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 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
from tensorflow.keras.models import load_model



def my_function():



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


  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('car3.jpg')


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


  # 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)




  # Using the Model that we Trained on Kaggle Dataset earlier
  model = load_model('licence_trained_model.h5', compile=False)



  # 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
        predictions = model.predict(img,verbose=0)
        y_ = np.argmax(predictions, axis=1)[0]  # get index of the maximum probability
        character = dic[y_]
        output.append(character)  # storing the result in a list

    plate_number = ''.join(output)

    return plate_number

  print(show_results())

my_function()














def run():
    st.write('##### Number plate Recognition')
    # Making Form
    # Create a Streamlit form
    with st.form(key='Number plate Recognition'):
        # Add a file uploader to the form
        uploaded_files = st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)

        # Check if any file is uploaded
        if uploaded_files:
            for uploaded_file in uploaded_files:
                st.write("filename:", uploaded_file.name)
        # Close the form
        submitted = st.form_submit_button('Recognize')
    
    if submitted:
        my_function()
        
        
if __name__ == '__main__':
    run()