繼續這個問題：
以這樣一種方式，您可以識別幾個不同像素的集群并重寫檔案，以便每個集群都具有唯一的顏色，例如：
以下是我在幾個來源的幫助下嘗試實作它的方法，包括stackoverflow用戶@Rabinzel：（主要代碼塊下方的詳細推理）
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 加載影像：
im = Image.open(r"C:\\Users\\scoop\\Desktop\\prov_test.png")
我創建了一個陣列以更輕松地（？）訪問影像陣列，它將每個像素的顏色存盤為元組形式的 RGB 顏色代碼。然后這個方法識別相關的像素簇。
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)
然后我創建一個唯一 RGB 代碼串列，其長度為存在的像素簇的長度。
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，我用與之前的唯一集群關聯的新 RGB 代碼重寫每個像素（并保存影像）。
#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，這個腳本存在多個問題，我覺得它已經退化為一些廢話。主要問題似乎是訪問 .PNG Image 類的 RGB 元組并將它們更改為整數以正確識別它們以及區分不同的集群而不僅僅是不同的顏色。到目前為止，我什至無法讓腳本將影像寫成純色。
作為參考，我希望能夠將其放大以處理這樣的影像：
并為每個小簇賦予獨特的顏色。任何和所有的幫助表示贊賞。
uj5u.com熱心網友回復：
好的，讓我們看看這是否適合你。如果我正確理解了您要實作的目標，那么這是我的（初學者）解決方案。
本質上，我以 3D 陣列的形式拍攝影像，找到圖片中所有唯一的顏色并將它們替換為整數（函式：）arr_to_int。然后找到所有具有函式的簇find_clusters。創建一個新顏色的字典，其顏色與簇的數量一樣多（因此每個簇的每個 int 都會再次被一種顏色替換）。最后，再次用顏色替換所有 int 并保存圖片。
這是我用來開始的影像：
這就是我得到的新圖片作為輸出：
如果您更改如何將它們應用到您要使用的特定顏色的程序，我認為我非常接近您想要實作的目標（希望如此:)）
import numpy as np
import cv2
from scipy import ndimage
# array of GBR colors to single int
def arr_to_int(arr, col_mask):
out = np.ndarray(shape=arr.shape[:2], dtype=int)
out[:,:] = -1
for rgb, idx in col_mask.items():
out[(arr==rgb).all(2)] = idx
return out
# 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
# Load image
im = cv2.imread("prov_test.png")
#im = cv2.resize(im, (2, 3)) #resize for debugging
#print('original image: \n', im, '\n')
#find all unique colors in image (cv2 presents in BGR format!!!)
unique_col_BGR = list(set(tuple(v) for m2d in im for v in m2d))
print('unique values: ', unique_col_BGR, '\n')
#create dict with GBR_colors as keys and unique integers as value
mask_GBR_int = {color:idx for idx,color in enumerate(unique_col_BGR)}
print('mask dict: ', mask_GBR_int, '\n')
#change all color values in im to a single int (mask)
im_with_ints = arr_to_int(im, mask_GBR_int)
#print('pic with mask values: \n', im_with_ints, '\n')
# due to replacing array of 3 values to a single int, new array has one dimension less
print('orig pic resized shape', im.shape)
print('Mask int pic shape', im_with_ints.shape, '\n')
clusters, cluster_count = find_clusters(im_with_ints)
print(f'Found {cluster_count} clusters', '\n')
#print(clusters)
#create dict with length equal to number of clusters and choose color of list_of_colors (random from the internet)
list_of_colors = [[192,192,192],[128,128,128],[128,0,0],[128,128,0],[0,128,0],[128,0,128],[0,128,128],[0,0,128],[255,0,0],[0,255,0],[0,0,255],[255,255,0],[0,255,255],[255,0,255]]
new_color_dict = {idx:val for idx,val in enumerate(list_of_colors[:cluster_count])}
print('new_color_dict: ', new_color_dict,'\n')
#change arr with int to colors again
res = np.array([*new_color_dict.values()])[clusters]
#print('image array with new colors: \n', res)
cv2.imwrite("prov_test_output.png", res)
Output:
unique values: [(0, 255, 0), (255, 0, 0), (0, 0, 255), (0, 255, 255)]
mask dict: {(0, 255, 0): 0, (255, 0, 0): 1, (0, 0, 255): 2, (0, 255, 255): 3}
orig pic resized shape (100, 100, 3)
Mask int pic shape (100, 100)
Found 9 clusters
new_color_dict: {0: [192, 192, 192], 1: [128, 128, 128], 2: [128, 0, 0], 3: [128, 128, 0], 4: [0, 128, 0], 5: [128, 0, 128], 6: [0, 128, 128], 7: [0, 0, 128], 8: [255, 0, 0]}
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