Update app.py

app.py

@@ -169,10 +169,16 @@ def detect_objects_in_video(video_path):
             frame_path = os.path.join(temp_frames_dir, f"frame_{frame_index}.jpg")
             cv2.imwrite(frame_path, frame)
 
-            # Deteksi objek pada frame
+            # Deteksi objek pada frame menggunakan YOLO
             predictions = yolo_model.predict(frame_path, confidence=60, overlap=80).json()
 
-            # Gambar bounding box pada frame
+            # Hitung jumlah objek per kelas dari YOLO
+            yolo_class_count = {}
+            for prediction in predictions['predictions']:
+                class_name = prediction['class']
+                yolo_class_count[class_name] = yolo_class_count.get(class_name, 0) + 1
+
+            # Gambar bounding box dan label pada frame
             for prediction in predictions['predictions']:
                 x, y, w, h = prediction['x'], prediction['y'], prediction['width'], prediction['height']
                 class_name = prediction['class']
@@ -180,6 +186,14 @@ def detect_objects_in_video(video_path):
                 cv2.rectangle(frame, (int(x - w/2), int(y - h/2)), (int(x + w/2), int(y + h/2)), color, 2)
                 cv2.putText(frame, class_name, (int(x - w/2), int(y - h/2 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
+            # Menyusun teks dinamis untuk jumlah objek yang terdeteksi
+            result_text = "Detected Objects:\n"
+            for class_name, count in yolo_class_count.items():
+                result_text += f"{class_name}: {count}\n"
+
+            # Menambahkan informasi jumlah objek terdeteksi pada video
+            cv2.putText(frame, result_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+
             # Tambahkan frame ke video keluaran
             output_video.write(frame)
             frame_index += 1