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This section addresses basic image manipulation and processing using the core scientific modules NumPy and SciPy. Some of the operations covered by this tutorial may be useful for other kinds of multidimensional array processing than image processing.
In particular, the submodule scipy. For more advanced image processing and image-specific routines, see the tutorial Scikit-image: For large data, use np. Use matplotlib and imshow to display an image inside a matplotlib figure:. See 3D plotting with Mayavi. Gaussian filter from scipy. Most local linear isotropic filters blur the image ndimage. Other local non-linear filters: More denoising filters are available in skimage. See wikipedia for a definition of mathematical morphology. Probe an image with a simple shape a structuring elementand modify this image according to how the shape locally fits or misses the image.
Replace the value of a pixel by the minimal value covered by the structuring element Use a gradient operator Sobel to find high intensity variations:. Check how a first denoising step e. More advanced segmentation algorithms are found in the scikit-image: Other Scientific Packages provide algorithms that can be useful for image processing.
In this example, we use the spectral clustering function of the scikit-learn in order to segment glued objects. Now reassign labels with np.
When regions are regular blocks, it is more efficient to use stride tricks Example: One example with mathematical morphology: Displaying a Racoon Face. Image manipulation and numpy arrays. Plot the block mean of an image. Display a Racoon Face.
Opening, erosion, and propagation. Find the bounding box of an object. Denoising an image with the median filter. Finding edges with Sobel filters. Cleaning segmentation with mathematical morphology. Segmentation with Gaussian mixture models.
Segmentation with spectral clustering. Gallery generated by Sphinx-Gallery. Edit it on Github. See also For more advanced image processing and image-specific routines, see the tutorial Scikit-image: See also 3-D visualization: Mayavi See 3D plotting with Mayavi. Image plane widgets Isosurfaces …. Exercise Open as an array the scikit-image logo http: Crop a meaningful part of the image, for example the python circle in the logo.
Display the image array using matplotlib. Change the interpolation method and zoom to see the difference. Transform your image to greyscale Increase the contrast of the image by changing its minimum and maximum values.
Save the array to two different file formats png, jpg, tiff. Add some noise e. Compare the histograms of the two different denoised images. Which one is the closest to the histogram of the original noise-free image? See also More denoising filters are available in skimage. Exercise Check how a first denoising step e. See also More advanced segmentation algorithms are found in the scikit-image: See also Other Scientific Packages provide algorithms that can be useful for image processing.
Download all examples in Python source code: Download all examples in Jupyter notebooks: See also More on image-processing: The chapter on Scikit-image Other, more powerful and complete modules: Table Of Contents 2. Image manipulation and processing using Numpy and Scipy 2. Opening and writing to image files 2.
Full code examples 2. Examples for the image processing chapter. Created using Sphinx 1.
This chapter describes how to use scikit-image on various image processing tasks, and insists on the link with other scientific Python modules such as NumPy and SciPy. For basic image manipulation, such as image cropping or simple filtering, a large number of simple operations can be realized with NumPy and SciPy only.
See Image manipulation and processing using Numpy and Scipy. Note that you should be familiar with the content of the previous chapter before reading the current one, as basic operations such as masking and labeling are a prerequisite. Recent versions of scikit-image is packaged in most Scientific Python distributions, such as Anaconda or Enthought Canopy. Most scikit-image functions take NumPy ndarrays as arguments. Different kinds of functions, from boilerplate utility functions to high-level recent algorithms.
An important if questionable skimage convention: Some image processing routines need to work with float arrays, and may hence output an array with a different type and the data range from the input array.
See the user guide for more details. Check the docstring for the expected dtype and data range of input images. Most functions of skimage can take 3D images as input arguments. Find a skimage function computing the histogram of an image and plot the histogram of each color channel. Local filters replace the value of pixels by a function of the values of neighboring pixels.
The function can be linear or non-linear. Non-local filters use a large region of the image or all the image to transform the value of one pixel:. See wikipedia for an introduction on mathematical morphology. Probe an image with a simple shape a structuring elementand modify this image according to how the shape locally fits or misses the image. Replace the value of a pixel by the minimal value covered by the structuring element Mathematical morphology operations are also available for non-binary grayscale images int or float type.
Erosion and dilation correspond to minimum resp. Basic mathematical morphology is also implemented in scipy. Image segmentation is the attribution of different labels to different regions of the image, for example in order to extract the pixels of an object of interest.
The Otsu method is a simple heuristic to find a threshold to separate the foreground from the background. Once you have separated foreground objects, it is use to separate them from each other.
For this, we can assign a different integer labels to each one. The random walker algorithm skimage. It is based on the idea of the diffusion of labels in the image:. Functions names are often self-explaining: Use skimage dedicated utility function:. Displaying a simple image. Computing horizontal gradients with the Sobel filter.
Equalizing the histogram of an image. Labelling connected components of an image. Watershed and random walker for segmentation. Gallery generated by Sphinx-Gallery. Edit it on Github. Emmanuelle Gouillart scikit-image is a Python package dedicated to image processing, and using natively NumPy arrays as image objects.
See also For basic image manipulation, such as image cropping or simple filtering, a large number of simple operations can be realized with NumPy and SciPy only. Basic filtering, mathematical morphology, regions properties Mahotas.
NumPy machinery Common filtering algorithms. Warning An important if questionable skimage convention: Exercise Open a color image on your disk as a NumPy array. Find a skimage function computing the histogram of an image and plot the histogram of each color channel Convert the image to grayscale and plot its histogram.
Grayscale mathematical morphology Mathematical morphology operations are also available for non-binary grayscale images int or float type. See also Basic mathematical morphology is also implemented in scipy. Example of filters comparison: Earlier scikit-image versions skimage. It is based on the idea of the diffusion of labels in the image: Postprocessing label images skimage provides several utility functions that can be used on label images ie images where different discrete values identify different regions.
Exercise Load the coins image from the data submodule. Separate the coins from the background by testing several segmentation methods: Otsu thresholding, adaptive thresholding, and watershed or random walker segmentation. See also for some properties, functions are available as well in scipy. Exercise continued Use the binary image of the coins and background from the previous exercise. Compute an image of labels for the different coins. Compute the size and eccentricity of all coins.
Some image processing operations: Download all examples in Python source code: Download all examples in Jupyter notebooks: Table Of Contents 3. Introduction and concepts 3. Otsu thresholding Labeling connected components of a discrete image 3. Marker based methods Watershed segmentation Random walker segmentation 3.
Data visualization and interaction 3. Feature extraction for computer vision 3. Full code examples 3. Examples for the scikit-image chapter. Created using Sphinx 1.