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Phase-Stretch Transform (PST)

Code Architect

class PST:
    def __init__(self, h=None, w=None):

    def load_img(self, img_file=None, img_array=None):

    def init_kernel(self, S, W):
    
    def apply_kernel(self, sigma_LPF, thresh_min, thresh_max, morph_flag):
    
    def run(self, img_file, phase_strength, warp_strength, \
            sigma_LPF, thresh_min, thresh_max, morph_flag):

  • The __init__ method has two parameters h (height) and w (width), which indicates the size of image the algorithm will operate on. They are set to None by default. If you leave them as None by default, their value will be determined when calling the following load_img method.

  • The load_img method can load the image from either an image file indicated by img_file or an image array indicated by img_array. Then it converts the image to greyscale if it is in RGB format, if h and w are not indicated in the __init__ method, they will be determined by the shape of the image. Otherwise, the image will be reshaped to the indicated size.

  • The init_kernel method initializes the PST kernel according to phase strength S and warp strength W. Please refer to [3], [4] for the explanation of these parameters.

  • The apply_kernel method first denoises the loaded image with a low-pass filter characterized by sigma_LPF, then it applies the initialized kernel to the denoised image, finally it applies morphological operation if morph_flag==1. The thresholds in the morphological operation are indicated by thresh_min and thresh_max.

  • The run method wraps load_img, init_kernel, apply_kernel together.

GPU Acceleration

The GPU version of PST significantly accelerates the PST algorithm by using GPU(s). As defined in phycv/pst_gpu.py , the architect of the PST_GPU class is similar to the original PST class with same attributes and methods. The main differences are:

  1. You have to indicate the device (in a PyTorch fashion) when instantiating the class.
  2. Image IO is done by torchvision instead of opencv, matrix operation is done by torch instead of numpy.
  3. The returned result locates on GPU and is in the form of torch.Tensor.

Examples

Example 1 - Single Image ( scripts/run_pst.py )

In this example, we run PST on a single image and all steps are performed in a single run method.

Example 2 - Video ( scripts/run_pst_video.py )

In this example, we run PST on a video, when using fixed parameters for all frames, the init_kernel can be called only once to save computation time. Note that running this script takes some time because saving processed results and putting them together to generate a video is time-consuming.