add online demo

.gitignore

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+.DS_Store

app.py

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+import os
+import glob
+from itertools import groupby
+
+import cv2
+import numpy as np
 import gradio as gr
+import tensorflow as tf
+
+
+def get_sample_images():
+    list_ = glob.glob(os.path.join(os.path.dirname(__file__), 'samples/*.jpg'))
+    return [[i] for i in list_]
+
+
+def inference(image):
+    # load model
+    demo = TFliteDemo(os.path.join(os.path.dirname(__file__), 'model.tflite'))
+    # load image
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    image = center_fit(image, 128, 64, top_left=True)
+    image = np.reshape(image, (1, *image.shape, 1)).astype(np.uint8)
+    # inference
+    pred = demo.inference(image)
+    # decode
+    dict = load_dict(os.path.join(os.path.dirname(__file__), 'label.names'))
+    res = decode_label(pred, dict)
+    return res
+
+
+class TFliteDemo:
+    def __init__(self, model_path):
+        self.interpreter = tf.lite.Interpreter(model_path=model_path)
+        self.interpreter.allocate_tensors()
+        self.input_details = self.interpreter.get_input_details()
+        self.output_details = self.interpreter.get_output_details()
+    
+    def inference(self, x):
+        self.interpreter.set_tensor(self.input_details[0]['index'], x)
+        self.interpreter.invoke()
+        return self.interpreter.get_tensor(self.output_details[0]['index'])
+
+
+def center_fit(img, w, h, inter=cv2.INTER_NEAREST, top_left=True):
+    # get img shape
+    img_h, img_w = img.shape[:2]
+    # get ratio
+    ratio = min(w / img_w, h / img_h)
+
+    if len(img.shape) == 3:
+        inter = cv2.INTER_AREA
+    # resize img
+    img = cv2.resize(img, (int(img_w * ratio), int(img_h * ratio)), interpolation=inter)
+    # get new img shape
+    img_h, img_w = img.shape[:2]
+    # get start point
+    start_w = (w - img_w) // 2
+    start_h = (h - img_h) // 2
+
+    if top_left:
+        start_w = 0
+        start_h = 0
+
+    if len(img.shape) == 2:
+        # create new img
+        new_img = np.zeros((h, w), dtype=np.uint8)
+        new_img[start_h:start_h+img_h, start_w:start_w+img_w] = img
+    else:
+        new_img = np.zeros((h, w, 3), dtype=np.uint8)
+        new_img[start_h:start_h+img_h, start_w:start_w+img_w, :] = img
+
+    return new_img
+
+
+def load_dict(dict_path='label.names'):
+    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
+
+
+def decode_label(mat, chars) -> str:
+    # mat is the output of model
+    # get char indices along best path
+    best_path_indices = np.argmax(mat[0], axis=-1)
+    # collapse best path (using itertools.groupby), map to chars, join char list to string
+    best_chars_collapsed = [chars[k] for k, _ in groupby(best_path_indices) if k != len(chars)]
+    res = ''.join(best_chars_collapsed)
+    return res
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+interface = gr.Interface(
+    fn=inference,
+    inputs="image",
+    outputs="text",
+    title="South Korean License Plate Recognition",
+    examples=get_sample_images(),
+)
+interface.launch()

label.names

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+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+가
+나
+다
+라
+마
+거
+너
+더
+러
+머
+버
+서
+어
+저
+고
+노
+도
+로
+모
+보
+소
+오
+조
+구
+누
+두
+루
+무
+부
+수
+우
+주
+하
+허
+호
+바
+사
+아
+자
+배
+서울
+부산
+대구
+인천
+광주
+대전
+울산
+세종
+경기
+강원
+충북
+충남
+전북
+전남
+경북
+경남
+제주
+북
+제
+종
+전
+충
+천
+울
+원
+인
+대
+세
+산
+남
+강
+기
+경
+광

model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a16ab2c9446d3edfa5e620ae418ed68952dd0047d8656eba2001a269341c0f81
+size 307488