Hough-TMF 0.2.1

A package for template matching using Torch

hd-tmf

A high-dimensional template matching framework based on PyTorch.

Installation

pip install Hough-TMF

Usage

from hd_tmf import tmf

TMF Example

# generate a random template
tmp = np.random.rand(10, 100, 20)
# generate a random image
data = np.random.rand(100, 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.

Hough Example

from hd_tmf import hough
data = np.random.randn(256, 256)
hough(data,freq=100,bandpass=[2,8],sl=[10,20],resample=1, sigma=1.3, low_threshold=3, high_threshold=6,theta=np.linspace(np.pi/2/90*10/100,np.pi/2/90*10,99), fil='bandpass', S_L=True,beta=0,kernel=(3,3))

Parameters

• data (numpy.ndarray or torch.Tensor): The image to search for the template.
• freq (int, optional): The frequency of the template. Defaults to 100.
• bandpass (list, optional): The bandpass filter to apply to the image. Defaults to [2,8].
• sl (list, optional): The size of the template. Defaults to [10,20].
• resample (int, optional): The resample rate of the image. Defaults to 1.
• sigma (float, optional): The sigma of the Gaussian filter. Defaults to 1.3.
• low_threshold (float, optional): The low threshold of the Canny edge detector. Defaults to 3.
• high_threshold (float, optional): The high threshold of the Canny edge detector. Defaults to 6.
• theta (numpy.ndarray, optional): The theta of the Hough transform. Defaults to np.linspace(np.pi/2/9010/100,np.pi/2/9010,99).
• fil (str, optional): The filter to apply to the image. Defaults to 'bandpass'.
• S_L (bool, optional): Whether to apply the Laplacian filter to the image. Defaults to True.
• beta (float, optional): The beta of the Laplacian filter. Defaults to 0.
• kernel (tuple, optional): The kernel size of the Laplacian filter. Defaults to (3,3).

License

MIT License

Copyright (c) [2023] []

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

References