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Introduction
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Simple Python and skimage (scikit-image) techniques can be used to solve genuine morphometric and colorimetric problems.
Morphometric problems involve the number, shape, and / or size of the objects in an image.
Colorimetric problems involve analyzing the color of the objects in an image.
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Image Basics
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Digital images are represented as rectangular arrays of square pixels.
Digital images use a left-hand coordinate system, with the origin in the upper left corner, the x-axis running to the right, and the y-axis running down.
Most frequently, digital images use an additive RGB model, with eight bits for the red, green, and blue channels.
Lossless compression retains all the details in an image, but lossy compression results in loss of some of the original image detail.
BMP images are uncompressed, meaning they have high quality but also that their file sizes are large.
JPEG images use lossy compression, meaning that their file sizes are smaller, but image quality may suffer.
TIFF images can be uncompressed or compressed with lossy or lossless compression.
Depending on the camera or sensor, various useful pieces of information may be stored in an image file, in the image metadata.
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Image representation in skimage
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skimage images are stored as three-dimensional NumPy arrays.
In skimage images, the red channel is specified first, then the green, then the blue, i.e. RGB.
Images are read from disk with the skimage.io.imread() function.
We create a window that automatically scales the displayed image with skimage.viewer.ImageViewer() and calling view() on the viewer object.
Color images can be transformed to grayscale using skimage.color.rgb2gray() or be read as grayscale directly by passing the argument as_gray=True to skimage.io.imread().
We can resize images with the skimage.transform.resize() function.
NumPy array commands, like img[img < 128] = 0, and be used to manipulate the pixels of an image.
Command-line arguments are accessed via the sys.argv list; sys.argv[1] is the first parameter passed to the program, sys.argv[2] is the second, and so on.
Array slicing can be used to extract sub-images or modify areas of images, e.g., clip = img[60:150, 135:480, :].
Metadata is not retained when images are loaded as skimage images.
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Drawing and Bitwise Operations
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We can use the NumPy zeros() function to create a blank, black image.
We can draw on skimage images with functions such as skimage.draw.rectangle(), skimage.draw.circle(), skimage.draw.line(), and more.
The drawing functions return indices to pixels that can be set directly.
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Creating Histograms
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We can load images in grayscale by passing the as_gray=True parameter to the skimage.io.imread() function.
We can create histograms of images with the np.histogram function.
We can separate the RGB channels of an image using slicing operations.
We can display histograms using the matplotlib pyplot figure(), title(), xlabel(), ylabel(), xlim(), plot(), and show() functions.
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Blurring images
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Applying a low-pass blurring filter smooths edges and removes noise from an image.
Blurring is often used as a first step before we perform Thresholding, Edge Detection, or before we find the Contours of an image.
The Gaussian blur can be applied to an image with the skimage.filters.gaussian() function.
Larger sigma values may remove more noise, but they will also remove detail from an image.
The float() function can be used to parse a string into an float.
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Thresholding
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Thresholding produces a binary image, where all pixels with intensities above (or below) a threshold value are turned on, while all other pixels are turned off.
The binary images produced by thresholding are held in two-dimensional NumPy arrays, since they have only one color value channel. They are boolean, hence they contain the values 0 (off) and 1 (on).
Thresholding can be used to create masks that select only the interesting parts of an image, or as the first step before Edge Detection or finding Contours.
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Edge Detection
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The skimage.viewer.ImageViewer is extended using a skimage.viewer.plugins.Plugin.
We supply a filter function callback when creating a Plugin.
Parameters of the callback function are manipulated interactively by creating sliders with the skimage.viewer.widgets.slider() function and adding them to the plugin.
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Connected Component Analysis
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skimage.measure.label is used to generate objects.
We use skimage.measure.regionprops to measure properties of labelled objects.
Color objects according to feature values.
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Challenges
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