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README.md

@@ -8,10 +8,10 @@ Note: Data preprocessing stage to generate input files for AI analysis via ir-tp
 ### 1.1. Extracting images of interest from multi-layer 3D TIFF files
 
 Example:
-''' 
+``` 
 python3 ./Preprocessing_2DPhaseCorrelation.py --crop_border --input $i --targetdisp $TargetDispImage \
 		--groundtruth $GroundTruthImage --confidence $ConfidenceImage --disp_lma $DispLMAImage --corr $CorrImage --verbose
-'''
+```
 
 Notes:
  - Input file is a special multi-layer TIFF file
@@ -21,10 +21,10 @@ Notes:
 ### 1.2. Generating 3D TIFF files as direct input files to neural network ir-tp-net
 
 Example:
-''' 
+``` 
 python3 ./Preprocessing_CombinedImages.py --corr $CorrImage --targetdisp $TargetDispImage \
 		--groundtruth $GroundTruthImage --confidence $ConfidenceImage --disp_lma $DispLMAImage --output $CombinedImage
-''' 
+``` 
 Notes: 
  - Output file is a 3D image with 124 layers
   - scaled to the size of the correlation image
@@ -41,20 +41,20 @@ Note: Data postprocessing stage to analyze outputs from AI analysis
 ###  3.1. Density analysis
 
 Examples:
-''' 
+``` 
 python3 ./Compute_Density.py --pred $i --groundtruth $GroundTruthImage --adjtilesdim 1 --output $Density_CSVFile --inclusionmask $InclusionMask_File --exclusionmask $ExclusionMask_File --threshold 2.0 --verbose
-''' 
+``` 
 
 Note: input file is the predicted disparity map
 
 ###  3.2. RMSE analysis
 
 Example:
-''' 
+``` 
 python3 ./Compute_RMSE_WithThreshold.py --pred $i --groundtruth $GroundTruthImage --adjtilesdim $AdjTilesDim --threshold $RMSE_Threshold --output $RMSE_CSVFile
 			
 python3 ./Compute_RMSE_WithFiltering.py --pred $i --groundtruth $GroundTruthImage --confidence $ConfidenceImage --disp_lma $DispLMAImage --adjtilesdim 1 --threshold $Threshold --output $RMSE_CSVFile --output_mask $RMSEFiltering_MaskFile
-''' 
+``` 
 
 Note: input file is the predicted disparity map
 
@@ -63,9 +63,9 @@ Note: input file is the predicted disparity map
 
 Quality control stage generating multi-layer 3D tiff file
 
-''' 
+``` 
 python3 ./Compute_Inference_QCImage.py --pred $i --groundtruth $GroundTruthImage --targetdisp $TargetDispImage --mask $DataFilteringMask --threshold 2.0 --output $InferenceQC_File --verbose
-''' 
+``` 
 
 Notes: 
  - input file is the predicted disparity map