Opencv交通标志识别

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Opencv交通标志识别

2024-07-17 20:23| 来源: 网络整理| 查看: 265

文章目录前言效果预览数据集下载地址训练模型模型预测项目结构及源码下载

前言

本文使用的数据集包含43种交通标志，使用opencv以及卷积神经网络训练模型，识别交通标志，使用pyqt5制作交通标志识别GUI的界面。

效果预览

如视频中所示，可以选择交通标志，然后可以进行图像预处理操作，如灰度化，边缘检测等，最后可以点击识别按钮进行识别。

交通标志识别

数据集下载地址

数据集中共包含43种交通标志！数据集下载地址：https://pan.baidu.com/wap/init?surl=5v14ieSPZntBTDzKVckEgA 提取码：39q4

训练模型

下面是训练模型的代码

import numpy as np import matplotlib.pyplot as plt from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam from tensorflow.python.keras.utils.np_utils import to_categorical from tensorflow.keras.layers import Dropout, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D import cv2 from sklearn.model_selection import train_test_split import pickle import os import pandas as pd import random from tensorflow.keras.preprocessing.image import ImageDataGenerator ################# Parameters ##################### path = "./data/myData" # folder with all the class folders labelFile = './data/labels.csv' # file with all names of classes batch_size_val = 50 # how many to process together steps_per_epoch_val = 446 # 迭代次数 epochs_val = 10 # 整个训练集训练次数 imageDimesions = (32, 32, 3) # 32*32的彩色图 testRatio = 0.2 # if 1000 images split will 200 for testing 测试集占比 validationRatio = 0.2 # if 1000 images 20% of remaining 800 will be 160 for validation 验证机占比 ################################################### ############################### Importing of the Images 加载图像与标签 count = 0 images = [] classNo = [] myList = os.listdir(path) print("Total Classes Detected:", len(myList)) noOfClasses = len(myList) print("Importing Classes.....") for x in range(0, len(myList)): myPicList = os.listdir(path + "/" + str(count)) for y in myPicList: curImg = cv2.imread(path + "/" + str(count) + "/" + y) images.append(curImg) classNo.append(count) print(count, end=" ") count += 1 print(" ") # 存着对应的图片信息和标签 images = np.array(images) classNo = np.array(classNo) ############################### Split Data 分割test集和验证集 X_train, X_test, y_train, y_test = train_test_split(images, classNo, test_size=testRatio) X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validationRatio) # X_train = ARRAY OF IMAGES TO TRAIN # y_train = CORRESPONDING CLASS ID ############################### TO CHECK IF NUMBER OF IMAGES MATCHES TO NUMBER OF LABELS FOR EACH DATA SET print("Data Shapes") print("Train", end=""); print(X_train.shape, y_train.shape) print("Validation", end=""); print(X_validation.shape, y_validation.shape) print("Test", end=""); print(X_test.shape, y_test.shape) assert (X_train.shape[0] == y_train.shape[ 0]), "The number of images in not equal to the number of lables in training set" assert (X_validation.shape[0] == y_validation.shape[ 0]), "The number of images in not equal to the number of lables in validation set" assert (X_test.shape[0] == y_test.shape[0]), "The number of images in not equal to the number of lables in test set" assert (X_train.shape[1:] == (imageDimesions)), " The dimesions of the Training images are wrong " assert (X_validation.

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Opencv交通标志识别

Opencv交通标志识别

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