Mat file to image python
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I managed to convert one image, use a loop to convert all. Show
Please read the comments. Note: When converting images to A large number of datasets for data science and research, utilize .mat files. In this article, we’ll learn to work with .mat files in Python and explore them in detail. The purpose of a .mat file may not seem obvious right off the bat. But when working with large
datasets, the information contained within these files is absolutely crucial for data science/machine learning projects! This is because the .mat files contain the metadata of every object/record in the dataset. While the files are not exactly designed for the sole purpose of creating annotations, a lot of researchers use MATLAB for their research and data collection, causing a lot of the annotations that we use in Machine Learning to be present in the form
of .mat files. So, it’s important for a data scientist to understand how to use the .mat files for your projects. These also help you better work with training and testing data sets instead of working with regular CSV files. Let’s get started! By default, Python is not capable of reading .mat files. We need to import a library that knows how to handle the file format. 1. Install scipySimilar to how we use the CSV module to work with .csv files, we’ll import the scipy libary to work with .mat files in Python. If you don’t already have scipy, you can use the pip command to install the same Now that we have scipy set up and ready to use, the next step is to open up your python script to finally get the data required from the file. 2. Import the scipy.io.loadmat moduleIn this example, I will be using the accordion annotations provided by Caltech, in 101 Object Categories. from scipy.io import loadmat
annots = loadmat('annotation_0001.mat')
print(annots)
Upon execution, printing out annots would provide us with this as the output. {'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN, Created on: Tue Dec 14 15:57:03 2004', '__version__': '1.0', '__globals__': [], 'box_coord': array([[ 2, 300, 1, 260]], dtype=uint16), 'obj_contour': array([[ 37.16574586, 61.94475138, 89.47697974, 126.92081031,
169.32044199, 226.03683241, 259.07550645, 258.52486188,
203.46040516, 177.5801105 , 147.84530387, 117.0092081 ,
1.37384899, 1.37384899, 7.98158379, 0.82320442,
16.2412523 , 31.65930018, 38.81767956, 38.81767956],
[ 58.59300184, 44.27624309, 23.90239411, 0.77532228,
2.97790055, 61.34622468, 126.87292818, 214.97605893,
267.83793738, 270.59116022, 298.67403315, 298.67403315,
187.99447514, 94.93554328, 90.53038674, 77.31491713,
62.44751381, 62.99815838, 56.94106814, 56.94106814]])}
Starting off, you can see that this single .mat file provides information regarding the version of MATLAB used, the platform, the date of its creation, and a lot more. The part that we should be focusing on is, however, the 3. Parse the .mat file structureIf you’ve gone through the information regarding the Annotations provided by Caltech, you’d know that these numbers are the outlines of the corresponding image in the dataset. In a little more detail, this means that the object present in image 0001, consists of these outlines. A little further down in the article, we’ll be sorting through the numbers, so, don’t worry about it for now. Parsing through this file structure, we could assign all the contour values to a new Python list. con_list = [[element for element in upperElement] for upperElement in annots['obj_contour']] If we printed out [[37.16574585635357, 61.94475138121544, 89.47697974217309, 126.92081031307546, 169.32044198895025, 226.03683241252295, 259.0755064456721, 258.52486187845295, 203.4604051565377, 177.58011049723754, 147.84530386740326, 117.0092081031307, 1.3738489871086301, 1.3738489871086301, 7.98158379373848, 0.8232044198894926, 16.24125230202577, 31.65930018416205, 38.81767955801104, 38.81767955801104], [58.59300184162066, 44.27624309392269, 23.90239410681403, 0.7753222836096256, 2.9779005524862328, 61.34622467771641, 126.87292817679563, 214.97605893186008, 267.83793738489874, 270.59116022099454, 298.6740331491713, 298.6740331491713, 187.9944751381216, 94.93554327808477, 90.53038674033152, 77.31491712707185, 62.44751381215474, 62.998158379373876, 56.94106813996319, 56.94106813996319]] 4. Use Pandas dataframes to work with the dataNow that you have the information and the data retrieved, how would you work with it? Continue to use lists? Definitely not. We use Dataframes as the structure to work with, in that it functions much like a table of data. Neat to look at, and extremely simple to use. Now, to work with Dataframes, we’ll need to import yet another module, Pandas. Pandas is an open source data analysis tool, that is used by machine learning enthusiasts and data scientists throughout the world. The operations provided by it are considered vital and fundamental in a lot of data science applications. We’ll only be working with DataFrames in this article, but, keep in mind that the opportunities provided by Pandas are immense. Working with the data we’ve received above can be simplified by using pandas to construct a data frame with rows and columns for the data. # zip provides us with both the x and y in a tuple. newData = list(zip(con_list[0], con_list[1])) columns = ['obj_contour_x', 'obj_contour_y'] df = pd.DataFrame(newData, columns=columns) Now, we have our data in a neat DataFrame! obj_contour_x obj_contour_y 0 37.165746 58.593002 1 61.944751 44.276243 2 89.476980 23.902394 3 126.920810 0.775322 4 169.320442 2.977901 5 226.036832 61.346225 6 259.075506 126.872928 7 258.524862 214.976059 8 203.460405 267.837937 9 177.580110 270.591160 10 147.845304 298.674033 11 117.009208 298.674033 12 1.373849 187.994475 13 1.373849 94.935543 14 7.981584 90.530387 15 0.823204 77.314917 16 16.241252 62.447514 17 31.659300 62.998158 18 38.817680 56.941068 19 38.817680 56.941068 As you can see, we have the X and Y coordinates for the image’s outline in a simple DataFrame of two columns. This should provide you with some clarity about the nature of the data in the file. The process of creating DataFrames for each .mat file is different but, with experience and practice, creating them out of .mat files should come naturally to you. That’s all for this article! ConclusionYou now know how to work with .mat files in Python, and how to create dataframes in pandas with its content. The next steps to work with this data would be to and create your own models, or employ existing ones for training or testing your copy of the dataset. References
How do I convert a mat file to an image?Convert Mat To Jpg. Download and install the latest version of Filestar.. Right click on one or more Mat file(s) on your desktop and select Convert with Filestar.. Type convert to jpg in the search box.. Press Convert.. How do I read a .MAT image in Python?By default, Python is not capable of reading . mat files. We need to import a library that knows how to handle the file format.. Install scipy. Similar to how we use the CSV module to work with . ... . Import the scipy. io. ... . Parse the . ... . Use Pandas dataframes to work with the data.. How do you process a .MAT file in Python?mat files in Python?. First read the documentation.. Use a hex editor (such as HxD) and look into a reference . mat -file you want to parse.. Try to figure out the meaning of each byte by saving the bytes to a . ... . Use classes to save each data element (such as miCOMPRESSED , miMATRIX , mxDOUBLE , or miINT32 ). How do you convert .MAT to .jpg in MATLAB?Direct link to this answer. % Load the image variable from the MAT file into a structure s.. s = load(yourFileName);. % Extract the image from the structure.. rgbImage = s. ... . % Write to disk in a jpg format, which you should never use for image analysis.. imwrite(rgbImage, 'my image.jpg');. |
