validation.py

"""
Phase 5 -- Scientific Validation (PSNR / SSIM / RMSE)
satellite_sr_deploy/validation.py

We use the standard Super-Resolution evaluation protocol since true 
higher-resolution ground truth (e.g. 2.5m WorldView) is unavailable 
for this specific Sentinel-2 scene. 

Protocol:
1. Load original 10m Sentinel-2 image (Ground Truth).
2. Crop to dimensions divisible by 4.
3. Downsample by 4x using Bicubic interpolation (simulating 40m input).
4. Upsample the 40m image back to 10m using Bicubic, ESRGAN, and HATSAT.
5. Compute PSNR, SSIM, and RMSE between the upsampled images and the GT.
"""

import sys
import math
import time
from pathlib import Path

import torch
import numpy as np
from PIL import Image

# Ensure satellite_sr_deploy is in path
ROOT = Path(__file__).resolve().parent
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from model_router import SatelliteSRRouter

def calculate_psnr(img1, img2):
    """Calculate Peak Signal-to-Noise Ratio (PSNR) in RGB space."""
    # img1 and img2 can be numpy arrays
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))

def calculate_rmse(img1, img2):
    """Calculate Root Mean Square Error (RMSE)."""
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    return np.sqrt(np.mean((img1 - img2) ** 2))

def ssim_core(img1, img2, C1=6.5025, C2=58.5225):
    """
    Simplified SSIM over the entire image. 
    Standard SSIM uses a gaussian window, but for a quick numpy implementation 
    we can approximate or compute global/block-based SSIM.
    For robust SSIM without adding scikit-image dependency, we'll compute 
    global mean/variance which gives a rough structural similarity.
    (Note: True SSIM uses local sliding windows, so we'll implement a simple 
    11x11 sliding window in numpy if possible, or just use the global version 
    for demonstration since this is a local metric).
    """
    # For strict SSIM, we should ideally use torchvision or skimage.
    # Let's see if skimage is available. 
    pass

try:
    from skimage.metrics import structural_similarity as ssim
    HAS_SKIMAGE = True
except ImportError:
    HAS_SKIMAGE = False
    print("skimage not found. SSIM will be calculated using a global approximation.")

def calculate_ssim(img1, img2):
    if HAS_SKIMAGE:
        # skimage expects channel_axis=-1 for RGB
        return ssim(img1, img2, channel_axis=-1, data_range=255)
    else:
        # Global approximation (Not strictly standard SSIM, but gives a relative metric)
        img1 = img1.astype(np.float64)
        img2 = img2.astype(np.float64)
        mu1 = img1.mean()
        mu2 = img2.mean()
        sigma1 = img1.var()
        sigma2 = img2.var()
        cov = np.cov(img1.flatten(), img2.flatten())[0, 1]
        
        c1 = (0.01 * 255)**2
        c2 = (0.03 * 255)**2
        
        num = (2 * mu1 * mu2 + c1) * (2 * cov + c2)
        den = (mu1**2 + mu2**2 + c1) * (sigma1 + sigma2 + c2)
        return num / den

def run_validation():
    print("=" * 60)
    print("PHASE 5: SCIENTIFIC VALIDATION")
    print("=" * 60)
    
    gt_path = ROOT.parent / "data" / "processed" / "sentinel2_rgb_10m.png"
    if not gt_path.exists():
        print(f"Error: Could not find GT image at {gt_path}")
        sys.exit(1)
        
    gt_img = Image.open(gt_path).convert("RGB")
    orig_w, orig_h = gt_img.size
    print(f"Original Ground Truth (GT) size: {orig_w}x{orig_h}")
    
    # 1. Crop GT to a multiple of 4
    w = (orig_w // 4) * 4
    h = (orig_h // 4) * 4
    gt_img = gt_img.crop((0, 0, w, h))
    print(f"Cropped GT size (divisible by 4): {w}x{h}")
    
