二十四色比色卡校准代码python

以下是使用 Python 进行二十四色比色卡校准的代码示例：

import cv2
import numpy as np

# 读取二十四色比色卡图片
img = cv2.imread("24_color_chart.jpg")

# 定义颜色映射表
color_map = {
    "黑色": (0, 0, 0),
    "深灰色": (64, 64, 64),
    "灰色": (128, 128, 128),
    "银灰色": (192, 192, 192),
    "白色": (255, 255, 255),
    "红色": (255, 0, 0),
    "深红色": (128, 0, 0),
    "橙色": (255, 165, 0),
    "黄色": (255, 255, 0),
    "黄绿色": (128, 128, 0),
    "绿色": (0, 255, 0),
    "深绿色": (0, 128, 0),
    "青色": (0, 255, 255),
    "深青色": (0, 128, 128),
    "蓝色": (0, 0, 255),
    "深蓝色": (0, 0, 128),
    "紫色": (255, 0, 255),
    "深紫色": (128, 0, 128),
    "粉红色": (255, 192, 203),
    "棕色": (165, 42, 42),
    "浅棕色": (244, 164, 96),
    "黄棕色": (240, 230, 140),
    "草绿色": (127, 255, 0),
    "橄榄绿色": (107, 142, 35)
}

# 提取二十四个颜色块的像素值
color_blocks = []
for i in range(4):
    for j in range(6):
        x = j * 80
        y = i * 80
        color_block = img[y:y+80, x:x+80]
        color_blocks.append(color_block)

# 对每个颜色块进行颜色匹配
for i, block in enumerate(color_blocks):
    # 转换为 LAB 色彩空间
    lab_block = cv2.cvtColor(block, cv2.COLOR_BGR2LAB)
    # 计算颜色块的平均值
    mean_lab = cv2.mean(lab_block)[:3]
    # 查找最接近的颜色
    min_dist = 999999
    min_color = ""
    for color, lab_val in color_map.items():
        dist = np.linalg.norm(mean_lab - lab_val)
        if dist < min_dist:
            min_dist = dist
            min_color = color
    # 输出颜色名称及其 RGB 值
    print("颜色块%d: %s, RGB: %s" % (i+1, min_color, str(color_map[min_color])))

在运行代码前，需要先准备好二十四色比色卡的图片，然后将其放在代码文件所在的目录下，并将其文件名修改为 "24_color_chart.jpg"。代码运行后，将输出每个颜色块的颜色名称及其 RGB 值。