問題描述
我有以灰度 16 位 tiff 格式編碼的圖像.他們使用 16 位顏色深度的變體，其中最大強度為 4,096.
I have images encoded in grayscale 16-bit tiff format. They use a variant of 16-bit color depth where the max intensity is 4,096.
我相信 openCV 中的默認最大強度是 65,536，所以我的圖像使用以下代碼顯示為黑色.
I believe the default max intensity in openCV is 65,536, so my image shows up as black using the following code.
import cv2
image = cv2.imread("test.tif", -1)
cv2.imshow('tiff', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
print(image)
我可以在matplotlib中使用vmin和vmax來配置顏色映射:
I can use vmin and vmax in matplotlib to configure the color mapping:
import cv2
import matplotlib.pyplot as plt
image = cv2.imread("test.tif", -1)
plt.imshow(image, cmap="gray", vmin=0, vmax=4096)
plt.show()
顯示圖片的內容:
我要堅持使用openCV的原因是matplotlib不支持顯示16位RGB圖像.
The reason why I want to stick with openCV is matplotlib doesn't support displaying 16-bit RGB images.
cv2 的文檔.imshow 并不是很有幫助.有沒有辦法在 Python openCV 中顯示 16 位 4096 強度圖像?
The documentation of cv2.imshow is not really helpful. Are there ways to display 16-bit 4096 intensity images in Python openCV?
測試圖像 test.tif 可以找到 這里.
The testing image test.tif can be found here.
推薦答案
你會想要使用 cv2.normalize() 在顯示之前縮放圖像.
You'll want to use cv2.normalize() to scale the image before displaying.
您可以設置圖像的最小值/最大值，它會適當地縮放圖像(通過將圖像的最小值移動到 alpha 并將圖像的最大值移動到 beta).假設你的 img 已經是 uint16:
You can set the min/max of the image and it will scale the image appropriately (by moving the min of the image to alpha and max of the image to beta). Supposing your img is already a uint16:
img_scaled = cv2.normalize(img, dst=None, alpha=0, beta=65535, norm_type=cv2.NORM_MINMAX)
然后就可以正常觀看了.
And then you can view as normal.
默認情況下，cv2.normalize() 將生成與輸入圖像相同類型的圖像，因此如果您想要一個無符號的 16 位結果，您的輸入應該是 uint16.
By default, cv2.normalize() will result in an image the same type as your input image, so if you want an unsigned 16-bit result, your input should be uint16.
再次，請注意，這會線性拉伸您的圖像范圍 --- 如果您的圖像從未真正達到 0 并且說最小值是 100，那么在您標準化之后，該最小值將是您設置的任何值 alpha 到.如果您不希望這樣，正如其中一條評論所建議的那樣，您可以簡單地將您的圖像乘以 16，因為它目前只上升到 4095.使用 * 16，它將上升到 65535.
Again, note that this linearly stretches your image range---if your image never actually hit 0 and say the lowest value was 100, after you normalize, that lowest value will be whatever you set alpha to. If you don't want that, as one of the comments suggests, you can simply multiply your image by 16, since it's currently only going up to 4095. With * 16, it will go up to 65535.
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