├── car.png ├── car6.jpg ├── car6 - Copy.jpg ├── train20X20 ├── 0 │ ├── 0_0.jpg │ ├── 0_1.jpg │ ├── 0_2.jpg │ ├── 0_3.jpg │ ├── 0_4.jpg │ ├── 0_5.jpg │ ├── 0_6.jpg │ ├── 0_7.jpg │ ├── 0_8.jpg │ └── 0_9.jpg ├── 1 │ ├── 1_0.jpg │ ├── 1_1.jpg │ ├── 1_2.jpg │ ├── 1_3.jpg │ ├── 1_4.jpg │ ├── 1_5.jpg │ ├── 1_6.jpg │ ├── 1_7.jpg │ ├── 1_8.jpg │ ├── 1_9.jpg │ └── 1_10.jpg ├── 2 │ ├── 5.jpg │ ├── 2_0.jpg │ ├── 2_1.jpg │ ├── 2_2.jpg │ ├── 2_3.jpg │ ├── 2_4.jpg │ ├── 2_5.jpg │ ├── 2_6.jpg │ ├── 2_7.jpg │ ├── 2_8.jpg │ ├── 2_9.jpg │ └── 2_10.jpg ├── 3 │ ├── 2.jpg │ ├── 3_0.jpg │ ├── 3_1.jpg │ ├── 3_2.jpg │ ├── 3_3.jpg │ ├── 3_4.jpg │ ├── 3_5.jpg │ ├── 3_6.jpg │ ├── 3_7.jpg │ ├── 3_8.jpg │ ├── 3_9.jpg │ └── 3_10.jpg ├── 4 │ ├── 4_0.jpg │ ├── 4_1.jpg │ ├── 4_2.jpg │ ├── 4_3.jpg │ ├── 4_4.jpg │ ├── 4_5.jpg │ ├── 4_6.jpg │ ├── 4_7.jpg │ ├── 4_8.jpg │ └── 4_9.jpg ├── 5 │ ├── 5_0.jpg │ ├── 5_1.jpg │ ├── 5_2.jpg │ ├── 5_3.jpg │ ├── 5_4.jpg │ ├── 5_5.jpg │ ├── 5_6.jpg │ ├── 5_7.jpg │ ├── 5_8.jpg │ └── 5_9.jpg ├── 6 │ ├── 6_0.jpg │ ├── 6_1.jpg │ ├── 6_2.jpg │ ├── 6_3.jpg │ ├── 6_4.jpg │ ├── 6_5.jpg │ ├── 6_6.jpg │ ├── 6_7.jpg │ ├── 6_8.jpg │ └── 6_9.jpg ├── 7 │ ├── 2.jpg │ ├── 5.jpg │ ├── 7_0.jpg │ ├── 7_1.jpg │ ├── 7_2.jpg │ ├── 7_3.jpg │ ├── 7_4.jpg │ ├── 7_5.jpg │ ├── 7_6.jpg │ ├── 7_7.jpg │ ├── 7_8.jpg │ └── 7_9.jpg ├── 8 │ ├── 8_0.jpg │ ├── 8_1.jpg │ ├── 8_2.jpg │ ├── 8_3.jpg │ ├── 8_4.jpg │ ├── 8_5.jpg │ ├── 8_6.jpg │ ├── 8_7.jpg │ ├── 8_8.jpg │ └── 8_9.jpg ├── 9 │ ├── 7.jpg │ ├── 9_0.jpg │ ├── 9_1.jpg │ ├── 9_2.jpg │ ├── 9_3.jpg │ ├── 9_4.jpg │ ├── 9_5.jpg │ ├── 9_6.jpg │ ├── 9_7.jpg │ ├── 9_8.jpg │ └── 9_9.jpg ├── A │ ├── 9.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── A_0.jpg │ ├── A_1.jpg │ ├── A_2.jpg │ ├── A_3.jpg │ ├── A_4.jpg │ ├── A_5.jpg │ ├── A_6.jpg │ ├── A_7.jpg │ ├── A_8.jpg │ ├── A_9.jpg │ ├── A_10.jpg │ ├── A_11.jpg │ ├── A_12.jpg │ ├── A_13.jpg │ ├── A_15.jpg │ ├── A_16.jpg │ └── A_14 (2).jpg ├── E │ ├── 3.jpg │ ├── 7.jpg │ ├── E_0.jpg │ ├── E_1.jpg │ ├── E_2.jpg │ ├── E_3.jpg │ ├── E_4.jpg │ ├── E_5.jpg │ ├── E_6.jpg │ ├── E_7.jpg │ ├── E_8.jpg │ ├── E_9.jpg │ └── E_10.jpg ├── J │ ├── 6.jpg │ ├── 7.jpg │ ├── J_0.jpg │ ├── J_1.jpg │ ├── J_2.jpg │ ├── J_3.jpg │ ├── J_4.jpg │ ├── J_5.jpg │ ├── J_6.jpg │ ├── J_7.jpg │ ├── J_8.jpg │ └── J_9.jpg ├── K │ ├── 1.jpg │ ├── 4.jpg │ ├── 7.jpg │ ├── K_0.jpg │ ├── K_1.jpg │ ├── K_2.jpg │ ├── K_3.jpg │ ├── K_4.jpg │ ├── K_5.jpg │ ├── K_6.jpg │ ├── K_7.jpg │ ├── K_8.jpg │ ├── K_9.jpg │ ├── K_10.jpg │ └── K_11.jpg ├── B │ ├── B_0.jpg │ ├── B_1.jpg │ ├── B_2.jpg │ ├── B_3.jpg │ ├── B_4.jpg │ ├── B_5.jpg │ ├── B_6.jpg │ ├── B_7.jpg │ ├── B_8.jpg │ └── B_9.jpg ├── C │ ├── C_0.jpg │ ├── C_1.jpg │ ├── C_2.jpg │ ├── C_3.jpg │ ├── C_4.jpg │ ├── C_5.jpg │ ├── C_6.jpg │ ├── C_7.jpg │ ├── C_8.jpg │ └── C_9.jpg ├── D │ ├── D_0.jpg │ ├── D_1.jpg │ ├── D_2.jpg │ ├── D_3.jpg │ ├── D_4.jpg │ ├── D_5.jpg │ ├── D_6.jpg │ ├── D_7.jpg │ ├── D_8.jpg │ └── D_9.jpg ├── F │ ├── F_0.jpg │ ├── F_1.jpg │ ├── F_2.jpg │ ├── F_3.jpg │ ├── F_4.jpg │ ├── F_5.jpg │ ├── F_6.jpg │ ├── F_7.jpg │ ├── F_8.jpg │ └── F_9.jpg ├── G │ ├── G_0.jpg │ ├── G_1.jpg │ ├── G_2.jpg │ ├── G_3.jpg │ ├── G_4.jpg │ ├── G_5.jpg │ ├── G_6.jpg │ ├── G_7.jpg │ ├── G_8.jpg │ └── G_9.jpg ├── H │ ├── H_0.jpg │ ├── H_1.jpg │ ├── H_2.jpg │ ├── H_3.jpg │ ├── H_4.jpg │ ├── H_5.jpg │ ├── H_6.jpg │ ├── H_7.jpg │ ├── H_8.jpg │ └── H_9.jpg ├── L │ ├── L_0.jpg │ ├── L_1.jpg │ ├── L_2.jpg │ ├── L_3.jpg │ ├── L_4.jpg │ ├── L_5.jpg │ ├── L_6.jpg │ ├── L_7.jpg │ ├── L_8.jpg │ └── L_9.jpg ├── M │ ├── M_0.jpg │ ├── M_1.jpg │ ├── M_2.jpg │ ├── M_3.jpg │ ├── M_4.jpg │ ├── M_5.jpg │ ├── M_6.jpg │ ├── M_7.jpg │ ├── M_8.jpg │ └── M_9.jpg ├── N │ ├── N_0.jpg │ ├── N_1.jpg │ ├── N_2.jpg │ ├── N_3.jpg │ ├── N_4.jpg │ ├── N_5.jpg │ ├── N_6.jpg │ ├── N_7.jpg │ ├── N_8.jpg │ └── N_9.jpg ├── P │ ├── P_0.jpg │ ├── P_1.jpg │ ├── P_2.jpg │ ├── P_3.jpg │ ├── P_4.jpg │ ├── P_5.jpg │ ├── P_6.jpg │ ├── P_7.jpg │ ├── P_8.jpg │ └── P_9.jpg ├── Q │ ├── Q_0.jpg │ ├── Q_1.jpg │ ├── Q_2.jpg │ ├── Q_3.jpg │ ├── Q_4.jpg │ ├── Q_5.jpg │ ├── Q_6.jpg │ ├── Q_7.jpg │ ├── Q_8.jpg │ └── Q_9.jpg ├── R │ ├── R_0.jpg │ ├── R_1.jpg │ ├── R_2.jpg │ ├── R_3.jpg │ ├── R_4.jpg │ ├── R_5.jpg │ ├── R_6.jpg │ ├── R_7.jpg │ ├── R_8.jpg │ └── R_9.jpg ├── S │ ├── S_0.jpg │ ├── S_1.jpg │ ├── S_2.jpg │ ├── S_3.jpg │ ├── S_4.jpg │ ├── S_5.jpg │ ├── S_6.jpg │ ├── S_7.jpg │ ├── S_8.jpg │ └── S_9.jpg ├── T │ ├── T_0.jpg │ ├── T_1.jpg │ ├── T_2.jpg │ ├── T_3.jpg │ ├── T_4.jpg │ ├── T_5.jpg │ ├── T_6.jpg │ ├── T_7.jpg │ ├── T_8.jpg │ └── T_9.jpg ├── U │ ├── U_0.jpg │ ├── U_1.jpg │ ├── U_2.jpg │ ├── U_3.jpg │ ├── U_4.jpg │ ├── U_5.jpg │ ├── U_6.jpg │ ├── U_7.jpg │ ├── U_8.jpg │ └── U_9.jpg ├── V │ ├── V_0.jpg │ ├── V_1.jpg │ ├── V_2.jpg │ ├── V_3.jpg │ ├── V_4.jpg │ ├── V_5.jpg │ ├── V_6.jpg │ ├── V_7.jpg │ ├── V_8.jpg │ └── V_9.jpg ├── W │ ├── W_0.jpg │ ├── W_1.jpg │ ├── W_2.jpg │ ├── W_3.jpg │ ├── W_4.jpg │ ├── W_5.jpg │ ├── W_6.jpg │ ├── W_7.jpg │ ├── W_8.jpg │ └── W_9.jpg ├── X │ ├── X_0.jpg │ ├── X_1.jpg │ ├── X_2.jpg │ ├── X_3.jpg │ ├── X_4.jpg │ ├── X_5.jpg │ ├── X_6.jpg │ ├── X_7.jpg │ ├── X_8.jpg │ └── X_9.jpg ├── Y │ ├── Y_0.jpg │ ├── Y_1.jpg │ ├── Y_2.jpg │ ├── Y_3.jpg │ ├── Y_4.jpg │ ├── Y_5.jpg │ ├── Y_6.jpg │ ├── Y_7.jpg │ ├── Y_8.jpg │ └── Y_9.jpg ├── Z │ ├── Z_0.jpg │ ├── Z_1.jpg │ ├── Z_2.jpg │ ├── Z_3.jpg │ ├── Z_4.jpg │ ├── Z_5.jpg │ ├── Z_6.jpg │ ├── Z_7.jpg │ ├── Z_8.jpg │ └── Z_9.jpg ├── bae │ ├── bae_0.jpg │ ├── bae_1.jpg │ ├── bae_2.jpg │ ├── bae_3.jpg │ ├── bae_4.jpg │ ├── bae_5.jpg │ ├── bae_6.jpg │ ├── bae_7.jpg │ ├── bae_8.jpg │ └── bae_9.jpg ├── dal │ ├── dal_0.jpg │ ├── dal_1.jpg │ ├── dal_2.jpg │ ├── dal_3.jpg │ ├── dal_4.jpg │ ├── dal_5.jpg │ ├── dal_6.jpg │ ├── dal_7.jpg │ ├── dal_8.jpg │ └── dal_9.jpg ├── hae │ ├── hae_0.jpg │ ├── hae_1.jpg │ ├── hae_2.jpg │ ├── hae_3.jpg │ ├── hae_4.jpg │ ├── hae_5.jpg │ ├── hae_6.jpg │ ├── hae_7.jpg │ ├── hae_8.jpg │ └── hae_9.jpg ├── alef │ ├── alef_0.jpg │ ├── alef_1.jpg │ ├── alef_2.jpg │ ├── alef_3.jpg │ ├── alef_4.jpg │ ├── alef_5.jpg │ ├── alef_6.jpg │ ├── alef_7.jpg │ ├── alef_8.jpg │ └── alef_9.jpg └── jeem │ ├── jeem_0.jpg │ ├── jeem_1.jpg │ ├── jeem_2.jpg │ ├── jeem_3.jpg │ ├── jeem_4.jpg │ ├── jeem_5.jpg │ ├── jeem_6.jpg │ ├── jeem_7.jpg │ ├── jeem_8.jpg │ └── jeem_9.jpg ├── requirements.txt ├── PredictCharacters.py ├── SegmentCharacters.py ├── TrainRecognizeCharacters.py └── DetectPlate.py /car.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RedaElmar/moroccanLPR/main/car.png -------------------------------------------------------------------------------- /car6.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RedaElmar/moroccanLPR/main/car6.jpg -------------------------------------------------------------------------------- /car6 - Copy.jpg: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cycler==0.10.0 2 | decorator==4.4.2 3 | imageio==2.8.0 4 | imutils==0.5.3 5 | joblib==0.15.1 6 | kiwisolver==1.2.0 7 | matplotlib==3.2.1 8 | networkx==2.4 9 | numpy==1.18.4 10 | opencv-python==4.2.0.34 11 | Pillow==8.1.1 12 | pyparsing==2.4.7 13 | python-dateutil==2.8.1 14 | PyWavelets==1.1.1 15 | scikit-image==0.17.2 16 | scikit-learn==0.23.1 17 | scipy==1.4.1 18 | six==1.15.0 19 | threadpoolctl==2.0.0 20 | tifffile==2020.5.25 21 | -------------------------------------------------------------------------------- /PredictCharacters.py: -------------------------------------------------------------------------------- 1 | import SegmentCharacters 2 | import pickle 3 | print("Loading model") 4 | filename = './finalized_model.sav' 5 | model = pickle.load(open(filename, 'rb')) 6 | 7 | print('Model loaded. Predicting characters of number plate') 8 | classification_result = [] 9 | for each_character in SegmentCharacters.characters: 10 | # converts it to a 1D array 11 | each_character = each_character.reshape(1, -1); 12 | result = model.predict(each_character) 13 | classification_result.append(result) 14 | 15 | print('Classification result') 16 | print(classification_result) 17 | 18 | plate_string = '' 19 | for eachPredict in classification_result: 20 | plate_string += eachPredict[0] 21 | 22 | print('Predicted license plate') 23 | print(plate_string) 24 | 25 | # it's possible the characters are wrongly arranged 26 | # since that's a possibility, the column_list will be 27 | # used to sort the