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import os
import cv2
import gradio as gr
import numpy as np
# from configs.config import Configuration
from scripts.charExtraction import CharExtraction
from scripts.bboxAnnotator import BBOXAnnotator
wPathPlat = "licence_plat.pt"
wPathChar = "licence_character.pt"
classList = np.array(['A','B','C','D','E','F','G','H','I','J','K','L','M',
'N','O','P','Q','R','S','T','U','V','W','X','Y','Z',
'0','1','2','3','4','5','6','7','8','9'])
sizePlat = (416,200)
# imageSample = Configuration['imageSample']
extractor = CharExtraction(wPlatePath=wPathPlat, wCharacterPath=wPathChar, classList=classList,
sizePlate=sizePlat, conf=0.5)
annotator = BBOXAnnotator()
def getAnnotatedImage(frame, conf):
bbox, plateNum, confidence = extractor.predict(image=frame, conf=conf)
annotateImg, plateNum = annotator.draw_bbox(frame, bbox, plateNum)
prob_dict = {key: value for key, value in zip(plateNum, confidence)}
# pathOu = os.path.join(os.path.abspath(''), "output/sample.jpg")
# cv2.imwrite(pathOu, annotateImg)
return annotateImg, prob_dict
def video_identity(frame, conf=0.45):
image, plateNumber = getAnnotatedImage(frame, conf)
return image, plateNumber
demo = gr.Interface(fn=video_identity,
inputs=[gr.Image(label='Input image'), gr.Slider(minimum=0.1, maximum=1.0, label='Conf value')],
outputs=[gr.Image(label='Result'), gr.Label(label='Plate Number Detected')],
examples = [["sample_image2.jpg"], ["sample_image3.jpg"], ["sample_image5.jpg"], ["sample_image6.jpg"]],
cache_examples=True)
if __name__ == "__main__":
demo.queue()
demo.launch() |