Python影像辨識筆記(五)：使用Open CV找出圖片主導顏色

#20190610更新

透過分群演算法

from sklearn.cluster import KMeans
from collections import Counter
from matplotlib import pyplot as plt
import cv2
# Utility function, rgb to hex
def rgb2hex(rgb):
    hex = "#{:02x}{:02x}{:02x}".format(int(rgb[0]), int(rgb[1]), int(rgb[2]))
    return hex
print(rgb2hex([255, 0, 0]))

列出圖片中所有顏色(且包含色碼表)

def plot_image_info(path, k=6):
    # load image
    img_bgr = cv2.imread(path)
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
# resize image to speed up processing time
    resized_img_rgb = cv2.resize(img_rgb, (64, 64), interpolation=cv2.INTER_AREA)
# reshape the image to be a list of pixels
    img_list = resized_img_rgb.reshape((resized_img_rgb.shape[0] * resized_img_rgb.shape[1], 3))
# cluster the pixels and assign labels
    clt = KMeans(n_clusters=k)
    labels = clt.fit_predict(img_list)
        
    # count labels to find most popular
    label_counts = Counter(labels)
    total_count = sum(label_counts.values())
# subset out most popular centroid
    center_colors = list(clt.cluster_centers_)
    ordered_colors = [center_colors[i]/255 for i in label_counts.keys()]
    color_labels = [rgb2hex(ordered_colors[i]*255) for i in label_counts.keys()]
    
    #print(label_counts.values())
    #print(color_labels)
    
    # plots
    plt.figure(figsize=(14, 8))
    plt.subplot(221)
    plt.imshow(img_rgb)
    plt.axis('off')
    
    plt.subplot(222)
    plt.pie(label_counts.values(), labels=color_labels, colors=ordered_colors, startangle=90)
    plt.axis('equal')
    plt.show()

使用

plot_image_info('pathToIMG/1.jpg')