Getting all black images

[shreyaspimpalgaonkar]

The .tiff images that I got after running python demo/tomopy_rec.py tomo_00007.h5 --axis 1232 --nsino 0.7 are almost black.

After doing normalisation (normalising values between 0 and 255) I can see a big circle like here -

https://ibb.co/hPAsx8

[decarlof]

@shreyas-7 what you get is correct. These data are from nanoCT with low S/N plus because of a large load cell there are several blocked views ([141,226]).

[shreyaspimpalgaonkar]

Thanks, so how do I get this image? - I have downloaded it from the tomohome website

Or maybe the left part of the image is same as my reconstruction?

[decarlof]

the left image is a projection (raw data transmission image at one angle); the right image is a reconstructed slice (output of tomopy_rec.py). This last is different from yours because you selected a different slice to be reconstructed (--nsino 0.7 => slice 1512 = 0.7 * ccd_y, in this case ccd_y = 2160)

[shreyaspimpalgaonkar]

Thank you. Also, for normalising the values, what was used to get that image?

I did - img = img - min(img) img = img*255/max(img)

min and max are minimum and maximum values of pixels in the image

[decarlof]

I assume img is the reconstructed slice, I think what you are doing will compress the dynamic range too much. Can you please give me the link to the downloadable tiff as generated by tomopy for img? I want to repeat the reconstruction and compare.

[shreyaspimpalgaonkar]

Here are some of the reconstructions of the same. (They are in gaps of 10 slices each)

[decarlof]

here is a png image obtained by loading recon_01132.tiff into imageJ and using auto-rescaling (Image/Adjust/Brightness-contrast) option. In this way the number of bits is automatically adjusted, I think your rescaling forcing 8 bits is compressing the dynamic range too much. Again these are difficult images with low S/N.