Frames only containing background about trackpy HOT 11 CLOSED

Could you share some/any of your images?

Try thresholding your images before passing them in.

It would also be helpful if you could post the exact parameters you are using.

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3,minmass=50000 (for example), threshold=8)

https://drive.google.com/file/d/0B9-erPeWaEjGc0N4MWdYZ0NRaGc/edit?usp=sharing
The first images have one or two particles detected with no problem, but when it disappears (other tifs) I have a bunch of other particles.
Thanks

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I poked around a little and saved my work in this notebook.

The later images in your example are full of scattered bright spots, and many of these bright spots have a mass much higher than 50000. Given how the images appear, I don't think locate/batch is doing anything surprising. The question might be why the field of view is suddenly filled with bright spots: is your camera's sensitivity changing during the experiment? Perhaps using a higher mass cutoff, as least twice as high as 50000, or some higher threshold, would eliminate those but retain the objects you want to track.

(For you images, typical mass numbers are higher than the ones in my walkthrough example because your image are 16-bit, where the examples are 8-bit.)

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The value of these bright spots is at the level of background when
comparing to images with real particles. But when I only have background
they are located. If you contrast the images you'll see they will
disappear. If not I gave you bad examples.

I tried higher Mass cut but I have situations where real events are around
50000, and even then it does not detect the background spots, only when
they disappear.
Thanks for the help.
On Apr 3, 2014 9:00 AM, "Dan Allan" [email protected] wrote:

I poked around a little and saved my work in this notebookhttp://nbviewer.ipython.org/gist/anonymous/2f0b6277559ddd53608d
.

The later images in your example are full of scattered bright spots, and
many of these bright spots have a mass much higher than 50000. Given how
the images appear, I don't think locate/batch is doing anything surprising.
The question might be why the field of view is suddenly filled with bright
spots: is your camera's sensitivity changing during the experiment? Perhaps
using a higher mass cutoff, as least twice as high as 50000, or some higher
threshold, would eliminate those but retain the objects you want to track.

(For you images, typical mass numbers are higher than the ones in my
walkthrough example because your image are 16-bit, where the examples are
8-bit.)

Reply to this email directly or view it on GitHubhttps://github.com//issues/99#issuecomment-39454077
.

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Ah, I think imshow was autoscaling them, which I should have realized. I will revisit later today. This should be doable.

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Just to check, the features if interest are the ~100 pixel across features? Shouldn't diameter be much larger than three? I strongly suspect that this can be handled with pre-processing and that part of the problem is the range stretching step (https://github.com/soft-matter/trackpy/blob/master/trackpy/feature.py#L503) that blows up the importance of noise that survives the too-small smoothing window.

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And as a further comment, if you only have three features per frame and they don't move much between frames while they are fading, you might be better off just finding them when they are bright and then just computing the sum over the same window in every frame.

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No, those are cells. It's just auto-fluorescence. I think that there is only one feature in those first samples (x:150 y: 270 aprox.) Here's an example with 3 or 4 particles.

https://drive.google.com/file/d/0B9-erPeWaEjGeW1CaW5Dcnc0bnM/edit?usp=sharing
I uploaded that example to show you that 1 event is fine, 0 events gives a lot of results that shouldn't be there.

I do have frames with 100+ events, but I have no problem with those. I can upload it if you'd like.

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And about the sum. I usually have more events, and they move a lot (virus). What I do with trackpy is to track the particles and then calculate a few things at the x.y provided by trackpy.

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Another example:
https://drive.google.com/file/d/0B9-erPeWaEjGWnFveG5vU0RUZUE/edit?usp=sharing

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Ah, sorry I was making up context from looking at the very small images in Dan's notebook.

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