letters in the right order 28 | 29 | column_list_copy = SegmentCharacters.column_list[:] 30 | SegmentCharacters.column_list.sort() 31 | rightplate_string = '' 32 | for each in SegmentCharacters.column_list: 33 | rightplate_string += plate_string[column_list_copy.index(each)] 34 | 35 | print('License plate') 36 | print(rightplate_string) -------------------------------------------------------------------------------- /SegmentCharacters.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from skimage.transform import resize 3 | from skimage import measure 4 | from skimage.measure import regionprops 5 | import matplotlib.patches as patches 6 | import matplotlib.pyplot as plt 7 | import DetectPlate 8 | 9 | # The invert was done so as to convert the black pixel to white pixel and vice versa 10 | license_plate = np.invert(DetectPlate.plate_like_objects[0]) 11 | 12 | labelled_plate = measure.label(license_plate) 13 | 14 | fig, ax1 = plt.subplots(1) 15 | ax1.imshow(license_plate, cmap="gray") 16 | # the next two lines is based on the assumptions that the width of 17 | # a license plate should be between 5% and 15% of the license plate, 18 | # and height should be between 35% and 60% 19 | # this will eliminate some 20 | character_dimensions = (0.35*license_plate.shape[0], 0.60*license_plate.shape[0], 0.05*license_plate.shape[1], 0.15*license_plate.shape[1]) 21 | min_height, max_height, min_width, max_width = character_dimensions 22 | 23 | characters = [] 24 | counter=0 25 | column_list = [] 26 | for regions in regionprops(labelled_plate): 27 | y0, x0, y1, x1 = regions.bbox 28 | region_height = y1 - y0 29 | region_width = x1 - x0 30 | 31 | if region_height > min_height and region_height < max_height and region_width > min_width and region_width < max_width: 32 | roi = license_plate[y0:y1, x0:x1] 33 | 34 | # draw a red bordered rectangle over the character. 35 | rect_border = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, edgecolor="red", 36 | linewidth=2, fill=False) 37 | ax1.add_patch(rect_border) 38 | 39 | # resize the characters to 20X20 and then append each character into the characters list 40 | resized_char = resize(roi, (20, 20)) 41 | characters.append(resized_char) 42 | 43 | # this is just to keep track of the arrangement of the characters 44 | column_list.append(x0) 45 | # print(characters) 46 | plt.show() -------------------------------------------------------------------------------- /TrainRecognizeCharacters.