# -*- 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")