标签：elif return img cv2 opencv train classNo 识别 交通标志
一共可以识别43种交通标志！！！
界面展示：
视频演示地址：
交通标志识别
训练网络代码：
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.shape[1:] == (imageDimesions)), " The dimesionas of the Validation images are wrong "
assert (X_test.shape[1:] == (imageDimesions)), " The dimesionas of the Test images are wrong"
############################### READ CSV FILE
data = pd.read_csv(labelFile)
print("data shape ", data.shape, type(data))
############################### DISPLAY SOME SAMPLES IMAGES OF ALL THE CLASSES
# 可视化部分图标及类别
num_of_samples = []
cols = 5
num_classes = noOfClasses
fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5, 300))
fig.tight_layout()
for i in range(cols):
for j, row in data.iterrows():
x_selected = X_train[y_train == j]
axs[j][i].imshow(x_selected[random.randint(0, len(x_selected) - 1), :, :], cmap=plt.get_cmap("gray"))
axs[j][i].axis("off")
if i == 2:
axs[j][i].set_title(str(j) + "-" + row["Name"])
num_of_samples.append(len(x_selected))
############################### DISPLAY A BAR CHART SHOWING NO OF SAMPLES FOR EACH CATEGORY
# 对类别分布做一个统计 饼图
print(num_of_samples)
plt.figure(figsize=(12, 4))
plt.bar(range(0, num_classes), num_of_samples)
plt.title("Distribution of the training dataset")
plt.xlabel("Class number")
plt.ylabel("Number of images")
plt.show()
############################### PREPROCESSING THE IMAGES
# 灰度
def grayscale(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img
# 直方图均衡化
def equalize(img):
img = cv2.equalizeHist(img)
return img
def preprocessing(img):
img = grayscale(img) # CONVERT TO GRAYSCALE
img = equalize(img) # STANDARDIZE THE LIGHTING IN AN IMAGE
img = img / 255 # TO NORMALIZE VALUES BETWEEN 0 AND 1 INSTEAD OF 0 TO 255
return img
# 对所有数据进行预处理
X_train = np.array(list(map(preprocessing, X_train))) # TO IRETATE AND PREPROCESS ALL IMAGES
X_validation = np.array(list(map(preprocessing, X_validation)))
X_test = np.array(list(map(preprocessing, X_test)))
#cv2.imshow("GrayScale Images",
#X_train[random.randint(0, len(X_train) - 1)]) # TO CHECK IF THE TRAINING IS DONE PROPERLY
############################### ADD A DEPTH OF 1
# 增加一维
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_validation = X_validation.reshape(X_validation.shape[0], X_validation.shape[1], X_validation.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)
############################### AUGMENTATAION OF IMAGES: TO MAKEIT MORE GENERIC
# width_shift_range 图像偏移 width_shift_range*width
# height_shift_range height_shift_range*height
# zoom_range: Float or [lower, upper]. Range for random zoom. 随机缩放范围
# shear_range: Float. Shear Intensity (Shear angle in counter-clockwise direction in degrees) # 剪切-剪切角度-逆时针剪切
# rotation_range: Int. Degree range for random rotations. 随机旋转的角度范围
dataGen = ImageDataGenerator(width_shift_range=0.1,
# 0.1 = 10% IF MORE THAN 1 E.G 10 THEN IT REFFERS TO NO. OF PIXELS EG 10 PIXELS
height_shift_range=0.1,
zoom_range=0.2, # 0.2 MEANS CAN GO FROM 0.8 TO 1.2
shear_range=0.1, # MAGNITUDE OF SHEAR ANGLE
rotation_range=10) # DEGREES
dataGen.fit(X_train)
batches = dataGen.flow(X_train, y_train,
batch_size=20) # REQUESTING DATA GENRATOR TO GENERATE IMAGES BATCH SIZE = NO. OF IMAGES CREAED EACH TIME ITS CALLED
X_batch, y_batch = next(batches)
# TO SHOW AGMENTED IMAGE SAMPLES
fig, axs = plt.subplots(1, 15, figsize=(20, 5))
fig.tight_layout()
for i in range(15):
axs[i].imshow(X_batch[i].reshape(imageDimesions[0], imageDimesions[1]))
axs[i].axis('off')
plt.show()
# one-hot
y_train = to_categorical(y_train, noOfClasses)
y_validation = to_categorical(y_validation, noOfClasses)
y_test = to_categorical(y_test, noOfClasses)
############################### CONVOLUTION NEURAL NETWORK MODEL 定义模型
# 卷积-卷积-池化 卷积-卷积-池化 drop
def myModel():
no_Of_Filters = 60
size_of_Filter = (5, 5) # THIS IS THE KERNEL THAT MOVE AROUND THE IMAGE TO GET THE FEATURES.
