scikit-image | Image processing in Python | Computer Vision library

 by   scikit-image Python Version: 0.21.0rc1 License: Non-SPDX

kandi X-RAY | scikit-image Summary

kandi X-RAY | scikit-image Summary

scikit-image is a Python library typically used in Artificial Intelligence, Computer Vision, OpenCV, Numpy applications. scikit-image has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However scikit-image has a Non-SPDX License. You can install using 'pip install scikit-image' or download it from GitHub, PyPI.

Image processing in Python

            kandi-support Support

              scikit-image has a medium active ecosystem.
              It has 5422 star(s) with 2157 fork(s). There are 187 watchers for this library.
              There were 10 major release(s) in the last 6 months.
              There are 487 open issues and 1970 have been closed. On average issues are closed in 345 days. There are 212 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of scikit-image is 0.21.0rc1

            kandi-Quality Quality

              scikit-image has 0 bugs and 0 code smells.

            kandi-Security Security

              scikit-image has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              scikit-image code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              scikit-image has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              scikit-image releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              scikit-image saves you 24051 person hours of effort in developing the same functionality from scratch.
              It has 54306 lines of code, 3630 functions and 609 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed scikit-image and discovered the below as its top functions. This is intended to give you an instant insight into scikit-image implemented functionality, and help decide if they suit your requirements.
            • Slice an image
            • Prepare a numpy array
            • Convert an RGB image to a Lab
            • Convert RGB to XYZ
            • Simplified image
            • Normalize a block
            • Generate the gradient of a channel
            • Runs the RANSAC algorithm
            • Determines the maximum number of trials
            • Blink a image
            • Calculate the peak local max_distance
            • Calculate Channel VAN energy distribution
            • Find corner points in image
            • Denoise a wavelet image
            • Perform flood of an image
            • Resize an image
            • Denoise an image
            • Unsupervised wfener
            • Generate a contour on an image
            • Generate a random walker
            • Warp an image
            • Compute the region properties for each image
            • Phase cross correlation between two images
            • Calculate reconstruction of a seed image
            • Generate random noise
            • Calculate peak local max distance
            • Calculate the density of the image
            Get all kandi verified functions for this library.

            scikit-image Key Features

            Image processing in Python

            scikit-image Examples and Code Snippets

            copy iconCopy
            from skimage import io
            img = io.imread('1.jpeg', as_gray=True)[...,None]
            img = tf.image.resize(img, [224, 256])
            # ------------------------- ^^^  ^^^ this is size of your input model
            img = tf.expand_dims(img, axis=0)
            How to tweak my code to get this smoother/fatter distance map using cv2.distanceTransform?
            Pythondot img2Lines of Code : 9dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            inv_m = cv2.bitwise_not(m)
            dist_img = cv2.distanceTransform(inv_m, cv2.DIST_L2, 3)
            norm_img = cv2.normalize(dist_img, dist_img, 0, 1.0, cv2.NORM_MINMAX)
            scale_img = skimage.exposure.rescale_intensity(norm_img, in_r
            Multiple template matching with Scikit-image
            Pythondot img3Lines of Code : 311dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def correlation_coefficient(patch1, patch2):
                product = np.mean((patch1 - patch1.mean()) * (patch2 - patch2.mean()))
                stds = patch1.std() * patch2.std()
                if stds == 0:
                    return 0
                    product /= stds
            Python Image segementation and extraction
            Pythondot img4Lines of Code : 47dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            from scipy import ndimage as ndi
            from skimage import io
            from skimage.filters import gaussian, threshold_li
            from skimage.morphology import remove_small_holes, remove_small_objects
            from skimage.segmentation import watershe
            Average diameter of complex shapes from pixels in df, Python
            Pythondot img5Lines of Code : 6dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            props = measure.regionprops_table(groups, properties = ['label', 'equivalent_diameter', 'perimeter'])
            df = pd.DataFrame(props)
            equivalent_diameter_area: float
            The diameter of a circle with the same area as the regi
            Extracting contours multiple times with Python
            Pythondot img6Lines of Code : 10dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            from skimage import measure
            # Construct some test data
            x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
            r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
            # Find contours at a constant value of 0.8
            contours = measu
            Cutting a patch around a segment of a segmented image
            Pythondot img7Lines of Code : 24dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from import astronaut
            import matplotlib.pyplot as plt
            from skimage.segmentation import slic
            from skimage.segmentation import mark_boundaries
            from skimage.measure import regionprops
            import matplotlib.patches as mpatches
            img = 
            Counting Objects in a noised Image
            Pythondot img8Lines of Code : 82dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from scipy import ndimage
            from skimage import filters, feature, measure, color
            import numpy as np
            import tifffile
            import matplotlib.pyplot as plt
            from scipy.ndimage import median_filter, morphology
            from scipy.fft import fftfreq, fft2, ffts
            Generate image back after changing blocks with view_as_blocks in Python
            Pythondot img9Lines of Code : 22dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from skimage import data, util
            import matplotlib.pyplot as plt
            image =
            blocked = util.view_as_blocks(image, (256, 256))
            blocked[0, 0] *= 0
            fig, ax = plt.subplots()
            Calculate average pixel intensity for each frame of tif movie
            Pythondot img10Lines of Code : 18dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from skimage import io
            im1 = io.imread('movie.tif')
            for i in range(im1.shape[0]):
            from skimage import io
            import matplotlib.pyplot as plt
            im1 = io.imread('movie.tif')
            y = []
            for i in r

            Community Discussions


            How to import Skimage to segment an image with watershed?
            Asked 2022-Mar-14 at 01:01

            I'm trying to use Skimage to segment an image with watershed, but I always get this error. Do you have a solution please?

