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skimage.filters.scharr_v(image, mask=None) [source]
Find the vertical edges of an image using the Scharr transform. Parameters
image2-D array
Image to process
mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to p... | skimage.api.skimage.filters#skimage.filters.scharr_v |
skimage.filters.sobel(image, mask=None, *, axis=None, mode='reflect', cval=0.0) [source]
Find edges in an image using the Sobel filter. Parameters
imagearray
The input image.
maskarray of bool, optional
Clip the output image to this mask. (Values where mask=0 will be set to 0.)
axisint or sequence of int,... | skimage.api.skimage.filters#skimage.filters.sobel |
skimage.filters.sobel_h(image, mask=None) [source]
Find the horizontal edges of an image using the Sobel transform. Parameters
image2-D array
Image to process.
mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to ... | skimage.api.skimage.filters#skimage.filters.sobel_h |
skimage.filters.sobel_v(image, mask=None) [source]
Find the vertical edges of an image using the Sobel transform. Parameters
image2-D array
Image to process.
mask2-D array, optional
An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to pr... | skimage.api.skimage.filters#skimage.filters.sobel_v |
skimage.filters.threshold_isodata(image=None, nbins=256, return_all=False, *, hist=None) [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... | skimage.api.skimage.filters#skimage.filters.threshold_isodata |
skimage.filters.threshold_li(image, *, tolerance=None, initial_guess=None, iter_callback=None) [source]
Compute threshold value by Li’s iterative Minimum Cross Entropy method. Parameters
imagendarray
Input image.
tolerancefloat, optional
Finish the computation when the change in the threshold in an iteratio... | skimage.api.skimage.filters#skimage.filters.threshold_li |
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 ... | skimage.api.skimage.filters#skimage.filters.threshold_local |
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. Returns
thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. R... | skimage.api.skimage.filters#skimage.filters.threshold_mean |
skimage.filters.threshold_minimum(image=None, nbins=256, max_iter=10000, *, hist=None) [source]
Return threshold value based on minimum method. The histogram of the input image is computed if not provided and smoothed until there are only two maxima. Then the minimum in between is the threshold value. Either image or... | skimage.api.skimage.filters#skimage.filters.threshold_minimum |
skimage.filters.threshold_multiotsu(image, classes=3, nbins=256) [source]
Generate classes-1 threshold values to divide gray levels in image. The threshold values are chosen to maximize the total sum of pairwise variances between the thresholded graylevel classes. See Notes and [1] for more details. Parameters
im... | skimage.api.skimage.filters#skimage.filters.threshold_multiotsu |
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 d... | skimage.api.skimage.filters#skimage.filters.threshold_niblack |
skimage.filters.threshold_otsu(image=None, nbins=256, *, hist=None) [source]
Return threshold value based on Otsu’s method. Either image or hist must be provided. If hist is provided, the actual histogram of the image is ignored. Parameters
image(N, M) ndarray, optional
Grayscale input image.
nbinsint, option... | skimage.api.skimage.filters#skimage.filters.threshold_otsu |
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)... | skimage.api.skimage.filters#skimage.filters.threshold_sauvola |
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.
nbinsint, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns
threshold... | skimage.api.skimage.filters#skimage.filters.threshold_triangle |
skimage.filters.threshold_yen(image=None, nbins=256, *, hist=None) [source]
Return threshold value based on Yen’s method. Either image or hist must be provided. In case hist is given, the actual histogram of the image is ignored. Parameters
image(N, M) ndarray, optional
Input image.
nbinsint, optional
Numbe... | skimage.api.skimage.filters#skimage.filters.threshold_yen |
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.
figsizetuple, optional
Figure size (in inches).
verbosebool, optional
Print function name for each method.... | skimage.api.skimage.filters#skimage.filters.try_all_threshold |
skimage.filters.unsharp_mask(image, radius=1.0, amount=1.0, multichannel=False, preserve_range=False) [source]
Unsharp masking filter. The sharp details are identified as the difference between the original image and its blurred version. These details are then scaled, and added back to the original image. Parameters... | skimage.api.skimage.filters#skimage.filters.unsharp_mask |
skimage.filters.wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None) [source]
Minimum Mean Square Error (Wiener) inverse filter. Parameters
data(M,N) ndarray
Input data.
