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[英]Python : count the number of different values for a given attribute in a list of objects
[英]Count the number of objects of different colors in an image in Python
这是一张我要从中计算每种颜色的对象数的 图表 。 什么是不使用opencv的简单方法?
[编辑2]:我尝试过的方法如下:(1)有色物体计数
from PIL import Image
im = Image.open('./colored-polka-dots.png').getcolors()
im.sort(key=lambda k: (k[0]), reverse=True)
print('Top 5 colors: {}'.format((im[:5])))
# View non-background colors
color_values = []
for color in im[1:5]:
color_values.append(color[1])
arr = np.asarray(color[1]).reshape(1,1,4).astype(np.uint8)
plt.imshow(arr)
plt.show() # get top 4 frequent colors as green,blue,pink,ornage
# Create a dict of color names and their corressponding rgba values
color_dict = {}
for color_name,color_val in zip(['green','blue','pink','orange'],color_values):
color_dict[color_name] = color_val
# Make use of ndimage.measurement.labels from scipy
# to get the number of distinct connected features that satisfy a given threshold
for color_name,color_val in color_dict.items():
b = ((img[:,:,0] ==color_val[0]) * (img[:,:,1] ==color_val[1]) * (img[:,:,2] ==color_val[2]))*1
labeled_array, num_features = scipy.ndimage.measurements.label(b.astype('Int8'))
print('Color:{} Count:{}'.format(color_name,num_features))
>输出:
orange: 288
green: 288
pink: 288
blue: 288
尽管这达到了目的,但我想知道是否有更有效,更优雅的方法来解决此问题。
这是一个基于scikit-image
的简单解决方案:
代码 :
import numpy as np
from skimage import io, morphology, measure
from sklearn.cluster import KMeans
img = io.imread('https://i.stack.imgur.com/du0XZ.png')
rows, cols, bands = img.shape
X = img.reshape(rows*cols, bands)
kmeans = KMeans(n_clusters=5, random_state=0).fit(X)
labels = kmeans.labels_.reshape(rows, cols)
for i in np.unique(labels):
blobs = np.int_(morphology.binary_opening(labels == i))
color = np.around(kmeans.cluster_centers_[i])
count = len(np.unique(measure.label(blobs))) - 1
print('Color: {} >> Objects: {}'.format(color, count))
输出 :
Color: [ 254. 253. 253. 255.] >> Objects: 1
Color: [ 255. 144. 36. 255.] >> Objects: 288
Color: [ 39. 215. 239. 255.] >> Objects: 288
Color: [ 255. 38. 135. 255.] >> Objects: 288
Color: [ 192. 231. 80. 255.] >> Objects: 288
备注 :
聚类中心的RGB坐标已四舍五入around
仅用于可视化目的。
我还通过binary_opening
执行了打开操作,以摆脱孤立的像素。
必须将标签产生的label
数量减去1
,以仅考虑那些带有考虑颜色标签的连接区域。
输出的第一行显然对应于白色背景。
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