app.py

import gradio as gr
import subprocess
import cv2
import ffmpeg

def convert_video(input_file, output_file, codec='mp4v'):
    try:
        # Define input and output files
        input_path = input_file
        output_path = output_file

        # Read input video file
        cap = cv2.VideoCapture(input_path)

        # Get video codec and frame dimensions
        fourcc = cv2.VideoWriter_fourcc(*codec)
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = cap.get(cv2.CAP_PROP_FPS)

        # Create output video writer
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))

        # Process and write video frames
        while cap.isOpened():
            ret, frame = cap.read()
            if ret:
                out.write(frame)
            else:
                break

        # Release video objects
        cap.release()
        out.release()

        # Process and write audio stream using ffmpeg
        stream = ffmpeg.input(input_path)
        stream = ffmpeg.output(stream, output_path, acodec='aac', vcodec=codec, strict='experimental', loglevel='error')
        # Run FFmpeg command and capture stderr output
        try:
            ffmpeg.run(stream, capture_stderr=True)
        except subprocess.CalledProcessError as e:
            # Print stderr output if an error occurs
            print(f"FFmpeg error: {e.stderr.decode()}")
            raise e

        print(f"Video converted successfully: {output_path}")

    except Exception as e:
        print(f"Error converting video: {e}")

def execute_command(command: str) -> None:
    subprocess.run(command, check=True)

def infer():

    output_name = "acknowledgement_english@M030_front_neutral_level1_001@male_face"

    command = [
        f"python",
        f"inference_for_demo_video.py",
        f"--wav_path=data/audio/acknowledgement_english.m4a",
        f"--style_clip_path=data/style_clip/3DMM/M030_front_neutral_level1_001.mat",
        f"--pose_path=data/pose/RichardShelby_front_neutral_level1_001.mat",
        f"--image_path=data/src_img/uncropped/male_face.png",
        f"--cfg_scale=1.0",
        f"--max_gen_len=30",
        f"--output_name={output_name}"
    ]

    execute_command(command)

    # Convert video to compatible codecs
    input_file_path = f"output_video/{output_name}.mp4"
    output_file_path = f"{output_name}.mp4"
    #convert_video(input_file_path, output_file_path)

    return f"output_video/{output_name}.mp4"

with gr.Blocks() as demo:
    with gr.Column():
        with gr.Row():
            with gr.Column():
                run_btn = gr.Button("Run")
            with gr.Column():
                output_video = gr.Video(format="mp4")
    
    run_btn.click(
        fn = infer,
        inputs = [],
        outputs = [output_video]
    )

demo.launch()