Kfloat or (M,N) ndarray
Ratio between power spectrum of noise and undegraded image.
impulse_responsecal... | skimage.api.skimage.filters#skimage.filters.wiener |
skimage.filters.window(window_type, shape, warp_kwargs=None) [source]
Return an n-dimensional window of a given size and dimensionality. Parameters
window_typestring, float, or tuple
The type of window to be created. Any window type supported by scipy.signal.get_window is allowed here. See notes below for a cur... | skimage.api.skimage.filters#skimage.filters.window |
Module: future Functionality with an experimental API. Although you can count on the functions in this package being around in the future, the API may change with any version update and will not follow the skimage two-version deprecation path. Therefore, use the functions herein with care, and do not use them in produc... | skimage.api.skimage.future |
skimage.future.fit_segmenter(labels, features, clf) [source]
Segmentation using labeled parts of the image and a classifier. Parameters
labelsndarray of ints
Image of labels. Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented.
featuresndarray
Array of features, with t... | skimage.api.skimage.future#skimage.future.fit_segmenter |
Module: future.graph
skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph.
skimage.future.graph.cut_threshold(labels, …) Combine regions separated by weight less than threshold.
skimage.future.graph.merge_hierarchical(…) Perform hierarchical merging of a RAG.... | skimage.api.skimage.future.graph |
skimage.future.graph.cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0, *, random_state=None) [source]
Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a... | skimage.api.skimage.future.graph#skimage.future.graph.cut_normalized |
skimage.future.graph.cut_threshold(labels, rag, thresh, in_place=True) [source]
Combine regions separated by weight less than threshold. Given an image’s labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold. Parameters
labelsndarray
The a... | skimage.api.skimage.future.graph#skimage.future.graph.cut_threshold |
skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func) [source]
Perform hierarchical merging of a RAG. Greedily merges the most similar pair of nodes until no edges lower than thresh remain. Parameters
labelsndarray
The array of labels.
ragRAG
The Reg... | skimage.api.skimage.future.graph#skimage.future.graph.merge_hierarchical |
skimage.future.graph.ncut(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0, *, random_state=None) [source]
Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph ... | skimage.api.skimage.future.graph#skimage.future.graph.ncut |
class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr) [source]
Bases: networkx.classes.graph.Graph The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph Parameters
label_imagearray of int
An initial segmentation, with each region labeled as a different integer. Eve... | skimage.api.skimage.future.graph#skimage.future.graph.RAG |
add_edge(u, v, attr_dict=None, **attr) [source]
Add an edge between u and v while updating max node id. See also networkx.Graph.add_edge(). | skimage.api.skimage.future.graph#skimage.future.graph.RAG.add_edge |
add_node(n, attr_dict=None, **attr) [source]
Add node n while updating the maximum node id. See also networkx.Graph.add_node(). | skimage.api.skimage.future.graph#skimage.future.graph.RAG.add_node |
copy() [source]
Copy the graph with its max node id. See also networkx.Graph.copy(). | skimage.api.skimage.future.graph#skimage.future.graph.RAG.copy |
fresh_copy() [source]
Return a fresh copy graph with the same data structure. A fresh copy has no nodes, edges or graph attributes. It is the same data structure as the current graph. This method is typically used to create an empty version of the graph. This is required when subclassing Graph with networkx v2 and do... | skimage.api.skimage.future.graph#skimage.future.graph.RAG.fresh_copy |
merge_nodes(src, dst, weight_func=<function min_weight>, in_place=True, extra_arguments=[], extra_keywords={}) [source]
Merge node src and dst. The new combined node is adjacent to all the neighbors of src and dst. weight_func is called to decide the weight of edges incident on the new node. Parameters
src, dstin... | skimage.api.skimage.future.graph#skimage.future.graph.RAG.merge_nodes |
next_id() [source]
Returns the id for the new node to be inserted. The current implementation returns one more than the maximum id. Returns
idint
The id of the new node to be inserted. | skimage.api.skimage.future.graph#skimage.future.graph.RAG.next_id |
__init__(label_image=None, connectivity=1, data=None, **attr) [source]
Initialize a graph with edges, name, or graph attributes. Parameters
incoming_graph_datainput graph (optional, default: None)
Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, or any Network... | skimage.api.skimage.future.graph#skimage.future.graph.RAG.__init__ |
skimage.future.graph.rag_boundary(labels, edge_map, connectivity=2) [source]
Comouter RAG based on region boundaries Given an image’s initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the s... | skimage.api.skimage.future.graph#skimage.future.graph.rag_boundary |
skimage.future.graph.rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0) [source]
Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of p... | skimage.api.skimage.future.graph#skimage.future.graph.rag_mean_color |
skimage.future.graph.show_rag(labels, rag, image, border_color='black', edge_width=1.5, edge_cmap='magma', img_cmap='bone', in_place=True, ax=None) [source]
Show a Region Adjacency Graph on an image. Given a labelled image and its corresponding RAG, show the nodes and edges of the RAG on the image with the specified ... | skimage.api.skimage.future.graph#skimage.future.graph.show_rag |
skimage.future.manual_lasso_segmentation(image, alpha=0.4, return_all=False) [source]
Return a label image based on freeform selections made with the mouse. Parameters
image(M, N[, 3]) array
Grayscale or RGB image.
