Initial commit for push folder

recognization/code/gui_app.py

@@ -0,0 +1,92 @@
+"""
+Native Desktop GUI for Supermarket Shelf Product Recognition
+Opens a desktop OpenCV window displaying image.jpg with bounding boxes only.
+Users can click on any product directly on screen to inspect price, stock,
+and feature matching inliers in real-time.
+"""
+
+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 pipeline import ShelfRecognitionPipeline
+
+
+def main():
+    print("=" * 60)
+    print("STARTING NATIVE DESKTOP INTERFACE")
+    print("Image: image.jpg | Mode: Hybrid (Paper Method)")
+    print("Instructions: Click on ANY product to identify & view info.")
+    print("Press 'q' or 'ESC' on the window to exit.")
+    print("=" * 60)
+
+    pipeline = ShelfRecognitionPipeline()
+    init_res = pipeline.set_active_shelf_image("image.jpg")
+
+    if init_res.get("status") != "success":
+        print("[Error] Failed to load image.jpg")
+        return
+
+    boxes = init_res["boxes"]
+    base_image = pipeline.current_image.copy()
+
+    # Draw ONLY bounding boxes (no class names, no confidence labels)
+    display_image = pipeline.detector.draw_detections(base_image, boxes, draw_labels=False)
+
+    window_name = "Smart Shelf - Click Any Product (Press Q to quit)"
+    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+    cv2.resizeWindow(window_name, 1280, 850)
+
+    def on_mouse(event, x, y, flags, param):
+        if event == cv2.EVENT_LBUTTONDOWN:
+            print(f"\n[Click detected at coordinates: ({x}, {y})]")
+            res = pipeline.process_click(x, y, method="hybrid")
+
+            if res.get("status") == "success":
+                prod = res["inventory"]
+                cls_info = res["classification"]
+                box_info = res["target_box"]
+
+                print("-" * 55)
+                print(f"📦 Product Identified : {prod['product_name']}")
+                print(f"🏷️  SKU ID             : {prod['sku_id']}")
+                print(f"💰 Price              : ¥{prod['price']}")
+                print(f"📊 In Stock (On-Shelf): {prod['number_on']} units")
+                print(f"🚚 Received / Sold    : {prod['number_in']} in / {prod['number_out']} out")
+                print(f"📅 Expiration Date    : {prod['expiration_date']}")
+                print(f"🎯 Match Confidence   : {cls_info['confidence'] * 100:.1f}% (Score: {cls_info['score']})")
+                print("-" * 55)
+
+                # Update main window with selected box highlight (in red)
+                selected_display = pipeline.detector.draw_detections(
+                    base_image, boxes, selected_box_id=box_info["id"], draw_labels=False
+                )
+                cv2.imshow(window_name, selected_display)
+
+                # Show visual match comparison window if available
+                if res.get("match_visualization") is not None:
+                    match_win = "Feature Matcher (Shelf Crop vs Stock Template)"
+                    cv2.namedWindow(match_win, cv2.WINDOW_NORMAL)
+                    cv2.resizeWindow(match_win, 800, 400)
+                    cv2.imshow(match_win, res["match_visualization"])
+            else:
+                print(f"[Info] {res.get('message', 'No product found at coordinates.')}")
+
+    cv2.setMouseCallback(window_name, on_mouse)
+
+    while True:
+        cv2.imshow(window_name, display_image)
+        key = cv2.waitKey(20) & 0xFF
+        if key in [ord('q'), ord('Q'), 27]:
+            break
+
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()