Update app.py

app.py

@@ -4,7 +4,6 @@ from matplotlib import gridspec
 import matplotlib.pyplot as plt
 import numpy as np
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
-from transformers import DetrImageProcessor, DetrForObjectDetection
 import torch
 import tensorflow as tf
 from PIL import ImageDraw
@@ -102,34 +101,6 @@ def sepia(inputs, button_text):
     """객체 검출 또는 세그멘테이션을 수행하고 결과를 반환하는 함수입니다."""
 
     input_img = Image.fromarray(inputs)
-    # if button_text == "detection":
-    #     inputs_detection = processor_detection(images=input_img, return_tensors="pt")
-    #     outputs_detection = model_detection(**inputs_detection)
-
-    #     target_sizes = torch.tensor([input_img.size[::-1]])
-    #     results_detection = processor_detection.post_process_object_detection(
-    #         outputs_detection, target_sizes=target_sizes, threshold=0.9
-    #     )[0]
-
-    #     draw = ImageDraw.Draw(input_img)
-    #     for score, label, box in zip(
-    #         results_detection["scores"],
-    #         results_detection["labels"],
-    #         results_detection["boxes"],
-    #     ):
-    #         box = [round(i, 2) for i in box.tolist()]
-    #         label_name = model_detection.config.id2label[label.item()]
-    #         print(
-    #             f"Detected {label_name} with confidence "
-    #             f"{round(score.item(), 3)} at location {box}"
-    #         )
-    #         draw.rectangle(box, outline="red", width=3)
-    #         draw.text((box[0], box[1]), label_name, fill="red", font=None)
-
-    #     fig = plt.figure(figsize=(20, 15))
-    #     plt.imshow(input_img)
-    #     plt.axis("off")
-    #     return fig
 
     inputs_segmentation = feature_extractor(images=input_img, return_tensors="tf")
     outputs_segmentation = model_segmentation(**inputs_segmentation)