+    classes_txt_file = 'config_files/yolo_config.txt'

+# def test2():

+#     import time

+#     import glob

+#

+#     # Path to your ONNX model and classes text file

+#     model_path = 'yoloseg/weight/best.onnx'

+#     classes_txt_file = 'config_files/yolo_config.txt'

+#

+#     model_input_shape = (640, 640)

+#     inference_engine = Inference(

+#         onnx_model_path=model_path,

+#         model_input_shape=model_input_shape,

+#         classes_txt_file=classes_txt_file,

+#         run_with_cuda=True

+#     )

+#

+#     image_dir = glob.glob("/home/juni/사진/sample_data/ex1/*.png")

+#

+#     for iteration, image_path in enumerate(image_dir):

+#         img = cv2.imread(image_path)

+#         if img is None:

+#             print("Error loading image_binary")

+#             return

+#         img = cv2.resize(img, model_input_shape)

+#         # Run inference

+#         t1 = time.time()

+#         detections, mask_maps = inference_engine.run_inference(img)

+#         t2 = time.time()

+#

+#         print(t2-t1)

+#

+#         # Display results

+#         # for detection in detections:

+#         #     x, y, w, h = detection['box']

+#         #     class_name = detection['class_name']

+#         #     confidence = detection['confidence']

+#         #     cv2.rectangle(img, (x, y), (x+w, y+h), detection['color'], 2)

+#         #     label = f"{class_name}: {confidence:.2f}"

+#         #     cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, detection['color'], 2)

+#         #

+#         if len(mask_maps) > 0 :

+#             seg_image = overlay_mask(img, mask_maps[0], color=(0, 255, 0), alpha=0.3)

+#             cv2.imwrite(f"result/{iteration}.png", seg_image)

+    pass

+    # test2()