    # 2. Downsample by 4x to simulate 40m input
    lr_w, lr_h = w // 4, h // 4
    lr_img = gt_img.resize((lr_w, lr_h), Image.BICUBIC)
    print(f"Low-Resolution (LR) 40m simulated size: {lr_w}x{lr_h}")
    
    # 3. Upsample using Bicubic
    print("\n--- Running Baseline: Bicubic Interpolation ---")
    start_time = time.time()
    bicubic_img = lr_img.resize((w, h), Image.BICUBIC)
    bicubic_time = time.time() - start_time
    
    # Initialize Models
    print("\n--- Initializing Deep Learning Models ---")
    router = SatelliteSRRouter()
    
    # 4. Upsample using ESRGAN
    print("\n--- Running ESRGAN ---")
    start_time = time.time()
    esrgan_img = router.predict(lr_img, "esrgan")
    esrgan_time = time.time() - start_time
    
    # 5. Upsample using HATSAT
    print("\n--- Running HATSAT ---")
    start_time = time.time()
    hatsat_img = router.predict(lr_img, "hatsat")
    hatsat_time = time.time() - start_time
    
    # 6. Evaluation
    print("\n" + "=" * 60)
    print("EVALUATION RESULTS (vs Ground Truth)")
    print("=" * 60)
    
    gt_np = np.array(gt_img)
    bicubic_np = np.array(bicubic_img)
    esrgan_np = np.array(esrgan_img)
    hatsat_np = np.array(hatsat_img)
    
    results = {}
    for name, img_np, t in [("Bicubic", bicubic_np, bicubic_time), 
                            ("ESRGAN", esrgan_np, esrgan_time), 
                            ("HATSAT", hatsat_np, hatsat_time)]:
        # Ensure dimensions match (in case of any cropping/padding discrepancy)
        if img_np.shape != gt_np.shape:
            # Crop to match GT exactly
            img_np = img_np[:h, :w, :]
            
        psnr_val = calculate_psnr(gt_np, img_np)
        ssim_val = calculate_ssim(gt_np, img_np)
        rmse_val = calculate_rmse(gt_np, img_np)
        
        results[name] = {
            "PSNR": psnr_val,
            "SSIM": ssim_val,
            "RMSE": rmse_val,
            "Time": t
        }
        
        print(f"{name.upper()}:")
        print(f"  PSNR: {psnr_val:.2f} dB (higher is better)")
        print(f"  SSIM: {ssim_val:.4f} (higher is better)")
        print(f"  RMSE: {rmse_val:.2f} (lower is better)")
        print(f"  Time: {t:.2f} s")
        print("-" * 40)
        
    print("SUMMARY CONCLUSION:")
    best_psnr = max(results, key=lambda k: results[k]["PSNR"])
    best_ssim = max(results, key=lambda k: results[k]["SSIM"])
    best_rmse = min(results, key=lambda k: results[k]["RMSE"])
    
    print(f"Highest PSNR: {best_psnr} ({results[best_psnr]['PSNR']:.2f} dB)")
    print(f"Highest SSIM: {best_ssim} ({results[best_ssim]['SSIM']:.4f})")
    print(f"Lowest RMSE:  {best_rmse} ({results[best_rmse]['RMSE']:.2f})")
    
    # Save the synthetic LR and upsampled variants for visual inspection
    out_dir = ROOT / "outputs" / "validation"
    out_dir.mkdir(parents=True, exist_ok=True)
    
    gt_img.save(out_dir / "01_GroundTruth.png")
    lr_img.save(out_dir / "02_SimulatedLR.png")
    bicubic_img.save(out_dir / "03_Bicubic.png")
    esrgan_img.save(out_dir / "04_ESRGAN.png")
    hatsat_img.save(out_dir / "05_HATSAT.png")
    print(f"\nSaved visual comparison images to: {out_dir}")
    print("=" * 60)

if __name__ == "__main__":
    run_validation()