Comments (3)
tanchaozhen
commented on June 6, 2024
I have the same problem.
Have you solved it?
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fatihergin
commented on June 6, 2024
I faced with the same issue and solved with converting the mask files from grayscale to RGBA.
It looks like your mask files are RGB and should also be converted to RGBA.
Here is the imagemagick batch convert that I used:
mogrify -define png:format=png32 -type TrueColor data/masks/*.png
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tanchaozhen
commented on June 6, 2024
Thanks for your help! I will try it.
…------------------ 原始邮件 ------------------
发件人: "fatih ergin"<[email protected]>;
发送时间: 2019年12月15日(星期天) 晚上11:00
收件人: "Cheng-Lin-Li/SegCaps"<[email protected]>;
抄送: "T_optimus"<[email protected]>; "Comment"<[email protected]>;
主题: Re: [Cheng-Lin-Li/SegCaps] Can not load images and masks (#16)
I faced with the same issue and solved with converting the mask files from grayscale to RGBA.
It looks like your mask files are RGB and should also be converted to RGBA.
Here is the imagemagick batch convert that I used:
mogrify -define png:format=png32 -type TrueColor data/masks/*.png
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Related Issues (20)
- Creating MSCOCO dataset HOT 2
- What does num_atom mean?
- Instruction for running the code from .jpg files HOT 3
- TypeError: deconv_length() missing 1 required positional argument: 'output_padding' HOT 13
- bad mask HOT 1
- RuntimeError: Exception thrown in SimpleITK ReadImage: /opt/miniconda2/conda-bld/simpleitk_1546539363356/work/Code/IO/src/sitkImageReaderBase.cxx:107: sitk::ERROR: Unable to determine ImageIO reader for "data/imgs/train470.png"
- Final output and raw output are always same no matter what input images are given HOT 1
- Why transformation matrix shared by different child capsule types?
- Training image and mask error HOT 4
- Did you realize the Experiments Results stated in paper of Capsules for Object Segmentation?
- RGB images with binary masks
- dependencies version
- Value Error
- ValueError: No gradients provided for any variable HOT 1
- ValueError: Dimension 0 in both shapes must be equal, but are 3 and 16.
- Proplem with deconv_length from keras.utils.conv_utils
- Iteration Time Issue
- Training Performance Do Not Improve HOT 4
- expected out_recon shape (512, 512, 1) HOT 2
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