如何对某个目录中的多个图像进行K均值聚类并将其保存到另一个目录中？ （在本地）

Question

import numpy as np
import cv2

img = cv2.imread('home.jpg')
Z = img.reshape((-1,3))

# convert to np.float32
Z = np.float32(Z)

# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 8
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))

cv2.imshow('res2',res2)
cv2.waitKey(0)
cv2.destroyAllWindows()

上面的代码是简单的基本K均值聚类代码，适用于“单个”图像。 但是，我需要一个用于目录中多个图像的代码。

因此，我创建了代码，但无法正常工作：“ PngImageFile”对象没有属性“ reshape”（已解决的问题）

但是在那之后我遇到了错误的问题：'numpy.ndarray'对象没有属性'save'。 我认为这是因为我更改了

img = Image.open(fullpath)
#to
img = np.array(Image.open(fullpath))

以下是我正在处理的代码。

path = "Desktop/Gray/fmtial_gb/good_crop/"
sub_path = "Desktop/Gray/fmtial_gb/good_crop_result/"
dirs = os.listdir(path)
def kmean():
    from os import listdir,makedirs
    from os.path import isfile,join
    import matplotlib.pylab as plt
    import matplotlib.image as mpimg
    import cv2
    import numpy as np
    from PIL import Image
    import os.path, sys
    for item in dirs:
        fullpath = os.path.join(path,item)
        pathos = os.path.join(sub_path,item)
        if os.path.isfile(fullpath):
            #img = Image.open(fullpath)
            img = np.array(Image.open(fullpath))
            f, e = os.path.splitext(pathos)
            #img = cv2.imread('Desktop/Gray/fmtial_gb/good_crop/RD091090(80)Cropped.bmp')
            Z = img.reshape((-1,3))

            # convert to np.float32
            Z = np.float32(Z)

            # define criteria, number of clusters(K) and apply kmeans()
            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
            K = 2
            ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

            # Now convert back into uint8, and make original image
            center = np.uint8(center)
            res = center[label.flatten()]
            res2 = res.reshape((img.shape))

            #cv2.imshow('res2',res2)
            #cv2.waitKey(0)
            #cv2.destroyAllWindows()

            Image.fromarray(res2).save(f + 'kmeans.png', "png", quality=100)     
kmean()   

Answer 1

我相信这是因为您正在尝试重塑PIL图像对象而不是numpy数组。

尝试将img = Image.open(fullpath)更改为img = np.array(Image.open(fullpath)) ，它应该可以工作。