alphafloat, optional
Transparency value for polygons drawn over the image.
return_allbool, ... | skimage.api.skimage.future#skimage.future.manual_lasso_segmentation |
skimage.future.manual_polygon_segmentation(image, alpha=0.4, return_all=False) [source]
Return a label image based on polygon selections made with the mouse. Parameters
image(M, N[, 3]) array
Grayscale or RGB image.
alphafloat, optional
Transparency value for polygons drawn over the image.
return_allbool,... | skimage.api.skimage.future#skimage.future.manual_polygon_segmentation |
skimage.future.predict_segmenter(features, clf) [source]
Segmentation of images using a pretrained classifier. Parameters
featuresndarray
Array of features, with the last dimension corresponding to the number of features, and the other dimensions are compatible with the shape of the image to segment, or a flatt... | skimage.api.skimage.future#skimage.future.predict_segmenter |
class skimage.future.TrainableSegmenter(clf=None, features_func=None) [source]
Bases: object Estimator for classifying pixels. Parameters
clfclassifier object, optional
classifier object, exposing a fit and a predict method as in scikit-learn’s API, for example an instance of RandomForestClassifier or LogisticR... | skimage.api.skimage.future#skimage.future.TrainableSegmenter |
compute_features(image) [source] | skimage.api.skimage.future#skimage.future.TrainableSegmenter.compute_features |
fit(image, labels) [source]
Train classifier using partially labeled (annotated) image. Parameters
imagendarray
Input image, which can be grayscale or multichannel, and must have a number of dimensions compatible with self.features_func.
labelsndarray of ints
Labeled array of shape compatible with image (sa... | skimage.api.skimage.future#skimage.future.TrainableSegmenter.fit |
predict(image) [source]
Segment new image using trained internal classifier. Parameters
imagendarray
Input image, which can be grayscale or multichannel, and must have a number of dimensions compatible with self.features_func. Raises
NotFittedError if self.clf has not been fitted yet (use self.fit). | skimage.api.skimage.future#skimage.future.TrainableSegmenter.predict |
__init__(clf=None, features_func=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.future#skimage.future.TrainableSegmenter.__init__ |
Module: graph
skimage.graph.route_through_array(array, …) Simple example of how to use the MCP and MCP_Geometric classes.
skimage.graph.shortest_path(arr[, reach, …]) Find the shortest path through an n-d array from one side to another.
