"""
Scientifically controlled spatial-scale experiment.

Extract the ORIGINAL 8 DeCoF frames from a 2048x1152 Veo video,
center-crop to square, then test MULTIPLE spatial resolutions through
DeCoF's preprocessing (which resizes each to 224x224).

This isolates spatial downsampling from video transcoding artifacts.
"""
import os
import sys
import cv2
import numpy as np
import torch
from pathlib import Path

project_root = Path(__file__).parent
sys.path.insert(0, str(project_root))
sys.path.insert(0, str(project_root / 'sdk'))

import importlib.util
spec = importlib.util.spec_from_file_location(
    'decof_detector', project_root / 'models' / 'video' / 'DeCoF' / 'detector.py'
)
module = importlib.util.module_from_spec(spec)
sys.modules['decof_detector'] = module
spec.loader.exec_module(module)

# Test videos (2048x1152 originals)
videos = [
    'D:/veo/veo/veo_example_014_jellyfish.mp4',
    'D:/veo/veo/veo_example_006_northern_lights.mp4',
    'D:/veo/veo/veo_cowboy_sun_1.mp4',
    'D:/veo/veo/veo_example_043_alpacas.mp4',
    'D:/veo/veo/veo_example_011_lighthouse.mp4',
    'D:/veo/veo/veo_example_012_elephant.mp4',
]

# Spatial scales to test (square side length)
scales = [1152, 960, 720, 576, 448, 336, 224]

print("=" * 70)
print("DeCoF Spatial-Scale Experiment (controlled, no transcoding)")
print("=" * 70)

print("\n[1] Loading detector...")
detector = module.DeCoFDetector()
detector.load()

print("\n[2] Extracting original frames and testing spatial scales...\n")

# For each video
for video_path in videos:
    name = os.path.basename(video_path)
    print(f"\n--- {name} ---")
    
    # Extract the 8 original DeCoF frames (2048x1152)
    frames = detector._extract_decof_frames(video_path)
    
    # Center-crop each to square (1152x1152) - the ORIGINAL pixels
    square_frames = []
    for frame in frames:
        h, w = frame.shape[:2]
        side = min(w, h)
        left = (w - side) // 2
        top = (h - side) // 2
        square_frames.append(frame[top:top+side, left:left+side])
    
    # For each scale, resize the square frames and run DeCoF
    print(f"  {'Scale':>6s}  {'fake_prob':>10s}")
    for scale in scales:
        # Resize square frames to (scale, scale)
        resized_frames = []
        for frame in square_frames:
            resized = cv2.resize(frame, (scale, scale), interpolation=cv2.INTER_LINEAR)
            resized_frames.append(resized)
        
        # Run DeCoF on these frames (its preprocessing will resize to 224x224)
        features = detector.extract_clip_features(resized_frames)
        with torch.no_grad():
            logits = detector.small_vit(features)
            probs = torch.softmax(logits, dim=-1)
            fake_prob = float(probs[0, 1].item())
        
        print(f"  {scale:>6d}  {fake_prob:10.4f}")

print("\n" + "=" * 70)
print("Done. If fake_prob increases as scale decreases,")
print("spatial downsampling is driving DeCoF's response.")
print("=" * 70)