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | from sklearn.svm import SVC 4 | from sklearn.model_selection import cross_val_score 5 | from skimage.io import imread 6 | from skimage.filters import threshold_otsu 7 | 8 | letters = [ 9 | '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 10 | 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 11 | 'U', 'V', 'W', 'X', 'Y', 'Z' 12 | ] 13 | 14 | def read_training_data(training_directory): 15 | image_data = [] 16 | target_data = [] 17 | for each_letter in letters: 18 | for each in range(10): 19 | image_path = os.path.join(training_directory, each_letter, each_letter + '_' + str(each) + '.jpg') 20 | # read each image of each character 21 | img_details = imread(image_path, as_gray=True) 22 | # converts each character image to binary image 23 | binary_image = img_details < threshold_otsu(img_details) 24 | # the 2D array of each image is flattened because the machine learning 25 | # classifier requires that each sample is a 1D array 26 | # therefore the 20*20 image becomes 1*400 27 | # in machine learning terms that's 400 features with each pixel 28 | # representing a feature 29 | flat_bin_image = binary_image.reshape(-1) 30 | image_data.append(flat_bin_image) 31 | target_data.append(each_letter) 32 | 33 | return (np.array(image_data), np.array(target_data)) 34 | 35 | def cross_validation(model, num_of_fold, train_data, train_label): 36 | # this uses the concept of cross validation to measure the accuracy 37 | # of a model, the num_of_fold determines the type of validation 38 | # e.g if num_of_fold is 4, then we are performing a 4-fold cross validation 39 | # it will divide the dataset into 4 and use 1/4 of it for testing 40 | # and the remaining 3/4 for the training 41 | accuracy_result = cross_val_score(model, train_data, train_label, 42 | cv=num_of_fold) 43 | print("Cross Validation Result for ", str(num_of_fold), " -fold") 44 | 45 | print(accuracy_result * 100) 46 | 47 | 48 | # current_dir = os.path.dirname(os.path.realpath(__file__)) 49 | # 50 | # training_dataset_dir = os.path.join(current_dir, 'train') 51 | print('reading data') 52 | training_dataset_dir = './train20X20' 53 | image_data, target_data = read_training_data(training_dataset_dir) 54 | print('reading data completed') 55 | 56 | # the kernel can be 'linear', 'poly' or 'rbf' 57 | # the probability was set to True so as to show 58 | # how sure the model is of it's prediction 59 | svc_model = SVC(kernel='linear', probability=True) 60 | 61 | cross_validation(svc_model, 4, image_data, target_data) 62 | 63 | print('training model') 64 | 65 | # let's train the model with all the input data 66 | svc_model.fit(image_data, target_data) 67 | 68 | import pickle 69 | print("model trained.saving model..") 