Update app.py

app.py

@@ -8,7 +8,7 @@ DECODE_DICT = {i:char for i, char in enumerate(CHARS)}
 
 interpreter = tf.lite.Interpreter(model_path='detection.tflite')
 interpreter.allocate_tensors()
-recog_interpreter = tf.lite.Interpreter(model_path='recognition.tflite')
+recog_interpreter = tf.lite.Interpreter(model_path='recognition2.tflite')
 recog_input_details = recog_interpreter.get_input_details()
 recog_output_details = recog_interpreter.get_output_details()
 recog_interpreter.resize_tensor_input(recog_input_details[0]['index'], (1, 24, 94, 3))
@@ -46,7 +46,7 @@ def execute_text_recognition_tflite( boxes, frame, interpreter, input_details, o
     return text,cv2.resize(save_frame,(94,24))
 
 def greet(image):
-    resized = cv2.resize(image, (480,480), interpolation=cv2.INTER_CUBIC)
+    resized = cv2.resize(image, (320,320), interpolation=cv2.INTER_CUBIC)
     input_data = resized.astype(np.float32)          # Set as 3D RGB float array
     input_data /= 255.                               # Normalize
     input_data = np.expand_dims(input_data, axis=0)  # Batch dimension (wrap in 4D)