Python图像纹理分割

骑猪看日落 2023-10-02 17:33 35阅读 0赞

将下图左右两种不同类型的纹理区域分开，方法输出结果是一幅与该图像等大小的二值图像，左边为0，右边为1，或者相反，灰色边框线在设计的方法中不作考虑，自行去除。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16]

import matplotlib.image as mpimg
    import matplotlib.pyplot as plt
    import numpy as np
    from cv2 import cv2
    from sklearn.multiclass import OneVsRestClassifier
    from sklearn.svm import SVR
    from skimage import feature as skft
    from PIL import Image,ImageDraw,ImageFont
    
    class Texture():
       #n_point选取中心像素周围的像素点的个数
       #radius选取的区域的半径
        def __init__(self):
            self.radius = 1
            self.n_point = self.radius * 8
        #加载灰度图片
        def loadPicture(self):
            train_index = 0
            test_index = 0
            train_data = np.zeros((10, 171, 171))
            test_data = np.zeros((8, 171, 171))
            train_label = np.zeros(10)
            test_label = np.zeros(8)
            for i in np.arange(2):
                image = mpimg.imread('dataset1/'+str(i)+'.tiff')
                data = np.zeros((513, 513))
                data[0:image.shape[0], 0:image.shape[1]] = image
                index = 0
                #图片分成9块，5块训练4块预测
                for row in np.arange(3):
                    for col in np.arange(3):
                        if index < 5:
                          train_data[train_index, :, :] = data[171*row:171*(row+1),171*col:171*(col+1)]
                          train_label[train_index] = i
                          train_index += 1
                        else:
                          test_data[test_index, :, :] = data[171*row:171*(row+1),171*col:171*(col+1)]
                          test_label[test_index] = i
                          test_index += 1
                        index += 1
    
            return train_data, test_data, train_label, test_label
        #纹理检测
        def texture_detect(self):
            train_data, test_data, train_label, test_label = self.loadPicture()
            n_point = self.n_point
            radius = self.radius
            train_hist = np.zeros((10, 256))
            test_hist = np.zeros((8, 256))
            #LBP特征提取
            for i in np.arange(10):
                # 使用LBP方法提取图像的纹理特征.
                lbp=skft.local_binary_pattern(train_data[i], n_point, radius, 'default')
                # 统计图像的直方图
                max_bins = int(lbp.max() + 1)
                # hist size:256
                train_hist[i], _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins))
    
            for i in np.arange(8):
                lbp = skft.local_binary_pattern(test_data[i], n_point, radius, 'default')
                max_bins = int(lbp.max() + 1)
                # hist size:256
                test_hist[i], _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins))
    
            return train_hist, test_hist
        #训练分类器 SVM支持向量机分类
        def classifer(self):
            train_data, test_data, train_label, test_label = self.loadPicture()
            train_hist, test_hist = self.texture_detect()
            #构建SVM支持向量机
            svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
            model= OneVsRestClassifier(svr_rbf, -1)
            model.fit(train_hist, train_label)
    
            image=cv2.imread('dataset1/image.png',cv2.IMREAD_GRAYSCALE)
            image=cv2.resize(image,(588,294))
            img_ku=skft.local_binary_pattern(image,8,1,'default')
            thresh=cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
            #对图像进行识别分类
            img_data=np.zeros((1,98,98))
            data=np.zeros((294,588))
            data[0:image.shape[0],0:image.shape[1]]=image
            img = Image.open('texture.png')
            draw = ImageDraw.Draw(img)
            # 设置字体和大小
            myfont = ImageFont.truetype('C:/windows/fonts/Arial.ttf', size=180)
            # 设置字体颜色
            fillcolor = "#008B8B"
            # 读取图片的size，也就是宽度和高度
            width, height = img.size
    
            #通过行列将图像分为九块
            for row in np.arange(3):
                for col in np.arange(6):
                    img_data[0,:,:]=data[98*row:98*(row+1),98*col:98*(col+1)]
                    lbp= skft.local_binary_pattern(img_data[0], 8, 1, 'default')
                    max_bins=int(lbp.max()+1)
                    img_hist, _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins))
                    predict=model.predict(img_hist.reshape(1,-1))
                    if(predict==0):
                        draw.text((width/6, height/6), '0', font=myfont, fill=fillcolor)
                        data[98*row:98*(row+1),98*col:98*(col+1)]=0
                    else:
                        draw.text((2*width /3, height/6), '1', font=myfont, fill=fillcolor)
                        data[98*row:98*(row+1),98*col:98*(col+1)]=255
            plt.subplot(211)
            plt.imshow(img_ku,'gray')
            #plt.subplot(312)
            #plt.imshow(thresh,'gray')
            plt.subplot(212)
            plt.imshow(img)
            plt.show()
    
    
    
    if __name__ == '__main__':
          test = Texture()
          # 计算分类准确度部分
          accuracy = test.classifer()
          #print('Final Accuracy = '+str(accuracy)+'%')

上面一张图是结果LBP算子特征提取后的图片，下面一张图使用SVM进行纹理分类后得到的结果，与预测的左上图纹理相同标为0，与右上图相同标为1。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 1]

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[watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16]: https://img-blog.csdnimg.cn/2dcf461ccfb74aaa8a2c434a5f3552c6.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBA5oyv5Y2OT1BQTw==,size_20,color_FFFFFF,t_70,g_se,x_16
[watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 1]: https://img-blog.csdnimg.cn/0cfdf80e7b4248989cec93c20a838561.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBA5oyv5Y2OT1BQTw==,size_20,color_FFFFFF,t_70,g_se,x_16