/scikit-image

# Module: filters

 `skimage.filters.inverse`(data[, …]) Apply the filter in reverse to the given data. `skimage.filters.wiener`(data[, …]) Minimum Mean Square Error (Wiener) inverse filter. `skimage.filters.gaussian`(image[, sigma, …]) Multi-dimensional Gaussian filter. `skimage.filters.median`(image[, selem, out, …]) Return local median of an image. `skimage.filters.sobel`(image[, mask]) Find the edge magnitude using the Sobel transform. `skimage.filters.sobel_h`(image[, mask]) Find the horizontal edges of an image using the Sobel transform. `skimage.filters.sobel_v`(image[, mask]) Find the vertical edges of an image using the Sobel transform. `skimage.filters.scharr`(image[, mask]) Find the edge magnitude using the Scharr transform. `skimage.filters.scharr_h`(image[, mask]) Find the horizontal edges of an image using the Scharr transform. `skimage.filters.scharr_v`(image[, mask]) Find the vertical edges of an image using the Scharr transform. `skimage.filters.prewitt`(image[, mask]) Find the edge magnitude using the Prewitt transform. `skimage.filters.prewitt_h`(image[, mask]) Find the horizontal edges of an image using the Prewitt transform. `skimage.filters.prewitt_v`(image[, mask]) Find the vertical edges of an image using the Prewitt transform. `skimage.filters.roberts`(image[, mask]) Find the edge magnitude using Roberts’ cross operator. `skimage.filters.roberts_pos_diag`(image[, mask]) Find the cross edges of an image using Roberts’ cross operator. `skimage.filters.roberts_neg_diag`(image[, mask]) Find the cross edges of an image using the Roberts’ Cross operator. `skimage.filters.laplace`(image[, ksize, mask]) Find the edges of an image using the Laplace operator. `skimage.filters.rank_order`(image) Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of `image`, aka the rank-order value. `skimage.filters.gabor_kernel`(frequency[, …]) Return complex 2D Gabor filter kernel. `skimage.filters.gabor`(image, frequency[, …]) Return real and imaginary responses to Gabor filter. `skimage.filters.try_all_threshold`(image[, …]) Returns a figure comparing the outputs of different thresholding methods. `skimage.filters.frangi`(image[, scale_range, …]) Filter an image with the Frangi filter. `skimage.filters.hessian`(image[, …]) Filter an image with the Hessian filter. `skimage.filters.threshold_adaptive`(image, …) Deprecated function. `skimage.filters.threshold_otsu`(image[, nbins]) Return threshold value based on Otsu’s method. `skimage.filters.threshold_yen`(image[, nbins]) Return threshold value based on Yen’s method. `skimage.filters.threshold_isodata`(image[, …]) Return threshold value(s) based on ISODATA method. `skimage.filters.threshold_li`(image) Compute threshold value by Li’s iterative Minimum Cross Entropy method. `skimage.filters.threshold_local`(image, …) Compute a threshold mask image based on local pixel neighborhood. `skimage.filters.threshold_minimum`(image[, …]) Return threshold value based on minimum method. `skimage.filters.threshold_mean`(image) Return threshold value based on the mean of grayscale values. `skimage.filters.threshold_niblack`(image[, …]) Applies Niblack local threshold to an array. `skimage.filters.threshold_sauvola`(image[, …]) Applies Sauvola local threshold to an array. `skimage.filters.threshold_triangle`(image[, …]) Return threshold value based on the triangle algorithm. `skimage.filters.apply_hysteresis_threshold`(…) Apply hysteresis thresholding to `image`. `skimage.filters.LPIFilter2D`(…) Linear Position-Invariant Filter (2-dimensional) `skimage.filters.rank`

## inverse

`skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None)` [source]

Apply the filter in reverse to the given data.

Parameters: Other Parameters: `data : (M,N) ndarray` Input data. `impulse_response : callable f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. `filter_params : dict` Additional keyword parameters to the impulse_response function. `max_gain : float` Limit the filter gain. Often, the filter contains zeros, which would cause the inverse filter to have infinite gain. High gain causes amplification of artefacts, so a conservative limit is recommended. `predefined_filter : LPIFilter2D` If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here.

