训练文件

在项目中建立training文件夹，在其中存放你想要训练的图像，其中的小文件夹就是你训练生成的图像名称，可自行更改。

识别图像

建立testing文件夹，在其中存放你想要识别的图像。

LocalBinrayPatterns

from skimage import feature
import numpy as np

class LocalBinrayPatterns:
    def __init__(self,numPoints,radius):
        self.numPoints = numPoints
        self.radius = radius

    def describle(self,image,esp=1e-7):
        lbp = feature.local_binary_pattern(image,self.numPoints,self.radius,method="uniform")
        (hist,_) = np.histogram(lbp.ravel(),bins=np.arange(0,self.numPoints + 3),range=(0,self.numPoints + 2))

        hist = hist.astype("float")
        hist /= (hist.sum() + esp)
        return hist

recognize

import cv2
from LocalBinrayPatterns import LocalBinrayPatterns
from sklearn.svm import LinearSVC
from imutils import paths
import argparse
import os


ap = argparse.ArgumentParser()
ap.add_argument("-t","--training",default="./training",help="path to the training images")
ap.add_argument("-e","--testing",default="./testing",help="path to the testing images")
args = vars(ap.parse_args())


desc = LocalBinrayPatterns(24,8)
data = []
labels = []


for imagePath in paths.list_images(args["training"]):
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    hist = desc.describle(gray)

    labels.append(imagePath.split(os.path.sep)[-2])
    data.append(hist)

model = LinearSVC(C=100.0,random_state=42)
model.fit(data, labels)



for imagePath in paths.list_images(args["testing"]):

    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    hist = desc.describle(gray)
    prediction = model.predict(hist.reshape(1,-1))


    cv2.putText(image, prediction[0],(10,30),cv2.FONT_HERSHEY_SIMPLEX,1.0,(0,0,255),3)
    cv2.imshow("Image",image)
    cv2.waitKey(0)

cv2.destroyWindow()