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/sierpinski.mp4">
#    </video>
#

######################################################################
# 3. Custom output streams
# ------------------------
#
# When defining an output stream, you can use
# :py:meth:`~torchaudio.io.StreamReader.add_audio_stream` and
# :py:meth:`~torchaudio.io.StreamReader.add_video_stream` methods.
#
# These methods take ``filter_desc`` argument, which is a string
# formatted according to ffmpeg's
# `filter expression <https://ffmpeg.org/ffmpeg-filters.html>`_.
#
# The difference between ``add_basic_(audio|video)_stream`` and
# ``add_(audio|video)_stream`` is that ``add_basic_(audio|video)_stream``
# constructs the filter expression and passes it to the same underlying
# implementation. Everything ``add_basic_(audio|video)_stream`` can be
# achieved with ``add_(audio|video)_stream``.
#
# .. note::
#
#    - When applying custom filters, the client code must convert
#      the audio/video stream to one of the formats that torchaudio
#      can convert to tensor format.
#      This can be achieved, for example, by applying
#      ``format=pix_fmts=rgb24`` to video stream and
#      ``aformat=sample_fmts=fltp`` to audio stream.
#    - Each output stream has separate filter graph. Therefore, it is
#      not possible to use different input/output streams for a
#      filter expression. However, it is possible to split one input
#      stream into multiple of them, and merge them later.
#

######################################################################
# 3.1. Custom audio streams
# -------------------------
#
#

# fmt: off
descs = [
    # No filtering
    "anull",
    # Apply a highpass filter then a lowpass filter
    "highpass=f=200,lowpass=f=1000",
    # Manipulate spectrogram
    (
        "afftfilt="
        "real='hypot(re,im)*sin(0)':"
        "imag='hypot(re,im)*cos(0)':"
        "win_size=512:"
        "overlap=0.75"
    ),
    # Manipulate spectrogram
    (
        "afftfilt="
        "real='hypot(re,im)*cos((random(0)*2-1)*2*3.14)':"
        "imag='hypot(re,im)*sin((random(1)*2-1)*2*3.14)':"
        "win_size=128:"
        "overlap=0.8"
    ),
]
# fmt: on

######################################################################
#

sample_rate = 8000

streamer = StreamReader(AUDIO_URL)
for desc in descs:
    streamer.add_audio_stream(
        frames_per_chunk=40000,
        filter_desc=f"aresample={sample_rate},{desc},aformat=sample_fmts=fltp",
    )

chunks = next(streamer.stream())


def _display(i):
    print("filter_desc:", streamer.get_out_stream_info(i).filter_description)
    _, axs = plt.subplots(2, 1)
    waveform = chunks[i][:, 0]
    axs[0].plot(waveform)
    axs[0].grid(True)
    axs[0].set_ylim([-1, 1])
    plt.setp(axs[0].get_xticklabels(), visible=False)
    axs[1].specgram(waveform, Fs=sample_rate)
    return IPython.display.Audio(chunks[i].T, rate=sample_rate)


######################################################################
# Original
# ~~~~~~~~
#

_display(0)

######################################################################
# Highpass / lowpass filter
# ~~~~~~~~~~~~~~~~~~~~~~~~~
#

_display(1)

######################################################################
# FFT filter - Robot 🤖
# ~~~~~~~~~~~~~~~~~~~~~
#

_display(2)

######################################################################
# FFT filter - Whisper
# ~~~~~~~~~~~~~~~~~~~~
#

_display(3)

######################################################################
# 3.2. Custom video streams
# -------------------------
#

# fmt: off
descs = [
    # No effect
    "null",
    # Split the input stream and apply horizontal flip to the right half.
    (
        "split [main][tmp];"
        "[tmp] crop=iw/2:ih:0:0, hflip [flip];"
        "[main][flip] overlay=W/2:0"
    ),
    # Edge detection
    "edgedetect=mode=canny",
    # Rotate image by randomly and fill the background with brown
    "rotate=angle=-random(1)*PI:fillcolor=brown",
    # Manipulate pixel values based on the coordinate
    "geq=r='X/W*r(X,Y)':g='(1-X/W)*g(X,Y)':b='(H-Y)/H*b(X,Y)'"
]
# fmt: on

######################################################################
#

streamer = StreamReader(VIDEO_URL)
for desc in descs:
    streamer.add_video_stream(
        frames_per_chunk=30,
        filter_desc=f"fps=10,{desc},format=pix_fmts=rgb24",
    )

streamer.seek(12)

chunks = next(streamer.stream())


def _display(i):
    print("filter_desc:", streamer.get_out_stream_info(i).filter_description)
    _, axs = plt.subplots(1, 3, figsize=(8, 1.9))
    chunk = chunks[i]
    for j in range(3):
        axs[j].imshow(chunk[10 * j + 1].permute(1, 2, 0))
        axs[j].set_axis_off()
    plt.tight_layout()
    plt.show(block=False)


######################################################################
# Original
# ~~~~~~~~

_display(0)

######################################################################
# Mirror
# ~~~~~~

_display(1)

######################################################################
# Edge detection
# ~~~~~~~~~~~~~~~

_display(2)

######################################################################
# Random rotation
# ~~~~~~~~~~~~~~~

_display(3)

######################################################################
# Pixel manipulation
# ~~~~~~~~~~~~~~~~~~

_display(4)
