image-quality-assessment | Convolutional Neural Networks to predict | Machine Learning library

├── data
│   ├── dped -> /home/***/datasets/dped/
│   ├── __init__.py
│   ├── load_dataset.py
│   └── pretrain_models
├── demo
├── experiments
│   ├── config
│   └── logs
├── loss
│   

# example for FR metric with dirs 
python inference_iqa.py -n LPIPS[or lpips] -i ./ResultsCalibra/dist_dir -r ./ResultsCalibra/ref_dir 

# example for NR metric with single image
python inference_iqa.py -n brisque -i ./ResultsCalibra/dist_dir/I03.bmp  

conda create -n iqa python=3.7 -y && conda activate iqa
python -m pip install pyyaml opencv-python tqdm pandas

# for psnr/ssim
python -m pip install scikit-image==0.18.2

# for ms-ssim/lpips
# test under cuda 10.x
python -m pip install torch  

def ssim_multiscale(img1,
                    img2,
                    max_val,
                    power_factors=_MSSSIM_WEIGHTS,
                    filter_size=11,
                    filter_sigma=1.5,
                    k1=0.01,
                 

def ssim(img1,
         img2,
         max_val,
         filter_size=11,
         filter_sigma=1.5,
         k1=0.01,
         k2=0.03):
  """Computes SSIM index between img1 and img2.

  This function is based on the standard SSIM implementation fro  

def _ssim_per_channel(img1,
                      img2,
                      max_val=1.0,
                      filter_size=11,
                      filter_sigma=1.5,
                      k1=0.01,
                      k2=0.03):
  """Computes SSIM