## wiener

`skimage.filters.wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None)` [source]

Minimum Mean Square Error (Wiener) inverse filter.

Parameters: Other Parameters: `data : (M,N) ndarray` Input data. `K : float or (M,N) ndarray` Ratio between power spectrum of noise and undegraded image. `impulse_response : callable f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. `filter_params : dict` Additional keyword parameters to the impulse_response function. `predefined_filter : LPIFilter2D` If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here.

## gaussian

`skimage.filters.gaussian(image, sigma=1, output=None, mode='nearest', cval=0, multichannel=None, preserve_range=False, truncate=4.0)` [source]

Multi-dimensional Gaussian filter.

Parameters: `image : array-like` Input image (grayscale or color) to filter. `sigma : scalar or sequence of scalars, optional` Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. `output : array, optional` The `output` parameter passes an array in which to store the filter output. `mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional` The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to ‘constant’. Default is ‘nearest’. `cval : scalar, optional` Value to fill past edges of input if `mode` is ‘constant’. Default is 0.0 `multichannel : bool, optional (default: None)` Whether the last axis of the image is to be interpreted as multiple channels. If True, each channel is filtered separately (channels are not mixed together). Only 3 channels are supported. If `None`, the function will attempt to guess this, and raise a warning if ambiguous, when the array has shape (M, N, 3). `preserve_range : bool, optional` Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. `truncate : float, optional` Truncate the filter at this many standard deviations. `filtered_image : ndarray` the filtered array

#### Notes

This function is a wrapper around `scipy.ndi.gaussian_filter()`.

Integer arrays are converted to float.

The multi-dimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

#### Examples

```>>> a = np.zeros((3, 3))
>>> a[1, 1] = 1
>>> a
array([[ 0.,  0.,  0.],
[ 0.,  1.,  0.],
[ 0.,  0.,  0.]])
>>> gaussian(a, sigma=0.4)  # mild smoothing
array([[ 0.00163116,  0.03712502,  0.00163116],
[ 0.03712502,  0.84496158,  0.03712502],
[ 0.00163116,  0.03712502,  0.00163116]])
>>> gaussian(a, sigma=1)  # more smoothing
array([[ 0.05855018,  0.09653293,  0.05855018],
[ 0.09653293,  0.15915589,  0.09653293],
[ 0.05855018,  0.09653293,  0.05855018]])
>>> # Several modes are possible for handling boundaries
>>> gaussian(a, sigma=1, mode='reflect')
array([[ 0.08767308,  0.12075024,  0.08767308],
[ 0.12075024,  0.16630671,  0.12075024],
[ 0.08767308,  0.12075024,  0.08767308]])
>>> # For RGB images, each is filtered separately
>>> from skimage.data import astronaut
>>> image = astronaut()
>>> filtered_img = gaussian(image, sigma=1, multichannel=True)
```

## median

`skimage.filters.median(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False)` [source]

Return local median of an image.

Parameters: `image : 2-D array (uint8, uint16)` Input image. `selem : 2-D array, optional` The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, a full square of size 3 is used. `out : 2-D array (same dtype as input)` If None, a new array is allocated. `mask : ndarray` Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). `shift_x, shift_y : int` Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). `out : 2-D array (same dtype as input image)` Output image.

#### Examples

```>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import median
>>> img = data.camera()
>>> med = median(img, disk(5))
```

## sobel

`skimage.filters.sobel(image, mask=None)` [source]

Find the edge magnitude using the Sobel transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Sobel edge map.

See also

`scharr`, `prewitt`, `roberts`, `feature.canny`

#### Notes

Take the square root of the sum of the squares of the horizontal and vertical Sobels to get a magnitude that’s somewhat insensitive to direction.

The 3x3 convolution kernel used in the horizontal and vertical Sobels is an approximation of the gradient of the image (with some slight blurring since 9 pixels are used to compute the gradient at a given pixel). As an approximation of the gradient, the Sobel operator is not completely rotation-invariant. The Scharr operator should be used for a better rotation invariance.

