Python实现数字图像处理之5种彩色空间转换（单图+多图+视频）

本文主要运用用Python代码实现了5种彩色空间之间的转换！

具体而言，包括：

• 1）RGB → CMY；
• 2)  CMY → RGB；
• 3)  RGB → HSI；
• 4)  HSI → RGB；
• 5)  RGB → YIQ；
• 6)  YIQ → RGB；
• 7)  RGB → YUV；
• 8)  YUV → RGB；
• 9)  RGB → YCbCr；
• 10) YCbCr → RGB；

文末还附有两方面的扩展：

• 1）对“多图”的处理
• 2）对“视频”的处理

快来一起交流学习吧！

目录

1 RGB → CMY

1.1 转换公式

1.2 代码实现 

1.3 运行结果 

2 CMY → RGB

2.1 公式转换

2.2 代码实现

2.3 运行效果

3 RGB → HSI

3.1 公式转换

3.2 代码实现

3.3 运行效果

4 HSI → RGB

4.1 公式转换

4.2 代码实现

4.3 运行效果

5 RGB → YIQ

5.1 公式转换

5.2 代码实现

5.3 运行效果

6 YIQ → RGB

6.1 公式转换

6.2 代码实现

6.3 运行效果

7 RGB → YUV

7.1 公式转换

7.2 代码实现

7.3 运行效果

8 YUV → RGB

8.1 公式转换

8.2 代码实现

8.3 运行效果

9 RGB → YCbCr

9.1 公式转换

9.2 代码实现

9.3 运行效果

10 YCbCr → RGB

10.1 公式转换

10.2 代码实现

10.3 运行效果

11 扩展1——多图处理

11.1 代码示例

11.2 运行效果

12 扩展2——视频处理

12.1 附：视频爬取代码

12.2 代码实现

12.3 运行效果

---

1 RGB → CMY

1.1 转换公式

1.2 代码实现 

'''-----------------RGB → CMY------------------------'''
import cv2
import imutils

def rgb_cmy(img):
    r, g, b = cv2.split(img)  # split the channels
    # normalization [0,1]
    r = r / 255.0
    g = g / 255.0
    b = b / 255.0
    c = 1 - r
    m = 1 - g
    y = 1 - b
    result = cv2.merge((c, m, y))  # merge the channels
    return result

if __name__ == '__main__':
    img = cv2.imread("E:/1.PNG")
    img_CMY = rgb_cmy(img)
    img_NEW = img_CMY * 255
    cv2.imwrite('F:/img_CMY.PNG', img_NEW)
    cv2.imshow("CMY image", imutils.resize(img_CMY, 666))
    cv2.imshow("original image", imutils.resize(img, 666))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

1.3 运行结果 

2 CMY → RGB

2.1 公式转换

2.2 代码实现

'''-----------------CMY → RGB------------------------'''
import cv2
import imutils

def cmy_rgb(img):
    c, m, y = cv2.split(img)  # split the channels
    # normalization[0,1]
    c = c / 255.0
    m = m / 255.0
    y = y / 255.0
    r = 1 - c
    g = 1 - m
    b = 1 - y
    result = cv2.merge((r, g, b))  # merge the channels
    print(result)
    return result

if __name__ == '__main__':
    img = cv2.imread("F:/img_CMY.PNG")
    img_CMY = cmy_rgb(img)
    cv2.imshow("RGB image", imutils.resize(img_CMY, 666))
    cv2.imshow("original image", imutils.resize(img, 666))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

2.3 运行效果

3 RGB → HSI

3.1 公式转换

3.2 代码实现

'''-----------------RGB → HSI------------------------'''
import cv2
import math
import imutils
import numpy as np

def rgb_hsi(rgb_Img):
    img_rows = int(rgb_Img.shape[0])
    img_cols = int(rgb_Img.shape[1])
    b, g, r = cv2.split(rgb_Img)
    # normalization[0,1]
    r = r / 255.0
    g = g / 255.0
    b = b / 255.0
    hsi_Img = rgb_Img.copy()
    H, S, I = cv2.split(hsi_Img)
    for i in range(img_rows):
        for j in range(img_cols):
            num = 0.5 * ((r[i, j]-g[i, j])+(r[i, j]-b[i, j]))
            den = np.sqrt((r[i, j]-g[i, j])**2+(r[i, j]-b[i, j])*(g[i, j]-b[i, j]))
            theta = float(np.arccos(num/den))

            if den == 0:
                H = 0
            elif b[i, j] <= g[i, j]:
                H = theta
            else:
                H = math.pi - theta

            min_RGB = min(min(b[i, j], g[i, j]), r[i, j])
            sum = b[i, j]+g[i, j]+r[i, j]
            if sum == 0:
                S = 0
            else:
                S = 1 - 3*min_RGB/sum

