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#!/usr/bin/env python3
"""
Golf Swing Analysis - Main Application
"""
import os
import sys
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Add the app directory to the path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils.video_downloader import download_youtube_video, cleanup_video_file
from utils.video_processor import process_video
from models.pose_estimator import analyze_pose
from models.swing_analyzer import segment_swing_pose_based, analyze_trajectory
from models.llm_analyzer import generate_swing_analysis
from utils.visualizer import create_annotated_video
def main():
"""Main application function"""
print("\n===== Golf Swing Analysis =====\n")
# Step 1: Get YouTube URL from user
youtube_url = input("Enter YouTube URL of golf swing: ")
# Step 2: Configure analysis options
enable_gpt = input(
"\nEnable GPT analysis? (y/n, default: y): ").lower() != 'n'
sample_rate_input = input(
"\nFrame processing rate for YOLO (1-10, default: 1 for all frames): ")
sample_rate = 1 # Default value - process all frames
if sample_rate_input.isdigit():
sample_rate = max(1, min(10, int(sample_rate_input)))
video_path = None # Initialize video_path for cleanup
try:
# Step 3: Download the video
print("\nDownloading video...")
video_path = download_youtube_video(youtube_url)
print(f"Video downloaded to: {video_path}")
# Step 4: Process video and detect golfer, club, and ball
print("\nProcessing video and detecting objects...")
frames, detections = process_video(video_path, sample_rate=sample_rate)
# Step 5: Analyze pose throughout the swing
print("\nAnalyzing golfer's pose...")
pose_data, world_landmarks = analyze_pose(frames)
# Step 6: Segment swing into phases
print("\nSegmenting swing phases...")
swing_phases = segment_swing_pose_based(pose_data,
detections,
sample_rate=sample_rate,
fps=30.0)
# Step 7: Analyze trajectory and speed
print("\nAnalyzing trajectory and speed...")
trajectory_data = analyze_trajectory(frames,
detections,
swing_phases,
sample_rate=sample_rate)
# Step 8: Generate swing analysis using LLM (if enabled)
if enable_gpt:
print("\nGenerating swing analysis and coaching tips...")
analysis = generate_swing_analysis(pose_data, swing_phases,
trajectory_data)
# Display results
print("\n===== Swing Analysis Results =====\n")
print(analysis)
else:
print("\nGPT analysis disabled. Skipping swing evaluation.")
# Step 9: Create annotated video (optional)
create_video = input(
"\nCreate annotated video? (y/n): ").lower() == 'y'
if create_video:
print("\nCreating annotated video...")
output_path = create_annotated_video(video_path,
frames,
detections,
pose_data,
swing_phases,
trajectory_data,
sample_rate=sample_rate)
print(f"Annotated video saved to: {output_path}")
# Telemetry removed per request
print("\nAnalysis complete!")
except Exception as e:
print(f"\nError: {str(e)}")
finally:
# Clean up the original downloaded video file after processing
if video_path:
print("\nCleaning up downloaded video file...")
cleanup_video_file(video_path)
if __name__ == "__main__":
main()
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