Note that `scipy.ndimage.sobel` returns a directional Sobel which has to be further processed to perform edge detection.

#### Examples

```>>> from skimage import data
>>> camera = data.camera()
>>> from skimage import filters
>>> edges = filters.sobel(camera)
```

## sobel_h

`skimage.filters.sobel_h(image, mask=None)` [source]

Find the horizontal edges of an image using the Sobel transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Sobel edge map.

#### Notes

We use the following kernel:

``` 1   2   1
0   0   0
-1  -2  -1
```

## sobel_v

`skimage.filters.sobel_v(image, mask=None)` [source]

Find the vertical edges of an image using the Sobel transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Sobel edge map.

#### Notes

We use the following kernel:

```1   0  -1
2   0  -2
1   0  -1
```

## scharr

`skimage.filters.scharr(image, mask=None)` [source]

Find the edge magnitude using the Scharr transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Scharr edge map.

See also

`sobel`, `prewitt`, `canny`

#### Notes

Take the square root of the sum of the squares of the horizontal and vertical Scharrs to get a magnitude that is somewhat insensitive to direction. The Scharr operator has a better rotation invariance than other edge filters such as the Sobel or the Prewitt operators.

#### References

  D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.

#### Examples

```>>> from skimage import data
>>> camera = data.camera()
>>> from skimage import filters
>>> edges = filters.scharr(camera)
```

## scharr_h

`skimage.filters.scharr_h(image, mask=None)` [source]

Find the horizontal edges of an image using the Scharr transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Scharr edge map.

#### Notes

We use the following kernel:

``` 3   10   3
0    0   0
-3  -10  -3
```

#### References

  D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.

## scharr_v

`skimage.filters.scharr_v(image, mask=None)` [source]

Find the vertical edges of an image using the Scharr transform.

Parameters: `image : 2-D array` Image to process `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Scharr edge map.

#### Notes

We use the following kernel:

``` 3   0   -3
10   0  -10
3   0   -3
```

#### References

  D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.

## prewitt

`skimage.filters.prewitt(image, mask=None)` [source]

Find the edge magnitude using the Prewitt transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Prewitt edge map.

See also

#### Notes

Return the square root of the sum of squares of the horizontal and vertical Prewitt transforms. The edge magnitude depends slightly on edge directions, since the approximation of the gradient operator by the Prewitt operator is not completely rotation invariant. For a better rotation invariance, the Scharr operator should be used. The Sobel operator has a better rotation invariance than the Prewitt operator, but a worse rotation invariance than the Scharr operator.

#### Examples

```>>> from skimage import data
>>> camera = data.camera()
>>> from skimage import filters
>>> edges = filters.prewitt(camera)
```

## prewitt_h

`skimage.filters.prewitt_h(image, mask=None)` [source]

Find the horizontal edges of an image using the Prewitt transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Prewitt edge map.

#### Notes

We use the following kernel:

``` 1   1   1
0   0   0
-1  -1  -1
```

## prewitt_v

`skimage.filters.prewitt_v(image, mask=None)` [source]

Find the vertical edges of an image using the Prewitt transform.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Prewitt edge map.

#### Notes

We use the following kernel:

```1   0  -1
1   0  -1
1   0  -1
```

## roberts

`skimage.filters.roberts(image, mask=None)` [source]

Find the edge magnitude using Roberts’ cross operator.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Roberts’ Cross edge map.

See also

`sobel`, `scharr`, `prewitt`, `feature.canny`

#### Examples

```>>> from skimage import data
>>> camera = data.camera()
>>> from skimage import filters
>>> edges = filters.roberts(camera)
```

## roberts_pos_diag

`skimage.filters.roberts_pos_diag(image, mask=None)` [source]

Find the cross edges of an image using Roberts’ cross operator.

The kernel is applied to the input image to produce separate measurements of the gradient component one orientation.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Robert’s edge map.