            H = H/(math.pi)
            I = sum/3.0
            # 输出HSI图像，扩充到255以方便显示，一般H分量在[0,2pi]之间，S和I在[0,1]之间
            hsi_Img[i, j, 0] = H*255
            hsi_Img[i, j, 1] = S*255
            hsi_Img[i, j, 2] = I*255
    return hsi_Img

if __name__ == '__main__':
    rgb_Img = cv2.imread("E:/1.PNG")
    hsi_Img = rgb_hsi(rgb_Img)

    cv2.imwrite('F:/img_HSI.PNG', hsi_Img)

    cv2.imshow('original image', imutils.resize(rgb_Img, 600))
    cv2.imshow('HSI image', imutils.resize(hsi_Img, 600))

    key = cv2.waitKey(0) & 0xFF
    if key == ord('q'):
        cv2.destroyAllWindows()

3.3 运行效果

4 HSI → RGB

4.1 公式转换

4.2 代码实现

'''-----------------HSI → RGB------------------------'''
import cv2
import math
import imutils

def hsi_rgb(hsi_img):
    img_rows = int(hsi_img.shape[0])
    img_cols = int(hsi_img.shape[1])
    H, S, I = cv2.split(hsi_img)
    # normalization[0,1]
    H = H / 255.0
    S = S / 255.0
    I = I / 255.0
    bgr_img = hsi_img.copy()
    B, G, R = cv2.split(bgr_img)
    for i in range(img_rows):
        for j in range(img_cols):
            if S[i, j] < 1e-6:
                R = I[i, j]
                G = I[i, j]
                B = I[i, j]
            else:
                H[i, j] *= 360
                if H[i, j] > 0 and H[i, j] <= 120:
                    B = I[i, j] * (1 - S[i, j])
                    R = I[i, j] * (1 + (S[i, j] * math.cos(H[i, j] * math.pi / 180)) / math.cos((60 - H[i, j]) * math.pi / 180))
                    G = 3 * I[i, j] - (R + B)
                elif H[i, j] > 120 and H[i, j] <= 240:
                    H[i, j] = H[i, j] - 120
                    R = I[i, j] * (1 - S[i, j])
                    G = I[i, j] * (1 + (S[i, j] * math.cos(H[i, j] * math.pi / 180)) / math.cos((60 - H[i, j]) * math.pi / 180))
                    B = 3 * I[i, j] - (R + G)
                elif H[i, j] > 240 and H[i, j] <= 360:
                    H[i, j] = H[i, j] - 240
                    G = I[i, j] * (1 - S[i, j])
                    B = I[i, j] * (1 + (S[i, j] * math.cos(H[i, j] * math.pi / 180)) / math.cos((60 - H[i, j]) * math.pi / 180))
                    R = 3 * I[i, j] - (G + B)
            bgr_img[i, j, 0] = B * 255
            bgr_img[i, j, 1] = G * 255
            bgr_img[i, j, 2] = R * 255
    return bgr_img

if __name__ == '__main__':
    hsi_Img = cv2.imread("F:/img_HSI.PNG")
    rgb_Img = hsi_rgb(hsi_Img)

    cv2.imshow('original image', imutils.resize(rgb_Img, 600))
    cv2.imshow('RGB image', imutils.resize(hsi_Img, 600))

    key = cv2.waitKey(0) & 0xFF
    if key == ord('q'):
        cv2.destroyAllWindows()

4.3 运行效果

5 RGB → YIQ

5.1 公式转换

5.2 代码实现

Python

'''-----------------RGB → YIQ------------------------'''
import cv2
import imutils
import numpy as np

def rgb_yiq(rgb_Img):
    img_rows = int(rgb_Img.shape[0])
    img_cols = int(rgb_Img.shape[1])
    yiq_image = rgb_Img.copy()
    R, G, B = cv2.split(yiq_image)

    for x in range(img_rows):
        for y in range(img_cols):
            right_matrix = np.array([[R[x,y]],
                                     [G[x,y]],
                                     [B[x,y]]])
            left_matrix = np.array([[0.299,0.587,0.114],
                                    [0.596,-0.275,-0.321],
                                    [0.212,-0.528,0.311]])
            matrix = np.dot(left_matrix,right_matrix)
            r = matrix[0][0]
            g = matrix[1][0]
            b = matrix[2][0]
            yiq_image[x, y] = (r, g, b)
    return yiq_image

