Error in image test

[Luis Entrena]

I'am just trying to complete my first run. After solving some issues (previous answers were very helpful, thanks!), I got stuck at the image test. I got the following output:

Loading config
Loading hip_main.dat
Filtering stars
Generating DB
Ready

rgb.solve_image('xmas/median_image.png')

Time1: 7.86781311035e-06
Traceback (most recent call last):
  File "startracker.py", line 532, in <module>
    exec(data)
  File "<string>", line 1, in <module>
  File "startracker.py", line 377, in solve_image
    self.current_image=star_image(imagefile,self.median_image)
  File "startracker.py", line 169, in __init__
    contours,heirachy = cv2.findContours(thresh,1,2);
ValueError: too many values to unpack

Traceback (most recent call last):
  File "startracker.py", line 532, in <module>
    exec(data)
  File "<string>", line 1, in <module>
  File "startracker.py", line 377, in solve_image
    self.current_image=star_image(imagefile,self.median_image)
  File "startracker.py", line 169, in __init__
    contours,heirachy = cv2.findContours(thresh,1,2);
ValueError: too many values to unpack

Can you help me? I've tried to use pdb, but it didn't work with the nc pipe. I will appreciate any recommendation to execute the code in debug mode.

Thank you very much in advance.

[Andrew Tennenbaum]

Hmm, sounds like it’s not calibrated properly. What images did you use when you ran the calibration?

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[Umair Khan]

Something else to note: findContours in OpenCV returns 3 things, not 2. So you need another variable to catch all of the results.

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[Luis Entrena]

I used the images in xmas for calibration. I've just followed the steps provided in the github for basic setup.

Umair, you're right. I discovered the findContours problem in the meantime and now it works.

By the way, I found a couple of problems in simulator.py. The following lines gave errors because there were negative arguments for sqrt and log10:

flux += flux_per_photon*np.random.normal(0, self.norm_gaussian(self.sigma_psf)*np.sqrt(flux/flux_per_photon), len(flux))

return -2.5 * np.log10(flux / self.base_flux)

I circumvented the problem by using the abs of the argument, but I am pretty sure this is not the right solution!

Thank you all for your fast answer.