#### Notes

We use the following kernel:

```1   0
0  -1
```

## roberts_neg_diag

`skimage.filters.roberts_neg_diag(image, mask=None)` [source]

Find the cross edges of an image using the Roberts’ Cross operator.

The kernel is applied to the input image to produce separate measurements of the gradient component one orientation.

Parameters: `image : 2-D array` Image to process. `mask : 2-D array, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : 2-D array` The Robert’s edge map.

#### Notes

We use the following kernel:

``` 0   1
-1   0
```

## laplace

`skimage.filters.laplace(image, ksize=3, mask=None)` [source]

Find the edges of an image using the Laplace operator.

Parameters: `image : ndarray` Image to process. `ksize : int, optional` Define the size of the discrete Laplacian operator such that it will have a size of (ksize,) * image.ndim. `mask : ndarray, optional` An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. `output : ndarray` The Laplace edge map.

#### Notes

The Laplacian operator is generated using the function skimage.restoration.uft.laplacian().

## rank_order

`skimage.filters.rank_order(image)` [source]

Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of `image`, aka the rank-order value.

Parameters: image: ndarray labels: ndarray of type np.uint32, of shape image.shape New array where each pixel has the rank-order value of the corresponding pixel in `image`. Pixel values are between 0 and n - 1, where n is the number of distinct unique values in `image`. original_values: 1-D ndarray Unique original values of `image`

#### Examples

```>>> a = np.array([[1, 4, 5], [4, 4, 1], [5, 1, 1]])
>>> a
array([[1, 4, 5],
[4, 4, 1],
[5, 1, 1]])
>>> rank_order(a)
(array([[0, 1, 2],
[1, 1, 0],
[2, 0, 0]], dtype=uint32), array([1, 4, 5]))
>>> b = np.array([-1., 2.5, 3.1, 2.5])
>>> rank_order(b)
(array([0, 1, 2, 1], dtype=uint32), array([-1. ,  2.5,  3.1]))
```

## gabor_kernel

`skimage.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0)` [source]

Return complex 2D Gabor filter kernel.

Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation.

Parameters: `frequency : float` Spatial frequency of the harmonic function. Specified in pixels. `theta : float, optional` Orientation in radians. If 0, the harmonic is in the x-direction. `bandwidth : float, optional` The bandwidth captured by the filter. For fixed bandwidth, `sigma_x` and `sigma_y` will decrease with increasing frequency. This value is ignored if `sigma_x` and `sigma_y` are set by the user. `sigma_x, sigma_y : float, optional` Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If `theta = pi/2`, then the kernel is rotated 90 degrees so that `sigma_x` controls the vertical direction. `n_stds : scalar, optional` The linear size of the kernel is n_stds (3 by default) standard deviations `offset : float, optional` Phase offset of harmonic function in radians. `g : complex array` Complex filter kernel.

#### Examples

```>>> from skimage.filters import gabor_kernel
>>> from skimage import io
>>> from matplotlib import pyplot as plt  # doctest: +SKIP
```
```>>> gk = gabor_kernel(frequency=0.2)
>>> plt.figure()        # doctest: +SKIP
>>> io.imshow(gk.real)  # doctest: +SKIP
>>> io.show()           # doctest: +SKIP
```
```>>> # more ripples (equivalent to increasing the size of the
>>> # Gaussian spread)
>>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1)
>>> plt.figure()        # doctest: +SKIP
>>> io.imshow(gk.real)  # doctest: +SKIP
>>> io.show()           # doctest: +SKIP
```

## gabor

`skimage.filters.gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0)` [source]

Return real and imaginary responses to Gabor filter.

The real and imaginary parts of the Gabor filter kernel are applied to the image and the response is returned as a pair of arrays.

Gabor filter is a linear filter with a Gaussian kernel which is modulated by a sinusoidal plane wave. Frequency and orientation representations of the Gabor filter are similar to those of the human visual system. Gabor filter banks are commonly used in computer vision and image processing. They are especially suitable for edge detection and texture classification.