skimage.graph.MCP(costs[, offsets, …]) A class for finding the minimum cost p... | skimage.api.skimage.graph |
class skimage.graph.MCP(costs, offsets=None, fully_connected=True, sampling=None)
Bases: object A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points... | skimage.api.skimage.graph#skimage.graph.MCP |
find_costs()
Find the minimum-cost path from the given starting points. This method finds the minimum-cost path to the specified ending indices from any one of the specified starting indices. If no end positions are given, then the minimum-cost path to every position in the costs array will be found. Parameters
s... | skimage.api.skimage.graph#skimage.graph.MCP.find_costs |
goal_reached()
int goal_reached(int index, float cumcost) This method is called each iteration after popping an index from the heap, before examining the neighbours. This method can be overloaded to modify the behavior of the MCP algorithm. An example might be to stop the algorithm when a certain cumulative cost is r... | skimage.api.skimage.graph#skimage.graph.MCP.goal_reached |
traceback(end)
Trace a minimum cost path through the pre-calculated traceback array. This convenience function reconstructs the the minimum cost path to a given end position from one of the starting indices provided to find_costs(), which must have been called previously. This function can be called as many times as ... | skimage.api.skimage.graph#skimage.graph.MCP.traceback |
__init__(costs, offsets=None, fully_connected=True, sampling=None)
See class documentation. | skimage.api.skimage.graph#skimage.graph.MCP.__init__ |
class skimage.graph.MCP_Connect(costs, offsets=None, fully_connected=True)
Bases: skimage.graph._mcp.MCP Connect source points using the distance-weighted minimum cost function. A front is grown from each seed point simultaneously, while the origin of the front is tracked as well. When two fronts meet, create_connect... | skimage.api.skimage.graph#skimage.graph.MCP_Connect |
create_connection()
create_connection id1, id2, pos1, pos2, cost1, cost2) Overload this method to keep track of the connections that are found during MCP processing. Note that a connection with the same ids can be found multiple times (but with different positions and costs). At the time that this method is called, b... | skimage.api.skimage.graph#skimage.graph.MCP_Connect.create_connection |
__init__(*args, **kwargs)
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.graph#skimage.graph.MCP_Connect.__init__ |
class skimage.graph.MCP_Flexible(costs, offsets=None, fully_connected=True)
Bases: skimage.graph._mcp.MCP Find minimum cost paths through an N-d costs array. See the documentation for MCP for full details. This class differs from MCP in that several methods can be overloaded (from pure Python) to modify the behavior ... | skimage.api.skimage.graph#skimage.graph.MCP_Flexible |
examine_neighbor(index, new_index, offset_length)
This method is called once for every pair of neighboring nodes, as soon as both nodes are frozen. This method can be overloaded to obtain information about neightboring nodes, and/or to modify the behavior of the MCP algorithm. One example is the MCP_Connect class, wh... | skimage.api.skimage.graph#skimage.graph.MCP_Flexible.examine_neighbor |
travel_cost(old_cost, new_cost, offset_length)
This method calculates the travel cost for going from the current node to the next. The default implementation returns new_cost. Overload this method to adapt the behaviour of the algorithm. | skimage.api.skimage.graph#skimage.graph.MCP_Flexible.travel_cost |
update_node(index, new_index, offset_length)
This method is called when a node is updated, right after new_index is pushed onto the heap and the traceback map is updated. This method can be overloaded to keep track of other arrays that are used by a specific implementation of the algorithm. For instance the MCP_Conne... | skimage.api.skimage.graph#skimage.graph.MCP_Flexible.update_node |
__init__(costs, offsets=None, fully_connected=True, sampling=None)
See class documentation. | skimage.api.skimage.graph#skimage.graph.MCP_Flexible.__init__ |
class skimage.graph.MCP_Geometric(costs, offsets=None, fully_connected=True)
Bases: skimage.graph._mcp.MCP Find distance-weighted minimum cost paths through an n-d costs array. See the documentation for MCP for full details. This class differs from MCP in that the cost of a path is not simply the sum of the costs alo... | skimage.api.skimage.graph#skimage.graph.MCP_Geometric |
__init__(costs, offsets=None, fully_connected=True, sampling=None)
See class documentation. | skimage.api.skimage.graph#skimage.graph.MCP_Geometric.__init__ |
skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True) [source]
Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters
arrayndarray
Array of costs.
startiter... | skimage.api.skimage.graph#skimage.graph.route_through_array |
skimage.graph.shortest_path(arr, reach=1, axis=-1, output_indexlist=False) [source]
Find the shortest path through an n-d array from one side to another. Parameters
arrndarray of float64
reachint, optional
By default (reach = 1), the shortest path can only move one row up or down for every step it moves forwa... | skimage.api.skimage.graph#skimage.graph.shortest_path |
skimage.img_as_bool(image, force_copy=False) [source]
Convert an image to boolean format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of bool (bool_)
Output image. Notes The upper half of the input ... | skimage.api.skimage#skimage.img_as_bool |
skimage.img_as_float(image, force_copy=False) [source]
Convert an image to floating point format. This function is similar to img_as_float64, but will not convert lower-precision floating point arrays to float64. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespe... | skimage.api.skimage#skimage.img_as_float |
skimage.img_as_float32(image, force_copy=False) [source]
Convert an image to single-precision (32-bit) floating point format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of float32
Output image. Not... | skimage.api.skimage#skimage.img_as_float32 |
skimage.img_as_float64(image, force_copy=False) [source]
Convert an image to double-precision (64-bit) floating point format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of float64
Output image. Not... | skimage.api.skimage#skimage.img_as_float64 |
skimage.img_as_int(image, force_copy=False) [source]
Convert an image to 16-bit signed integer format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of int16
Output image. Notes The values are scaled ... | skimage.api.skimage#skimage.img_as_int |
skimage.img_as_ubyte(image, force_copy=False) [source]
Convert an image to 8-bit unsigned integer format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of ubyte (uint8)
Output image. Notes Negative in... | skimage.api.skimage#skimage.img_as_ubyte |
skimage.img_as_uint(image, force_copy=False) [source]
Convert an image to 16-bit unsigned integer format. Parameters
imagendarray
Input image.
