Update app.py

app.py

@@ -27,23 +27,25 @@ project = rf.workspace(workspace).project(project_name)
 yolo_model = project.version(model_version).model
 
 # ========== Fungsi Deteksi Kombinasi ==========
-def detect_combined(image):
-    # Konversi gambar PIL ke format numpy array
-    image_np = np.array(image)
-
-    # Resize gambar ke 640x640 tanpa mengubah warna atau format
-    image_resized = cv2.resize(image_np, (640, 640))
+from PIL import Image
 
-    # Simpan gambar input yang sudah di-resize ke file sementara
+# Fungsi untuk deteksi dengan resize
+def detect_combined(image):
+    # Simpan gambar input ke file sementara
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        cv2.imwrite(temp_file.name, image_resized)
+        image.save(temp_file, format="JPEG")
         temp_path = temp_file.name
 
     try:
-        # ========== [1] YOLO: Deteksi Produk Nestlé ==========
+        # Resize gambar input menjadi 640x640
+        img = Image.open(temp_path)
+        img = img.resize((640, 640), Image.ANTIALIAS)
+        img.save(temp_path, format="JPEG")
+
+        # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ==========
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
 
-        # Hitung jumlah objek per kelas dan simpan bounding box
+        # Hitung per class Nestlé dan simpan bounding box (format: (x_center, y_center, width, height))
         nestle_class_count = {}
         nestle_boxes = []
         for pred in yolo_pred['predictions']:
@@ -54,13 +56,16 @@ def detect_combined(image):
         total_nestle = sum(nestle_class_count.values())
 
         # ========== [2] OWLv2: Deteksi Kompetitor ==========
-        headers = {"Authorization": "Basic " + OWLV2_API_KEY}
-        data = {"prompts": OWLV2_PROMPTS, "model": "owlv2"}
-        
+        headers = {
+            "Authorization": "Basic " + OWLV2_API_KEY,
+        }
+        data = {
+            "prompts": OWLV2_PROMPTS,
+            "model": "owlv2"
+        }
         with open(temp_path, "rb") as f:
             files = {"image": f}
             response = requests.post("https://api.landing.ai/v1/tools/text-to-object-detection", files=files, data=data, headers=headers)
-        
         result = response.json()
         owlv2_objects = result['data'][0] if 'data' in result else []
 
@@ -69,6 +74,7 @@ def detect_combined(image):
         for obj in owlv2_objects:
             if 'bounding_box' in obj:
                 bbox = obj['bounding_box']  # Format: [x1, y1, x2, y2]
+                # Filter objek yang sudah terdeteksi oleh YOLO (Overlap detection)
                 if not is_overlap(bbox, nestle_boxes):
                     class_name = obj.get('label', 'unknown').strip().lower()
                     competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
@@ -85,14 +91,13 @@ def detect_combined(image):
         for class_name, count in nestle_class_count.items():
             result_text += f"{class_name}: {count}\n"
         result_text += f"\nTotal Products Nestle: {total_nestle}\n\n"
-
         if competitor_class_count:
             result_text += f"Total Unclassified Products: {total_competitor}\n"
         else:
             result_text += "No Unclassified Products detected\n"
 
         # ========== [4] Visualisasi ==========
-        img = image_resized.copy()
+        img = cv2.imread(temp_path)
 
         # Gambar bounding box untuk produk Nestlé (Hijau)
         for pred in yolo_pred['predictions']:
@@ -110,7 +115,6 @@ def detect_combined(image):
             cv2.putText(img, f"{display_name} {comp['confidence']:.2f}",
                         (int(x1), int(y1 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
 
-        # Simpan gambar hasil deteksi
         output_path = "/tmp/combined_output.jpg"
         cv2.imwrite(output_path, img)
 
@@ -121,7 +125,6 @@ def detect_combined(image):
     finally:
         os.remove(temp_path)
 
-
 def is_overlap(box1, boxes2, threshold=0.3):
     """
     Fungsi untuk mendeteksi overlap bounding box.
@@ -254,4 +257,4 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", ne
         outputs=[output_image, output_text]
     )
 
-iface.launch()
+iface.launch()