import os
import re
import sys
import imagehash
from PIL import Image
import cv2
import yt_dlp

VIDEOS = {
    "Forth Day 2020 - Preview of x64 & ColorForth & SPIR V - Onat.txt": "https://youtu.be/ajZAECYdJvE",
    "Neokineogfx - 4th And Beyond - Transcript.txt": "https://youtu.be/Awkdt30Ruvk",
    "Silicon Valley Forth Interest Group - Metaprogramming VAMP in KYRA, a Next-gen Forth-like language --- Onat Türkçüoğlu -- 2025-04-26.txt": "https://youtu.be/J9U_5tjdegY"
}

REFERENCES_DIR = "C:/projects/forth/bootslop/references"
SNAPSHOTS_DIR = os.path.join(REFERENCES_DIR, "snapshots")

os.makedirs(SNAPSHOTS_DIR, exist_ok=True)

def parse_timestamps(filepath):
    timestamps = []
    with open(filepath, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            # Match MM:SS or H:MM:SS
            if re.match(r'^(\d+:)?\d+:\d{2}$', line):
                parts = list(map(int, line.split(':')))
                if len(parts) == 2:
                    seconds = parts[0] * 60 + parts[1]
                else:
                    seconds = parts[0] * 3600 + parts[1] * 60 + parts[2]
                timestamps.append(seconds)
    return sorted(list(set(timestamps)))

def download_video(url, output_path):
    ydl_opts = {
        'format': 'best[ext=mp4]/best',
        'outtmpl': output_path,
        'quiet': True,
        'no_warnings': True,
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])

def extract_and_dedup_frames(video_path, timestamps, out_dir):
    os.makedirs(out_dir, exist_ok=True)
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print(f"Failed to open video {video_path}")
        return

    fps = cap.get(cv2.CAP_PROP_FPS)
    saved_hashes = []
    
    for t_sec in timestamps:
        # Set video position
        cap.set(cv2.CAP_PROP_POS_MSEC, t_sec * 1000)
        ret, frame = cap.read()
        if not ret:
            continue
            
        # Convert CV2 BGR to RGB for Pillow
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        pil_img = Image.fromarray(frame_rgb)
        
        # Calculate perceptual hash
        h = imagehash.phash(pil_img)
        
        # Check against previous hashes (hamming distance < 5 is very similar)
        is_duplicate = False
        for saved_h in saved_hashes:
            if h - saved_h < 5:
                is_duplicate = True
                break
                
        if not is_duplicate:
            saved_hashes.append(h)
            out_filename = os.path.join(out_dir, f"frame_{t_sec:04d}s.jpg")
            pil_img.save(out_filename, quality=90)
            print(f"Saved {out_filename}")

    cap.release()

def main():
    for transcript_file, url in VIDEOS.items():
        filepath = os.path.join(REFERENCES_DIR, transcript_file)
        if not os.path.exists(filepath):
            print(f"Transcript not found: {filepath}")
            continue
            
        print(f"Processing {transcript_file}...")
        timestamps = parse_timestamps(filepath)
        print(f"Found {len(timestamps)} timestamps.")
        
        video_name = os.path.splitext(transcript_file)[0]
        video_path = os.path.join(SNAPSHOTS_DIR, f"{video_name}.mp4")
        out_dir = os.path.join(SNAPSHOTS_DIR, video_name)
        
        if not os.path.exists(video_path):
            print(f"Downloading video from {url}...")
            download_video(url, video_path)
            
        print("Extracting frames...")
        extract_and_dedup_frames(video_path, timestamps, out_dir)
        
        # Clean up video to save space
        if os.path.exists(video_path):
            os.remove(video_path)

if __name__ == '__main__':
    main()