app.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

def main():
    st.title("number plate recognition")

    img = cv2.imread('car.jpg')
    if img is not None:
        print("Error: Unable to read the image.")

        
#    img = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])




    # 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()

    img = cv2.imread('car.jpg')
    if img is None:
        print("Error: Unable to read the image.")


        
    # Continue processing the image
    display(img, 'input image')



    # Getting plate prom the processed image
    output_img, plate = detect_plate(img)



    display(output_img, 'detected license plate in the input image')



    display(plate, 'extracted license plate from the image')




    # Match contours to license plate or character template
    def find_contours(dimensions, img) :

        # Find all contours in the image
        cntrs, _ = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        # Retrieve potential dimensions
        lower_width = dimensions[0]
        upper_width = dimensions[1]
        lower_height = dimensions[2]
        upper_height = dimensions[3]
    
        # Check largest 5 or  15 contours for license plate or character respectively
        cntrs = sorted(cntrs, key=cv2.contourArea, reverse=True)[:15]
    
        ii = cv2.imread('contour.jpg')
    
        x_cntr_list = []
        target_contours = []
        img_res = []
        for cntr in cntrs :
            # detects contour in binary image and returns the coordinates of rectangle enclosing it
            intX, intY, intWidth, intHeight = cv2.boundingRect(cntr)
        
            # checking the dimensions of the contour to filter out the characters by contour's size
            if intWidth > lower_width and intWidth < upper_width and intHeight > lower_height and intHeight < upper_height :
                x_cntr_list.append(intX) #stores the x coordinate of the character's contour, to used later for indexing the contours

                char_copy = np.zeros((44,24))
                # extracting each character using the enclosing rectangle's coordinates.
                char = img[intY:intY+intHeight, intX:intX+intWidth]
                char = cv2.resize(char, (20, 40))
            
                cv2.rectangle(ii, (intX,intY), (intWidth+intX, intY+intHeight), (50,21,200), 2)
                plt.imshow(ii, cmap='gray')

                # Make result formatted for classification: invert colors
                char = cv2.subtract(255, char)

                # Resize the image to 24x44 with black border
                char_copy[2:42, 2:22] = char
                char_copy[0:2, :] = 0
                char_copy[:, 0:2] = 0
                char_copy[42:44, :] = 0
                char_copy[:, 22:24] = 0

                img_res.append(char_copy) # List that stores the character's binary image (unsorted)
            
        # Return characters on ascending order with respect to the x-coordinate (most-left character first)
            
        plt.show()
        # arbitrary function that stores sorted list of character indeces
        indices = sorted(range(len(x_cntr_list)), key=lambda k: x_cntr_list[k])
        img_res_copy = []
        for idx in indices:
            img_res_copy.append(img_res[idx])# stores character images according to their index
        img_res = np.array(img_res_copy)

        return img_res





    # Find characters in the resulting images
    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[0:3,:] = 255
        img_binary_lp[:,0:3] = 255
        img_binary_lp[72:75,:] = 255
        img_binary_lp[:,330:333] = 255

        # Estimations of character contours sizes of cropped license plates
        dimensions = [LP_WIDTH/6,
                           LP_WIDTH/2,
                           LP_HEIGHT/10,
                           2*LP_HEIGHT/3]
        plt.imshow(img_binary_lp, cmap='gray')
        plt.show()
        cv2.imwrite('contour.jpg',img_binary_lp)

        # Get contours within cropped license plate
        char_list = find_contours(dimensions, img_binary_lp)

        return char_list



    # Let's see the segmented characters
    char = segment_characters(plate)




    for i in range(10):
        plt.subplot(1, 10, i+1)
        plt.imshow(char[i], cmap='gray')
        plt.axis('off')





    # Predicting the output
    def fix_dimension(img): 
      new_img = np.zeros((28,28,3))
      for i in range(3):
        new_img[:,:,i] = img
      return new_img
  
    def show_results():
        dic = {}
        characters = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
        for i,c in enumerate(characters):
            dic[i] = c
    
        output = []
        for i,ch in enumerate(char): #iterating over the characters
            img_ = cv2.resize(ch, (28,28), interpolation=cv2.INTER_AREA)
            img = fix_dimension(img_)
            img = img.reshape(1,28,28,3) #preparing image for the model
            y_ = model.predict_classes(img)[0] #predicting the class
            character = dic[y_] #
            output.append(character) #storing the result in a list
        
        plate_number = ''.join(output)
    
        return plate_number

    print(show_results())



if __name__ == "__main__":
    main()