import argparse
import os
import sys

import cv2

from src.data.renderer import generate_high_fidelity_spinda
from src.models.inference import SpindaInference
from src.registry.database import SpindaRegistry
from src.utils.detector import SpindaDetector
from src.utils.resolver import SpindaResolver


def identify_spinda(
    image_path: str,
    model_path: str = "models/best_resnet34_model.pth",
    backbone: str = "resnet34",
) -> None:
    if not os.path.exists(image_path):
        print(f"Error: File {image_path} not found.")
        return

    print(f"--- Identifying Spinda in {image_path} ---")

    # 1. Detect and crop
    detector = SpindaDetector()
    cropped_img = detector.detect_and_crop(image_path)
    if cropped_img is None:
        print("Error: Could not detect Spinda in the image.")
        return

    cv2.imwrite("detected_spinda_crop.png", cropped_img)
    print("Detected Spinda saved to detected_spinda_crop.png")

    # 2. Inference — pass the BGR array directly, no temp file needed
    inf = SpindaInference(model_path=model_path, backbone=backbone)
    coords, fingerprint = inf.predict(cropped_img)

    print(f"Visual Fingerprint: {fingerprint}")
    print(f"Predicted Grid Coordinates: {coords}")

    # 3. Resolve to PIDs
    resolved = SpindaResolver.resolve_fingerprint(fingerprint)
    print("\nPossible PIDs:")
    print(f"  Standard (Gen 3-8, HOME): 0x{resolved['standard']}")
    print(f"  BDSP (Big-Endian Flip):   0x{resolved['bdsp']}")

    # 4. Visual verification
    print("\nGenerating visual verification image...")
    verify_img = generate_high_fidelity_spinda(int(resolved['standard'], 16))
    cv2.imwrite("prediction_verify.png", verify_img)
    print("Verification image saved to: prediction_verify.png")

    # 5. Registry lookup
    reg = SpindaRegistry()
    matches = reg.lookup_by_fingerprint(fingerprint)
    if matches:
        print("\nMatches found in Global Registry:")
        for pid in matches:
            print(f"  - Registered PID: 0x{pid}")
    else:
        print("\nNo matching entries in Global Registry.")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("image_path")
    parser.add_argument("--backbone",   type=str, default="resnet34",
                        choices=["resnet18", "resnet34", "convnext_tiny"])
    parser.add_argument("--model_path", type=str, default="models/best_resnet34_model.pth")
    args = parser.parse_args()
    identify_spinda(args.image_path, model_path=args.model_path, backbone=args.backbone)