Initial commit for push folder

recognization/code/test_image.py

@@ -0,0 +1,89 @@
+"""
+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()