            AttributeError: module 'skimage.morphology' has no attribute 'watershed'

            Source code :



            Answered 2022-Mar-14 at 01:01

            You are for some reason looking at the old documentation for scikit-image, version 0.12. (See the 0.12.x in the URL that you shared.) You can look at the examples for the latest released version at:


            Concretely for your code, you need to update the import to from skimage.segmentation import watershed.



            Python Script on Startup:: ValueError:numpy.ndarray size changed, may indicate binary incompatibility. Expected 48 from C header, got 40 from PyObject
            Asked 2022-Mar-09 at 14:22

            The Error occurs only with the shell script after reboot!

            The issue:

            I am trying to run my python script on Startup in Raspberry Pi. So, I wrote a Shell script called as follows:



            Answered 2022-Mar-09 at 14:22

            After a rigorous search, I got some hints from this accepted answer. I checked my cronlog file and followed the python import attempt until the error had occurred. The error had occurred just after import Interval attempt. Therefore, I installed the numpy as follows:



            Using plotly express to show image and plot with animation
            Asked 2022-Mar-07 at 18:28

            I need some help making an animation with plotly express where each frame will include an image and a graph (preferably a plotly express graph) side by side. My goal is to have the animation be corresponding to time steps from each frame. Plotly express has an example of this with just images shown in the link below. But I do not know how to make a plotly express graph take the place of one of the images in the example.

            The link below will lead you to the plotly express example for "Combining animations and facets"

            Link to plotly for specific example: 'Combining animations and facets' (




            Answered 2022-Mar-07 at 18:28

            There are a number of things you need to be systematic with

            1. make sure each frame has a name
            2. make sure each trace is using correct axes
            3. build sliders from list of frames



            ModuleNotFoundError: No module named 'skimage.measure.simple_metrics'
            Asked 2022-Feb-19 at 05:29

            I am using google colab. I installed scikit-image. When I execute this code, I am getting error:-
            'from skimage.measure.simple_metrics import compare_psnr'
            ModuleNotFoundError: No module named 'skimage.measure.simple_metrics'

            import math
            import torch
            import torch.nn as nn
            import numpy as np
            import cv2
            from skimage.measure.simple_metrics import compare_psnr

            def weights_init_kaiming(m):
            classname = m.__class__.__name__
            if classname.find('Conv') != -1:
            nn.init.kaiming_normal(, a=0, mode='fan_in')
            elif classname.find('Linear') != -1:
            nn.init.kaiming_normal(, a=0, mode='fan_in')
            elif classname.find('BatchNorm') != -1:
            # nn.init.uniform(, 1.0, 0.02)
  , std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025)
            nn.init.constant(, 0.0)

            def batch_PSNR(img, imclean, data_range):
            Img =
            Iclean =
            PSNR = 0
            for i in range(Img.shape[0]):
            PSNR += compare_psnr(Iclean[i,:,:,:], Img[i,:,:,:], data_range=data_range)
            return (PSNR/Img.shape[0])



            Answered 2022-Feb-19 at 05:29

            Since scikit-image version 0.16, that function was renamed from skimage.measure.compare_psnr to skimage.metrics.peak_signal_noise_ratio.

            Please check your scikit-image version to confirm it.




            How do skimage.morphology.remove_small_holes and skimage.morphology.remove_small_objects differ?
            Asked 2022-Jan-26 at 01:55

            Both of these methods are included in the Scikit-Image library for Python. I'm trying to extract certain objects from images and ran into these two methods in a senior dev's code, written for the same purpose.

            I have read the documentation for both skimage.morphology.remove_small_holes and skimage.morphology.remove_small_objects. But I can't understand what difference these two methods pose when they are run on a ndarray containing an image.



            Answered 2022-Jan-26 at 01:55

            One removes holes (value 0) within objects (any other single value), the other removes objects. Note that it acts on either binary images (ndarray of dtype bool) or segmentation masks (ndarray of dtype int, where each value represents one object). Hopefully this example clarifies their use:



            How to find the intersection between 2 skimage.measure.LineModelND objects?
            Asked 2022-Jan-04 at 14:08

            Here is a description of what I am trying to accomplish. I have a set of 3D points and I am trying to fit 2 lines on those points and after defining those lines I want to find their intersection.

            Here are more details about what I have done so far, starting from the begin. I have a set of 3D points stored in an np.ndarray of shape (N, 3) one can find the points here.