# THIS WOULD REMOVE 2 PIXELS FROM EACH BORDER WHEN USING 32 32 IMAGE
size_of_Filter2 = (3, 3)
size_of_pool = (2, 2) # SCALE DOWN ALL FEATURE MAP TO GERNALIZE MORE, TO REDUCE OVERFITTING
no_Of_Nodes = 500 # NO. OF NODES IN HIDDEN LAYERS
model = Sequential()
model.add((Conv2D(no_Of_Filters, size_of_Filter, input_shape=(imageDimesions[0], imageDimesions[1], 1),
activation='relu'))) # ADDING MORE CONVOLUTION LAYERS = LESS FEATURES BUT CAN CAUSE ACCURACY TO INCREASE
model.add((Conv2D(no_Of_Filters, size_of_Filter, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool)) # DOES NOT EFFECT THE DEPTH/NO OF FILTERS
model.add((Conv2D(no_Of_Filters // 2, size_of_Filter2, activation='relu')))
model.add((Conv2D(no_Of_Filters // 2, size_of_Filter2, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(no_Of_Nodes, activation='relu'))
model.add(Dropout(0.5)) # INPUTS NODES TO DROP WITH EACH UPDATE 1 ALL 0 NONE
model.add(Dense(noOfClasses, activation='softmax')) # OUTPUT LAYER
# COMPILE MODEL
model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
return model
############################### TRAIN
model = myModel()
print(model.summary())
# 开始训练
history = model.fit_generator(dataGen.flow(X_train, y_train, batch_size=batch_size_val),
steps_per_epoch=steps_per_epoch_val, epochs=epochs_val,
validation_data=(X_validation, y_validation), shuffle=1)
############################### PLOT
plt.figure(1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['training', 'validation'])
plt.title('loss')
plt.xlabel('epoch')
plt.figure(2)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training', 'validation'])
plt.title('Acurracy')
plt.xlabel('epoch')
plt.show()
# 开始评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test Score:', score[0])
print('Test Accuracy:', score[1])
#保持模型
model.save('traffic.h5')
# STORE THE MODEL AS A PICKLE OBJECT
# pickle_out = open("model_trained.p", "wb") # wb = WRITE BYTE
# pickle.dump(model, pickle_out)
# pickle_out.close()
# cv2.waitKey(0)
预测代码：
import numpy as np
import cv2
import pickle
import tensorflow as tf
#############################################
# 设置帧参数 长宽 亮度 阈值 字体
frameWidth = 640 # CAMERA RESOLUTION
frameHeight = 480
brightness = 180
threshold = 0.75 # PROBABLITY THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
##############################################
# SETUP THE VIDEO CAMERA
# cap = cv2.VideoCapture(0)
# cap.set(3, frameWidth)
# cap.set(4, frameHeight)
# cap.set(10, brightness)
# IMPORT THE TRANNIED MODEL
# 导入训练好的模型参数
model=tf.keras.models.load_model('traffic.h5')
# pickle_in = open("model_trained.p", "rb") ## rb = READ BYTE
# model = pickle.load(pickle_in)
# 转灰度
def grayscale(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img
# 灰度图像均衡化
# https://zhuanlan.zhihu.com/p/54771264
def equalize(img):
img = cv2.equalizeHist(img)
return img
# 灰度均衡化归一化
def preprocessing(img):
img = grayscale(img)
img = equalize(img)
img = img / 255
return img
# 标志代表含义
def getCalssName(classNo):
if classNo == 0:
return 'Speed Limit 20 km/h'
elif classNo == 1:
return 'Speed Limit 30 km/h'
elif classNo == 2:
return 'Speed Limit 50 km/h'
elif classNo == 3:
return 'Speed Limit 60 km/h'
elif classNo == 4:
return 'Speed Limit 70 km/h'
elif classNo == 5:
return 'Speed Limit 80 km/h'
elif classNo == 6:
return 'End of Speed Limit 80 km/h'
elif classNo == 7:
return 'Speed Limit 100 km/h'
elif classNo == 8:
return 'Speed Limit 120 km/h'
elif classNo == 9:
return 'No passing'
elif classNo == 10:
return 'No passing for vechiles over 3.5 metric tons'
elif classNo == 11:
return 'Right-of-way at the next intersection'
elif classNo == 12:
return 'Priority road'
elif classNo == 13:
return 'Yield'
elif classNo == 14:
