Initial commit for push folder

recognization/code/benchmark.py

@@ -0,0 +1,103 @@
+"""
+Benchmark Suite for Supermarket Product Classification
+Compares SURF, ORB, BRISK, and Hybrid methods following
+MDPI Electronics 2023, 12(17), 3640.
+"""
+
+import os
+import sys
+import time
+import cv2
+import numpy as np
+from typing import Dict, List, Any
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from feature_matcher import ProductFeatureMatcher
+
+
+def run_benchmark(stock_dir: str = "stock", num_runs: int = 3):
+    print("=" * 70)
+    print("RETAIL SHELF PRODUCT RECOGNITION - ALGORITHM BENCHMARK")
+    print("Methods Evaluated: SURF | ORB | BRISK | HYBRID (Paper Method)")
+    print("=" * 70)
+
+    matcher = ProductFeatureMatcher(stock_dir=stock_dir)
+    templates = matcher.db.templates
+
+    if not templates:
+        print("[Error] No templates found in stock directory.")
+        return
+
+    methods = ["surf", "orb", "brisk", "hybrid"]
+    results = {m: {"latencies": [], "inliers": [], "keypoints_query": []} for m in methods}
+
+    # Test images: We will evaluate using crops synthesized with slight transformations
+    # (scale, rotation, noise) as well as the template images themselves
+    print(f"\nEvaluating across {len(templates)} catalog SKUs (repeat count: {num_runs})...\n")
+
+    for sku_id, tmpl in templates.items():
+        original_img = tmpl["image"]
+        h, w = original_img.shape[:2]
+
+        # Apply synthetic shelf condition (rotation by 10 deg, resize by 0.85)
+        M = cv2.getRotationMatrix2D((w / 2, h / 2), 10, 0.85)
+        query_img = cv2.warpAffine(original_img, M, (w, h))
+
+        for method in methods:
+            times = []
+            for _ in range(num_runs):
+                t0 = time.perf_counter()
+                res = matcher.classify_crop(query_img, method=method)
+                t1 = time.perf_counter()
+                times.append((t1 - t0) * 1000.0)  # ms
+
+            avg_latency = np.mean(times)
+            results[method]["latencies"].append(avg_latency)
+
+            # Record inliers/score
+            if res.get("status") == "success":
+                top_sku = res["best_sku"]
+                score = res["score"]
+                results[method]["inliers"].append(score)
+
+    # Summarize metrics
+    summary_rows = []
+    for method in methods:
+        latencies = results[method]["latencies"]
+        scores = results[method]["inliers"]
+
+        avg_lat = np.mean(latencies) if latencies else 0.0
+        p95_lat = np.percentile(latencies, 95) if latencies else 0.0
+        avg_score = np.mean(scores) if scores else 0.0
+
+        # Paper characteristics notes
+        notes = {
+            "surf": "Scale & rotation robust; Hessian gradient features",
+            "orb": "Ultra fast binary descriptor; FAST + BRIEF",
+            "brisk": "Scale-space AGAST; circular pattern descriptor",
+            "hybrid": "Paper fusion (SURF+ORB+BRISK); optimal accuracy/speed"
+        }[method]
+
+        summary_rows.append([
+            method.upper(),
+            f"{avg_lat:.2f} ms",
+            f"{p95_lat:.2f} ms",
+            f"{avg_score:.1f}",
+            notes
+        ])
+
+    headers = ["Method", "Avg Latency (ms)", "P95 Latency (ms)", "Avg Match Score", "Characteristics / Paper Findings"]
+    print("\n" + "-" * 105)
+    print(f"{headers[0]:<10} | {headers[1]:<18} | {headers[2]:<18} | {headers[3]:<16} | {headers[4]}")
+    print("-" * 105)
+    for row in summary_rows:
+        print(f"{row[0]:<10} | {row[1]:<18} | {row[2]:<18} | {row[3]:<16} | {row[4]}")
+    print("-" * 105)
+    print("\n[Benchmark Complete] All methods operational.")
+
+
+if __name__ == "__main__":
+    run_benchmark()