chore: Update TFliteDemo constructor parameters, model.tflite file, and preprocessing methods

app.py

@@ -7,7 +7,7 @@ import gradio as gr
 import tensorflow as tf
 
 
-def get_sample_images():
+def get_samples():
     list_ = glob.glob(os.path.join(os.path.dirname(__file__), 'samples/*.jpg'))
     # sort by name
     list_.sort(key=lambda x: int(x.split('/')[-1].split('.')[0]))
@@ -40,6 +40,7 @@ def center_fit(img, w, h, inter=cv2.INTER_NEAREST, top_left=True):
         inter = cv2.INTER_AREA
     # resize img
     img = cv2.resize(img, (int(img_w * ratio), int(img_h * ratio)), interpolation=inter)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     # get new img shape
     img_h, img_w = img.shape[:2]
     # get start point
@@ -69,9 +70,8 @@ def load_dict(dict_path='label.names'):
 
 
 class TFliteDemo:
-    def __init__(self, model_path, blank=0, conf_mode="min"):
+    def __init__(self, model_path, blank=0):
         self.blank = blank
-        self.conf_mode = conf_mode
         self.interpreter = tf.lite.Interpreter(model_path=model_path)
         self.interpreter.allocate_tensors()
         self.inputs = self.interpreter.get_input_details()
@@ -90,30 +90,28 @@ class TFliteDemo:
             if img is None:
                 raise ValueError('img is None')
             image = img.copy()
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
         image = center_fit(image, 192, 96, top_left=True)
         image = np.reshape(image, (1, *image.shape, 1)).astype(np.float32) / 255.0
         return image
 
-    def get_confidence(self, pred, mode="mean"):
+    def get_confidence(self, pred):
         _argmax = np.argmax(pred, axis=-1)
         _idx = _argmax != pred.shape[-1] - 1
         conf = pred[_idx, _argmax[_idx]]
-        conf = np.exp(conf)
-        return np.min(conf) if mode == "min" else np.mean(conf)
+        return np.min(np.exp(conf))
 
     def postprocess(self, pred):
         label = decode_label(pred, load_dict())
-        conf = self.get_confidence(pred[0], mode=self.conf_mode)
+        conf = self.get_confidence(pred[0])
         # keep 4 decimal places
         conf = float('{:.4f}'.format(conf))
         return label, conf
 
-
-def inference(img):
-    img = demo.preprocess(img)
-    pred = demo.inference(img)
-    return demo.postprocess(pred)
+    def get_results(self, img):
+        img = self.preprocess(img)
+        pred = self.inference(img)
+        return self.postprocess(pred)
 
 
 if __name__ == '__main__':
@@ -127,8 +125,8 @@ if __name__ == '__main__':
     '''
     # init model
     demo = TFliteDemo(os.path.join(os.path.dirname(__file__), 'model.tflite'))
-    interface = gr.Interface(
-        fn=inference,
+    app = gr.Interface(
+        fn=demo.get_results,
         inputs="image",
         outputs=[
             gr.Textbox(label="Plate Number", type="text"),
@@ -136,6 +134,6 @@ if __name__ == '__main__':
         ],
         title=_TITLE,
         description=_DESCRIPTION,
-        examples=get_sample_images(),
+        examples=get_samples(),
     )
-    interface.launch()
+    app.launch()