70 | filename = './finalized_model.sav' 71 | pickle.dump(svc_model, open(filename, 'wb')) 72 | print("model saved") -------------------------------------------------------------------------------- /DetectPlate.py: -------------------------------------------------------------------------------- 1 | from skimage.io import imread 2 | from skimage.filters import threshold_otsu 3 | import matplotlib.pyplot as plt 4 | import imutils 5 | import cv2 6 | 7 | from skimage import measure 8 | from skimage.measure import regionprops 9 | import matplotlib.pyplot as plt 10 | import matplotlib.patches as patches 11 | import os 12 | import shutil 13 | filename = './video12.mp4' 14 | 15 | if os.path.exists('output'): 16 | shutil.rmtree('output') 17 | 18 | os.makedirs('output') 19 | 20 | cap = cv2.VideoCapture(filename) 21 | # cap = cv2.VideoCapture(0) 22 | count = 0 23 | while cap.isOpened(): 24 | ret,frame = cap.read() 25 | if ret == True: 26 | cv2.imshow('window-name',frame) 27 | cv2.imwrite("./output/frame%d.jpg" % count, frame) 28 | count = count + 1 29 | if cv2.waitKey(10) & 0xFF == ord('q'): 30 | break 31 | else: 32 | break 33 | cap.release() 34 | cv2.destroyAllWindows() 35 | # 36 | # car image -> grayscale image -> binary image 37 | car_image = imread("./output/frame%d.jpg"%(count-1), as_gray=True) 38 | car_image = imutils.rotate(car_image, 270) 39 | # car_image = imread("car.png", as_gray=True) 40 | # it should be a 2 dimensional array 41 | print(car_image.shape) 42 | 43 | # the next line is not compulsory however, a grey scale pixel 44 | # in skimage ranges between 0 & 1. multiplying it with 255 45 | # will make it range between 0 & 255 (something we can relate better with 46 | 47 | gray_car_image = car_image * 255 48 | fig, (ax1, ax2) = plt.subplots(1, 2) 49 | ax1.imshow(gray_car_image, cmap="gray") 50 | threshold_value = threshold_otsu(gray_car_image) 51 | binary_car_image = gray_car_image > threshold_value 52 | # print(binary_car_image) 53 | ax2.imshow(binary_car_image, cmap="gray") 54 | # ax2.imshow(gray_car_image, cmap="gray") 55 | plt.show() 56 | 57 | # CCA (finding connected regions) of binary image 58 | 59 | 60 | # this gets all the connected regions and groups them together 61 | label_image = measure.label(binary_car_image) 62 | 63 | # print(label_image.shape[0]) #width of car img 64 | 65 | # getting the maximum width, height and minimum width and height that a license plate can be 66 | plate_dimensions = (0.03*label_image.shape[0], 0.08*label_image.shape[0], 0.15*label_image.shape[1], 0.3*label_image.shape[1]) 67 | plate_dimensions2 = (0.08*label_image.shape[0], 0.2*label_image.shape[0], 0.15*label_image.shape[1], 0.4*label_image.shape[1]) 68 | min_height, max_height, min_width, max_width = plate_dimensions 69 | plate_objects_cordinates = [] 70 | plate_like_objects = [] 71 | 72 | fig, (ax1) = plt.subplots(1) 73 | ax1.imshow(gray_car_image, cmap="gray") 74 | flag =0 75 | # regionprops creates a list of properties of all the labelled regions 76 | for region in regionprops(label_image): 77 | # print(region) 78 | if region.area < 50: 79 | #if the region is