Both are actively maintained and in many ways complementary, but for physics and astronomy scikit-image is more powerful at this time. Scikit-image is often compared to OpenCV, a collection of programs for computer vision that include live video. It is oriented toward extracting physical information from images, and has routines for reading, writing, and modifying images that are powerful, and fast. ![]() Among them, scikit-image is for image processing in Python. We've mentioned that SciKits is a searchable index of highly specialized tools that are built on SciPy and NumPy. With a little effort there are better choices. Python's core routines dependent on matplotlib may be used to display an image, but these are designed for graphics, and limited by the constraints of the matplotlib interface. Some more advanced techiques are available in SciPy too, courtesy of researchers who have contributed to SciKit as we will see. Many of the processing functions you will find in Python Imaging Library (PIL) are also available in SciPy where we have precise mathematical control over their definitions and operation. While PIL provides some essential functionality, for more practical uses in astronomy we need to read Flexible Image Transport or "FITS" files, and to enable numerical work on images in SciPy. Processing is not limited to "PNG" files, though that file type is preferred because it is not a lossy storage option. Mirror = anspose(Image.FLIP_LEFT_RIGHT).transpose(pil.ROTATE_90) You can transpose an image left-right with The saving operation converts the internal data back to the file format set by the extension used in the file name. Here we rotate the image 90 degrees, and then save it to a file with a new name. We operate on the image with the transpose function, which has an argument that controls what it does. This is an amazingly short program, because in opening the image the library handles all the conversions in formatting and stores the image internally so that you refer to it only by the name assigned when it is loaded. ![]() ![]() The PIL is imported with Image, and appears in the code as The first part of this is standard form to get the image name on the command line and make it available to the program. Outfilename = os.path.splitext(os.path.basename(infilename))+'_r90.png' Sys.exit("Usage: png_image_rotate file.png ") Parser= argparse.ArgumentParser(description = 'Rotate a png image 90 degrees') Here's how you would read it, rotate it 90 degrees, and write it out again using Pillow. Flexibile Image Transport System (FITS) files used for astronomy should be managed with astropy or pyfits.Īs a simple starting example, suppose you have an image that was taken with the camera turned so that "up" is to the side when the image is displayed. Once an image has been read into a numpy array, the full power of Python is available to process it, and we can turn to Pillow again to save a processed image in png or jpg or another format. Most import for us, Pillow has routines to read and write conventional image formats. Pillow uses the same "namespace" as PIL and older code should work, perhaps with a few modifications to allow for recent developments. with functions that are documented by the developers in a handbook describing the methods and giving some examples. Pillow includes the basics of image processing. Pillow will probably be on any packaged distribution of Python 3, or it may be installed with (note the capital "P") It continues to improve, and the features described here are tested with "Pillow 5.1" and Python 3.6 as of April 2018. With the coming of age of Python 3.x, a fork of the older version has evolved that is more suited for the new technologies and is in a package called "Pillow". The Python Imaging Library (PIL) was developed for Python 2.x and provided functions to manipulate images, including reading, modifying and saving in various standard image formats in a package called "PIL". ![]() 4.1 Reading and Writing a FITS File in Python.
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