2023-04-09

HSV空间替换纯色衣服颜色

本文将介绍如何在HSV空间替换衣服的颜色（仅限纯色衣服）。

完整代码可见我的Github：GitHub - catchcodes/ChangeCloth: 在HSV空间替换衣服颜色

包括单纯opencv和用pyqt5实现图形界面的。

HSV详解

HSV颜色空间介绍

HSV是一种将RGB色彩模型中的点在圆柱坐标系中的表示方法

色相（Hue）是色彩的基本属性，用角度度量，取值范围为0°～360°（在OpenCV中为0-180，是由于8bit的最大值为255），从红色开始按逆时针方向计算，红色为0°，绿色为120°,蓝色为240°。
饱和度（Saturation）表示颜色接近光谱色的程度。一种颜色，可以看成是某种光谱色与白色混合的结果。其中光谱色所占的比例愈大，颜色接近光谱色的程度就愈高，颜色的饱和度也就愈高。饱和度高，颜色则深而艳。光谱色的白光成分为0，饱和度达到最高。通常取值范围为0%～100%，值越大，颜色越饱和（在OpenCV中为0-255）。
明度（Value），明度表示颜色明亮的程度，对于光源色，明度值与发光体的光亮度有关；对于物体色，此值和物体的透射比或反射比有关，光照对此值影响最大。通常取值范围为0%（黑）到100%（在OpenCV中为0-255）。

RGB转换为HSV

$r = R/255.0, \ g = G/255.0 , \ b=B/255.0$

$max = \max(r, g, b), \ min = \min(r, g, b)$

HSV与HSL的对比

注：HSL也称HSI（I：Intensity）或HLS

上一张经典的对比图（图源Wikipedia）

在HSL中，亮度L分量跨越从黑色经过选择的色相到白色的完整范围；
在HSV中，V分量只走一半行程，从黑色到选择的色相。

下列左图为HSL，右图为HSV

代码实现

第一步：点击图片获取要替换的衣服BGR颜色（也可直接根据图片H分量范围去选择）

img = cv2.imread(r"图片路径")
rows, cols, _ = img_hsv.shape

cv2.namedWindow("ChangeColor")
cv2.resizeWindow("ChangeColor", rows, cols)
# 创建一个鼠标事件响应
cv2.setMouseCallback("ChangeColor", get_color_BGR)

# 这是setMouseCallback的声明原型<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>参数分别为窗口名<span class="bd-box"><h-char class="bd bd-beg"><h-inner>、</h-inner></h-char></span>鼠标响应函数<span class="bd-box"><h-char class="bd bd-beg"><h-inner>、</h-inner></h-char></span>传给鼠标响应函数参数
# def setMouseCallback(windowName: Any,
#                      onMouse: Any,
#                      param: Any = None) -> None

# 回调函数<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span> 即鼠标事件响应函数
def get_color_BGR(envent, x, y, flags, param):
    if envent == cv2.EVENT_LBUTTONDOWN:
        # 注意<span class="bd-box"><h-char class="bd bd-beg"><h-inner>：</h-inner></h-char></span>opencv的行列与坐标相反(row, col) -> (y, x)
        BGR = img[y, x].tolist()

第二步：将获取到的BGR值转为HSV

HSV = [0, 0, 256]  # 一个不存在的HSV值

def bgr2hsv(b, g, r):
    # 限定像素BGR值的范围
    assert (0 <= r < 256 and 0 <= g < 256 and 0 <= b < 256)

    r, g, b = r / 255.0, g / 255.0, b / 255.0
    Max = max(r, g, b)
    Min = min(r, g, b)
    D = Max - Min

    HSV[2] = int(Max * 255)

    HSV[1] = int(255 * (D / Max)) if Max != 0 else 0

    if D == 0:
        HSV[0] = 0
    elif Max == r:
        HSV[0] = (60 * ((g - b) / D)) % 360
    elif Max == g:
        HSV[0] = (120 + 60 * ((b - r) / D)) % 360
    elif Max == b:
        HSV[0] = (240 + 60 * ((r - g) / D)) % 360

    HSV[0] = int(HSV[0] / 2)
    return HSV

第三步：限定H分量的范围，找到衣服的区域

img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# HSV 的下界限
lower = np.array([HSV[0] - 7, 50, 50])
# HSV 的上界限
upper = np.array([HSV[0] + 7, 255, 255])
# 找到H分量在这个范围的区域
mask = cv2.inRange(img_hsv, lower, upper)

# 腐蚀和膨胀的核kernel<span class="bd-box"><h-char class="bd bd-end"><h-inner>（</h-inner></h-char></span>可根据图片自己设计<span class="bd-box"><h-char class="bd bd-beg"><h-inner>）</h-inner></h-char></span>
kernel = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
# 腐蚀图像<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>除去干扰点
eroded = cv2.erode(mask, kernel, iterations=1)
# 膨胀图像<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>恢复腐蚀前的大小
dilated = cv2.dilate(eroded, kernel, iterations=1)

