Marcel-Simon
commented
6 years ago It seems the reason why the 16 bit images are all gray is because the values are all pretty high, while still having very small variance.
In: np.unique(img, return_counts=True)
Out: (array([51241, 51361, 51381, 51401, 51421, 51441, 51461, 51481, 51501,
51521, 51541, 51561, 51581, 51601, 51621, 51641, 51661, 51681,
51701, 51721, 51741, 51761, 51781, 51801, 51821, 51841, 51861,
51881, 51901, 51921, 51941, 51961, 51981, 52001, 52021, 52041,
52061, 52101, 52181]),
array([ 1, 2, 13, 6, 31, 50, 204, 313, 880,
1086, 3079, 6116, 11211, 12168, 27619, 25012, 45961, 33892,
50997, 30580, 34756, 14925, 13214, 5228, 4758, 1819, 1709,
762, 615, 323, 163, 80, 40, 35, 10, 8,
10, 2, 2], dtype=int64))However, the image reading itself seems fine. We just need proper normalization. For example, try this:
img = np.array(PIL.Image.open(image_path)).astype(np.float64)
mi = np.percentile(img, 1)
ma = np.percentile(img, 99)
plt.imshow((img - mi)/(ma-mi), vmin=0, vmax=1)It is also important to set vmin=0, vmax=1 when using pyplot, as it will mess up scaling because there are negative and values >1 in the array after normalization. Also note that int is automatically converted to float during the devision as mi and ma are floats.
Also be careful when converting to 8 bit, because you will get unhandled overflows:
In: np.array([255,256,257]).astype(np.uint8)
Out: array([255, 0, 1], dtype=uint8)
jomalsan
pbaer
Our thermal data is in a raw 16-bit PNG format. We need to both a) figure out exactly what the raw values correspond to so that we can normalize the data (i.e. 0 degrees Celsius should be the same 16-bit pixel value in any image) for training and b) convert it an 8-bit representation for human visual inspection.