# -*- coding: utf-8 -*-
"""
Created on Tue Jun 4 18:04:45 2019
@author: David
"""
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from hog import HOG
import dataset
(digits, target) = dataset.load_digits("train.csv")
data = []
hog = HOG(orientations = 18, pixelsPerCell = (10, 10), cellsPerBlock = (1, 1), transform = True)
for image in digits:
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
data.append(hist)
model = LinearSVC(random_state = 42)
model.fit(data, target)
joblib.dump(model, "svm.cpickle")
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from hog import HOG
import dataset
import argparse
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())
(digits, target) = dataset.load_digits(args['dataset'])
data = []
hog = HOG(orientations = 18, pixelsPerCell=(10, 10),
cellsPerBlock=(1,1), normalize=True)
for image in digits:
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
data.append(hist)
model = LinearSVC(random_state = 42)
model.fit(data, target)
joblib.dump(model, args['model'])
# print 'I don\'t give a shit'
import dataset
import argparse
import numpy as np
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 bill be stored')
args = vars(ap.parse_args())
(digits, target) = dataset.load_digits(args['dataset'])
data = []
hog = HOG(orientations=18,
pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1),
transform=True)
for image in digits:
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
data.append(hist)
print(np.shape(data))
# model = LinearSVC(random_state=42)
# model.fit(data, target)
import pickle
import dataset
import argparse
from hog import HOG
from sklearn.svm import LinearSVC
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--train", required=True, help="train.csv path")
ap.add_argument("-m",
"--model",
required=True,
help="path of where model will be saved")
args = vars(ap.parse_args())
digits, labels = dataset.load_digits(args["train"])
hog = HOG(orientations=18,
pixels_per_cell=(6, 6),
cells_per_block=(1, 1),
transform=True)
data = []
for digit in digits:
hist = hog.describe(digit)
data.append(hist)
model = LinearSVC()
model.fit(data, labels)
pickle.dump(model, open(args["model"], 'wb'))
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from hog import HOG
import dataset
import argparse
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())
(digits, target) = dataset.load_digits(args["dataset"])
data = []
hog = HOG(orientations=18, pixelspercell=(10, 10),
cellsperblock=(1, 1), transform=True)
for image in digits:
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
data.append(hist)
# instantiate random for reproducible results
model = LinearSVC(random_state=42)
model.fit(data, target)
joblib.dump(model, args["model"])
__author__ = 'XJH'
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from hog import HOG
import dataset
(digits, target) = dataset.load_digits("digits.csv")
data = []
hog = HOG(orientations=18,
pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1),
transform=True)
for image in digits:
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
data.append(hist)
model = LinearSVC(random_state=42)
model.fit(data, target)
joblib.dump(model, "model")
