Hough-TMF 0.0.1

A package for template matching using Torch

hd-tmf

A high-dimensional template matching framework based on PyTorch.

Installation

pip install hd-tmf

Usage

import numpy as np
from hd_tmf import tmf

Example

# generate a random template
tmp = np.random.rand(10, 20, 20)
# generate a random image
data = np.random.rand(1000, 1000)
# calculate the cross-correlation between the template and the image
corr = tmf(tmp, data, step=1, device='cpu', moves=[], batch_size=-1, save_memory=False, max_workers=4)

Parameters

• tmp (numpy.ndarray or torch.Tensor): The template to be matched.
• data (numpy.ndarray or torch.Tensor): The image to search for the template.
• step (int, optional): The step size of the convolution. Defaults to 1.
• device (str, optional): The device to perform the computation on. Defaults to 'cpu'.
• moves (list, optional): A list of moves to apply to the template before matching. Defaults to [].
• batch_size (int, optional): The batch size to use for the computation. Defaults to -1.
• save_memory (bool, optional): Whether to use half-precision floating point numbers to save memory. Defaults to False.
• max_workers (int, optional): The maximum number of worker threads to use for the computation. Defaults to 4.

Returns

• numpy.ndarray: The cross-correlation between the template and the image.

License

Reference