train.py

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# Universidad Tecnica Particular de Loja    #
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# Professor:                                #
# Rodrigo Barba        lrbarba@utpl.edu.ec  #
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# Students:                                 #
# Marcelo Bravo        mdbravo4@utpl.edu.ec #
# Galo Celly           gscelly@utpl.edu.ec  #
# Nicholas Earley      nearley@utpl.edu.ec  #
#############################################

# python train.py --dataset data/digits.csv --model models/svm.cpickle

# import the necessary packages
from sklearn.svm import LinearSVC
from pyimagesearch.hog import HOG
from pyimagesearch import dataset
import argparse
import cPickle

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
	help = "path to the dataset file")
ap.add_argument("-m", "--model", required = True,
	help = "path to where the model will be stored")
args = vars(ap.parse_args())

# load the dataset and initialize the data matrix
(digits, target) = dataset.load_digits(args["dataset"])
data = []

# initialize the HOG descriptor
hog = HOG(orientations = 18, pixelsPerCell = (10, 10),
	cellsPerBlock = (1, 1), normalize = True)

# loop over the images
for image in digits:
	# deskew the image, center it
	image = dataset.deskew(image, 20)
	image = dataset.center_extent(image, (20, 20))

	# describe the image and update the data matrix
	hist = hog.describe(image)
	data.append(hist)

# train the model
model = LinearSVC(random_state = 42)
model.fit(data, target)

# dump the model to file
f = open(args["model"], "w")
f.write(cPickle.dumps(model))
f.close()