如何使用 cv2.imshow() 可视化 16 位灰度图像？

Question

I have a set of grayscale drone images in tiff format having 16-bit resolution where a person can be seen moving. How can I visualize these images in OpenCV as a normal image, so that I can see the information within the image in OpenCV? Currently, when I try to read and show the image, I see a black image.

import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())

image = cv2.imread(args["image"],IMREAD_ANYCOLOR | IMREAD_ANYDEPTH)

cv2.imshow("image", image)
cv2.waitKey(0)

I have tried the above code, but it still displays a complete black image. However, when I convert the image to a png and then use the above code, then it works fine which I do not want to do due to loss of information.

Here is the link to sample image. All images contain different information.

https://filebin.net/n3oidsqn70eq8a9x/gelmer_gas_0_Raw_316_4245_2942_1455208775.tif?t=c2m8vnsn

The image should be like the below. This was opened with another software just for visual purpose

Answer 1

As you stated before, loading is easy:

img = cv2.imread("a.tif", cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)

Then, one has different options to visualize a thermal image. The simple naive approach is to normalize from the min to the max value:

normed = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)

After, you can color it with a colormap:

color = cv2.applyColorMap(normed, cv2.COLORMAP_JET)

My suggestion would be to fix the temperature range and clip the rest of the values to get an image with colors that can be compare between several images or video. To do that, you can take the idea from this answer assuming your new min and max are 0 and 255 and your old min and max are the range you need.

To be more specific, in your case you need something like:

def normalizeImg(low, high, img):
    imgClip = np.clip(img, low, high)
    maxVal = np.max(imgClip)
    minVal = np.min(imgClip)
    return np.uint8((255.)/(maxVal-minVal)*(imgClip-maxVal)+255.)

Where low and high are the raw values you want to normalize to. And then you use it like:

def celsiusToPixel(val):
    return (val + 273.15) / 0.04

rangeToUse = [celsiusToPixel(20), celsiusToPixel(30)] # from 20-30° celsius
normed_range =  normalizeImg(rangeToUse[0], rangeToUse[1], img)

I hope a didn't miss anything, but if you have questions, just ask :)