use logger.info replace print

tools/infer/predict_cls.py

@@ -23,7 +23,7 @@ import copy
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -106,10 +106,10 @@ class TextClassifier(object):
                 norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
                 self.predictor.run([norm_img_batch])
             prob_out = self.output_tensors[0].copy_to_cpu()
-            cls_res = self.postprocess_op(prob_out)
+            cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
-            for rno in range(len(cls_res)):
-                label, score = cls_res[rno]
+            for rno in range(len(cls_result)):
+                label, score = cls_result[rno]
                 cls_res[indices[beg_img_no + rno]] = [label, score]
                 if '180' in label and score > self.cls_thresh:
                     img_list[indices[beg_img_no + rno]] = cv2.rotate(
@@ -133,8 +133,8 @@ def main(args):
         img_list.append(img)
     try:
         img_list, cls_res, predict_time = text_classifier(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -143,10 +143,10 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))
 
-    if __name__ == "__main__":
+if __name__ == "__main__":
     main(utility.parse_args())

tools/infer/predict_det.py

@@ -178,11 +178,12 @@ if __name__ == "__main__":
         if count > 0:
             total_time += elapse
         count += 1
-        print("Predict time of {}: {}".format(image_file, elapse))
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = os.path.split(image_file)[-1]
         img_path = os.path.join(draw_img_save,
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
     if count > 1:
-        print("Avg Time:", total_time / (count - 1))
+        logger.info("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -22,7 +22,7 @@ import cv2
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -135,8 +135,8 @@ def main(args):
         img_list.append(img)
     try:
         rec_res, predict_time = text_recognizer(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -145,9 +145,9 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))