Update app.py

app.py

@@ -28,7 +28,9 @@ yolo_model = project.version(model_version).model
 # ========== Fungsi untuk Mengecek Overlap antara YOLO dan CountGD ==========
 def is_overlap(box1, boxes2, threshold=0.5):
     """
-    Mengecek apakah box1 overlap dengan salah satu box di boxes2 berdasarkan IoU.
+    Mengecek apakah box1 (format: (x_min, y_min, x_max, y_max)) overlap
+    dengan salah satu box di boxes2 (format: (x_center, y_center, width, height))
+    berdasarkan IoU, menggunakan threshold yang ditetapkan.
     """
     x1_min, y1_min, x1_max, y1_max = box1
     for b2 in boxes2:
@@ -51,6 +53,7 @@ def is_overlap(box1, boxes2, threshold=0.5):
 def iou(boxA, boxB):
     """
     Menghitung Intersection over Union (IoU) antara dua bounding box.
+    Masing-masing box dalam format (x_min, y_min, x_max, y_max).
     """
     xA = max(boxA[0], boxB[0])
     yA = max(boxA[1], boxB[1])
@@ -59,11 +62,11 @@ def iou(boxA, boxB):
     interArea = max(0, xB - xA) * max(0, yB - yA)
     boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
     boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
-    iou_val = interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
-    return iou_val
+    return interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
 
 # ========== Fungsi Deteksi Kombinasi ==========
 def detect_combined(image):
+    # Simpan image ke file sementara
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
         image.save(temp_file, format="JPEG")
         temp_path = temp_file.name
@@ -71,6 +74,7 @@ def detect_combined(image):
     try:
         # ===== YOLO Detection =====
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
+        # Get YOLO boxes as (x_center, y_center, width, height)
         nestle_boxes = [(pred['x'], pred['y'], pred['width'], pred['height']) for pred in yolo_pred['predictions']]
         
         # ===== CountGD Detection =====
@@ -92,18 +96,21 @@ def detect_combined(image):
                     if 'bounding_box' in obj:
                         x1, y1, x2, y2 = obj['bounding_box']
                         countgd_box = (x1, y1, x2, y2)
-                        
-                        if not is_overlap(countgd_box, nestle_boxes, threshold=0.5):
-                            duplicate = False
-                            for existing_box in competitor_boxes:
-                                if iou(countgd_box, existing_box) > 0.4:
-                                    duplicate = True
-                                    break
-                            if not duplicate:
-                                competitor_boxes.append(countgd_box)
+                        # Prioritaskan deteksi YOLO: hapus jika overlap dengan YOLO (threshold 0.5)
+                        if is_overlap(countgd_box, nestle_boxes, threshold=0.5):
+                            continue
+                        # Hindari duplikasi antar CountGD: jika IoU dengan deteksi lain > 0.4, lewati
+                        duplicate = False
+                        for existing_box in competitor_boxes:
+                            if iou(countgd_box, existing_box) > 0.4:
+                                duplicate = True
+                                break
+                        if not duplicate:
+                            competitor_boxes.append(countgd_box)
         
         # ===== Visualisasi =====
         img = cv2.imread(temp_path)
+        # Gambar bounding box YOLO (hijau)
         for pred in yolo_pred['predictions']:
             x, y, w, h = pred['x'], pred['y'], pred['width'], pred['height']
             pt1 = (int(x - w/2), int(y - h/2))
@@ -111,7 +118,7 @@ def detect_combined(image):
             cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
             cv2.putText(img, pred['class'], (pt1[0], pt1[1]-10),
                         cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
-        
+        # Gambar bounding box CountGD (merah)
         for box in competitor_boxes:
             x1, y1, x2, y2 = box
             cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
@@ -120,7 +127,10 @@ def detect_combined(image):
         
         output_path = "/tmp/combined_output.jpg"
         cv2.imwrite(output_path, img)
-        return output_path
+        
+        # Buat result text untuk counting object
+        result_text = f"Total Produk Nestlé: {len(nestle_boxes)}\nTotal Unclassified Products: {len(competitor_boxes)}"
+        return output_path, result_text
     
     except Exception as e:
         return temp_path, f"Error: {str(e)}"
@@ -136,9 +146,16 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", ne
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(type="pil", label="Input Image")
+    with gr.Row():
+        with gr.Column():
             detect_image_button = gr.Button("Detect Image")
+    with gr.Row():
+        with gr.Column():
             output_image = gr.Image(label="Detect Object")
+    with gr.Row():
+        with gr.Column():
             output_text = gr.Textbox(label="Counting Object")
-            detect_image_button.click(fn=detect_combined, inputs=input_image, outputs=[output_image, output_text])
+    
+    detect_image_button.click(fn=detect_combined, inputs=input_image, outputs=[output_image, output_text])
 
 iface.launch()