"""
StreamReader Advanced Usages
============================

**Author**: `Moto Hira <moto@meta.com>`__

This tutorial is the continuation of
`StreamReader Basic Usages <./streamreader_basic_tutorial.html>`__.

This shows how to use :py:class:`~torchaudio.io.StreamReader` for

- Device inputs, such as microphone, webcam and screen recording
- Generating synthetic audio / video
- Applying preprocessing with custom filter expressions
"""

import torch
import torchaudio

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

import IPython
import matplotlib.pyplot as plt
from torchaudio.io import StreamReader

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"


######################################################################
# Audio / Video device input
# --------------------------
#
# .. seealso::
#
#    - `Accelerated Video Decoding with NVDEC <../hw_acceleration_tutorial.html>`__.
#    - `Online ASR with Emformer RNN-T <./online_asr_tutorial.html>`__.
#    - `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)
#

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

######################################################################
# Audio Examples
# ~~~~~~~~~~~~~~
#

######################################################################
# Sine wave
# ^^^^^^^^^
# 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%">
#

######################################################################
# Signal with arbitral 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%">
#

######################################################################
# Noise
# ^^^^^
# 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%">
#

######################################################################
# 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>
#

######################################################################
# Custom filters
# --------------
#
# 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.
#

######################################################################
# Audio Examples
# ~~~~~~~~~~~~~~
#
#

# 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)

######################################################################
# Video Examples
# ~~~~~~~~~~~~~~
#

# 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)

######################################################################
#
# Tag: :obj:`torchaudio.io`