if __name__ == '__main__':
    rgb_Img = cv2.imread("E:/1.PNG")
    yiq_Img = rgb_yiq(rgb_Img)
    cv2.imshow('original image', imutils.resize(rgb_Img, 600))
    cv2.imshow('YIQ image', imutils.resize(yiq_Img, 600))

    cv2.imwrite('F:/img_YIQ1.PNG', yiq_Img)

    key = cv2.waitKey(0) & 0xFF
    if key == ord('q'):
        cv2.destroyAllWindows()

MATLAB

% 清变量，关闭窗口
clear;
close all;
% 文件读取
img=imread('E:/1.PNG'); %获得256*256*3数组
imshow(img);title('原始RGB图像');

rgb=im2double(img); %将原图像转换到[0,1]空间
% figure;     %与原图像相同
% imshow(rgb);
r=rgb(:,:,1);
g=rgb(:,:,2);
b=rgb(:,:,3)

% rgb模型到yiq模型
y=0.299*r+0.587*g+0.114*b;
i=0.596*r-0.274*g-0.322*b;
q=0.211*r-0.523*g+0.312*b;

img_YIQ=cat(3,y,i,q);
figure;
imshow(img_YIQ);title('RGB2YIQ图像');

5.3 运行效果

6 YIQ → RGB

6.1 公式转换

6.2 代码实现

Python

'''-----------------YIQ → RGB------------------------'''
import cv2
import imutils
import numpy as np

def yiq_rgb(yiq_Img):
    img_rows = int(yiq_Img.shape[0])
    img_cols = int(yiq_Img.shape[1])
    rgb_image = yiq_Img.copy()
    Y, I, Q = cv2.split(rgb_image)

    for x in range(img_rows):
        for y in range(img_cols):
            right_matrix = np.array([[Y[x,y]],
                                     [I[x,y]],
                                     [Q[x,y]]])
            left_matrix = np.array([[1,0.956,0.620],
                                    [1,-0.272,-0.647],
                                    [1,-1.108,1.705]])
            matrix = np.dot(left_matrix,right_matrix)
            r = matrix[0][0]
            g = matrix[1][0]
            b = matrix[2][0]
            rgb_image[x, y] = (r, g, b)
    return rgb_image

if __name__ == '__main__':
    yiq_Img = cv2.imread("F:/img_YIQ1.PNG")
    rgb_Img = yiq_rgb(yiq_Img)
    cv2.imshow('original image', imutils.resize(yiq_Img, 600))
    cv2.imshow('RGB image', imutils.resize(rgb_Img, 600))

    key = cv2.waitKey(0) & 0xFF
    if key == ord('q'):
        cv2.destroyAllWindows()

MATLAB

% 清变量，关闭窗口
clear;
close all;
% 文件读取
img=imread('F:\img_YIQ.PNG'); %获得256*256*3数组
imshow(img);title('原始YIQ图像');

yiq=im2double(img); %将原图像转换到[0,1]空间
% figure;     %与原图像相同
% imshow(rgb);
y=yiq(:,:,1);
i=yiq(:,:,2);
q=yiq(:,:,3)

% rgb模型到yiq模型
r=1*y+0.956*i+0.620*q;
g=1*y-0.272*i-0.674*q;
b=1*y-1.108*i+1.705*q;

img_YIQ=cat(3,r,g,b);
figure;
imshow(img_YIQ);title('YIQ2RGB图像');

6.3 运行效果

7 RGB → YUV

7.1 公式转换

7.2 代码实现

'''-----------------RGB → YUV------------------------'''
import numpy as np
import cv2 as cv
import imutils


def rgb_yuv(rgb_img):
    W = np.array([
        [0.299, 0.587, 0.114],
        [-0.148, -0.289, 0.437],
        [0.615, -0.515, -0.100]
    ])
    rgb_Img = rgb_img.copy()
    rgb_Img = rgb_Img.astype(np.float)
    h, w, c = rgb_Img.shape
    for i in range(h):
        for j in range(w):
            rgb_Img[i, j] = np.dot(W, rgb_Img[i, j])
    imc = rgb_Img.astype(np.uint8)
    return imc

if __name__ == '__main__':
    img_rgb = cv.imread('E:/1.PNG')
    img_yuv1 = cv.cvtColor(img_rgb, cv.COLOR_RGB2YUV)
    img_yuv2 = rgb_yuv(img_rgb)

    cv.imwrite('F:/img_YUV.PNG', img_yuv1)
    # cv.imwrite('F:/img_YUV_self.PNG', img_yuv2)

    cv.imshow('original image', imutils.resize(img_rgb, 600))
    cv.imshow('OpenCV_YUV image', imutils.resize(img_yuv1, 600))
    cv.imshow('Self_YUV image', imutils.resize(img_yuv2, 600))

    cv.waitKey(0)

