基于OpenCV進(jìn)行車牌檢測(cè)！

# installing OpenCV>pip install opencv-python==4.1.0# Installing Keras>pip install keras# Installing Jupyter>pip install jupyter#Installing Scikit-Learn>pip install scikit-learn

#importing openCV>import cv2#importing numpy>import numpy as np#importing pandas to read the CSV file containing our data>import pandas as pd#importing keras and sub-libraries>from keras.models import Sequential>from keras.layers import Dense>from keras.layers import Dropout>from keras.layers import Flatten, MaxPool2D>from keras.layers.convolutional import Conv2D>from keras.layers.convolutional import MaxPooling2D>from keras import backend as K>from keras.utils import np_utils>from sklearn.model_selection import train_test_split

def extract_plate(img): # the function detects and perfors blurring on the number plate.  plate_img = img.copy()    #Loads the data required for detecting the license plates from cascade classifier.  plate_cascade = cv2.CascadeClassifier('./indian_license_plate.xml')
  # detects numberplates and returns the coordinates and dimensions of detected license plate's contours.  plate_rect = plate_cascade.detectMultiScale(plate_img, scaleFactor = 1.3, minNeighbors = 7)
  for (x,y,w,h) in plate_rect:    a,b = (int(0.02*img.shape[0]), int(0.025*img.shape[1])) #parameter tuning    plate = plate_img[y+a:y+h-a, x+b:x+w-b, :]    # finally representing the detected contours by drawing rectangles around the edges.    cv2.rectangle(plate_img, (x,y), (x+w, y+h), (51,51,255), 3)          return plate_img, plate # returning the processed image

# Find characters in the resulting imagesdef segment_characters(image) :
    # Preprocess cropped license plate image    img = cv2.resize(image, (333, 75))    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    _, img_binary = cv2.threshold(img_gray, 200, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)    img_erode = cv2.erode(img_binary, (3,3))    img_dilate = cv2.dilate(img_erode, (3,3))
    LP_WIDTH = img_dilate.shape[0]    LP_HEIGHT = img_dilate.shape[1]
    # Make borders white    img_dilate[0:3,:] = 255    img_dilate[:,0:3] = 255    img_dilate[72:75,:] = 255    img_dilate[:,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]
    # Get contours within cropped license plate    char_list = find_contours(dimensions, img_dilate)
    return char_list

import numpy as npimport cv2
# Match contours to license plate or character templatedef 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]
    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))
            # 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)        #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

## create model>model = Sequential()>model.add(Conv2D(filters=32, kernel_size=(5,5), input_shape=(28, 28, 1), activation='relu'))>model.add(MaxPooling2D(pool_size=(2, 2)))>model.add(Dropout(rate=0.4))>model.add(Flatten())>model.add(Dense(units=128, activation='relu'))>model.add(Dense(units=36, activation='softmax'))

import datetimeclass stop_training_callback(tf.keras.callbacks.Callback):  def on_epoch_end(self, epoch, logs={}):    if(logs.get('val_acc') > 0.992):      self.model.stop_training = True      log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
batch_size = 1callbacks = [tensorboard_callback, stop_training_callback()]model.fit_generator(train_generator,      steps_per_epoch = train_generator.samples // batch_size,      validation_data = validation_generator,       validation_steps = validation_generator.samples // batch_size,      epochs = 80, callbacks=callbacks)