Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection
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SorourMo
Each value of output tif from main_test.py is all around 0.5(much bigger than 0.0471) and get all black or white tif by evaluation.m
@SorourMo
First stage(In this project):
1.1 input:
GLOBAL_PATH = 'H:/cloudAndNoncloud/38-Cloud'
TEST_FOLDER = os.path.join(GLOBAL_PATH, '38-Cloud_test')
test_patches_csv_name = 'test_patches_38-cloud.csv'
2 process:
processed by main_tet.py with Cloud-Net_trained_on_38-Cloud_training_patches.h5(417.7MB)
1.3 output:
TIF file（384x384pix， 32bits）in Predictions folder
Q1：(1)Why all values of this TIF file are around 0.5(not around 0.0471)
Second stage(In 38-cloud project, evaluation folder .m files) 2.1 input: output TIF file from stage1, like patch_248_13_by_8_LC08_L1TP_003052_20160120_20170405_01_T1.TIF 2.2 process: 38-Cloud-A-Cloud-Segmentation-Dataset/tree/master/evaluation/evaluation.m file refactoring about complete_mask and gt, change complete_pred_mast and gt（clip manually by 248_13 tile location） to 384x384 2.3 output: All black or white TIF.
Q2:
(2)Is all black or white TIF result of stage2 caused by wrong output of stage1(384x384 tif, values are around 0.5)
(3)Why output of stage1 are all around 0.5 ? what step is wrong from my side? (I feel that all values should be around 0.471, so this predicted_patch = imbinarize(predicted_patch,thresh); could be make sense.
(4) If 0.0471 or 12/255 is settled, why not make evaluation.m file in main_test.py(in python) ?
Seems pretrained model Cloud-Net_trained_on_38-Cloud_training_patches.h5(417.7MB) not correct. I got different result tif, which can denote real cloud shape(even not that precise)about stage 1 by .h5 file trained by myself.