检查图像主要是黑白还是彩色

我不确定确切的用例,但是在经历过类似的问题之后,我使用了这篇相当有用的文章。

https://www.alanzucconi.com/2015/05/24/how-to-find-the-main-colours-in-an-image/

包含完整代码的github可以在这里找到: https://gist.github.com/jayapal/077f63f3163abbfb3c50c7d209524cc6

如果这是为了你自己的视觉直方图应该是足够的,但如果你试图自动化,它可能有助于舍入颜色值向上或向下,这将提供信息,如果图像是黑暗或轻于某个值。

从更大的角度看,您将此代码用于什么?也许这会有助于提供更充分的信息

编辑:上面的代码还提供了定义图像区域的能力,希望这将使您的选择更加准确

直接添加代码

from sklearn.cluster import KMeans
from sklearn import metrics
import cv2
import numpy as np
import cv2
image = cv2.imread("red.png")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Resize it
h, w, _ = image.shape
w_new = int(100 * w / max(w, h) )
h_new = int(100 * h / max(w, h) )

image = cv2.resize(image, (w_new, h_new));


# Reshape the image to be a list of pixels
image_array = image.reshape((image.shape[0] * image.shape[1], 3))
print image_array
# Clusters the pixels
clt = KMeans(n_clusters = 3)
clt.fit(image_array)

def centroid_histogram(clt):
    # grab the number of different clusters and create a histogram
    # based on the number of pixels assigned to each cluster
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    (hist, _) = np.histogram(clt.labels_, bins = numLabels)

    # normalize the histogram, such that it sums to one
    hist = hist.astype("float")
    hist /= hist.sum()

    # return the histogram
    return hist


# Finds how many pixels are in each cluster
hist = centroid_histogram(clt)

# Sort the clusters according to how many pixel they have
zipped = zip (hist, clt.cluster_centers_)
zipped.sort(reverse=True, key=lambda x : x[0])
hist, clt.cluster_centers = zip(*zipped)

# By Adrian Rosebrock
import numpy as np
import cv2



bestSilhouette = -1
bestClusters = 0;

for clusters in range(2, 10):
    # Cluster colours
    clt = KMeans(n_clusters = clusters)
    clt.fit(image_array)

    # Validate clustering result
    silhouette = metrics.silhouette_score(image_array, clt.labels_, 
    metric='euclidean')

    # Find the best one
    if silhouette > bestSilhouette:
        bestSilhouette = silhouette;
        bestClusters = clusters;

print bestSilhouette
print bestClusters