Update app.py

app.py

@@ -17,7 +17,7 @@ workspace = os.getenv("ROBOFLOW_WORKSPACE")
 project_name = os.getenv("ROBOFLOW_PROJECT")
 model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
 
-# CountGD Config
+# CountGD Config (Replace DINO-X)
 COUNTGD_API_KEY = os.getenv("COUNTGD_API_KEY")
 
 # Inisialisasi YOLO Model dari Roboflow
@@ -25,111 +25,106 @@ rf = Roboflow(api_key=rf_api_key)
 project = rf.workspace(workspace).project(project_name)
 yolo_model = project.version(model_version).model
 
-# ========== Fungsi untuk Menghitung IoU ==========
+# ========== 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.
+    """
+    x1_min, y1_min, x1_max, y1_max = box1
+    for b2 in boxes2:
+        x_center, y_center, w2, h2 = b2
+        x2_min = x_center - w2 / 2
+        x2_max = x_center + w2 / 2
+        y2_min = y_center - h2 / 2
+        y2_max = y_center + h2 / 2
+
+        dx = min(x1_max, x2_max) - max(x1_min, x2_min)
+        dy = min(y1_max, y2_max) - max(y1_min, y2_min)
+        if dx > 0 and dy > 0:
+            area_overlap = dx * dy
+            area_box1 = (x1_max - x1_min) * (y1_max - y1_min)
+            if area_box1 > 0 and (area_overlap / area_box1) > threshold:
+                return True
+    return False
+
+# ========== Fungsi untuk Menghitung IoU antar dua bounding box ==========
 def iou(boxA, boxB):
+    """
+    Menghitung Intersection over Union (IoU) antara dua bounding box.
+    """
     xA = max(boxA[0], boxB[0])
     yA = max(boxA[1], boxB[1])
     xB = min(boxA[2], boxB[2])
     yB = min(boxA[3], boxB[3])
-
     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])
-
-    return interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
+    iou_val = interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
+    return iou_val
 
 # ========== Fungsi Deteksi Kombinasi ==========
 def detect_combined(image):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
         image.save(temp_file, format="JPEG")
         temp_path = temp_file.name
-
+    
     try:
-        # YOLO Detection (Produk Nestlé)
+        # ===== YOLO Detection =====
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
-        nestle_class_count = {}
-        nestle_boxes = []  # (x_center, y_center, width, height)
-        for pred in yolo_pred['predictions']:
-            class_name = pred['class']
-            nestle_class_count[class_name] = nestle_class_count.get(class_name, 0) + 1
-            nestle_boxes.append((pred['x'], pred['y'], pred['width'], pred['height']))
-
-        total_nestle = sum(nestle_class_count.values())
-
-        # CountGD Detection (Produk Kompetitor)
+        nestle_boxes = [(pred['x'], pred['y'], pred['width'], pred['height']) for pred in yolo_pred['predictions']]
+        
+        # ===== CountGD Detection =====
         url = "https://api.landing.ai/v1/tools/text-to-object-detection"
-        competitor_class_count = {}
+        headers = {"Authorization": f"Basic {COUNTGD_API_KEY}"}
         competitor_boxes = []
         COUNTGD_PROMPTS = ["cans", "bottle", "mixed box"]
-        headers = {"Authorization": f"Basic {COUNTGD_API_KEY}"}
-
+        
         for prompt in COUNTGD_PROMPTS:
             with open(temp_path, "rb") as f:
                 files = {"image": f}
                 data = {"prompts": [prompt], "model": "countgd"}
                 response = requests.post(url, files=files, data=data, headers=headers)
             result = response.json()
-
+            
             if 'data' in result and result['data']:
                 detections = result['data'][0]
-                detections_sorted = sorted(detections, key=lambda obj: obj.get('confidence', 0), reverse=True)
-
-                for obj in detections_sorted:
+                for obj in detections:
                     if 'bounding_box' in obj:
                         x1, y1, x2, y2 = obj['bounding_box']
                         countgd_box = (x1, y1, x2, y2)
-
-                        # Hapus duplikasi dengan deteksi YOLO
-                        if any(iou(countgd_box, yolo_box) > 0.3 for yolo_box in nestle_boxes):
-                            continue
-
-                        # Hapus duplikasi antar deteksi CountGD
-                        if any(iou(countgd_box, existing_box) > 0.3 for existing_box in competitor_boxes):
-                            continue
-
-                        label = obj.get('label', prompt)
                         
-                        # Hapus "mixed box" jika ada "cans" atau "bottle" yang lebih spesifik
-                        if label == "mixed box" and ("cans" in competitor_class_count or "bottle" in competitor_class_count):
-                            continue
-
-                        competitor_class_count[label] = competitor_class_count.get(label, 0) + 1
-                        competitor_boxes.append(countgd_box)
-
-        total_competitor = sum(competitor_class_count.values())
-
-        # Format Output Text
-        result_text = "Product Nestlé\n\n"
-        for class_name, count in nestle_class_count.items():
-            result_text += f"{class_name}: {count}\n"
-        result_text += f"\nTotal Products Nestlé: {total_nestle}\n\n"
+                        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)
         
-        if total_competitor:
-            result_text += f"\nTotal Unclassified Products: {total_competitor}\n"
-        else:
-            result_text += "No Unclassified Products detected\n"
-
-        # Visualisasi Bounding Box
+        # ===== Visualisasi =====
         img = cv2.imread(temp_path)
         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))
             pt2 = (int(x + w/2), int(y + h/2))
             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)
-
+            cv2.putText(img, pred['class'], (pt1[0], pt1[1]-10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
+        
         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)
-            cv2.putText(img, "unclassified", (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
-
+            cv2.putText(img, "unclassified", (int(x1), int(y1)-10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
+        
         output_path = "/tmp/combined_output.jpg"
         cv2.imwrite(output_path, img)
-        return output_path, result_text
-
+        return output_path
+    
     except Exception as e:
         return temp_path, f"Error: {str(e)}"
-
+    
     finally:
         if os.path.exists(temp_path):
             os.remove(temp_path)