return 'Stop'
elif classNo == 15:
return 'No vechiles'
elif classNo == 16:
return 'Vechiles over 3.5 metric tons prohibited'
elif classNo == 17:
return 'No entry'
elif classNo == 18:
return 'General caution'
elif classNo == 19:
return 'Dangerous curve to the left'
elif classNo == 20:
return 'Dangerous curve to the right'
elif classNo == 21:
return 'Double curve'
elif classNo == 22:
return 'Bumpy road'
elif classNo == 23:
return 'Slippery road'
elif classNo == 24:
return 'Road narrows on the right'
elif classNo == 25:
return 'Road work'
elif classNo == 26:
return 'Traffic signals'
elif classNo == 27:
return 'Pedestrians'
elif classNo == 28:
return 'Children crossing'
elif classNo == 29:
return 'Bicycles crossing'
elif classNo == 30:
return 'Beware of ice/snow'
elif classNo == 31:
return 'Wild animals crossing'
elif classNo == 32:
return 'End of all speed and passing limits'
elif classNo == 33:
return 'Turn right ahead'
elif classNo == 34:
return 'Turn left ahead'
elif classNo == 35:
return 'Ahead only'
elif classNo == 36:
return 'Go straight or right'
elif classNo == 37:
return 'Go straight or left'
elif classNo == 38:
return 'Keep right'
elif classNo == 39:
return 'Keep left'
elif classNo == 40:
return 'Roundabout mandatory'
elif classNo == 41:
return 'End of no passing'
elif classNo == 42:
return 'End of no passing by vechiles over 3.5 metric tons'
# while True:
#
# # READ IMAGE
# success, imgOrignal = cap.read()
#
# # PROCESS IMAGE
# # 图片预处理
# img = np.asarray(imgOrignal)
# # 网络输入图片指定32*32
# img = cv2.resize(img, (32, 32))
# img = preprocessing(img)
# cv2.imshow("Processed Image", img)
# img = img.reshape(1, 32, 32, 1)
# cv2.putText(imgOrignal, "CLASS: ", (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
# cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
# # PREDICT IMAGE
# # 预测
# predictions = model.predict(img)
# classIndex = model.predict_classes(img)
# probabilityValue = np.amax(predictions)
# # 概率大于阈值才判断有效检测
# if probabilityValue > threshold:
# # print(getCalssName(classIndex))
# cv2.putText(imgOrignal, str(classIndex) + " " + str(getCalssName(classIndex)), (120, 35), font, 0.75,
# (0, 0, 255), 2, cv2.LINE_AA)
# cv2.putText(imgOrignal, str(round(probabilityValue * 100, 2)) + "%", (180, 75), font, 0.75, (0, 0, 255), 2,
# cv2.LINE_AA)
# cv2.imshow("Result", imgOrignal)
#
# if cv2.waitKey(1) and 0xFF == ord('q'):
# break
# 图片预处理
def pres(imgOrignal):
img = np.asarray(imgOrignal)
# 网络输入图片指定32*32
img = cv2.resize(img, (32, 32))
img = preprocessing(img)
#显示预处理图像
# cv2.imshow("Processed Image", img)
# cv2.waitKey(0)
img = img.reshape(1, 32, 32, 1)
#cv2.putText(imgOrignal, "CLASS: ", (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
#cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
# cv2.imshow('qw21',imgOrignal)
# cv2.waitKey(0)
# PREDICT IMAGE
# 预测
predictions = model.predict(img)
classIndex = model.predict_classes(img)
probabilityValue = np.argmax(predictions,axis=-1)
# 概率大于阈值才判断有效检测
if probabilityValue > threshold:
# print(getCalssName(classIndex))
return str(getCalssName(classIndex))
# cv2.putText(imgOrignal, str(classIndex) + " " + str(getCalssName(classIndex)), (120, 35), font, 0.75,
# (0, 0, 255), 2, cv2.LINE_AA)
# cv2.putText(imgOrignal, str(round(probabilityValue * 100, 2)) + "%", (180, 75), font, 0.75, (0, 0, 255), 2,
# cv2.LINE_AA)
# cv2.imshow("Result", imgOrignal)
# cv2.waitKey(0)
else:
return "No"
if __name__ == '__main__':
imgOrignal = cv2.imread('img.png')
out=pres(imgOrignal)
数据集下载地址：
全部项目下载地址（带界面）：
标签：elif,return,img,cv2,opencv,train,classNo,识别,交通标志 来源： https://blog.csdn.net/qq_45087786/article/details/121864804
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