Comments (2)
def pack_raw(im):
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
## R G G B
out = np.concatenate((im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :],
im[1:H:2, 1:W:2, :]), axis=2)
return out
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Hi
You need to clamp the output of the model_raw2rgb between 0 and 1.
Here is a snippet of code.
rgb_noisy = model_raw2rgb(raw_img_save, ccm_tensor)
rgb_noisy = torch.clamp(rgb_noisy, 0., 1.)
rgb_noisy = rgb_noisy.squeeze(0).permute(1, 2, 0)
Thanks
from cycleisp.
Related Issues (20)
- Raw2Rgb network doesnt match the one in paper
- SIDD validation's bayer pattern is unknown, how to stack per bayer's data as R-G-G-B sequence.
- Training code HOT 3
- training code
- training code and dataset
- About 'lycon' module HOT 1
- unpadding
- Training Code HOT 2
- Question about the SIDD raw results.
- How to test raw images without gt?
- Installation with lycon
- Version update for skimage.measure compare_psnr and compare_ssim
- Log Files from Training
- Training code
- Training code
- About the training code
- rgb2raw branch mosaic problem
- License information HOT 2
- Some questions about GPU settings HOT 1
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