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Download and unzip the CoNSeP dataset to the directory
../MCSpatNet_datasets
wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep.zip -P ../MCSpatNet_datasets unzip ../datasets/consep.zip -d ../MCSpatNet_datasets
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cd data_prepare/
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Edit
1_generate_dot_maps_consep.py
Set the variables:
in_dir
points to the CoNSeP train/test directory, and
out_root_dir
points to the training/testing data output directory, respectively.
Default values are:in_dir = '../../MCSpatNet_datasets/CoNSeP/Train' out_root_dir = '../../MCSpatNet_datasets/CoNSeP_train'
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Run
1_generate_dot_maps_consep.py
python 1_generate_dot_maps_consep.py
It will create 2 sub-directories:
images
andgt_custom
in the output folder.
The generated files are:- images/:
<img_name>.png
: the rescaled images by 0.5 (20x).
- gt_custom/:
<img_name>_gt_dots.npy
: the classification dot annotation map.<img_name>_gt_dots_all.npy
: the detection dot annotation map.<img_name>.npy
: the classification binary mask.<img_name>_all.npy
: the detection binary mask.<img_name>_s<class id>_binary.png
: visualization of the binary mask for each class (default: 1=inflammatory, 2=epithelial, 3=stromal).<img_name>_binary.png
: visualization of the detection binary mask.<img_name>_img_with_dots.jpg
: image with cells dot annotation visualization with different dot colors. (default: blue=inflammatory, red=epithelial, green=stromal).
- images/:
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Edit
2_calc_kmaps.py
Set the variables:
root_dir
points to the CoNSeP train/test directory created in the previous step
Default value is:root_dir = '../../MCSpatNet_datasets/CoNSeP_train'
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Run
2_calc_kmaps.py
python 2_calc_kmaps.py
It will create the sub-directory:
k_func_maps
in the output folder.
It generates the cross k function maps. The file names arek_func_maps/<img_name>_gt_kmap.npy
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Repeat steps 3-6 with the test data directory:
ReplaceCoNSeP/Train
withCoNSeP/Test
ReplaceCoNSeP_train
withCoNSeP_test