Python 阅读 .PNG，如何识别 colored颜色 簇并重写图像文件，使每个簇都有唯一的 RGB 代码

继续回答这个问题:How could you rewrite a list of lists so that "islands" of values are unique from one another?

简介:如何解析图像，例如:

通过这种方式，您可以识别不同像素的几个簇，并重写文件，使每个簇具有唯一的 colored颜色 ，例如:

以下是我如何在几个来源的帮助下实现它，包括stackoverflow user@Rabinzel:(主要代码块下面的详细推理)

    from scipy import ndimage
    import numpy as np
    from PIL import Image
    
    #set the file path to wherever your provinces.png is located
    im = Image.open(r"C:\\Users\\scoop\\Desktop\\prov_test.png")
    
    print('-------------------------------------------')
    #DEBUGGING: simply prints the format, size, and mode of your file
    print(im.format, im.size, im.mode)
    #saves the width and depth of the file
    im_xsize = im.size[0]
    im_ysize = im.size[1]
    #DEBUGGING: prints it
    print(im_xsize, im_ysize)
    #DEBUGGNG: prints data bands, should be R, G, B
    print(im.getbands())
    
    #DEBUGGING: prints RGB value of pixel of choice
    print(im.getpixel((0,0)))
    print('-------------------------------------------')
    
    #creates array for pixel RGBs
    rgb_array = [[None] * im_ysize for length in range(0,im_xsize)]
    
    #fills pixel RGB array
    for x in range(0,im_xsize):
        for y in range(0,im_ysize):
            rgb_array[x][y] = im.getpixel((x,y))
       
    #find unique clusters of identical RGB codes
    def find_clusters(array):
        clustered = np.empty_like(array)
        unique_vals = np.unique(array)
        cluster_count = 0
        for val in unique_vals:
            labelling, label_count = ndimage.label(array == val)
            for k in range(1, label_count + 1):
                clustered[labelling == k] = cluster_count
                cluster_count += 1
        return clustered, cluster_count
    
    clusters, cluster_count = find_clusters(rgb_array)
    print("Found {} clusters:".format(cluster_count))
    #print(clusters)
    
    #defining a list of unique colors
    province_color_list = [[0] * 3 for length in range(0,cluster_count)] 
    
    #DEBUGGING
    print('province count...', cluster_count)
    #variables
    r = 255
    g = 0
    b = 0
    count = 0
    
    #generating colors
for length in range(0,cluster_count):
    province_color_list[length][0] = r
    province_color_list[length][1] = g
    province_color_list[length][2] = b
    g += 25
    b += 25
    count += 1
    if count >= 11:
        r -= 1
        g = 0
        b = 0
        count = 0

#DEBUGGING
print('# of colors... ', len(province_color_list))
print(province_color_list)
print('-------------------------------------------')

#writing colors to pixels
for x in range(0,im_xsize):
    for y in range(0,im_ysize):
        #places province color based on which province current pixel is assigned to
        im.putpixel((x,y),   (province_color_list[0][0],   province_color_list[0][1],   province_color_list[0][2]))
         
#im.save(r"C:\\Users\\scoop\\Desktop\\prov_test.png", im.format)

我使用PIL加载图像:

im = Image.open(r"C:\\Users\\scoop\\Desktop\\prov_test.png")

我创建一个数组来更容易(？)访问图像数组，该数组以元组形式将每个像素的 colored颜色 存储为RGB colored颜色 代码.然后该方法识别相关的像素簇.

rgb_array = [[None] * im_ysize for length in range(0,im_xsize)]

#fills pixel RGB array
for x in range(0,im_xsize):
    for y in range(0,im_ysize):
        rgb_array[x][y] = im.getpixel((x,y))
   
#find unique clusters of identical RGB codes
def find_clusters(array):
    clustered = np.empty_like(array)
    unique_vals = np.unique(array)
    cluster_count = 0
    for val in unique_vals:
        labelling, label_count = ndimage.label(array == val)
        for k in range(1, label_count + 1):
            clustered[labelling == k] = cluster_count
            cluster_count += 1
    return clustered, cluster_count

clusters, cluster_count = find_clusters(rgb_array)

然后我创建了一个独特的RGB代码列表，即存在的#个像素簇的长度.

province_color_list = [[0] * 3 for length in range(0,cluster_count)] 

#DEBUGGING
print('province count...', cluster_count)
#variables
r = 255
g = 0
b = 0
count = 0

#generating colors
for length in range(0,cluster_count):
    province_color_list[length][0] = r
    province_color_list[length][1] = g
    province_color_list[length][2] = b
    g += 25
    b += 25
    count += 1
    if count >= 11:
        r -= 1
        g = 0
        b = 0
        count = 0

最后，我用新的RGB代码重写每个像素，该代码与之前的唯一簇关联(并保存图像).

#writing colors to pixels
for x in range(0,im_xsize):
    for y in range(0,im_ysize):
        #places province color based on which province current pixel is assigned to
        im.putpixel((x,y),   (province_color_list[clusters[x][y]][0],   province_color_list[clusters[x][y]][1],   province_color_list[clusters[x][y]][2]))
         
#im.save(r"C:\\Users\\scoop\\Desktop\\prov_test.png", im.format)

不幸的是，这个脚本有多个问题，我觉得它已经退化成了一点废话.主要的问题似乎是访问.PNG图像类，并将其更改为整数以正确识别它们，以及区分不同的簇，而不仅仅是不同的 colored颜色 .到目前为止，我甚至无法让脚本将图像写成平淡的 colored颜色 .

作为参考，我希望能够放大它来处理这样的图像:

给每个小簇一个独特的 colored颜色 .感谢所有帮助.