Rename app-complete-mantab.py to app.py

app-complete-mantab.py → app.py

@@ -23,7 +23,7 @@ model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
 
 # DINO-X Config
 DINOX_API_KEY = os.getenv("DINO_X_API_KEY")
-DINOX_PROMPT = "beverage . bottle . cans"  # Customize sesuai produk kompetitor : food . drink
+DINOX_PROMPT = "beverage . bottle . cans . boxed milk . milk"  # Customize sesuai produk kompetitor : food . drink
 
 # Inisialisasi Model
 rf = Roboflow(api_key=rf_api_key)
@@ -41,7 +41,7 @@ def detect_combined(image):
 
     try:
         # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ========== 
-        yolo_pred = yolo_model.predict(temp_path, confidence=60, overlap=80).json()
+        yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
 
         # Hitung per class Nestlé
         nestle_class_count = {}
@@ -85,15 +85,13 @@ def detect_combined(image):
         result_text = "Product Nestle\n\n"
         for class_name, count in nestle_class_count.items():
             result_text += f"{class_name}: {count}\n"
-        result_text += f"\nTotal Product Nestle: {total_nestle}\n\n"
+        result_text += f"\nTotal Products Nestle: {total_nestle}\n\n"
 
-        result_text += "Competitor Products\n\n"
+        #result_text += "Competitor Products\n\n"
         if competitor_class_count:
-            for class_name, count in competitor_class_count.items():
-                result_text += f"{class_name}: {count}\n"
+            result_text += f"Total Unclassified Products: {total_competitor}\n"  # Hanya total, tidak per kelas
         else:
-            result_text += "No competitors detected\n"
-        result_text += f"\nTotal Competitor: {total_competitor}"
+            result_text += "No Unclassified Products detected\n"
 
         # ========== [4] Visualisasi ========== 
         img = cv2.imread(temp_path)
@@ -105,13 +103,20 @@ def detect_combined(image):
             cv2.putText(img, pred['class'], (int(x-w/2), int(y-h/2-10)), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
 
-        # Kompetitor (Merah)
+        # Kompetitor (Merah) dengan nama 'unclassified'
         for comp in competitor_boxes:
             x1, y1, x2, y2 = comp['box']
-            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 2)
-            cv2.putText(img, f"{comp['class']} {comp['confidence']:.2f}", 
-                       (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2)
-
+            
+            # Define a list of target classes to rename
+            unclassified_classes = ["beverage", "cans", "bottle", "boxed milk", "milk"]
+            
+            # Normalize the class name to be case-insensitive and check if it's in the unclassified list
+            display_name = "unclassified" if any(class_name in comp['class'].lower() for class_name in unclassified_classes) else comp['class']
+            
+            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
+            cv2.putText(img, f"{display_name} {comp['confidence']:.2f}",
+                        (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+            
         output_path = "/tmp/combined_output.jpg"
         cv2.imwrite(output_path, img)
 
@@ -186,7 +191,7 @@ def detect_objects_in_video(video_path):
             cv2.imwrite(frame_path, frame)
 
             # Process predictions for frame
-            predictions = yolo_model.predict(frame_path, confidence=60, overlap=80).json()
+            predictions = yolo_model.predict(frame_path, confidence=50, overlap=80).json()
 
             # Update class count for this frame
             frame_class_count = {}
@@ -210,7 +215,6 @@ def detect_objects_in_video(video_path):
             nestle_total = sum(all_class_count.values())
 
             # Create a vertical layout for counts (dynamically updated)
-            count_text = "Cumulative Object Counts\n"
             for class_name, count in all_class_count.items():
                 count_text += f"{class_name}: {count}\n"
             count_text += f"\nTotal Product Nestlé: {nestle_total}"