Arrays in NumPy are multi-dimensional and can represent vectors, matrices, and images. When we loaded images in the previous examples, we converted them to NumPy array objects with the array() call but didn’t mention what that means. Figure 1-3 shows the contour and histogram plot. The method flatten() converts any array to a one-dimensional array with values taken row-wise. Note that the image needs to be flattened first, because hist() takes a one-dimensional array as input. The second argument specifies the number of bins to use. The visualization of the (graylevel) image histogram is done using the hist() function: figure() A number of bins is specified for the span of values and each bin gets a count of how many pixels have values in the bin’s range. # show contours with origin upper left cornerĪs before, the PIL method convert() does conversion to grayscale.Īn image histogram is a plot showing the distribution of pixel values. Im = array(Image.open('empire.jpg').convert('L')) Plot(x,y,'ks:') # black dotted line with square-markers Plot(x,y,'go-') # green line with circle-markers Use them like this: plot(x,y) # default blue solid line
The most useful are the short commands shown in Table 1-1, Table 1-2 and Table 1-3. There are many options for formatting color and styles when plotting. This will give a plot like the one on the right in Figure 1-2 instead. The axes are useful for debugging, but if you want a prettier plot, add: axis('off') Note that PyLab uses a coordinate origin at the top left corner as is common for images. You should call show() only once per script, usually at the end. This GUI loop blocks your scripts and they are paused until the last figure window is closed. The show() command starts the figure GUI and raises the figure windows. This plots the image, then four points with red star markers at the x and y coordinates given by the x and y lists, and finally draws a line (blue by default) between the two first points in these lists. # line plot connecting the first two points Here is an example of plotting an image with a few points and a line: from PIL import Image Most importantly, we want to be able to show things like interest points, correspondences, and detected objects using points and lines. Return Īlthough it is possible to create nice bar plots, pie charts, scatter plots, etc., only a few commands are needed for most computer vision purposes. """ Returns a list of filenames for all jpg images in a directory. Create a file called imtools.py to store some of these generally useful routines and add the following function: import os Here’s how you could create a list of filenames of all images in a folder. Throughout this book we are going to need lists of images to process. There is a simple check that the file is not already a JPEG file and a message is printed to the console if the conversion fails. PIL is smart enough to determine the image format from the file extension. The new filename will be the same as the original with the file ending “.jpg” instead. The PIL function open() creates a PIL image object and the save() method saves the image to a file with the given filename.
Outfile = os.path.splitext(infile) + ".jpg" Figure 1-1. Examples of processing images with PIL.
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