#! /usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2023 Imperial College London (Pingchuan Ma)
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

import torch
import torchaudio
import torchvision


class AVSRDataLoader:
    def __init__(self, modality, detector="retinaface", resize=None):
        self.modality = modality
        if modality == "video":
            if detector == "retinaface":
                from detectors.retinaface.detector import LandmarksDetector
                from detectors.retinaface.video_process import VideoProcess

                self.landmarks_detector = LandmarksDetector(device="cuda:0")
                self.video_process = VideoProcess(resize=resize)
            if detector == "mediapipe":
                from detectors.mediapipe.detector import LandmarksDetector
                from detectors.mediapipe.video_process import VideoProcess

                self.landmarks_detector = LandmarksDetector()
                self.video_process = VideoProcess(resize=resize)

    def load_data(self, data_filename, transform=True):
        if self.modality == "audio":
            audio, sample_rate = self.load_audio(data_filename)
            audio = self.audio_process(audio, sample_rate)
            return audio
        if self.modality == "video":
            landmarks = self.landmarks_detector(data_filename)
            video = self.load_video(data_filename)
            video = self.video_process(video, landmarks)
            video = torch.tensor(video)
            return video

    def load_audio(self, data_filename):
        waveform, sample_rate = torchaudio.load(data_filename, normalize=True)
        return waveform, sample_rate

    def load_video(self, data_filename):
        return torchvision.io.read_video(data_filename, pts_unit="sec")[0].numpy()

    def audio_process(self, waveform, sample_rate, target_sample_rate=16000):
        if sample_rate != target_sample_rate:
            waveform = torchaudio.functional.resample(waveform, sample_rate, target_sample_rate)
        waveform = torch.mean(waveform, dim=0, keepdim=True)
        return waveform

#! /usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2023 Imperial College London (Pingchuan Ma)
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

import torch
import torchaudio
import torchvision


class AVSRDataLoader:
    def __init__(self, modality, detector="retinaface", resize=None):
        self.modality = modality
        if modality == "video":
            if detector == "retinaface":
                from detectors.retinaface.detector import LandmarksDetector
                from detectors.retinaface.video_process import VideoProcess

                self.landmarks_detector = LandmarksDetector(device="cuda:0")
                self.video_process = VideoProcess(resize=resize)

    def load_data(self, data_filename, transform=True):
        if self.modality == "audio":
            audio, sample_rate = self.load_audio(data_filename)
            audio = self.audio_process(audio, sample_rate)
            return audio
        if self.modality == "video":
            landmarks = self.landmarks_detector(data_filename)
            video = self.load_video(data_filename)
            video = self.video_process(video, landmarks)
            video = torch.tensor(video)
            return video

    def load_audio(self, data_filename):
        waveform, sample_rate = torchaudio.load(data_filename, normalize=True)
        return waveform, sample_rate

    def load_video(self, data_filename):
        return torchvision.io.read_video(data_filename, pts_unit="sec")[0].numpy()

    def audio_process(self, waveform, sample_rate, target_sample_rate=16000):
        if sample_rate != target_sample_rate:
            waveform = torchaudio.functional.resample(waveform, sample_rate, target_sample_rate)
        waveform = torch.mean(waveform, dim=0, keepdim=True)
        return waveform