force_copybool, optional
Force a copy of the data, irrespective of its current dtype. Returns
outndarray of uint16
Output image. Notes Negative input val... | skimage.api.skimage#skimage.img_as_uint |
Module: io Utilities to read and write images in various formats. The following plug-ins are available:
Plugin Description
qt Fast image display using the Qt library. Deprecated since 0.18. Will be removed in 0.20.
imread Image reading and writing via imread
gdal Image reading via the GDAL Library (www.gdal.org... | skimage.api.skimage.io |
skimage.io.call_plugin(kind, *args, **kwargs) [source]
Find the appropriate plugin of ‘kind’ and execute it. Parameters
kind{‘imshow’, ‘imsave’, ‘imread’, ‘imread_collection’}
Function to look up.
pluginstr, optional
Plugin to load. Defaults to None, in which case the first matching plugin is used.
*args,... | skimage.api.skimage.io#skimage.io.call_plugin |
skimage.io.concatenate_images(ic) [source]
Concatenate all images in the image collection into an array. Parameters
ican iterable of images
The images to be concatenated. Returns
array_catndarray
An array having one more dimension than the images in ic. Raises
ValueError
If images in ic don’t have... | skimage.api.skimage.io#skimage.io.concatenate_images |
skimage.io.find_available_plugins(loaded=False) [source]
List available plugins. Parameters
loadedbool
If True, show only those plugins currently loaded. By default, all plugins are shown. Returns
pdict
Dictionary with plugin names as keys and exposed functions as values. | skimage.api.skimage.io#skimage.io.find_available_plugins |
class skimage.io.ImageCollection(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs) [source]
Bases: object Load and manage a collection of image files. Parameters
load_patternstr or list of str
Pattern string or list of strings to load. The filename path can be absolute or relative.
conse... | skimage.api.skimage.io#skimage.io.ImageCollection |
concatenate() [source]
Concatenate all images in the collection into an array. Returns
arnp.ndarray
An array having one more dimension than the images in self. Raises
ValueError
If images in the ImageCollection don’t have identical shapes. See also
concatenate_images | skimage.api.skimage.io#skimage.io.ImageCollection.concatenate |
property conserve_memory | skimage.api.skimage.io#skimage.io.ImageCollection.conserve_memory |
property files | skimage.api.skimage.io#skimage.io.ImageCollection.files |
reload(n=None) [source]
Clear the image cache. Parameters
nNone or int
Clear the cache for this image only. By default, the entire cache is erased. | skimage.api.skimage.io#skimage.io.ImageCollection.reload |
__init__(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs) [source]
Load and manage a collection of images. | skimage.api.skimage.io#skimage.io.ImageCollection.__init__ |
skimage.io.imread(fname, as_gray=False, plugin=None, **plugin_args) [source]
Load an image from file. Parameters
fnamestring
Image file name, e.g. test.jpg or URL.
as_graybool, optional
If True, convert color images to gray-scale (64-bit floats). Images that are already in gray-scale format are not converte... | skimage.api.skimage.io#skimage.io.imread |
skimage.io.imread_collection(load_pattern, conserve_memory=True, plugin=None, **plugin_args) [source]
Load a collection of images. Parameters
load_patternstr or list
List of objects to load. These are usually filenames, but may vary depending on the currently active plugin. See the docstring for ImageCollection... | skimage.api.skimage.io#skimage.io.imread_collection |
skimage.io.imread_collection_wrapper(imread) [source] | skimage.api.skimage.io#skimage.io.imread_collection_wrapper |
skimage.io.imsave(fname, arr, plugin=None, check_contrast=True, **plugin_args) [source]
Save an image to file. Parameters
fnamestr
Target filename.
arrndarray of shape (M,N) or (M,N,3) or (M,N,4)
Image data.
