66 lines
2.0 KiB
Markdown
66 lines
2.0 KiB
Markdown
# Seeing Stars
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**Category**: Astronomy, Astrophysics, Astrometry, Astrodynamics, AAAA
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**Points (final)**: 23
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**Solves**: 213
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> Here is the output from a CCD Camera from a star tracker, identify as many stars as you can! (in image reference coordinates)
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> Note: The camera prints pixels in the following order (x,y): (0,0), (1,0), (2,0)... (0,1), (1,1), (2,1)…
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>
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> Note that top left corner is (0,0)
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## Write-up
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by [hazel (`arcetera`)](https://qtp2t.club/)
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The CCD image given by the netcat is a 128x128 matrix of comma-separated values.
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We read the data into a NumPy array, and pass that into OpenCV.
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``` python
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data = []
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for line in rawdat.strip().split('\n'):
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data.append([int(x) for x in line.split(',')])
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x = np.array(data, dtype='uint8').T
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im = x
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```
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We then run a filter on the data, only grabbing values in [127, 255] to filter
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out data that is *obviously not* stars.
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We then run two dilates on the image post-filter, because otherwise
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we end up with a division by zero on centroid finding later for `M["m00"]`.
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Finally, we grabbed the contour of every object visible in the image.
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``` python
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ret, thresh = cv2.threshold(im.copy(), 127, 255, 0)
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kernel = np.ones((5, 5), np.uint8)
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dilated = cv2.dilate(thresh.copy(), kernel, iterations = 2)
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cnts, hier = cv2.findContours(dilated.copy(), \
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cv2.RETR_TREE, \
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cv2.CHAIN_APPROX_NONE)
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```
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For each contour, we grabbed its centroid:
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``` python
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solve = ''
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for c in cnts:
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M = cv2.moments(c)
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cX = int(M["m10"] / M["m00"])
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cY = int(M["m01"] / M["m00"])
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solve += (str(cX) + "," + str(cY)+'\n')
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return solve
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```
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We then automated this entire process using pwnlib to connect to the server and
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read the data.
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### Full code
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```{.python include=seeing-stars.py}
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```
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Run it, and the flag should be printed as a bytestring.
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## Resources and other writeups
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- <https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html>
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- <https://docs.opencv.org/trunk/dd/d49/tutorial_py_contour_features.html>
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