7.3 运行效果

8 YUV → RGB

8.1 公式转换

8.2 代码实现

'''-----------------YUV → RGB------------------------'''
import numpy as np
import cv2 as cv
import imutils


def yuv_rgb(yuv_img):
    W = np.array([
        [1, 0., 1.13983],
        [1, -0.39465, -0.58060],
        [1, 2.03211, 0.]
    ])
    rgb_img = yuv_img.copy()
    rgb_img = yuv_img.astype(np.float)
    h, w, c = rgb_img.shape
    for i in range(h):
        for j in range(w):
            rgb_img[i, j][0] -= 16  # Y
            rgb_img[i, j][1] -= 128  # U
            rgb_img[i, j][2] -= 128  # V
            rgb_img[i, j] = np.matmul(W, rgb_img[i, j])
    imc = rgb_img.astype(np.uint8)
    return imc

if __name__ == '__main__':
    img_rgb = cv.imread('F:/img_YUV.PNG')
    img_yuv1 = yuv_rgb(img_rgb)
    img_yuv2 = cv.cvtColor(img_rgb, cv.COLOR_YUV2RGB)

    cv.imshow('original image', imutils.resize(img_rgb, 600))
    cv.imshow('OpenCV_RGB image', imutils.resize(img_yuv2, 600))
    cv.imshow('Self_RGB image', imutils.resize(img_yuv1, 600))

    cv.waitKey(0)

8.3 运行效果

9 RGB → YCbCr

9.1 公式转换

9.2 代码实现

'''-----------------RGB → YCbCr------------------------'''
import numpy as np
import cv2 as cv
import imutils

def rgb2ycbcr(rgb_image):
    """convert rgb into ycbcr"""
    if len(rgb_image.shape)!=3 or rgb_image.shape[2]!=3:
        raise ValueError("input image is not a rgb image")
    rgb_image = rgb_image.astype(np.float32)
    # 1：创建变换矩阵，和偏移量
    transform_matrix = np.array([[0.257, 0.564, 0.098],
                                 [-0.148, -0.291, 0.439],
                                 [0.439, -0.368, -0.071]])
    shift_matrix = np.array([16, 128, 128])
    ycbcr_image = np.zeros(shape=rgb_image.shape)
    w, h, _ = rgb_image.shape
    # 2：遍历每个像素点的三个通道进行变换
    for i in range(w):
        for j in range(h):
            ycbcr_image[i, j, :] = np.dot(transform_matrix, rgb_image[i, j, :]) + shift_matrix
    return ycbcr_image

if __name__ == '__main__':
    img_rgb = cv.imread('E:/1.PNG')
    img_ycbcr = rgb2ycbcr(img_rgb)
    img_NEW = img_ycbcr / 255

    cv.imwrite('F:/img_YCbCr.PNG', img_ycbcr)

    cv.imshow('original image', imutils.resize(img_rgb, 600))
    cv.imshow('Self_YCbCr image', imutils.resize(img_NEW, 600))

    cv.waitKey(0)

9.3 运行效果

10 YCbCr → RGB

10.1 公式转换

10.2 代码实现

'''-----------------YCbCr → RGB------------------------'''
import numpy as np
import cv2 as cv
import imutils

def ycbcr2rgb(ycbcr_image):
    """convert ycbcr into rgb"""
    if len(ycbcr_image.shape)!=3 or ycbcr_image.shape[2]!=3:
        raise ValueError("input image is not a rgb image")
    ycbcr_image = ycbcr_image.astype(np.float32)
    transform_matrix = np.array([[0.257, 0.564, 0.098],
                                 [-0.148, -0.291, 0.439],
                                 [0.439, -0.368, -0.071]])
    transform_matrix_inv = np.linalg.inv(transform_matrix)
    shift_matrix = np.array([16, 128, 128])
    rgb_image = np.zeros(shape=ycbcr_image.shape)
    w, h, _ = ycbcr_image.shape
    for i in range(w):
        for j in range(h):
            rgb_image[i, j, :] = np.dot(transform_matrix_inv, ycbcr_image[i, j, :]) - np.dot(transform_matrix_inv, shift_matrix)
    return rgb_image.astype(np.uint8)