pluginstr, optional
Name of plugin to use. By default, the different plugins are tried (starting... | skimage.api.skimage.io#skimage.io.imsave |
skimage.io.imshow(arr, plugin=None, **plugin_args) [source]
Display an image. Parameters
arrndarray or str
Image data or name of image file.
pluginstr
Name of plugin to use. By default, the different plugins are tried (starting with imageio) until a suitable candidate is found. Other Parameters
plugin... | skimage.api.skimage.io#skimage.io.imshow |
skimage.io.imshow_collection(ic, plugin=None, **plugin_args) [source]
Display a collection of images. Parameters
icImageCollection
Collection to display.
pluginstr
Name of plugin to use. By default, the different plugins are tried until a suitable candidate is found. Other Parameters
plugin_argskeywor... | skimage.api.skimage.io#skimage.io.imshow_collection |
skimage.io.load_sift(f) [source]
Read SIFT or SURF features from externally generated file. This routine reads SIFT or SURF files generated by binary utilities from http://people.cs.ubc.ca/~lowe/keypoints/ and http://www.vision.ee.ethz.ch/~surf/. This routine does not generate SIFT/SURF features from an image. These ... | skimage.api.skimage.io#skimage.io.load_sift |
skimage.io.load_surf(f) [source]
Read SIFT or SURF features from externally generated file. This routine reads SIFT or SURF files generated by binary utilities from http://people.cs.ubc.ca/~lowe/keypoints/ and http://www.vision.ee.ethz.ch/~surf/. This routine does not generate SIFT/SURF features from an image. These ... | skimage.api.skimage.io#skimage.io.load_surf |
class skimage.io.MultiImage(filename, conserve_memory=True, dtype=None, **imread_kwargs) [source]
Bases: skimage.io.collection.ImageCollection A class containing all frames from multi-frame images. Parameters
load_patternstr or list of str
Pattern glob or filenames to load. The path can be absolute or relative.... | skimage.api.skimage.io#skimage.io.MultiImage |
property filename | skimage.api.skimage.io#skimage.io.MultiImage.filename |
__init__(filename, conserve_memory=True, dtype=None, **imread_kwargs) [source]
Load a multi-img. | skimage.api.skimage.io#skimage.io.MultiImage.__init__ |
skimage.io.plugin_info(plugin) [source]
Return plugin meta-data. Parameters
pluginstr
Name of plugin. Returns
mdict
Meta data as specified in plugin .ini. | skimage.api.skimage.io#skimage.io.plugin_info |
skimage.io.plugin_order() [source]
Return the currently preferred plugin order. Returns
pdict
Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value. | skimage.api.skimage.io#skimage.io.plugin_order |
skimage.io.pop() [source]
Pop an image from the shared image stack. Returns
imgndarray
Image popped from the stack. | skimage.api.skimage.io#skimage.io.pop |
skimage.io.push(img) [source]
Push an image onto the shared image stack. Parameters
imgndarray
Image to push. | skimage.api.skimage.io#skimage.io.push |
skimage.io.reset_plugins() [source] | skimage.api.skimage.io#skimage.io.reset_plugins |
skimage.io.show() [source]
Display pending images. Launch the event loop of the current gui plugin, and display all pending images, queued via imshow. This is required when using imshow from non-interactive scripts. A call to show will block execution of code until all windows have been closed. Examples >>> import sk... | skimage.api.skimage.io#skimage.io.show |
skimage.io.use_plugin(name, kind=None) [source]
Set the default plugin for a specified operation. The plugin will be loaded if it hasn’t been already. Parameters
namestr
Name of plugin.
kind{‘imsave’, ‘imread’, ‘imshow’, ‘imread_collection’, ‘imshow_collection’}, optional
Set the plugin for this function. B... | skimage.api.skimage.io#skimage.io.use_plugin |
skimage.lookfor(what) [source]
Do a keyword search on scikit-image docstrings. Parameters
whatstr
Words to look for. Examples >>> import skimage
>>> skimage.lookfor('regular_grid')
Search results for 'regular_grid'
---------------------------------
skimage.lookfor
Do a keyword search on scikit-image doc... | skimage.api.skimage#skimage.lookfor |
Module: measure
skimage.measure.approximate_polygon(coords, …) Approximate a polygonal chain with the specified tolerance.
skimage.measure.block_reduce(image, block_size) Downsample image by applying function func to local blocks.
skimage.measure.euler_number(image[, …]) Calculate the Euler characteristic in bina... | skimage.api.skimage.measure |
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