Hough-TMF 0.2.5
A package for template matching using Torch
License: MIT
Requires Python: >=3.9

# hd-tmf
A high-dimensional template matching framework based on PyTorch.

## Installation
```bash
pip install Hough-TMF
```

## Usage
```python

```

## TMF Example
```python
import numpy as np
# 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.tma(data,tmp, step=1,device='cpu',moves = [],is_sum=False,batch_size=-1,half=False,save_memory=False)
```

## 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.

## Returns
- `numpy.ndarray`: The cross-correlation between the template and the image.

## Hough Example
```python
from hd_tma 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/2/90*10/100,np.pi/2/90*10,99).
- `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
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## References