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import gradio as gr
import re, datetime,time, cv2, numpy as np, tensorflow as tf, sys
CHARS = "ABCDEFGHIJKLMNPQRSTUVWXYZ0123456789" # exclude I, O
CHARS_DICT = {char:i for i, char in enumerate(CHARS)}
DECODE_DICT = {i:char for i, char in enumerate(CHARS)}
interpreter = tf.lite.Interpreter(model_path='detection.tflite')
interpreter.allocate_tensors()
recog_interpreter = tf.lite.Interpreter(model_path='recognition2.tflite')
recog_input_details = recog_interpreter.get_input_details()
recog_output_details = recog_interpreter.get_output_details()
recog_interpreter.resize_tensor_input(recog_input_details[0]['index'], (1, 24, 94, 3))
recog_interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
def execute_text_recognition_tflite( boxes, frame, interpreter, input_details, output_details):
x1, x2, y1, y2 = boxes[1], boxes[3], boxes[0], boxes[2]
save_frame = frame[
max( 0, int(y1*1079) ) : min( 1079, int(y2*1079) ),
max( 0, int(x1*1920) ) : min( 1920, int(x2*1920) )
]
# Execute text recognition
print(frame.shape)
test_image = cv2.resize(save_frame,(94,24))/256
test_image = np.expand_dims(test_image,axis=0)
test_image = test_image.astype(np.float32)
interpreter.set_tensor(input_details[0]['index'], test_image)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
decoded = tf.keras.backend.ctc_decode(output_data,(24,),greedy=False)
text = ""
for i in np.array(decoded[0][0][0]):
if i >-1:
text += DECODE_DICT[i]
# Do nothing if text is empty
if not len(text): return
license_plate = text
text[:3].replace("0",'O')
return text,cv2.resize(save_frame,(94,24))
def greet(image):
resized = cv2.resize(image, (320,320), interpolation=cv2.INTER_AREA)
input_data = resized.astype(np.float32) # Set as 3D RGB float array
input_data /= 255. # Normalize
input_data = np.expand_dims(input_data, axis=0) # Batch dimension (wrap in 4D)
# Initialize input tensor
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
# Bounding boxes
boxes = interpreter.get_tensor(output_details[1]['index'])
text = None
# For index and confidence value of the first class [0]
for i, confidence in enumerate(output_data[0]):
if confidence > .3:
text, crop = execute_text_recognition_tflite(
boxes[0][i], image,
recog_interpreter, recog_input_details, recog_output_details,
)
return text, crop
image = gr.inputs.Image(shape=(1920,1080))
output_image =gr.outputs.Image(type="auto", label="Output")
title = "Automatic licence plate detection and recognition"
description = "Gradio demo for an automatic licence plate recognition system. To use it, simply upload your image of a car with a licence plate, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://ieeexplore.ieee.org/document/9071863'>Robust Real time Lightweight Automatic License plate Recognition System for Iranian License Plates</a> | <a href='https://github.com/clsandoval/LPRnet-keras'>Github Repo</a></p>"
iface = gr.Interface(
fn=greet,
inputs=image,
outputs=["text",output_image],
title = title,
description = description,
article=article,
examples = [
"3.jpg",
"4.jpg",
]
)
iface.launch() |