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| # remove warning message | |
| import json | |
| import os | |
| from typing import final | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' | |
| # required library | |
| import cv2 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import matplotlib.gridspec as gridspec | |
| from local_utils import detect_lp, getPath | |
| from os.path import splitext,basename | |
| from keras.models import model_from_json | |
| from keras.preprocessing.image import load_img, img_to_array | |
| from keras.applications.mobilenet_v2 import preprocess_input | |
| from sklearn.preprocessing import LabelEncoder | |
| import glob | |
| import gradio as gr | |
| from transfer import load_model | |
| def sort_contours(cnts,reverse = False): | |
| i = 0 | |
| boundingBoxes = [cv2.boundingRect(c) for c in cnts] | |
| (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), | |
| key=lambda b: b[1][i], reverse=reverse)) | |
| return cnts | |
| def predict_from_model(image,model,labels): | |
| image = cv2.resize(image,(80,80)) | |
| image = np.stack((image,)*3, axis=-1) | |
| prediction = labels.inverse_transform([np.argmax(model.predict(image[np.newaxis,:]))]) | |
| return prediction | |
| def classify(img,resize=False,Dmax=650, Dmin = 270): | |
| wpod_net_path = "wpod-net.json" | |
| wpod_net = load_model(wpod_net_path) | |
| ##preprocess_image | |
| #img = cv2.imread(image_path) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| img = img / 255 | |
| if resize: | |
| img = cv2.resize(img, (224,224)) | |
| ##get_plate | |
| vehicle = img | |
| ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2]) | |
| side = int(ratio * Dmin) | |
| bound_dim = min(side, Dmax) | |
| _ , LpImg, _, cor = detect_lp(wpod_net, vehicle, bound_dim, lp_threshold=0.5) | |
| if (len(LpImg)): #check if there is at least one license image | |
| # Scales, calculates absolute values, and converts the result to 8-bit. | |
| plate_image = cv2.convertScaleAbs(LpImg[0], alpha=(255.0)) | |
| # convert to grayscale and blur the image | |
| gray = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY) | |
| blur = cv2.GaussianBlur(gray,(7,7),0) | |
| # Applied inversed thresh_binary | |
| binary = cv2.threshold(blur, 180, 255, | |
| cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] | |
| kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) | |
| thre_mor = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel3) | |
| cont, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| test_roi = plate_image.copy() | |
| crop_characters = [] | |
| digit_w, digit_h = 30, 60 | |
| for c in sort_contours(cont): | |
| (x, y, w, h) = cv2.boundingRect(c) | |
| ratio = h/w | |
| if 1<=ratio<=3.5: # Only select contour with defined ratio | |
| if h/plate_image.shape[0]>=0.5: # Select contour which has the height larger than 50% of the plate | |
| # Draw bounding box arroung digit number | |
| cv2.rectangle(test_roi, (x, y), (x + w, y + h), (0, 255,0), 2) | |
| # Sperate number and gibe prediction | |
| curr_num = thre_mor[y:y+h,x:x+w] | |
| curr_num = cv2.resize(curr_num, dsize=(digit_w, digit_h)) | |
| _, curr_num = cv2.threshold(curr_num, 220, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) | |
| crop_characters.append(curr_num) | |
| #print("Detect {} letters...".format(len(crop_characters))) | |
| # Load model architecture, weight and labels | |
| json_file = open('MobileNets_character_recognition.json', 'r') | |
| loaded_model_json = json_file.read() | |
| json_file.close() | |
| model = model_from_json(loaded_model_json) | |
| model.load_weights("License_character_recognition_weight.h5") | |
| labels = LabelEncoder() | |
| labels.classes_ = np.load('license_character_classes.npy') | |
| #fig = plt.figure(figsize=(15,3)) | |
| #cols = len(crop_characters) | |
| #grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig) | |
| final_string = '' | |
| for i,character in enumerate(crop_characters): | |
| #fig.add_subplot(grid[i]) | |
| title = np.array2string(predict_from_model(character,model,labels)) | |
| #plt.title('{}'.format(title.strip("'[]"),fontsize=20)) | |
| final_string+=title.strip("'[]") | |
| #plt.axis(False) | |
| #plt.imshow(character,cmap='gray') | |
| try: | |
| cols = len(crop_characters) | |
| except ValueError: | |
| return "No Plate Detected" | |
| else: | |
| if len(crop_characters) == 0: | |
| return "No Plate Detected" | |
| else: | |
| return final_string | |
| gr.Interface(fn=classify, | |
| inputs=gr.inputs.Image(), | |
| outputs="text", | |
| title = "Plate Number Recognition", | |
| examples = ['29Z5550.jpeg', 'germany_car_plate.jpg', 'india_car_plate.jpg', 'turkey_car_plate.jpg', 'vietnam_car_rectangle_plate.jpg'], | |
| description="Automaticall Recognize the symbols contained in the number plates of a motor vehicle when read from an image provided. It will help the authorities to automatically detect motor vehicle that will violate number-coding scheme.", | |
| allow_flagging="never").launch(inbrowser=True) | |
| #classify("C:/Users/JomerJuan/Documents/Deep Learning/Plate Number Recognition/Plate_examples/germany_car_plate.jpg",resize=False,Dmax=650, Dmin = 270) | |