"""
Test Script for image.jpg
1. Runs YOLO detection on image.jpg.
2. Draws ONLY bounding box rectangles (no class names, no confidence labels).
3. Saves output to detected_image.jpg.
4. Matches the detected bounding boxes against stock templates.
"""

import os
import sys
import cv2
import numpy as np

current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

from detector import ShelfProductDetector
from feature_matcher import ProductFeatureMatcher
from inventory import InventoryManager


def main():
    image_path = "image.jpg"
    print(f"[1/4] Loading {image_path}...")
    image = cv2.imread(image_path)
    if image is None:
        print(f"[Error] Failed to read {image_path}")
        return

    h, w = image.shape[:2]
    print(f"      Image resolution: {w}x{h}")

    print("[2/4] Detecting products using YOLO...")
    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
    # You can adjust conf_threshold as needed (default 0.25)
    det_res = detector.detect(image, conf_threshold=0.25)
    boxes = det_res.get("boxes", [])
    print(f"      Total detected items: {len(boxes)}")

    print("[3/4] Drawing bounding boxes ONLY (no class names, no confidence)...")
    annotated = detector.draw_detections(image, boxes, draw_labels=False)
    output_path = "detected_image.jpg"
    cv2.imwrite(output_path, annotated)
    print(f"      Saved detection visualization to: {output_path}")

    print("[4/4] Matching against stock catalogue (SURF, ORB, BRISK, Hybrid)...")
    matcher = ProductFeatureMatcher("stock")
    inv = InventoryManager("stock")

    print("\n--- Stock Templates Present in image.jpg ---")
    matched_skus = set()
    sample_matches = []

    # Test top detections to find corresponding stock SKUs
    for b in boxes:
        crop = detector.crop_roi(image, b["box"])
        # Use hybrid method from the paper
        res = matcher.classify_crop(crop, method="hybrid")
        if res.get("status") == "success":
            sku_id = res["best_sku"]
            score = res["score"]
            conf = res["confidence"]
            prod = inv.get_product(sku_id)
            if prod:
                matched_skus.add(sku_id)
                sample_matches.append({
                    "box_id": b["id"],
                    "box": b["box"],
                    "sku_id": sku_id,
                    "product_name": prod["product_name"],
                    "price": prod["price"],
                    "number_on": prod["number_on"],
                    "score": score,
                    "confidence": conf
                })

    # Sort matches by confidence
    sample_matches.sort(key=lambda x: x["score"], reverse=True)

    print(f"\nSuccessfully matched {len(matched_skus)} distinct SKUs across detected shelf boxes:")
    for sm in sample_matches[:10]:
        print(f"Box #{sm['box_id']:<3} -> [{sm['sku_id']}] {sm['product_name']:<30} | Price: ¥{sm['price']:<5} | On-Shelf: {sm['number_on']} | Score: {sm['score']}")

    print("\n[Complete] Tested image.jpg successfully.")


if __name__ == "__main__":
    main()