Python怎么对图像补全并分割成多块补丁

这篇文章主要介绍了Python怎么对图像补全并分割成多块补丁的相关知识，内容详细易懂，操作简单快捷，具有一定借鉴价值，相信大家阅读完这篇Python怎么对图像补全并分割成多块补丁文章都会有所收获，下面我们一起来看看吧。

题目

编写一个程序，按照输入的宽高，将测试图像分割成多个补丁块，超出图像边界的部分用黑色像素补齐

思路

按照输入的宽高，先判断原始图像与其取模是否为零，判断需不需要进行图像填充

如果需要进行图像填充，先计算出新图像的宽和高（（整除后+1）* 指定宽高），然后新建一张全黑图像，将原图像默认为左上角位置粘贴进去

最后进行图像裁剪，使用两层for循环，步长设定为补丁的宽高，使用crop函数，指定补丁图片的左、上、右、下坐标

代码

import numpy as npfrom PIL import Image# 判断是否需要进行图像填充def judge(img, wi, he):    width, height = img.size    # 默认新图像尺寸初始化为原图像    new_width, new_height = img.size    if width % wi != 0:        new_width = (width//wi + 1) * wi    if height % he != 0:        new_height = (height//he + 1) * he    # 新建一张新尺寸的全黑图像    new_image = Image.new('RGB', (new_width, new_height), (0, 0, 0))    # 将原图像粘贴在new_image上，默认为左上角坐标对应    new_image.paste(img, box=None, mask=None)    new_image.show()    return new_image# 按照指定尺寸进行图片裁剪def crop_image(image, patch_w, patch_h):    width, height = image.size    # 补丁计数    cnt = 0    for w in range(0, width, patch_w):        for h in range(0, height, patch_h):            cnt += 1            # 指定原图片的左、上、右、下            img = image.crop((w, h, w+patch_w, h+patch_h))            img.save("dog-%d.jpg" % cnt)    print("图片补丁裁剪结束，共有{}张补丁".format(cnt))def main():    image_path = "dog.jpg"    img = Image.open(image_path)    # 查看图像形状    print("原始图像形状{}".format(np.array(img).shape))    # 输入指定的补丁宽高    print("输入补丁宽高：")    wi, he = map(int, input().split(" "))    # 进行图像填充    new_image = judge(img, wi, he)    # 图片补丁裁剪    crop_image(new_image, wi, he)if __name__ == '__main__':    main()

效果展示

原图像使用了黑色像素填充

图像裁剪，分割成小补丁

图像分割方法总结

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, th2 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)    th3 = cv2.adaptiveThreshold(        gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)    th4 = cv2.adaptiveThreshold(        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)    ret2, th5 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)    images = [th2, th3, th5, th4]    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()

关于"Python怎么对图像补全并分割成多块补丁"这篇文章的内容就介绍到这里，感谢各位的阅读！相信大家对"Python怎么对图像补全并分割成多块补丁"知识都有一定的了解，大家如果还想学习更多知识，欢迎关注行业资讯频道。