Update app.py

app.py

@@ -6,9 +6,7 @@ import os
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
 def process_video(video):
-    # 打开视频文件
     cap = cv2.VideoCapture(video)
-    # 创建一个 VideoWriter 对象以保存处理后的视频
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (int(cap.get(3)), int(cap.get(4))))
 
@@ -16,21 +14,35 @@ def process_video(video):
         ret, frame = cap.read()
         if not ret:
             break
-        # 处理帧，例如将其转换为灰度
         gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        # 将处理后的帧写入输出视频
         out.write(cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2BGR))
     
     cap.release()
     out.release()
     return 'output.mp4'
 
-# Define the title and description
 title = "Welcome to DDM DeepSort"
 description = "Upload a video to process it using DDM and DeepSORT."
 
-# Initialize Gradio interface with title and description
 with gr.Blocks() as demo:
+    gr.Markdown("""
+    # Welcome to My Neuroscience Project
+    The author is a third-year undergraduate student at the School of Intelligent Science and Technology, Nanjing University, Suzhou Campus.
+    ## Note
+    <div style="border: 2px solid rgba(0, 0, 0, 0.1); border-radius: 10px; background-color: rgba(255, 255, 255, 0.8); padding: 20px;">
+    Since this project uses Hugging Face's free CPU, the processing speed is very slow. In the worst case, even a video with a dozen frames can take several minutes to process. Therefore, if possible, it is recommended to deploy on a device with a better GPU.
+    Although the YOLOv5 model supports up to 80 classes, my project is primarily focused on autonomous driving. Therefore, objects other than people and cars will be excluded after object detection.
+    </div>
+    <div style="border: 2px solid rgba(0, 0, 0, 0.1); border-radius: 10px; background-color: rgba(255, 255, 255, 0.8); padding: 20px;">
+    **Tips for First-Time Users:**
+    - Ensure that the video includes at least people and cars.
+    - It's recommended that the video is not too long, ideally within 10 seconds.
+    </div>
+    """)
+
+    with gr.Row(align="center"):
+        gr.Video(value="DDMDeepsort1.mp4", label="Demo Video", width=720, height=435)
+
     gr.Interface(
         fn=app_main,
         inputs="video",
@@ -38,26 +50,5 @@ with gr.Blocks() as demo:
         title=title,
         description=description
     )
-    gr.Markdown("""
-# Welcome to My Neuroscience Project
-
-The author is a third-year undergraduate student at the School of Intelligent Science and Technology, Nanjing University, Suzhou Campus.
-
-## Note
-<div style="border: 2px solid rgba(0, 0, 0, 0.1); border-radius: 10px; background-color: rgba(255, 255, 255, 0.8); padding: 20px;">
-Since this project uses Hugging Face's free CPU, the processing speed is very slow. In the worst case, even a video with a dozen frames can take several minutes to process. Therefore, if possible, it is recommended to deploy on a device with a better GPU.
-
-Although the YOLOv5 model supports up to 80 classes, my project is primarily focused on autonomous driving. Therefore, objects other than people and cars will be excluded after object detection.
-</div>
-<div style="border: 2px solid rgba(0, 0, 0, 0.1); border-radius: 10px; background-color: rgba(255, 255, 255, 0.8); padding: 20px;">
-**Tips for First-Time Users:**
-- Ensure that the video includes at least people and cars.
-- It's recommended that the video is not too long, ideally within 10 seconds.
-</div>
-""")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Video(value="DDMDeepsort1.mp4", label="Demo Video", width=720, height=435)
 
 demo.launch()