添加了logging部分

config.py

@@ -1,4 +1,5 @@
-import torch
+import os
+
 import numpy as np
 
 
@@ -36,3 +37,6 @@ class Config:
 
     # save part
     offset_vertical = 0
+
+    # logging
+    root_dir = os.path.split(os.path.realpath(__file__))[0]

main.py

@@ -1,4 +1,6 @@
 import os
+import sys
+
 import cv2
 import time
 import numpy as np
@@ -6,6 +8,7 @@ import numpy as np
 import utils
 from config import Config
 from models import RgbDetector, SpecDetector
+import logging
 
 
 def main(only_spec=False, only_color=False):
@@ -34,27 +37,34 @@ def main(only_spec=False, only_color=False):
         if len(data) < 3:
             threshold = int(float(data))
             Config.spec_size_threshold = threshold
-            print("[INFO] Get spec threshold: ", threshold)
+            logging.info('[INFO] Get spec threshold: ', threshold)
         else:
             data_total = data
         os.close(fd_img)
-
         # rgb data read
         rgb_data = os.read(fd_rgb, total_rgb)
         if len(rgb_data) < 3:
+            try:
                 rgb_threshold = int(float(rgb_data))
                 Config.rgb_size_threshold = rgb_threshold
-            print("[INFO] Get rgb threshold", rgb_threshold)
+                logging.info(f'Get rgb threshold: {rgb_threshold}')
+            except Exception as e:
+                logging.error(f'毁灭性错误:收到长度小于3却无法转化为整数的数据，{e}.')
             continue
         else:
             rgb_data_total = rgb_data
         os.close(fd_rgb)
-
         # 识别
-        t1 = time.time()
+        since = time.time()
+        try:
             img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)) \
             .transpose(0, 2, 1)
+        except Exception as e:
+            logging.error(f'毁灭性错误!收到的光谱数据长度为{len(data_total)}无法转化成指定的形状 {e}')
+        try:
             rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
+        except Exception as e:
+            logging.error(f'毁灭性错误!收到的rgb数据长度为{len(rgb_data)}无法转化成指定形状 {e}')
         if only_spec:
             # 光谱识别
             mask_spec = spec_detector.predict(img_data)
@@ -66,7 +76,6 @@ def main(only_spec=False, only_color=False):
         else:
             mask_spec = spec_detector.predict(img_data)
             mask_rgb = rgb_detector.predict(rgb_data)
-
         # 进行喷阀的合并
         masks = [utils.valve_merge(mask, merge_size=Config.valve_merge_size) for mask in [mask_spec, mask_rgb]]
         # control the size of the output masks, 在resize前，图像的宽度是和喷阀对应的
@@ -77,8 +86,10 @@ def main(only_spec=False, only_color=False):
             fd_mask = os.open(fifo, os.O_WRONLY)
             os.write(fd_mask, mask.tobytes())
             os.close(fd_mask)
-        t3 = time.time()
+        time_spent = (time.time() - since) * 1000
-        print(f'total time is:{t3 - t1}')
+        logging.info(f'Total time is: {time_spent:.2f} ms')
+        if time_spent > 200:
+            logging.warning(f'警告预测超时,预测耗时超过了200ms,The prediction time is {time_spent:.2f} ms.')
 
 
 if __name__ == '__main__':
@@ -86,11 +97,22 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='主程序')
     parser.add_argument('-oc', default=False, action='store_true', help='只进行RGB彩色预测 only rgb', required=False)
     parser.add_argument('-os', default=False, action='store_true', help='只进行光谱预测 only spec', required=False)
+    parser.add_argument('-d', default=False, action='store_true', help='是否使用DEBUG模式', required=False)
     args = parser.parse_args()
     # fifo 参数
-    img_fifo_path = "/tmp/dkimg.fifo"
+    img_fifo_path = '/tmp/dkimg.fifo'
-    rgb_fifo_path = "/tmp/dkrgb.fifo"
+    rgb_fifo_path = '/tmp/dkrgb.fifo'
     # mask fifo
-    mask_fifo_path = "/tmp/dkmask.fifo"
+    mask_fifo_path = '/tmp/dkmask.fifo'
-    rgb_mask_fifo_path = "/tmp/dkmask_rgb.fifo"
+    rgb_mask_fifo_path = '/tmp/dkmask_rgb.fifo'
+    # logging相关
+    file_handler = logging.FileHandler(os.path.join(Config.root_dir, '.tobacco_algorithm.log'))
+    file_handler.setLevel(logging.WARNING)
+    console_handler = logging.StreamHandler(sys.stdout)
+    if args.d:
+        console_handler.setLevel(logging.DEBUG)
+    else:
+        console_handler.setLevel(logging.WARNING)
+    logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
+                        handlers=[file_handler, console_handler])
     main(only_spec=args.os, only_color=args.oc)