Python如何对图像补全并分割成多块补丁_python_

import numpy as np from 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() 

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

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)

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)

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

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]

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()