Update app.py

app.py

@@ -32,7 +32,7 @@ detector50 = pipeline(model="facebook/detr-resnet-50")
 detector101 = pipeline(model="facebook/detr-resnet-101")
 
 if torch.cuda.is_available():
-    print("##############------------use cuda!------------#################")
+    # print("##############------------use cuda!------------#################")
     detector50.model.to('cuda')
     detector101.model.to('cuda')
 
@@ -59,7 +59,7 @@ def query_data(model, in_pil_img: Image.Image):
         results = detector101(in_pil_img)
     else:
         results = detector50(in_pil_img)
-    print(f"检测结果：{results}")
+    # print(f"检测结果：{results}")
     return results
 
 
@@ -69,7 +69,7 @@ def get_figure(in_pil_img):
     plt.imshow(in_pil_img)
     
     ax = plt.gca()
-    print(f"图像尺寸：{in_pil_img.size}")
+    # print(f"图像尺寸：{in_pil_img.size}")
     in_results = query_data(model, in_pil_img)
 
     for prediction in in_results:
@@ -80,7 +80,7 @@ def get_figure(in_pil_img):
 
         ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
         ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
-        print(f"x: {x}, y: {y}, w: {w}, h: {h}, label: {prediction['label']}, score: {prediction['score']}")
+        # print(f"x: {x}, y: {y}, w: {w}, h: {h}, label: {prediction['label']}, score: {prediction['score']}")
 
     plt.axis("off")
 
@@ -88,7 +88,7 @@ def get_figure(in_pil_img):
 
 
 def process_single_frame(frame):
-    print(f"开始处理单帧")
+    # print(f"开始处理单帧")
     # 将 BGR 转换为 RGB
     rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     
@@ -107,7 +107,7 @@ def process_single_frame(frame):
 
 
 def infer_video(input_video_path):
-    print(f"开始处理视频 {input_video_path}")
+    # print(f"开始处理视频 {input_video_path}")
     with tempfile.TemporaryDirectory() as tmp_dir:
         # output_video_path = Path(tmp_dir) / "output.mp4"
         cap = cv2.VideoCapture(input_video_path)