python 图像分割方法总结

您所在的位置：网站首页 › 图像分割算法代码 › python 图像分割方法总结

python 图像分割方法总结

2023-11-16 22:00| 来源: 网络整理| 查看: 265

图像分割是一种常用的图像处理方法，可分为传统方法和深度学习的方法。深度学习的方法比如：mask rcnn这类实例分割模型，效果比传统的图像分割方法要好的多，所以目前图像分割领域都是用深度学习来做的。但是深度学习也有它的缺点，模型大、推理速度慢、可解释性差、训练数据要求高等。本文在这里仅讨论传统的图像分割算法，可供学习和使用。 1、阈值分割最简单的图像分割算法，只直接按照像素值进行分割，虽然简单，但是在一些像素差别较大的场景中表现不错，是一种简单而且稳定的算法。

def thresholdSegment(filename): gray = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) ret1, th1 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) th2 = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2) th3 = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) ret2, th4 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) images = [th1, th2, th4, th3] imgaesTitle = ['THRESH_BINARY', 'THRESH_MEAN', 'THRESH_OTSU', 'THRESH_GAUSSIAN'] plt.figure() for i in range(4): plt.subplot(2, 2, i+1) plt.imshow(images[i], 'gray') plt.title(imgaesTitle[i]) cv2.imwrite(imgaesTitle[i]+'.jpg', images[i]) plt.show() cv2.waitKey(0) return images

2、边界分割（边缘检测）

def edgeSegmentation(filename): # 读取图片 img = cv2.imread(filename) # 灰度化 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 高斯模糊处理:去噪(效果最好) blur = cv2.GaussianBlur(gray, (9, 9), 0) # Sobel计算XY方向梯度 gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0) gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1) # 计算梯度差 gradient = cv2.subtract(gradX, gradY) # 绝对值 gradient = cv2.convertScaleAbs(gradient) # 高斯模糊处理:去噪(效果最好) blured = cv2.GaussianBlur(gradient, (9, 9), 0) # 二值化 _, dst = cv2.threshold(blured, 90, 255, cv2.THRESH_BINARY) # 滑动窗口 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (107, 76)) # 形态学处理:形态闭处理(腐蚀) closed = cv2.morphologyEx(dst, cv2.MORPH_CLOSE, kernel) # 腐蚀与膨胀迭代 closed = cv2.erode(closed, None, iterations=4) closed = cv2.dilate(closed, None, iterations=4) # 获取轮廓 _, cnts, _ = cv2.findContours( closed.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) c = sorted(cnts, key=cv2.contourArea, reverse=True)[0] rect = cv2.minAreaRect(c) box = np.int0(cv2.boxPoints(rect)) draw_img = cv2.drawContours(img.copy(), [box], -1, (0, 0, 255), 3) #cv2.imshow("Box", draw_img) #cv2.imwrite('./test/monkey.png', draw_img) images = [blured, dst, closed, draw_img] imgaesTitle = ['blured', 'dst', 'closed', 'draw_img'] plt.figure() for i in range(4): plt.subplot(2, 2, i+1) plt.imshow(images[i], 'gray') plt.title(imgaesTitle[i]) #cv2.imwrite(imgaesTitle[i]+'.jpg', images[i]) plt.show() cv2.waitKey(0)

3、区域分割（区域生成）

def regionSegmentation(filename): # 读取图片 img = cv2.imread(filename) # 图片宽度 img_x = img.shape[1] # 图片高度 img_y = img.shape[0] # 分割的矩形区域 rect = (0, 0, img_x-1, img_y-1) # 背景模式,必须为1行,13x5列 bgModel = np.zeros((1, 65), np.float64) # 前景模式,必须为1行,13x5列 fgModel = np.zeros((1, 65), np.float64) # 图像掩模,取值有0,1,2,3 mask = np.zeros(img.shape[:2], np.uint8) # grabCut处理,GC_INIT_WITH_RECT模式 cv2.grabCut(img, mask, rect, bgModel, fgModel, 4, cv2.GC_INIT_WITH_RECT) # grabCut处理,GC_INIT_WITH_MASK模式 #cv2.grabCut(img, mask, rect, bgModel, fgModel, 4, cv2.GC_INIT_WITH_MASK) # 将背景0,2设成0,其余设成1 mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8') # 重新计算图像着色,对应元素相乘 img = img*mask2[:, :, np.newaxis] cv2.imshow("Result", img) cv2.waitKey(0)