if __name__ == '__main__':
    img_ycbcr = cv.imread('F:/img_YCbCr.PNG')
    img_rgb = ycbcr2rgb(img_ycbcr)
    img_NEW = img_rgb / 255

    cv.imshow('original image', imutils.resize(img_ycbcr, 600))
    cv.imshow('Self_RGB image', imutils.resize(img_NEW, 600))

    cv.waitKey(0)

10.3 运行效果

11 扩展1——多图处理

11.1 代码示例

'''-----------------RGB → CMY------------------------'''
import cv2
import imutils

def rgb_cmy(img):
    r, g, b = cv2.split(img)  # split the channels
    # normalization [0,1]
    r = r / 255.0
    g = g / 255.0
    b = b / 255.0
    c = 1 - r
    m = 1 - g
    y = 1 - b
    result = cv2.merge((c, m, y))  # merge the channels
    return result

if __name__ == '__main__':
    for i in range(3):
        img = cv2.imread("F:/{}.PNG".format(i))
        img_CMY = rgb_cmy(img)
        img_NEW = img_CMY * 255
        cv2.imwrite('F:/img_CMY.PNG', img_NEW)
        cv2.imshow("CMY image{}".format(i), imutils.resize(img_CMY, 666))
        cv2.imshow("original image{}".format(i), imutils.resize(img, 666))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

11.2 运行效果

12 扩展2——视频处理

12.1 附：视频爬取代码

import requests
import json
import re

def change_title(title):
    # 替换非法字符
    pattern = re.compile(r"[\/\\\:\*\?\"\<\>\|]")
    new_title = re.sub(pattern, "_", title)
    return new_title

# 示例爬取3页数据
for page in range(1, 4):
    print('---------------正在爬取第{}页小姐姐视频-------------------'.format(page))
    # 获取 URL 地址
    url = 'https://v.6.cn/minivideo/getlist.php?act=recommend&page={}&pagesize=20'.format(page)
    # headers 参数确定
    headers = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.122 Safari/537.36'
    }

    # 模拟浏览器发送 URL 地址请求
    response = requests.get(url, headers=headers)
    # 去除 response 响应对象中的文本数据
    response_data = response.text
    # print(response_data)


    # 转换数据类型
    dict_data = json.loads(response_data)    # 字典
    # print(dict_data)
    # 数据解析
    data_list = dict_data['content']['list']   # 列表
    # print(data_list)

    # 遍历
    for data in data_list:
        # print(data)
        video_title = data['title']      # 视频文件名
        video_alias = data['alias']       # 视频作者名
        video_playurl = data['playurl']   # 视频 url
        # print('视频:', video_title, '作者:', video_alias, 'url地址:', video_playurl)
        print('正在下载视频：', video_title)

        new_title = change_title(video_title)

        # 发送视频 URL 请求
        video = requests.get(video_playurl, headers=headers).content

        # 保存数据
        with open(r'F:\Beautiful Girl Video\\' + new_title + '_' + video_alias + '.mp4', 'wb') as video_file:
            video_file.write(video)

        print('视频下载成功…… \n')

    print('---------------第{}页小姐姐视频爬取完毕-------------------'.format(page))

12.2 代码实现

'''-----------------RGB → CMY------------------------'''
import cv2

def rgb_cmy(video):
    while True:
        ret, frame=video.read()
        if not ret:
            break
        else:
            r, g, b = cv2.split(frame)  # split the channels
            # normalization [0,1]
            r = r / 255.0
            g = g / 255.0
            b = b / 255.0
            c = 1 - r
            m = 1 - g
            y = 1 - b
            result = cv2.merge((c, m, y))  # merge the channels
            cv2.imshow('original video',frame)
            cv2.imshow('CMY video',result)
            cv2.waitKey(ret)

if __name__ == '__main__':
    img = cv2.VideoCapture(r"F:\Beautiful Girl Video\video.mp4")
    img_CMY = rgb_cmy(img)

12.3 运行效果

---

注：本文所有内容的讲解视频已发布到：https://space.bilibili.com/386691571

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2020年6月初于山西大同

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