Update app.py

app.py

@@ -10,7 +10,16 @@ import cv2
 from pathlib import Path
 import tempfile
 import imageio
-from tqdm import tqdm  # 新增: 导入tqdm
+from tqdm import tqdm
+import logging
+
+# 新增: 配置logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
 
 # 从环境变量获取密码
 APP_USERNAME = "admin"  # 用户名保持固定
@@ -29,9 +38,12 @@ auth_state = AuthState()
 
 def login(username, password):
     """登录验证"""
+    logger.info(f"用户尝试登录: {username}")
     if username == APP_USERNAME and password == APP_PASSWORD:
         auth_state.is_logged_in = True
+        logger.info("登录成功")
         return gr.update(visible=False), gr.update(visible=True), "登录成功"
+    logger.warning("登录失败：用户名或密码错误")
     return gr.update(visible=True), gr.update(visible=False), "用户名或密码错误"
 
 @spaces.GPU(duration=300)
@@ -44,14 +56,20 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
         conf_threshold: 置信度阈值(0-1)
         max_det: 每帧最大检测数量
     """
+    logger.info(f"开始处理视频: {video_path}")
+    logger.info(f"参数设置 - 处理时长: {process_seconds}秒, 置信度阈值: {conf_threshold}, 最大检测数: {max_det}")
+    
     if not auth_state.is_logged_in:
+        logger.warning("用户未登录，拒绝访问")
         return None, "请先登录"
     
     # 创建临时目录保存输出视频
+    logger.info("创建临时输出目录")
     with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
         output_path = tmp_file.name
     
     # 获取视频信息
+    logger.info("读取视频信息")
     cap = cv2.VideoCapture(video_path)
     fps = int(cap.get(cv2.CAP_PROP_FPS))
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
@@ -59,6 +77,8 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
     total_frames = int(process_seconds * fps) if process_seconds else int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     cap.release()
     
+    logger.info(f"视频信息 - FPS: {fps}, 分辨率: {width}x{height}, 总帧数: {total_frames}")
+    
     # 创建视频写入器
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     video_writer = cv2.VideoWriter(
@@ -72,7 +92,7 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
     base_size = min(width, height)
     line_thickness = max(1, int(base_size * 0.002))  # 0.2% 的最小边长
 
-    # 修改: 设置推理参数并处理视频,关闭verbose输出
+    logger.info("开始YOLO模型推理")
     results = model.predict(
         source=video_path,
         device=device,
@@ -90,7 +110,7 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
         verbose=False  # 关闭YOLO默认日志输出
     )
     
-    # 处理结果
+    logger.info("开始处理检测结果")
     frame_count = 0
     detection_info = []
     all_positions = []
@@ -147,6 +167,7 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
     
     pbar.close()  # 关闭进度条
     video_writer.release()
+    logger.info(f"视频处理完成，共处理 {frame_count} 帧")
 
     # 生成分析报告
     confidences = [float(det['confidence'].strip('%'))/100 for info in detection_info for det in info['detections']]
@@ -180,7 +201,7 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
             positions: 位置列表 [[x1,y1], [x2,y2],...]
             width: 视频宽度
             height: 视频高度
-            max_jump_distance: 允许的最大跳跃距离
+            max_jump_distance: 允许��最大跳跃距离
         """
         if len(positions) < 3:
             return positions
@@ -315,6 +336,7 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
             frame_heatmap_color = cv2.addWeighted(frame_heatmap_color, 0.7, np.full_like(frame_heatmap_color, 255), 0.3, 0)
             heatmap_frames.append(frame_heatmap_color)
 
+    logger.info("开始生成轨迹图和热力图")
     trajectory_gif_path = output_path.replace('.mp4', '_trajectory.gif')
     heatmap_gif_path = output_path.replace('.mp4', '_heatmap.gif')
     
@@ -326,6 +348,13 @@ def process_video(video_path, process_seconds=20, conf_threshold=0.2, max_det=8)
     cv2.imwrite(trajectory_path, trajectory_img)
     cv2.imwrite(heatmap_path, heatmap_colored)
     
+    logger.info("轨迹图和热力图生成完成")
+    logger.info("开始生成GIF动画")
+    imageio.mimsave(trajectory_gif_path, trajectory_frames, duration=50)
+    imageio.mimsave(heatmap_gif_path, heatmap_frames, duration=50)
+    logger.info("GIF动画生成完成")
+
+    logger.info("所有处理完成，准备返回结果")
     return output_path, trajectory_path, heatmap_path, trajectory_gif_path, heatmap_gif_path, report
 
 # 创建 Gradio 界面