This is the behavior of matplotlib. It finds the minimum and maximum of your picture, makes those black and white, and scales everything in between.

This is useful for arbitrary data that may have integer or floating point types, and value ranges between 0.0 and 1.0, or 0 .. 255, or anything else.

You can set those limits yourself with vmin and vmax arguments:

plt.imshow(imgGray, cmap='gray', vmin=0, vmax=255) # if your data ranges is uint8

OpenCV does no such auto-scaling. It has fixed rules. If it's floating point, 0.0 is black and 1.0 is white. If it's uint8, the range is 0 .. 255.

To get such auto-ranging in OpenCV, you'll have to scale the data before displaying:

normalized = cv.normalize(
    data, alpha=0.0, beta=1.0, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)

You are supposed to add another parameter in plt.imshow() so as to mention that you want a gray scale image.

Modify the last line like this: plt.imshow(img, cmap='gray')

Upon doing so I got the following:

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 :)