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Vehicle_License_Plate_Recognition

Module Name Vehicle_License_Plate_Recognition
Category text recognition
Network -
Dataset CCPD
Fine-tuning supported or not No
Module Size 111MB
Latest update date 2021-03-22
Data indicators -

I.Basic Information

  • Application Effect Display

    • Sample results：


  • Module Introduction

    • Vehicle_License_Plate_Recognition is a module for licence plate recognition, trained on CCPD dataset. This model can detect the position of licence plate and recognize the contents.

II.Installation

  • 1、Environmental Dependence

    • paddlepaddle >= 2.0.0

    • paddlehub >= 2.0.4

    • paddleocr >= 2.0.2

  • 2、Installation

III.Module API Prediction

  • 1、Prediction Code Example

    • import paddlehub as hub
import cv2

model = hub.Module(name="Vehicle_License_Plate_Recognition")
result = model.plate_recognition(images=[cv2.imread('/PATH/TO/IMAGE')])

  • 2、API

    • def plate_recognition(images)

      • Prediction API.

      • Parameters

        • images (list[numpy.ndarray]): image data, ndarray.shape is in the format [H, W, C], BGR;

      • Return

        • results(list(dict{'license', 'bbox'})): The list of recognition results, where each element is dict.

IV.Server Deployment

  • PaddleHub Serving can deploy an online service of text recognition.

  • Step 1: Start PaddleHub Serving

    • Run the startup command：

    • $ hub serving start -m Vehicle_License_Plate_Recognition

    • The servitization API is now deployed and the default port number is 8866.

    • NOTE: If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.

  • Step 2: Send a predictive request

    • With a configured server, use the following lines of code to send the prediction request and obtain the result

    • import requests
import json
import cv2
import base64


def cv2_to_base64(image):
  data = cv2.imencode('.jpg', image)[1]
  return base64.b64encode(data.tostring()).decode('utf8')

# Send an HTTP request
data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/Vehicle_License_Plate_Recognition"
r = requests.post(url=url, headers=headers, data=json.dumps(data))

# print prediction results
print(r.json()["results"])

V.Release Note

  • 1.0.0

    First release

    • $ hub install Vehicle_License_Plate_Recognition==1.0.0