难得被人求助一次, 这个必须回答一下 不过你的需求确实没有写得太清楚 根据k值算法出来的是主要颜色有三个, 所以我把三个颜色都打在记事本里了 如果和你的需求有误, 请自行解决吧
另外这里需要用到numpy的库, 希望你装了, 如果没装, 这个直接安装也比较麻烦, 可以看一下portablepython的绿色版。
代码如下:
# -- coding: utf-8 --
import Image
import random
import numpy
class Cluster(object):
def __init__(self):
selfpixels = []
selfcentroid = None
def addPoint(self, pixel):
selfpixelsappend(pixel)
def setNewCentroid(self):
R = [colour[0] for colour in selfpixels]
G = [colour[1] for colour in selfpixels]
B = [colour[2] for colour in selfpixels]
R = sum(R) / len(R)
G = sum(G) / len(G)
B = sum(B) / len(B)
selfcentroid = (R, G, B)
selfpixels = []
return selfcentroid
class Kmeans(object):
def __init__(self, k=3, max_iterations=5, min_distance=50, size=200):
selfk = k
selfmax_iterations = max_iterations
selfmin_distance = min_distance
selfsize = (size, size)
def run(self, image):
selfimage = image
selfimagethumbnail(selfsize)
selfpixels = numpyarray(imagegetdata(), dtype=numpyuint8)
selfclusters = [None for i in range(selfk)]
selfoldClusters = None
randomPixels = randomsample(selfpixels, selfk)
for idx in range(selfk):
selfclusters[idx] = Cluster()
selfclusters[idx]centroid = randomPixels[idx]
iterations = 0
while selfshouldExit(iterations) is False:
selfoldClusters = [clustercentroid for cluster in selfclusters]
print iterations
for pixel in selfpixels:
selfassignClusters(pixel)
for cluster in selfclusters:
clustersetNewCentroid()
iterations += 1
return [clustercentroid for cluster in selfclusters]
def assignClusters(self, pixel):
shortest = float('Inf')
for cluster in selfclusters:
distance = selfcalcDistance(clustercentroid, pixel)
if distance < shortest:
shortest = distance
nearest = cluster
nearestaddPoint(pixel)
def calcDistance(self, a, b):
result = numpysqrt(sum((a - b) 2))
return result
def shouldExit(self, iterations):
if selfoldClusters is None:
return False
for idx in range(selfk):
dist = selfcalcDistance(
numpyarray(selfclusters[idx]centroid),
numpyarray(selfoldClusters[idx])
)
if dist < selfmin_distance:
return True
if iterations <= selfmax_iterations:
return False
return True
# ############################################
# The remaining methods are used for debugging
def showImage(self):
selfimageshow()
def showCentroidColours(self):
for cluster in selfclusters:
image = Imagenew("RGB", (200, 200), clustercentroid)
imageshow()
def showClustering(self):
localPixels = [None] len(selfimagegetdata())
for idx, pixel in enumerate(selfpixels):
shortest = float('Inf')
for cluster in selfclusters:
distance = selfcalcDistance(
clustercentroid,
pixel
)
if distance < shortest:
shortest = distance
nearest = cluster
localPixels[idx] = nearestcentroid
w, h = selfimagesize
localPixels = numpyasarray(localPixels)\
astype('uint8')\
reshape((h, w, 3))
colourMap = Imagefromarray(localPixels)
colourMapshow()
if __name__=="__main__":
from PIL import Image
import os
k_image=Kmeans()
path = r'\\pics\\'
fp = open('file_colortxt','w')
for filename in oslistdir(path):
print path+filename
try:
color = k_imagerun(Imageopen(path+filename))
fpwrite('The color of '+filename+' is '+str(color)+'\n')
except:
print "This file format is not support"
fpclose()
这个就比较简单,一般手机中都有这样的工具的,一般我们使用工具就可以去进行 *** 作,答主就简单和你们分享一些经验吧!
一般我们识别中的文字可以利用手机上的软件去进行识别或者使用一些在线网站去进行文字识别
手机识别文字我们可以借助微信小程序
可以在小程序或者搜索框中搜索迅捷文字识别然后进行点击进入,将自己的照片或者上传上去就可以将进行文字识别了
识别效果图:
当然我们还可以选择进行复制转发以及翻译都是可以的,关键看自己的需要!
除了这些之外,我们还可以利用在线网站去将转换为文字,具体步骤我就不写了,小伙伴们可以自己去试一试!
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