第四步：在窗口中控制H分量和显示图像

# 在‘ChangeColor’窗口中创建滑动控制条
# 参数介绍 createTrackbar(const String& trackbarname, const String& winname, int *value, int count, TrackbarCallback onChange = 0, void *userdata = 0)
# 分别为滑动条名称<span class="bd-box"><h-char class="bd bd-beg"><h-inner>、</h-inner></h-char></span>窗口名称<span class="bd-box"><h-char class="bd bd-beg"><h-inner>、</h-inner></h-char></span>初始值<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>范围<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>滑块更改时的回调函数<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>传给回调函数的参数<span class="bd-box"><h-char class="bd bd-end"><h-inner>（</h-inner></h-char></span>默认为0<span class="bd-box"><h-char class="bd bd-beg"><h-inner>）</h-inner></h-char></span>
cv2.createTrackbar('H', 'ChangeColor', 140, 180, lambda x: None)
# cv2.createTrackbar('S', 'ChangeColor', 100, 255, lambda x: None)
# cv2.createTrackbar('V', 'ChangeColor', 100, 255, lambda x: None)

# 获取滑块的值
h = cv2.getTrackbarPos('H', 'ChangeColor')
# s = cv2.getTrackbarPos('S', 'ChangeColor')
# v = cv2.getTrackbarPos('V', 'ChangeColor')

for row in range(rows):
    for col in range(cols):
        if dilated[row, col] == 255:
            img_hsv.itemset((row, col, 0), h)
            # img_hsv.itemset((row, col, 1), s)
            # img_hsv.itemset((row, col, 2), v)

img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
cv2.imshow("ChangeColor", img)

完整代码

可以直接在我的Github上Clone：https://github.com/catchcodes/ChangeCloth

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :main.py
# @Time      :2023/4/7 19:46
# @Author    :Ouyang Bin

import cv2
import numpy as np

# 不存在的HSV值
HSV = [0, 0, 256]
newHSV = [0, 0, 256]


def bgr2hsv(b, g, r):
    # 限定像素BGR值的范围
    assert (0 <= r < 256 and 0 <= g < 256 and 0 <= b < 256)

    r, g, b = r / 255.0, g / 255.0, b / 255.0
    Max = max(r, g, b)
    Min = min(r, g, b)
    D = Max - Min

    HSV[2] = int(Max * 255)

    HSV[1] = int(255 * (D / Max)) if Max != 0 else 0

    if D == 0:
        HSV[0] = 0
    elif Max == r:
        HSV[0] = (60 * ((g - b) / D)) % 360
    elif Max == g:
        HSV[0] = (120 + 60 * ((b - r) / D)) % 360
    elif Max == b:
        HSV[0] = (240 + 60 * ((r - g) / D)) % 360

    HSV[0] = int(HSV[0] / 2)
    return HSV


# 回调函数
def get_color_BGR(envent, x, y, flags, param):
    if envent == cv2.EVENT_LBUTTONDOWN:
        # 注意<span class="bd-box"><h-char class="bd bd-beg"><h-inner>：</h-inner></h-char></span>opencv的行列与坐标相反(row, col) -> (y, x)
        BGR = img[y, x].tolist()
        bgr2hsv(BGR[0], BGR[1], BGR[2])


img = cv2.imread(r"C:\Users\19941\Pictures\cloth.jpg")
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
rows, cols, _ = img_hsv.shape

cv2.namedWindow("ChangeColor")
cv2.resizeWindow("ChangeColor", rows, cols)
cv2.setMouseCallback("ChangeColor", get_color_BGR)

cv2.createTrackbar('H', 'ChangeColor', 140, 180, lambda x: None)
# cv2.createTrackbar('S', 'ChangeColor', 100, 255, lambda x: None)
# cv2.createTrackbar('V', 'ChangeColor', 100, 255, lambda x: None)

times = 0

while True:
    cv2.imshow('ChangeColor', img)

    if newHSV != HSV:
        if times == 0:
            # HSV 的下界限
            lower = np.array([HSV[0] - 7, 50, 50])
            # HSV 的上界限
            upper = np.array([HSV[0] + 7, 255, 255])
            mask = cv2.inRange(img_hsv, lower, upper)

            # 核
            kernel = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8)
            # 腐蚀图像<span class="bd-box"><h-char class="bd bd-beg"><h-inner>，</h-inner></h-char></span>除去干扰点
            eroded = cv2.erode(mask, kernel, iterations=1)
            # 膨胀图像
            dilated = cv2.dilate(eroded, kernel, iterations=1)

            times += 1

        h = cv2.getTrackbarPos('H', 'ChangeColor')
        # s = cv2.getTrackbarPos('S', 'ChangeColor')
        # v = cv2.getTrackbarPos('V', 'ChangeColor')

        for row in range(rows):
            for col in range(cols):
                if dilated[row, col] == 255:
                    img_hsv.itemset((row, col, 0), h)
                    # img_hsv.itemset((row, col, 1), s)
                    # img_hsv.itemset((row, col, 2), v)

        img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
        cv2.imshow("ChangeColor", img)
    else:
        img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
        cv2.imshow("ChangeColor", img)

    if cv2.waitKey(1) & 0xff == 27:
        break

cv2.destroyAllWindows()

CatchCodes