from sklearn.externals import joblib

from sklearn.svm import LinearSVC

from utilities.hog import HOG

from utilities import dataset

# Define paths

dataset_path = 'data/digits.csv'

model_path = 'models/svm.cpickle'

# Load the dataset and initialize the data matrix

(digits, target) = dataset.load_digits(dataset_path)

data = []

# Initialize the HOG descriptor

hog = HOG(orientations=18, pixels_per_cell=(10, 10), cells_per_block=(1, 1), transform=True)

# Loop over the images

for image in digits:

# De-skew the image and center it

image = dataset.de_skew(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)

# Save the model to file

joblib.dump(model, model_path)