import cv2
import numpy as np
from pathlib import Path
from PIL import Image

# Select files using glob (for now, only process the first file for testing)
shots_dir = Path('data/shots')
files = sorted(shots_dir.glob('sample-000-0.webp'))  # Change pattern to 'sample-*.webp' for batch
if not files:
    print('No files found.')
    exit(1)

# Process each file serially (for now, just one file)
for webp_path in files:
    print(f'Processing {webp_path}')
    # Extract all frames from the animated webp using PIL
    frames = []
    frame_durations = []
    with Image.open(webp_path) as im:
        try:
            while True:
                frame = im.convert('RGB')
                frames.append(np.array(frame)[:, :, ::-1])  # Convert RGB to BGR for OpenCV
                # Get duration in ms for this frame (default to 100ms if not present)
                duration = im.info.get('duration', 100)
                frame_durations.append(duration)
                im.seek(im.tell() + 1)
        except EOFError:
            pass

    # Debug: check extracted frames
    print(f"Extracted {len(frames)} frames from {webp_path}")
    if len(frames) > 0:
        print(f"First frame shape: {frames[0].shape}, dtype: {frames[0].dtype}, min: {frames[0].min()}, max: {frames[0].max()}")

    # Compute dense optical flow and overlay visualization
    hsv = None
    motion_frames = []
    for i in range(1, len(frames)):
        prev = cv2.cvtColor(frames[i-1], cv2.COLOR_BGR2GRAY)
        curr = cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY)
        flow = cv2.calcOpticalFlowFarneback(prev, curr, None, 0.5, 3, 15, 3, 5, 1.2, 0)
        mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
        print(f"Frame {i}: flow mag min={mag.min()}, max={mag.max()}, mean={mag.mean()}")
        if np.all(mag == 0):
            print(f"Frame {i}: All zero motion, skipping.")
            continue
        # Draw flow as arrows on a grid
        step = 16  # grid size
        arrow_color = (0, 255, 0)  # green
        arrow_thickness = 1
        overlay = frames[i].copy()
        h, w = prev.shape
        for y in range(0, h, step):
            for x in range(0, w, step):
                fx, fy = flow[y, x]
                end_x = int(x + fx * 4)
                end_y = int(y + fy * 4)
                cv2.arrowedLine(overlay, (x, y), (end_x, end_y), arrow_color, arrow_thickness, tipLength=0.3)
        motion_frames.append(overlay)

    # Save as mp4
    if motion_frames:
        height, width, _ = motion_frames[0].shape
        # Calculate FPS from frame durations (use mean duration between frames)
        if len(frame_durations) > 1:
            # Use durations between frames (skip first frame)
            mean_duration = np.mean(frame_durations[1:])
        else:
            mean_duration = 100
        fps = 1000.0 / mean_duration if mean_duration > 0 else 10
        print(f"Using FPS: {fps:.2f} (mean frame duration: {mean_duration} ms)")
        if hasattr(cv2, 'VideoWriter_fourcc'):
            fourcc = cv2.VideoWriter_fourcc(*'avc1')  # More compatible MP4 codec for macOS
        else:
            raise RuntimeError('cv2.VideoWriter_fourcc is not available in your OpenCV installation. Please update OpenCV.')
        out_path = webp_path.parent / f"{webp_path.stem}.motion.mp4"
        out = cv2.VideoWriter(str(out_path), fourcc, fps, (width, height))
        for f in motion_frames:
            out.write(f)
        out.release()
        print(f'Saved {out_path}')
    else:
        print('No motion frames to save for', webp_path)