clementsan / disp-map-analysis

python scripts used for depth sensing pre-processing and post-processing stages

This project contains various python scripts used for disparity map analysis

1. Data pre-processing stage

Note: Data preprocessing stage to generate input files for AI analysis via ir-tp-net

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
• Output CorrImage is a 3D TIFF file with 120 layers
• Other output files are 2D images

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

2. AI analysis stage

Please see "ir-tp-net" project for neural network training and testing to generate predicted disparity map

3. Data post-processing stage

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

4. Data Quality control

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
• output file includes predicted disparity map, ground truth map, target disparity map and mask map