so small then it's likely not a license plate 80 | continue 81 | # the bounding box coordinates 82 | min_row, min_col, max_row, max_col = region.bbox 83 | # print(min_row) 84 | # print(min_col) 85 | # print(max_row) 86 | # print(max_col) 87 | 88 | region_height = max_row - min_row 89 | region_width = max_col - min_col 90 | # print(region_height) 91 | # print(region_width) 92 | 93 | # ensuring that the region identified satisfies the condition of a typical license plate 94 | if region_height >= min_height and region_height <= max_height and region_width >= min_width and region_width <= max_width and region_width > region_height: 95 | flag = 1 96 | plate_like_objects.append(binary_car_image[min_row:max_row, 97 | min_col:max_col]) 98 | plate_objects_cordinates.append((min_row, min_col, 99 | max_row, max_col)) 100 | rectBorder = patches.Rectangle((min_col, min_row), max_col - min_col, max_row - min_row, edgecolor="red", 101 | linewidth=2, fill=False) 102 | ax1.add_patch(rectBorder) 103 | # let's draw a red rectangle over those regions 104 | if(flag == 1): 105 | # print(plate_like_objects[0]) 106 | plt.show() 107 | 108 | 109 | 110 | 111 | if(flag==0): 112 | min_height, max_height, min_width, max_width = plate_dimensions2 113 | plate_objects_cordinates = [] 114 | plate_like_objects = [] 115 | 116 | fig, (ax1) = plt.subplots(1) 117 | ax1.imshow(gray_car_image, cmap="gray") 118 | 119 | # regionprops creates a list of properties of all the labelled regions 120 | for region in regionprops(label_image): 121 | if region.area < 50: 122 | #if the region is so small then it's likely not a license plate 123 | continue 124 | # the bounding box coordinates 125 | min_row, min_col, max_row, max_col = region.bbox 126 | # print(min_row) 127 | # print(min_col) 128 | # print(max_row) 129 | # print(max_col) 130 | 131 | region_height = max_row - min_row 132 | region_width = max_col - min_col 133 | # print(region_height) 134 | # print(region_width) 135 | 136 | # ensuring that the region identified satisfies the condition of a typical license plate 137 | if region_height >= min_height and region_height <= max_height and region_width >= min_width and region_width <= max_width and region_width > region_height: 138 | # print("hello") 139 | plate_like_objects.append(binary_car_image[min_row:max_row, 140 | min_col:max_col]) 141 | plate_objects_cordinates.append((min_row, min_col, 142 | max_row, max_col)) 143 | rectBorder = patches.Rectangle((min_col, min_row), max_col - min_col, max_row - min_row, edgecolor="red", 144 | linewidth=2, fill=False) 145 | ax1.add_patch(rectBorder) 146 | # let's draw a red rectangle over those regions 147 | # print(plate_like_objects[0]) 148 | plt.show() --------------------------------------------------------------------------------