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Phase-Stretch Adaptive Gradient-field Extractor (PAGE)

Code Architect

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

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

    def init_kernel(self, mu_1, mu_2, sigma_1, sigma_2, S1, S2):
    
    def apply_kernel(self, sigma_LPF, thresh_min, thresh_max, morph_flag):    

    def create_page_edge(self):

    def run(self, img_file, mu_1, mu_2, sigma_1, sigma_2, S1, S2,\
            sigma_LPF, thresh_min, thresh_max, morph_flag):

  • To instantiate a PAGE class, three parameters h (height), w (width), and direction_bins are needed. h and w 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 PAGE kernel according to the parameters mu_1, mu_2, sigma_1, sigma_2, S1, S2. In the CPU version, kernels for different frequency bins are initialized in serial. As for the derivation of the kernel and the physical meaning of parameters, please refer to references [9], [10].

  • 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 create_page_edge method creates a weighted color image of PAGE output to visualize directionality of edges.

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

GPU Acceleration

The GPU version of PST significantly accelerates the PST algorithm by using GPU(s). As defined in phycv/page_gpu.py , the architect of the PAGE_GPU class is similar to the original PAGE 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 init_kernel method initializes kernels for different direction bins in parallel by using broadcasting.
  4. The apply_kernel method applies kernels for different direction bins to the image in parallel by using broadcasting.
  5. The returned result locates on GPU and is in the form of torch.Tensor.

Examples

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

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

Example 2 - Video ( scripts/run_page_video.py )

In this example, we run PAGE 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.