import argparse

import os
import cv2
import torch
from insightface.app import FaceAnalysis
from imageio_ffmpeg import get_ffmpeg_exe

parser = argparse.ArgumentParser()
parser.add_argument('--video_path', type=str, default='')
parser.add_argument('--kps_sequence_save_path', type=str, default='')
parser.add_argument('--audio_save_path', type=str, default='')
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--gpu_id', type=int, default=0)
parser.add_argument('--insightface_model_path', type=str, default='./model_ckpts/insightface_models/')
parser.add_argument('--height', type=int, default=512)
parser.add_argument('--width', type=int, default=512)
args = parser.parse_args()

app = FaceAnalysis(
    providers=['CUDAExecutionProvider' if args.device == 'cuda' else 'CPUExecutionProvider'],
    provider_options=[{'device_id': args.gpu_id}] if args.device == 'cuda' else [],
    root=args.insightface_model_path,
)
app.prepare(ctx_id=0, det_size=(args.height, args.width))

os.system(f'{get_ffmpeg_exe()} -i "{args.video_path}" -y -vn "{args.audio_save_path}"')

kps_sequence = []
video_capture = cv2.VideoCapture(args.video_path)
frame_idx = 0
while video_capture.isOpened():
    ret, frame = video_capture.read()
    if not ret:
        break
    frame = cv2.resize(frame, (args.width, args.height))
    faces = app.get(frame)
    assert len(faces) == 1, f'There are {len(faces)} faces in the {frame_idx}-th frame. Only one face is supported.'

    kps = faces[0].kps[:3]
    kps_sequence.append(kps)
    frame_idx += 1
torch.save(kps_sequence, args.kps_sequence_save_path)