app.py

import gradio as gr
import cv2
import subprocess

from utils import get_mediapipe_pose
from process_frame import ProcessFrame
from thresholds import get_thresholds_beginner, get_thresholds_pro


# Initialize face mesh solution
POSE = get_mediapipe_pose()

def process_video(video_path, mode):

    output_video_file = f"output_recorded.mp4"
    
    if mode == 'Beginner':
        thresholds = get_thresholds_beginner()

    elif mode == 'Pro':
        thresholds = get_thresholds_pro()

    upload_process_frame = ProcessFrame(thresholds=thresholds)

    vf = cv2.VideoCapture(video_path)

    fps = int(vf.get(cv2.CAP_PROP_FPS))
    width = int(vf.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(vf.get(cv2.CAP_PROP_FRAME_HEIGHT))
    frame_size = (width, height)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    video_output = cv2.VideoWriter(output_video_file, fourcc, fps, frame_size)


    while vf.isOpened():
        ret, frame = vf.read()
        if not ret:
            break

        # convert frame from BGR to RGB before processing it.
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        out_frame, _ = upload_process_frame.process(frame, POSE)
        
        video_output.write(cv2.cvtColor(out_frame, cv2.COLOR_RGB2BGR))

        yield out_frame, None
    
    vf.release()
    video_output.release()
    
    # convertedVideo = f"output_h264.mp4"
    # subprocess.call(args=f"ffmpeg -y -i {output_video_file} -c:v libx264 {convertedVideo}".split(" "))

    yield None, output_video_file


input_video = gr.Video( label="Input Video")

output_frames = gr.Image(label="Output Frames")
output_video_file = gr.Video(label="Output video")

interface = gr.Interface(
                fn=process_video,
                inputs=[input_video, gr.Radio(choices=["Beginner", "Pro"], label="Select Mode")],
                outputs=[output_frames, output_video_file],
                title=f"AI Fitness Trainer: Squats Analysis",
                allow_flagging="never"
)

interface.queue().launch()