将灰色图像转换为蓝色和红色图像

Question

我想知道如何拍摄灰度图像并将暗像素变为红色，将白色像素变为蓝色？所有其他看起来灰色的像素（不是完全黑色或白色）应该是从红色到蓝色的过渡。我尝试了以下方法：获取灰度图像，将其转换为 RGB，然后尝试删除绿色通道。但图像看起来只是粉红色：im = cv2.imread('grey_img.jpg') im[:,:,1] = 0那么如何将灰色图像变成蓝色到红色的图像呢？

Answer 1

我接触到了一些数学方面的东西，它们很优雅：

img = cv2.imread('images/lena.png', cv2.IMREAD_GRAYSCALE)

# find some percentiles for grayscale range of src image

percentiles = np.percentile(img, [0, 25, 75, 100])

# define the same count of values to further interpolation

targets = np.geomspace(10, 255, 4)

# use interpolation from percentiles to targets for blue and red

b = np.interp(img, percentiles, targets).astype(np.uint8)

g = np.zeros_like(img)

r = np.interp(img, percentiles, targets[::-1]).astype(np.uint8)

# merge channels to BGR image

result = cv2.merge([b, g, r])

结果：

您可以通过更改百分位数或目标空间点来调整亮度

Answer 2

删除色带不会实现您所描述的效果，因为您正在尝试对图像进行着色，而不是对其进行脱色。决定如何处理每个像素的像素级函数是解决此问题的好方法。

from PIL import Image

def pixop_redblue(pixel):

    pixel, alpha = pixel[:3], pixel[3:]

    grey = sum(pixel) // len(pixel)

    redvalue = 255 - grey  # "darkness"

    bluevalue = grey  # "brightness"

    return (redvalue, 0, bluevalue) + alpha

img = Image.open('trees.jpg')

img2 = img.copy()

img2.putdata([pixop_redblue(pixel) for pixel in img.getdata()])

img2.show()

Answer 3

以下是使用 Python/OpenCV 将渐变颜色应用于灰度图像的一种方法。

 - Load the grayscale image

 - Convert it to 3 equal channels (only if image is 1 channel grayscale already)

 - Create a 1 pixel red image

 - Create a 1 pixel blue image

 - Concatenate the two

 - Resize linearly to 256 pixels as a Lookup Table (LUT)

 - Apply the LUT

 - Save the result

输入：

import cv2

import numpy as np

# load image as grayscale

img = cv2.imread('lena_gray.png', cv2.IMREAD_GRAYSCALE)

# convert to 3 equal channels (only if img is already 1 channel grayscale)

img = cv2.merge((img, img, img))

# create 1 pixel red image

red = np.zeros((1, 1, 3), np.uint8)

red[:] = (0,0,255)

# create 1 pixel blue image

blue = np.zeros((1, 1, 3), np.uint8)

blue[:] = (255,0,0)

# append the two images

lut = np.concatenate((red, blue), axis=0)

# resize lut to 256 values

lut = cv2.resize(lut, (1,256), interpolation=cv2.INTER_CUBIC)

# apply lut

result = cv2.LUT(img, lut)

# save result

cv2.imwrite('lena_red_blue_lut_mapped.png', result)

# display result

cv2.imshow('RESULT', result)

cv2.waitKey(0)

cv2.destroyAllWindows()