Parameters: `image : 2-D array` Input image. `frequency : float` Spatial frequency of the harmonic function. Specified in pixels. `theta : float, optional` Orientation in radians. If 0, the harmonic is in the x-direction. `bandwidth : float, optional` The bandwidth captured by the filter. For fixed bandwidth, `sigma_x` and `sigma_y` will decrease with increasing frequency. This value is ignored if `sigma_x` and `sigma_y` are set by the user. `sigma_x, sigma_y : float, optional` Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If `theta = pi/2`, then the kernel is rotated 90 degrees so that `sigma_x` controls the vertical direction. `n_stds : scalar, optional` The linear size of the kernel is n_stds (3 by default) standard deviations. `offset : float, optional` Phase offset of harmonic function in radians. `mode : {‘constant’, ‘nearest’, ‘reflect’, ‘mirror’, ‘wrap’}, optional` Mode used to convolve image with a kernel, passed to `ndi.convolve` `cval : scalar, optional` Value to fill past edges of input if `mode` of convolution is ‘constant’. The parameter is passed to `ndi.convolve`. `real, imag : arrays` Filtered images using the real and imaginary parts of the Gabor filter kernel. Images are of the same dimensions as the input one.

#### Examples

```>>> from skimage.filters import gabor
>>> from skimage import data, io
>>> from matplotlib import pyplot as plt  # doctest: +SKIP
```
```>>> image = data.coins()
>>> # detecting edges in a coin image
>>> filt_real, filt_imag = gabor(image, frequency=0.6)
>>> plt.figure()            # doctest: +SKIP
>>> io.imshow(filt_real)    # doctest: +SKIP
>>> io.show()               # doctest: +SKIP
```
```>>> # less sensitivity to finer details with the lower frequency kernel
>>> filt_real, filt_imag = gabor(image, frequency=0.1)
>>> plt.figure()            # doctest: +SKIP
>>> io.imshow(filt_real)    # doctest: +SKIP
>>> io.show()               # doctest: +SKIP
```

## try_all_threshold

`skimage.filters.try_all_threshold(image, figsize=(8, 5), verbose=True)` [source]

Returns a figure comparing the outputs of different thresholding methods.

Parameters: `image : (N, M) ndarray` Input image. `figsize : tuple, optional` Figure size (in inches). `verbose : bool, optional` Print function name for each method. `fig, ax : tuple` Matplotlib figure and axes.

#### Notes

The following algorithms are used:

• isodata
• li
• mean
• minimum
• otsu
• triangle
• yen

#### Examples

```>>> from skimage.data import text
>>> fig, ax = try_all_threshold(text(), figsize=(10, 6), verbose=False)
```

## frangi

`skimage.filters.frangi(image, scale_range=(1, 10), scale_step=2, beta1=0.5, beta2=15, black_ridges=True)` [source]

Filter an image with the Frangi filter.

This filter can be used to detect continuous edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.

Calculates the eigenvectors of the Hessian to compute the similarity of an image region to vessels, according to the method described in .

Parameters: `image : (N, M) ndarray` Array with input image data. `scale_range : 2-tuple of floats, optional` The range of sigmas used. `scale_step : float, optional` Step size between sigmas. `beta1 : float, optional` Frangi correction constant that adjusts the filter’s sensitivity to deviation from a blob-like structure. `beta2 : float, optional` Frangi correction constant that adjusts the filter’s sensitivity to areas of high variance/texture/structure. `black_ridges : boolean, optional` When True (the default), the filter detects black ridges; when False, it detects white ridges. `out : (N, M) ndarray` Filtered image (maximum of pixels across all scales).

#### Notes

Written by Marc Schrijver, 2/11/2001 Re-Written by D. J. Kroon University of Twente (May 2009)

#### References

  (1, 2) A. Frangi, W. Niessen, K. Vincken, and M. Viergever. “Multiscale vessel enhancement filtering,” In LNCS, vol. 1496, pages 130-137, Germany, 1998. Springer-Verlag.
  Kroon, D.J.: Hessian based Frangi vesselness filter.

