如何从图像中提取特定颜色

Question

我需要一些支持来向我的代码添加详细信息。在我的代码中，我仅使用 1 张图像（请参阅附件，名为“Photo1”）。

当我运行整个代码时，它将给出以下输出： ，“主色： ['#263f6c'、'#4c5d85'、'#d3d6d8'、'#9db1c6'、'#7388a7']"

我不希望输出为代码，我想确切地知道哪些颜色是主色（例如：红色、蓝色、黑色）并将其返回到名为“提取的颜色”的列中

如何在我的代码中添加这些详细信息？

import cv2
import numpy as np
from PIL import Image
from sklearn.cluster import KMeans

image_path = "Photo1.jpg"
num_colors = 5
num_clusters = 5

def get_dominant_colors(image_path, num_colors=10, num_clusters=5):
    image = Image.open(image_path)
    image = image.resize((200, 200))
    image = image.convert('RGB')
    img_array = np.array(image)
    pixels = img_array.reshape(-1, 3)
    kmeans = KMeans(n_clusters=num_clusters, random_state=0)
    labels = kmeans.fit_predict(pixels)
    centers = kmeans.cluster_centers_
    color_counts = {}
    for label in np.unique(labels):
        color = tuple(centers[label].astype(int))
        color_counts[color] = np.count_nonzero(labels == label)
    sorted_colors = sorted(color_counts.items(), key=lambda x: x[1], reverse=True)
    dominant_colors = [color for color, count in sorted_colors[:num_colors]]
    color_occurrences = [count for color, count in sorted_colors[:num_colors]]
    dominant_colors_hex = ['#%02x%02x%02x' % color for color in dominant_colors]
    return dominant_colors_hex, color_occurrences

dominant_colors, color_occurrences = get_dominant_colors(image_path, num_colors, num_clusters)

print("Dominant Colors:")
print(dominant_colors)

palette_height = 100
palette_width = 100 * num_colors
palette = np.zeros((palette_height, palette_width, 3), dtype=np.uint8)

start_x = 0
for color_hex in dominant_colors:
    color_rgb = tuple(int(color_hex[i:i+2], 16) for i in (1, 3, 5))
    end_x = start_x + 100
    palette[:, start_x:end_x] = color_rgb
    start_x = end_x

palette_image = Image.fromarray(palette)
palette_bgr = cv2.cvtColor(np.array(palette_image), cv2.COLOR_RGB2BGR)

cv2.imshow("Palette", palette_bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

其中一种可能性是使用 webcolors 包。我修改了您的代码以查找调色板上最接近匹配颜色的名称：

import cv2
import numpy as np
from PIL import Image
from sklearn.cluster import KMeans
import webcolors
from webcolors import CSS3_HEX_TO_NAMES, hex_to_rgb

image_path = "Photo1.png"
num_colors = 5
num_clusters = 5

def get_dominant_colors(image_path, num_colors=10, num_clusters=5):
    image = Image.open(image_path)
    image = image.resize((200, 200))
    image = image.convert('RGB')
    img_array = np.array(image)
    pixels = img_array.reshape(-1, 3)
    kmeans = KMeans(n_clusters=num_clusters, random_state=0)
    labels = kmeans.fit_predict(pixels)
    centers = kmeans.cluster_centers_
    color_counts = {}
    for label in np.unique(labels):
        color = tuple(centers[label].astype(int))
        color_counts[color] = np.count_nonzero(labels == label)
    sorted_colors = sorted(color_counts.items(), key=lambda x: x[1], reverse=True)
    dominant_colors = [color for color, count in sorted_colors[:num_colors]]
    color_occurrences = [count for color, count in sorted_colors[:num_colors]]
    dominant_colors_hex = ['#%02x%02x%02x' % color for color in dominant_colors]
    return dominant_colors_hex, color_occurrences

dominant_colors, color_occurrences = get_dominant_colors(image_path, num_colors, num_clusters)

def closest_color(hex):
    colors = {}
    for key, name in CSS3_HEX_TO_NAMES.items():
        r_c, g_c, b_c = hex_to_rgb(key)
        rd = (int(hex[1:3], 16) - r_c) ** 2
        gd = (int(hex[3:5], 16) - g_c) ** 2
        bd = (int(hex[5:7], 16) - b_c) ** 2
        colors[(rd + gd + bd)] = name
    return colors[min(colors.keys())]
print("Dominant colors: ")

for col in dominant_colors:
    col_name = closest_color(col)
    print(col_name)

palette_height = 100
palette_width = 100 * num_colors
palette = np.zeros((palette_height, palette_width, 3), dtype=np.uint8)

start_x = 0
for color_hex in dominant_colors:
    color_rgb = tuple(int(color_hex[i:i+2], 16) for i in (1, 3, 5))
    end_x = start_x + 100
    palette[:, start_x:end_x] = color_rgb
    start_x = end_x

palette_image = Image.fromarray(palette)
palette_bgr = cv2.cvtColor(np.array(palette_image), cv2.COLOR_RGB2BGR)

cv2.imshow("Palette", palette_bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()