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View Code? Open in Web Editor NEWCMSC 471 project 3
CMSC 471 project 3
Jeremy Aguillon CMSC 471 Project 3 Contents: pickling.py - program that pickles the given data into data.pickle - not generalizible only created based on specific data set data.pickle - pickled data that contains all of the featurized images and their labels proj3.ipynb - program that creates the confusion matrix and creates a graph of it for visualization classify.py - this is the requested program that takes in a command line argument for the path to the image to test and predicts the type of image - uses data.pickle instead of folders of files so it is more generalized - can use any other data set in the form of [ <list of vectorized images>, <list of corresponding labels> ] named pickle.data project3Report.tex - tex source code for the report project3Report.pdf - readable version of the report To install: My program uses scikitlearn and all the imports are already at the top of my programs. If you need to install scikit learn => From their website Scikit-learn requires: Python (>= 2.6 or >= 3.3), NumPy (>= 1.6.1), SciPy (>= 0.9) This is how I did conda install scikit-learn Another option pip install -U scikit-learn To Run: python/python3 classify.py <path to img> Examples: python3 classify.py "../Training/00/01.jpg" python classify.py "test.jpg"
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