OCR_part/ocr_interface.py

@@ -2,16 +2,25 @@ import numpy as np
 from paddleocr import TextRecognition
 import cv2
 
+class OCRProcessor:
+    def __init__(self):
+        self.model = TextRecognition(model_name="PP-OCRv5_server_rec")
+        print("OCR模型初始化完成（占位）")
+
+    def predict(self, image_array):
+        # 保持原有模型调用方式
+        output = self.model.predict(input=image_array)
+        # 结构化输出结果
+        results = output[0]["rec_text"]
+        placeholder_result = results.split(',')
+        return placeholder_result
+
+# 保留原有函数接口
+_processor = OCRProcessor()
+
 def initialize_ocr_model():
-    model = TextRecognition(model_name="PP-OCRv5_server_rec")
-    print("OCR模型初始化完成（占位）")
-    return model
+    return _processor
 
 def ocr_predict(image_array):
-    # 保持原有模型调用方式
-    output = initialize_ocr_model().predict(input=image_array)
-    # 结构化输出结果
-    results = output[0]["rec_text"]
-    placeholder_result = results.split(',')
-    return placeholder_result
+    return _processor.predict(image_array)
 

main.py

@@ -9,10 +9,10 @@ from PyQt5.QtCore import QTimer, Qt, pyqtSignal, QThread
 from PyQt5.QtGui import QImage, QPixmap, QFont, QPainter, QPen, QColor
 import os
 from yolopart.detector import LicensePlateYOLO
-#from OCR_part.ocr_interface import ocr_predict
-#from OCR_part.ocr_interface import initialize_ocr_model
+from OCR_part.ocr_interface import ocr_predict
+from OCR_part.ocr_interface import initialize_ocr_model
 # 使用CRNN进行车牌字符识别
-from CRNN_part.crnn_interface import crnn_predict
+# from CRNN_part.crnn_interface import crnn_predict
 from CRNN_part.crnn_interface import initialize_crnn_model
 
 class CameraThread(QThread):
@@ -164,8 +164,8 @@ class MainWindow(QMainWindow):
         self.init_camera()
 
         # 初始化OCR/CRNN模型（具体用哪个模块识别车牌号就写在这儿）
-        #initialize_ocr_model()
-        initialize_crnn_model()
+        initialize_ocr_model()
+        # initialize_crnn_model()
 
     
     def init_ui(self):
@@ -392,8 +392,8 @@ class MainWindow(QMainWindow):
         try:
             # 使用OCR接口进行识别
             # 可以根据需要切换为CRNN: crnn_predict(corrected_image)
-            #result = ocr_predict(corrected_image)
-            result = crnn_predict(corrected_image)
+            result = ocr_predict(corrected_image)
+            # result = crnn_predict(corrected_image)
             
             # 将字符列表转换为字符串
             if isinstance(result, list) and len(result) >= 7: