Python中RGB与TSL色彩空间互转无法还原图像的问题排查

我用Python实现了RGB到TSL（色调Tint、饱和度Saturation、明度Lightness）色彩空间的转换函数，同时编写了反向转换函数用于验证图像能否正确还原，以此确认实现逻辑的准确性。但目前反向转换无法还原出原始输入图像，不确定错误出在哪个环节。

TSL转RGB的实现参考了维基百科对应公式，RGB转TSL则参考了原始论文，且已采纳@statemachine的修改建议。

实现代码 #

import cv2
import numpy as np

def rgb_tsl(image_path, gamma_factor):
    # OpenCV默认读取BGR顺序
    object_data = cv2.imread(image_path)

    # 归一化到0-1范围
    scaled_data = object_data / 255

    # Gamma校正
    corrected_image_gamma = np.power(scaled_data, gamma_factor)

    # 拆分通道（BGR顺序）
    base_blue_channel = corrected_image_gamma[:, :, 0]
    base_green_channel = corrected_image_gamma[:, :, 1]
    base_red_channel = corrected_image_gamma[:, :, 2]

    # 计算T、S、L分量
    common_divisor = (base_red_channel + base_green_channel + base_blue_channel)
    small_r = base_red_channel / common_divisor
    small_g = base_green_channel / common_divisor
    r_hyphen = small_r - (1/3)
    g_hyphen = small_g - (1/3)

    luma = (0.299 * base_red_channel) + (0.587 * base_green_channel) + (0.114 * base_blue_channel)
    saturation = np.sqrt((9/5)*(np.power(r_hyphen, 2) + np.power(g_hyphen, 2)))

    # 计算色调Tint
    tint_arr = np.zeros_like(g_hyphen)
    for index, item in np.ndenumerate(g_hyphen):
        if item == 0:
            tint_arr[index] = 0
        else:
            corresponding_r_hyphen = r_hyphen[index]
            if item < 0:
                arctan_value_less_zero = ((np.arctan(corresponding_r_hyphen / item)) / (2 * np.pi)) + (3/4)
                tint_arr[index] = arctan_value_less_zero
            else:
                arctan_value_greater_zero = ((np.arctan(corresponding_r_hyphen / item)) / (2 * np.pi)) + (1 / 4)
                tint_arr[index] = arctan_value_greater_zero

    merged_image = cv2.merge([tint_arr, saturation, luma])
    merged_image = (255 * merged_image).astype(np.uint8)
    # 修复原函数返回值缺失问题，保证调用时能解构出原始图像
    return object_data, merged_image


def tsl_rgb(image):
    tint = image[:, :, 0] / 255.0
    saturation = image[:, :, 1] / 255.0
    luma = image[:, :, 2] / 255.0

    x_val = np.power(np.tan((2 * np.pi) * (tint - (1/4))), 2)
    r_hyphen_tsl = np.sqrt((5 * np.power(saturation, 2)) / 9 * ((1/x_val) + 1))
    g_hyphen_tsl = np.sqrt((5 * np.power(saturation, 2)) / 9 * (x_val + 1))

    r_tsl = r_hyphen_tsl + (1/3)
    g_tsl = g_hyphen_tsl + (1/3)

    k = luma / ((0.185 * r_tsl) + (0.473 * g_tsl) + 0.114)

    final_r = k * r_tsl
    final_g = k * g_tsl
    final_b = k * (1-r_tsl-g_tsl)

    # 修复通道顺序问题，匹配OpenCV的BGR格式
    final_rgb_image = cv2.merge([final_b, final_g, final_r])
    clippedImg = np.clip(final_rgb_image, 0, 1)
    final_rgb_image = (255 * clippedImg).astype(np.uint8)
    return final_rgb_image

# 测试路径
current_image_path = '/home/xyz/Data_Science/Skin Cancer/ISIC_0034202_dullrazor.jpg'
# Gamma校正系数参考行业标准值
original_img, converted_image_tsl = rgb_tsl(current_image_path, 1.5)
reverse_image = tsl_rgb(converted_image_tsl)

cv2.imshow("Original RGB image", original_img)
cv2.imshow("TSL image", converted_image_tsl)
cv2.imshow("Re-constructed RGB image", reverse_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

当前输出效果 #

• 原始RGB图像
  
• RGB转TSL后的图像
  
• TSL还原后的RGB图像
  

内容的提问来源于stack exchange，提问作者Dhruv Marwha