classifier.py

import matplotlib.pyplot as plt
import numpy as np
import cv2
import tensorflow as tf
from sklearn.metrics import f1_score 
from tensorflow.keras.models import load_model
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Flatten, MaxPooling2D, Dropout, Conv2D
import streamlit as st


# Loads the data required for detecting the license plates from cascade classifier.
plate_cascade = cv2.CascadeClassifier('indian_license_plate.xml')
# add the path to 'india_license_plate.xml' file.


model = load_model('licence_trained_model.h5', compile=False)




def detect_plate(img, text=''): # the function detects and perfors blurring on the number plate.
    plate_img = img.copy()
    roi = img.copy()
    plate_rect = plate_cascade.detectMultiScale(plate_img, scaleFactor = 1.2, minNeighbors = 7) # detects numberplates and returns the coordinates and dimensions of detected license plate's contours.
    for (x,y,w,h) in plate_rect:
        roi_ = roi[y:y+h, x:x+w, :] # extracting the Region of Interest of license plate for blurring.
        plate = roi[y:y+h, x:x+w, :]
        cv2.rectangle(plate_img, (x+2,y), (x+w-3, y+h-5), (51,181,155), 3) # finally representing the detected contours by drawing rectangles around the edges.
    if text!='':
        plate_img = cv2.putText(plate_img, text, (x-w//2,y-h//2), 
                                cv2.FONT_HERSHEY_COMPLEX_SMALL , 0.5, (51,181,155), 1, cv2.LINE_AA)
        
    return plate_img, plate # returning the processed image.




# Testing the above function
def display(img, title=''):
    img = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
    fig = plt.figure(figsize=(10,6))
    ax = plt.subplot(111)
    ax.imshow(img)
    plt.axis('off')
    plt.title(title)
    plt.show()
    


display(img,'input image')


'''



def segment_characters(image) :

    # Preprocess cropped license plate image
    img_lp = cv2.resize(image, (333, 75))
    img_gray_lp = cv2.cvtColor(img_lp, cv2.COLOR_BGR2GRAY)
    _, img_binary_lp = cv2.threshold(img_gray_lp, 200, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    img_binary_lp = cv2.erode(img_binary_lp, (3,3))
    img_binary_lp = cv2.dilate(img_binary_lp, (3,3))

    LP_WIDTH = img_binary_lp.shape[0]
    LP_HEIGHT = img_binary_lp.shape[1]

    # Make borders white
    img_binary_lp
'''



# Streamlit app
def run():
    st.write('##### Traffic Sign Classifier')
    # Making Form
    # Create a Streamlit form
    with st.form(key='vehicle number plate'):
        # Add a file uploader to the form
        #img= st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)
        img= st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)


        
if __name__ == '__main__':
    run()