feat: demo

README.md

@@ -1,6 +1,6 @@
 ---
 title: Multi-line License Plate Recognition
-emoji: 🚗
+emoji: 🚘
 colorFrom: indigo
 colorTo: blue
 sdk: gradio

app.py

@@ -0,0 +1,92 @@
+import os, glob
+from pathlib import Path
+
+import cv2
+import numpy as np
+import gradio as gr
+from ai_edge_litert.interpreter import Interpreter
+
+
+def get_samples():
+    list_ = glob.glob(os.path.join(os.path.dirname(__file__), 'data/*.jpg'))
+    list_.sort(key=lambda x: int(Path(x).stem))
+    return list_
+
+
+def cv2_imread(path):
+    return cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+
+
+def load_dict(dict_path='label.names'):
+    dict_path = os.path.join(os.path.dirname(__file__), dict_path)
+    with open(dict_path, 'r', encoding='utf-8') as f:
+        _dict = f.read().splitlines()
+    _dict = {i: _dict[i] for i in range(len(_dict))}
+    return _dict
+
+
+class TFliteDemo:
+    def __init__(self, model_path, blank=0):
+        self.blank = blank
+        self.interpreter = Interpreter(model_path=model_path, num_threads=os.cpu_count())
+        self.interpreter.allocate_tensors()
+        self.inputs = self.interpreter.get_input_details()
+        self.outputs = self.interpreter.get_output_details()
+
+    def inference(self, x):
+        self.interpreter.set_tensor(self.inputs[0]['index'], x)
+        self.interpreter.invoke()
+        return self.interpreter.get_tensor(self.outputs[0]['index'])
+
+    def preprocess(self, img):
+        if isinstance(img, str):
+            image = cv2_imread(img)
+        else:
+            if img is None:
+                raise ValueError('img is None')
+            image = img.copy()
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        image = cv2.resize(image, (96, 32), interpolation=cv2.INTER_LINEAR)
+        image = np.reshape(image, (1, 1, *image.shape)).astype(np.float32) / 255.0
+        return image
+
+    def postprocess(self, pred):
+        label_dict = load_dict()
+        pred_probs = pred[0]
+        pred_indices = np.argmax(pred_probs, axis=-1)
+        pred_label = [label_dict[i] for i in pred_indices]
+        label = ''.join(pred_label)
+        conf = np.min(np.max(pred_probs, axis=-1))
+        conf = float(f'{conf:.4f}')
+        return label, conf
+
+    def get_results(self, img):
+        img = self.preprocess(img)
+        pred = self.inference(img)
+        return self.postprocess(pred)
+
+
+if __name__ == '__main__':
+    _TITLE = '''Lightweight South Korean Multi-line License Plate Recognition'''
+    _DESCRIPTION = '''
+    <div>
+    <p style="text-align: center; font-size: 1.3em">This is a demo of Lightweight South Korean Multi-line License Plate Recognition.
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/noahzhy/SALPR'><img src='https://img.shields.io/github/stars/noahzhy/SALPR?style=social' /></a>
+    </p>
+    </div>
+    '''
+    # init model
+    demo = TFliteDemo(os.path.join(os.path.dirname(__file__), 'tinyLPR.tflite'))
+    app = gr.Interface(
+        fn=demo.get_results,
+        inputs="image",
+        outputs=[
+            gr.Textbox(label="Plate Number", type="text"),
+            gr.Textbox(label="Confidence", type="text"),
+        ],
+        title=_TITLE,
+        description=_DESCRIPTION,
+        examples=get_samples(),
+    )
+    app.launch()

requirements.txt

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+numpy
+opencv-python
+ai-edge-litert

tinyLPR.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf5f2dc5e2032347830ab49057ed3b10475037ca8b7ac96a9d13ac72b50f33ae
+size 543036