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Normalize each channel for flat #189

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Merged
vladtcvs merged 2 commits into from
Dec 17, 2024
Merged

Normalize each channel for flat #189

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20891e3
Normalize each channel for flat
vladtcvs Dec 17, 2024
abc293e
Fix test
vladtcvs Dec 17, 2024
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4 changes: 3 additions & 1 deletion src/vstarstack/library/calibration/flat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,8 @@ def prepare_flat_sky(images : vstarstack.library.common.IImageSource,
if not flat.get_channel_option(channel, "signal"):
continue
layer = cv2.GaussianBlur(layer, (15, 15), 0)
flat.add_channel(layer, channel, **opts)
layer = layer / np.amax(layer)
flat.replace_channel(layer, channel, **opts)
for channel in list(flat.get_channels()):
if not flat.get_channel_option(channel, "weight"):
continue
Expand Down Expand Up @@ -201,5 +202,6 @@ def approximate_flat_image(flat : vstarstack.library.data.DataFrame) -> vstarsta
ky = ky * k**2
layer_approximated = generate_flat(layer.shape[1], layer.shape[0], x0, y0, val0, kx, ky)
layer_approximated = detect_spots(layer, layer_approximated)
layer_approximated = layer_approximated / np.amax(layer_approximated)
flat.replace_channel(layer_approximated, channel, **opts)
return flat
1 change: 1 addition & 0 deletions tests/test_flat_approximation.py
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Expand Up @@ -22,6 +22,7 @@ def test_flat():
dfg = vstarstack.library.loaders.classic.readjpeg(os.path.join(dir_path, 'flat/flat.jpg'))
df = next(dfg)
flat,_ = df.get_channel('L')
flat = flat / np.amax(flat)
approx = vstarstack.library.calibration.flat.approximate_flat_image(df.copy())
flat_approx,_ = approx.get_channel('L')
assert flat_approx.shape == flat.shape
Expand Down