## hessian

`skimage.filters.hessian(image, scale_range=(1, 10), scale_step=2, beta1=0.5, beta2=15)` [source]

Filter an image with the Hessian filter.

This filter can be used to detect continuous edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.

Almost equal to Frangi filter, but uses alternative method of smoothing. Refer to  to find the differences between Frangi and Hessian filters.

Parameters: `image : (N, M) ndarray` Array with input image data. `scale_range : 2-tuple of floats, optional` The range of sigmas used. `scale_step : float, optional` Step size between sigmas. `beta1 : float, optional` Frangi correction constant that adjusts the filter’s sensitivity to deviation from a blob-like structure. `beta2 : float, optional` Frangi correction constant that adjusts the filter’s sensitivity to areas of high variance/texture/structure. `out : (N, M) ndarray` Filtered image (maximum of pixels across all scales).

#### Notes

Written by Marc Schrijver, 2/11/2001 Re-Written by D. J. Kroon University of Twente (May 2009)

#### References

  (1, 2) Choon-Ching Ng, Moi Hoon Yap, Nicholas Costen and Baihua Li, “Automatic Wrinkle Detection using Hybrid Hessian Filter”.

## threshold_adaptive

`skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None)` [source]

Deprecated function. Use `threshold_local` instead.

## threshold_otsu

`skimage.filters.threshold_otsu(image, nbins=256)` [source]

Return threshold value based on Otsu’s method.

Parameters: `image : (N, M) ndarray` Grayscale input image. `nbins : int, optional` Number of bins used to calculate histogram. This value is ignored for integer arrays. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. ValueError If `image` only contains a single grayscale value.

#### Notes

The input image must be grayscale.

#### References

  Wikipedia, http://en.wikipedia.org/wiki/Otsu’s_Method

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_otsu(image)
>>> binary = image <= thresh
```

## threshold_yen

`skimage.filters.threshold_yen(image, nbins=256)` [source]

Return threshold value based on Yen’s method.

Parameters: `image : (N, M) ndarray` Input image. `nbins : int, optional` Number of bins used to calculate histogram. This value is ignored for integer arrays. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.

#### References

  Yen J.C., Chang F.J., and Chang S. (1995) “A New Criterion for Automatic Multilevel Thresholding” IEEE Trans. on Image Processing, 4(3): 370-378. DOI:10.1109/83.366472
  Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165, DOI:10.1117/1.1631315 http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf
  ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_yen(image)
>>> binary = image <= thresh
```

## threshold_isodata

`skimage.filters.threshold_isodata(image, nbins=256, return_all=False)` [source]

Return threshold value(s) based on ISODATA method.

Histogram-based threshold, known as Ridler-Calvard method or inter-means. Threshold values returned satisfy the following equality:

```threshold = (image[image <= threshold].mean() +
image[image > threshold].mean()) / 2.0
```

That is, returned thresholds are intensities that separate the image into two groups of pixels, where the threshold intensity is midway between the mean intensities of these groups.

For integer images, the above equality holds to within one; for floating- point images, the equality holds to within the histogram bin-width.

Parameters: `image : (N, M) ndarray` Input image. `nbins : int, optional` Number of bins used to calculate histogram. This value is ignored for integer arrays. return_all: bool, optional If False (default), return only the lowest threshold that satisfies the above equality. If True, return all valid thresholds. `threshold : float or int or array` Threshold value(s).

#### References

  Ridler, TW & Calvard, S (1978), “Picture thresholding using an iterative selection method” IEEE Transactions on Systems, Man and Cybernetics 8: 630-632, DOI:10.1109/TSMC.1978.4310039
  Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165, http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf DOI:10.1117/1.1631315
  ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold

#### Examples

```>>> from skimage.data import coins
>>> image = coins()
>>> thresh = threshold_isodata(image)
>>> binary = image > thresh
```

## threshold_li

`skimage.filters.threshold_li(image)` [source]

Compute threshold value by Li’s iterative Minimum Cross Entropy method.

Parameters: `image : ndarray` Input image. `tolerance : float, optional` Finish the computation when the change in the threshold in an iteration is less than this value. By default, this is half of the range of the input image, divided by 256. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.

