基于Python查找图像中最常见的颜色

重磅干货，第一时间送达

01. 准备工作

02. 常用方法

img_temp = img.copy()img_temp[:,:,0], img_temp[:,:,1], img_temp[:,:,2] = np.average(img, axis=(0,1))
img_temp_2 = img_2.copy()img_temp_2[:,:,0], img_temp_2[:,:,1], img_temp_2[:,:,2] = np.average(img_2, axis=(0,1))
show_img_compar(img, img_temp)show_img_compar(img_2, img_temp_2)

img_temp = img.copy()unique, counts = np.unique(img_temp.reshape(-1, 3), axis=0, return_counts=True)img_temp[:,:,0], img_temp[:,:,1], img_temp[:,:,2] = unique[np.argmax(counts)]
img_temp_2 = img_2.copy()unique, counts = np.unique(img_temp_2.reshape(-1, 3), axis=0, return_counts=True)img_temp_2[:,:,0], img_temp_2[:,:,1], img_temp_2[:,:,2] = unique[np.argmax(counts)]
show_img_compar(img, img_temp)show_img_compar(img_2, img_temp_2)


def palette(clusters): width=300 palette = np.zeros((50, width, 3), np.uint8) steps = width/clusters.cluster_centers_.shape[0] for idx, centers in enumerate(clusters.cluster_centers_): palette[:, int(idx*steps):(int((idx+1)*steps)), :] = centers return palette

clt_1 = clt.fit(img.reshape(-1, 3))show_img_compar(img, palette(clt_1))
clt_2 = clt.fit(img_2.reshape(-1, 3))show_img_compar(img_2, palette(clt_2))

def palette(clusters): width=300 palette = np.zeros((50, width, 3), np.uint8) steps = width/clusters.cluster_centers_.shape[0] for idx, centers in enumerate(clusters.cluster_centers_): palette[:, int(idx*steps):(int((idx+1)*steps)), :] = centers return palette
clt_3 = KMeans(n_clusters=3)clt_3.fit(img_2.reshape(-1, 3))show_img_compar(img_2, palette(clt_3))

from collections import Counter
def palette_perc(k_cluster): width = 300 palette = np.zeros((50, width, 3), np.uint8)
n_pixels = len(k_cluster.labels_) counter = Counter(k_cluster.labels_) # count how many pixels per cluster perc = {} for i in counter: perc[i] = np.round(counter[i]/n_pixels, 2) perc = dict(sorted(perc.items()))
#for logging purposes print(perc) print(k_cluster.cluster_centers_)
step = 0
for idx, centers in enumerate(k_cluster.cluster_centers_): palette[:, step:int(step + perc[idx]*width+1), :] = centers step += int(perc[idx]*width+1)
return palette
clt_1 = clt.fit(img.reshape(-1, 3))show_img_compar(img, palette_perc(clt_1))
clt_2 = clt.fit(img_2.reshape(-1, 3))show_img_compar(img_2, palette_perc(clt_2))

03. 结论