            After that I use the scikit-image library to define the lines using ransac. And I get lines of the type LineModelND which are defined by a point (origin) and a unit vector (direction) using ransac. Here is a code snippet. And after one line is fitted to the data, I fit on the outliers another line, so now I have 2 LineModelND objects and I want find their intersection. Any ideas ?



            Answered 2022-Jan-04 at 14:08

            The solution I've found so far is to use the skspatial.objects.Line.intersect_line method. This method requires the points to be co-planar and the lines to actually have an intersection or in other words to not be parallel. The LineModelND returns a line model defined by point and direction as the line object definition of the skspatial.objects.Line object, so after defining the skspatial Lines you can use the intersect_line method of this class.

            In my case, the lines are not coplanar. So I had to project the points to a plane, find the 2D intersection, compute the 3D axis value from the planar equation and then reverse the transformation. This results to the following output (I can't post images so I've posted a link to the image.



            How does skimage.segmentation.slic achieve segmentation under non-binary masks?
            Asked 2021-Dec-29 at 17:15

            Slic can implement segmentation under binarized masks, as shown in the figure below


            But if I need to divide the superpixels of different adjacent regions, what should I do?

            Each color represents an area, each region requires independent superpixel segmentation



            Answered 2021-Dec-29 at 17:15

            There is not currently any way to handle a mask with multiple regions in a single call. For your use case you will have to split each region into a separate mask and then call slic once per mask. You can combine the multiple segmentations into one by incrementing the labels appropriately.

            Pasted below is a concrete example of this for two separate masked regions (adapted from the existing example you referenced):



            Is there an alternative to Numba for functions that use many features not supported by Numba?
            Asked 2021-Dec-24 at 02:01

            I know Numba does not support all Python features nor all NumPy features. However I really need to speed up the execution time of the following function, which is block_reduce available in the scikit-image library (I've not downloaded the whole package, I've just taken block_reduce and view_as_blocks from it).

            Here is the original code (I've just removed the examples from the docstring).




            Answered 2021-Dec-24 at 02:01

            Have you tried running detailed profiling of your code? If you are dissatisfied with the performance of your program I think it can be very helpful to use a tool such as cProfile or py-spy. This can identify bottlenecks in your program and which parts specifically need to be sped up.

            That being said, as @CJR said, if your program is spending the bulk of the compute time in NumPy, there likely is no reason to worry about speeding it up using a just-in-time compiler or similar modifications to your setup. As explained in more detail here, NumPy is fast due to it implementing compute-intensive tasks in compiled languages, so it saves you from worrying about that and abstracts it away.

            Depending on what exactly you are planning to do, it is possible that your efficiency could be improved by parallelism, but this is not something I would worry about yet.

            To end on a more general note: while optimizing code efficiency is of course very important, it is imperative to do so carefully and deliberately. As Donald Knuth is famous for saying "premature optimization is the root of all evil (or at least most of it) in programming". See this stack exchange thread for some more discussion on this.



            Solving conda environment stuck
            Asked 2021-Dec-22 at 18:02

            I'm trying to install conda environment using the command:



            Answered 2021-Dec-22 at 18:02

            This solves fine (), but is indeed a complex solve mainly due to:

            • underspecification
            • lack of modularization

            This particular environment specification ends up installing well over 300 packages. And there isn't a single one of those that are constrained by the specification. That is a huge SAT problem to solve and Conda will struggle with this. Mamba will help solve faster, but providing additional constraints can vastly reduce the solution space.

            At minimum, specify a Python version (major.minor), such as python=3.9. This is the single most effective constraint.

            Beyond that, putting minimum requirements on central packages (those that are dependencies of others) can help, such as minimum NumPy.

            Lack of Modularization

            I assume the name "devenv" means this is a development environment. So, I get that one wants all these tools immediately at hand. However, Conda environment activation is so simple, and most IDE tooling these days (Spyder, VSCode, Jupyter) encourages separation of infrastructure and the execution kernel. Being more thoughtful about how environments (emphasis on the plural) are organized and work together, can go a long way in having a sustainable and painless data science workflow.

            The environment at hand has multiple red flags in my book:

            • conda-build should be in base and only in base
            • snakemake should be in a dedicated environment
            • notebook (i.e., Jupyter) should be in a dedicated environment, co-installed with nb_conda_kernels; all kernel environments need are ipykernel

            I'd probably also have the linting/formatting packages separated, but that's less an issue. The real killer though is snakemake - it's just a massive piece of infrastructure and I'd strongly encourage keeping that separated.



            How to make conda use its own gcc version?
            Asked 2021-Dec-12 at 16:12

            I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:



            Answered 2021-Dec-12 at 16:12

            Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install scikit-image

            Also see installing scikit-image.
            Debian/Ubuntu: sudo apt-get install python-skimage
            OSX: pip install scikit-image
            Anaconda: conda install -c conda-forge scikit-image
            Then, install scikit-image using:.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • PyPI

            pip install scikit-image

          • CLONE
          • HTTPS


          • CLI

            gh repo clone scikit-image/scikit-image

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link