#### References

  Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 DOI:10.1016/0031-3203(93)90115-D
  Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Minimum Cross Entropy Thresholding” Pattern Recognition Letters, 18(8): 771-776 DOI:10.1016/S0167-8655(98)00057-9
  Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165 DOI:10.1117/1.1631315
  ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_li(image)
>>> binary = image > thresh
```

## threshold_local

`skimage.filters.threshold_local(image, block_size, method='gaussian', offset=0, mode='reflect', param=None, cval=0)` [source]

Compute a threshold mask image based on local pixel neighborhood.

Also known as adaptive or dynamic thresholding. The threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a given function, using the ‘generic’ method.

Parameters: `image : (N, M) ndarray` Input image. `block_size : int` Odd size of pixel neighborhood which is used to calculate the threshold value (e.g. 3, 5, 7, …, 21, …). `method : {‘generic’, ‘gaussian’, ‘mean’, ‘median’}, optional` Method used to determine adaptive threshold for local neighbourhood in weighted mean image. ‘generic’: use custom function (see `param` parameter) ‘gaussian’: apply gaussian filter (see `param` parameter for custom sigma value) ‘mean’: apply arithmetic mean filter ‘median’: apply median rank filter By default the ‘gaussian’ method is used. `offset : float, optional` Constant subtracted from weighted mean of neighborhood to calculate the local threshold value. Default offset is 0. `mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional` The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’. `param : {int, function}, optional` Either specify sigma for ‘gaussian’ method or function object for ‘generic’ method. This functions takes the flat array of local neighbourhood as a single argument and returns the calculated threshold for the centre pixel. `cval : float, optional` Value to fill past edges of input if mode is ‘constant’. `threshold : (N, M) ndarray` Threshold image. All pixels in the input image higher than the corresponding pixel in the threshold image are considered foreground.

#### Examples

```>>> from skimage.data import camera
>>> image = camera()[:50, :50]
>>> binary_image1 = image > threshold_local(image, 15, 'mean')
>>> func = lambda arr: arr.mean()
>>> binary_image2 = image > threshold_local(image, 15, 'generic',
...                                         param=func)
```

## threshold_minimum

`skimage.filters.threshold_minimum(image, nbins=256, max_iter=10000)` [source]

Return threshold value based on minimum method.

The histogram of the input `image` is computed and smoothed until there are only two maxima. Then the minimum in between is the threshold value.

Parameters: `image : (M, N) ndarray` Input image. `nbins : int, optional` Number of bins used to calculate histogram. This value is ignored for integer arrays. max_iter: int, optional Maximum number of iterations to smooth the histogram. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. RuntimeError If unable to find two local maxima in the histogram or if the smoothing takes more than 1e4 iterations.

#### References

  C. A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993.
  Prewitt, JMS & Mendelsohn, ML (1966), “The analysis of cell images”, Annals of the New York Academy of Sciences 128: 1035-1053 DOI:10.1111/j.1749-6632.1965.tb11715.x

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_minimum(image)
>>> binary = image > thresh
```

## threshold_mean

`skimage.filters.threshold_mean(image)` [source]

Return threshold value based on the mean of grayscale values.

Parameters: `image : (N, M[, …, P]) ndarray` Grayscale input image. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.

#### References

  C. A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993. DOI:10.1006/cgip.1993.1040

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_mean(image)
>>> binary = image > thresh
```

## threshold_niblack

`skimage.filters.threshold_niblack(image, window_size=15, k=0.2)` [source]

Applies Niblack local threshold to an array.

A threshold T is calculated for every pixel in the image using the following formula:

```T = m(x,y) - k * s(x,y)
```

where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation.

Parameters: image: (N, M) ndarray Grayscale input image. `window_size : int, optional` Odd size of pixel neighborhood window (e.g. 3, 5, 7…). `k : float, optional` Value of parameter k in threshold formula. `threshold : (N, M) ndarray` Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground.

#### Notes

This algorithm is originally designed for text recognition.

#### References

  Niblack, W (1986), An introduction to Digital Image Processing, Prentice-Hall.

#### Examples

