如何使用 python 显示 16 位彩色 FITS 图像

发布于 2025-01-12 03:22:41 字数 1067 浏览 3 评论 0原文

我想在 python 中显示 FITS 图像。我使用 astropy（FITS 加载程序）、matplotlib 和灰度图像完成所有这些工作。然而，我有 16 位颜色的图像，带有 RGGB 拜耳矩阵，但我不知道如何将其显示为 RGB 彩色图像。

这适用于灰度图像：

import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
m42 = fits.open('FITS Data/frame-u-006073-4-0063.fits')
imagedata = m42[0].data
plt.imshow(imagedata, cmap='gray')
plt.colorbar()

但是我有第二个图像，每像素 16 位，我不知道如何将这些位映射到 r、g、b 值并在 matplotlib 中显示它们。

例如（第一个像素是 3148）：

pixel = imagedata[0][0]
r = (pixel & 0b1111000000000000) >> 12
g = (pixel & 0b0000111100000000) >> 8
g = int((g + ((pixel & 0b0000000011110000) >> 4)) / 2)
b = pixel & 0b0000000000001111

分别为红色、绿色和蓝色给出 0、8 和 12。如何将整个数组imagedata映射到RGB并让matplotlib显示它？还假设您对两个绿色值进行平均？任何帮助表示赞赏。

更新：我是否误解了带有拜耳矩阵的 16 位图像的格式？每像素完整 16 位是 R、G、G 还是 B？在什么情况下我需要先对图像进行去马赛克/去拜耳处理？

原文

I want to display a FITS image in python. I have all this working using astropy (the FITS loader) and matplotlib and a greyscale image. However I have images that are 16 bit colour with a Bayer matrix of RGGB and I don't know how to display this as an RGB colour image.

This works for a greyscale image:

import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
m42 = fits.open('FITS Data/frame-u-006073-4-0063.fits')
imagedata = m42[0].data
plt.imshow(imagedata, cmap='gray')
plt.colorbar()

However I have a second image that is 16 bits per pixel and I don't know how to map the bits to r, g, b values and display them in matplotlib.

For example (first pixel is 3148):

pixel = imagedata[0][0]
r = (pixel & 0b1111000000000000) >> 12
g = (pixel & 0b0000111100000000) >> 8
g = int((g + ((pixel & 0b0000000011110000) >> 4)) / 2)
b = pixel & 0b0000000000001111

Gives 0, 8, and 12 for red, green, and blue respectively. How do I map the entire array imagedata to RGB and get matplotlib to display it? Also assuming you average the two green values? Any help appreciated.

Update: Have I misunderstood the format of a 16 bit image with a Bayer matrix? Are the full 16 bits per pixel either R, G, G, or B? In which case do I need to look at demosaicing / debayering the image first?

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祁梦 2025-01-19 03:22:41

答案是使用 OpenCV 对图像进行去拜耳处理，然后对数据进行标准化。

import cv2
debayered_image = cv2.cvtColor(imagedata, cv2.COLOR_BayerBG2BGR)
normalised_image = cv2.normalize(debayered_image, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
imgplot = plt.imshow(normalised_image)

The answer was to use OpenCV to debayer the image and then normalise the data.

import cv2
debayered_image = cv2.cvtColor(imagedata, cv2.COLOR_BayerBG2BGR)
normalised_image = cv2.normalize(debayered_image, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
imgplot = plt.imshow(normalised_image)

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