4、SVM分割（支持向量机）

def svmSegment(pic): img = Image.open(pic) img.show() # 显示原始图像 img_arr = np.asarray(img, np.float64) #选取图像上的关键点RGB值(10个) lake_RGB = np.array( [[147, 168, 125], [151, 173, 124], [143, 159, 112], [150, 168, 126], [146, 165, 120], [145, 161, 116], [150, 171, 130], [146, 112, 137], [149, 169, 120], [144, 160, 111]]) # 选取待分割目标上的关键点RGB值(10个) duck_RGB = np.array( [[81, 76, 82], [212, 202, 193], [177, 159, 157], [129, 112, 105], [167, 147, 136], [237, 207, 145], [226, 207, 192], [95, 81, 68], [198, 216, 218], [197, 180, 128]] ) RGB_arr = np.concatenate((lake_RGB, duck_RGB), axis=0) # 按列拼接 # lake 用 0标记，duck用1标记 label = np.append(np.zeros(lake_RGB.shape[0]), np.ones(duck_RGB.shape[0])) # 原本 img_arr 形状为(m,n,k),现在转化为(m*n,k) img_reshape = img_arr.reshape( [img_arr.shape[0]*img_arr.shape[1], img_arr.shape[2]]) svc = SVC(kernel='poly', degree=3) # 使用多项式核，次数为3 svc.fit(RGB_arr, label) # SVM 训练样本 predict = svc.predict(img_reshape) # 预测测试点 lake_bool = predict == 0. lake_bool = lake_bool[:, np.newaxis] # 增加一列(一维变二维) lake_bool_3col = np.concatenate( (lake_bool, lake_bool, lake_bool), axis=1) # 变为三列 lake_bool_3d = lake_bool_3col.reshape( (img_arr.shape[0], img_arr.shape[1], img_arr.shape[2])) # 变回三维数组(逻辑数组) img_arr[lake_bool_3d] = 255. img_split = Image.fromarray(img_arr.astype('uint8')) # 数组转image img_split.show() # 显示分割之后的图像 img_split.save('split_duck.jpg') # 保存

5、分水岭分割

def watershedSegment(filename): img = cv2.imread(filename) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) # noise removal kernel = np.ones((3,3),np.uint8) opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) # sure background area sure_bg = cv2.dilate(opening,kernel,iterations=3) # Finding sure foreground area dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5) ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0) # Finding unknown region sure_fg = np.uint8(sure_fg) unknown = cv2.subtract(sure_bg,sure_fg) # Marker labelling ret, markers = cv2.connectedComponents(sure_fg) # Add one to all labels so that sure background is not 0, but 1 markers = markers+1 # Now, mark the region of unknown with zero markers[unknown==255]=0 markers = cv2.watershed(img,markers) img[markers == -1] = [255,0,0]

6、Kmeans分割

def kmeansSegment(filename,k): f = open(filename,'rb') #二进制打开 data = [] img = Image.open(f) #以列表形式返回图片像素值 m,n = img.size #图片大小 for i in range(m): for j in range(n): #将每个像素点RGB颜色处理到0-1范围内并存放data x,y,z = img.getpixel((i,j)) data.append([x/256.0,y/256.0,z/256.0]) f.close() img_data=np.mat(data) row=m col=n label = KMeans(n_clusters=k).fit_predict(img_data) #聚类中心的个数为3 label = label.reshape([row,col]) #聚类获得每个像素所属的类别 pic_new = Image.new("L",(row,col)) #创建一张新的灰度图保存聚类后的结果 for i in range(row): #根据所属类别向图片中添加灰度值 for j in range(col): pic_new.putpixel((i,j),int(256/(label[i][j]+1))) pic_new.save('keans_'+str(k)+'.jpg') plt.imshow(pic_new) plt.show()

【本文地址】

python 图像分割方法总结

python 图像分割方法总结

今日新闻

推荐新闻