```>>> from skimage import data
>>> image = data.page()
>>> binary_image = threshold_niblack(image, window_size=7, k=0.1)
```

## threshold_sauvola

`skimage.filters.threshold_sauvola(image, window_size=15, k=0.2, r=None)` [source]

Applies Sauvola local threshold to an array. Sauvola is a modification of Niblack technique.

In the original method a threshold T is calculated for every pixel in the image using the following formula:

```T = m(x,y) * (1 + k * ((s(x,y) / R) - 1))
```

where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation. R is the maximum standard deviation of a greyscale image.

Parameters: image: (N, M) ndarray Grayscale input image. `window_size : int, optional` Odd size of pixel neighborhood window (e.g. 3, 5, 7…). `k : float, optional` Value of the positive parameter k. `r : float, optional` Value of R, the dynamic range of standard deviation. If None, set to the half of the image dtype range. `threshold : (N, M) ndarray` Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground.

#### Notes

This algorithm is originally designed for text recognition.

#### References

  J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recognition 33(2), pp. 225-236, 2000. DOI:10.1016/S0031-3203(99)00055-2

#### Examples

```>>> from skimage import data
>>> image = data.page()
>>> t_sauvola = threshold_sauvola(image, window_size=15, k=0.2)
>>> binary_image = image > t_sauvola
```

## threshold_triangle

`skimage.filters.threshold_triangle(image, nbins=256)` [source]

Return threshold value based on the triangle algorithm.

Parameters: `image : (N, M[, …, P]) ndarray` Grayscale input image. `nbins : int, optional` Number of bins used to calculate histogram. This value is ignored for integer arrays. `threshold : float` Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.

#### References

  Zack, G. W., Rogers, W. E. and Latt, S. A., 1977, Automatic Measurement of Sister Chromatid Exchange Frequency, Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753 DOI:10.1177/25.7.70454
  ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold

#### Examples

```>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_triangle(image)
>>> binary = image > thresh
```

## apply_hysteresis_threshold

`skimage.filters.apply_hysteresis_threshold(image, low, high)` [source]

Apply hysteresis thresholding to `image`.

This algorithm finds regions where `image` is greater than `high` OR `image` is greater than `low` and that region is connected to a region greater than `high`.

Parameters: `image : array, shape (M,[ N, …, P])` Grayscale input image. `low : float, or array of same shape as image` Lower threshold. `high : float, or array of same shape as image` Higher threshold. `thresholded : array of bool, same shape as image` Array in which `True` indicates the locations where `image` was above the hysteresis threshold.

#### References

  J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; vol. 8, pp.679-698. DOI: 10.1109/TPAMI.1986.4767851

#### Examples

```>>> image = np.array([1, 2, 3, 2, 1, 2, 1, 3, 2])
>>> apply_hysteresis_threshold(image, 1.5, 2.5).astype(int)
array([0, 1, 1, 1, 0, 0, 0, 1, 1])
```

## LPIFilter2D

`class skimage.filters.LPIFilter2D(impulse_response, **filter_params)` [source]

Bases: `object`

Linear Position-Invariant Filter (2-dimensional)

`__init__(impulse_response, **filter_params)` [source]
Parameters: `impulse_response : callable f(r, c, **filter_params)` Function that yields the impulse response. `r` and `c` are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r,c),(r,c) etc. `**filter_params` are passed through. In other words, `impulse_response` would be called like this: ```>>> def impulse_response(r, c, **filter_params): ... pass >>> >>> r = [0,0,0,1,1,1,2,2,2] >>> c = [0,1,2,0,1,2,0,1,2] >>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3} >>> impulse_response(r, c, **filter_params) ```

#### Examples

Gaussian filter: Use a 1-D gaussian in each direction without normalization coefficients.

```>>> def filt_func(r, c, sigma = 1):
...     return np.exp(-np.hypot(r, c)/sigma)
>>> filter = LPIFilter2D(filt_func)
```

© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.14.x/api/skimage.filters.html