# USAGE
# python train.py --dataset data/digits.csv --model models/svm.cpickle
# import the necessary packages
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from pyimagesearch.hog import HOG
from pyimagesearch import dataset
import argparse
dataset_path = "data/digits.csv"
models_path = "models/svm.cpickle_01"
# load the dataset and initialize the data matrix
(digits, target) = dataset.load_digits(dataset_path)
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)
import argparse
# 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),
transform=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
from pyimagesearch.hog import HOG
from pyimagesearch 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 = []
# 18 orientations for the gradient magnitude histogram, 10 pixels for each
# cell, and 1 cell per block.
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))
# HOG feature vector is computed for the pre-processed image by calling
# the describe method
hist = hog.describe(image)
data.appned(hist)
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)
import cv2
image = cv2.imread('/Users/ryanzotti/Downloads/IMG_0302.JPG')
#image = cv2.imread('/Users/ryanzotti/Documents/repos/OpenCvDigits/images/cellphone.png')
#print(image.shape[0])
#print(image.shape[1])
# import the necessary packages
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from pyimagesearch.hog import HOG
from pyimagesearch import dataset
import argparse
# load the dataset and initialize the data matrix
(digits, target) = dataset.load_digits(
"/Users/ryanzotti/Documents/repos/OpenCvDigits/data/digits.csv")
data = []
# initialize the HOG descriptor
hog = HOG(orientations=18,
pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1),
normalize=True)
image = digits[0]
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
# loop over the images
for image in digits:
# deskew the image, center it
image = cv2.imread('/Users/ryanzotti/Downloads/IMG_0302.JPG')
#image = cv2.imread('/Users/ryanzotti/Documents/repos/OpenCvDigits/images/cellphone.png')
#print(image.shape[0])
#print(image.shape[1])
# import the necessary packages
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from pyimagesearch.hog import HOG
from pyimagesearch import dataset
import argparse
# load the dataset and initialize the data matrix
(digits, target) = dataset.load_digits("/Users/ryanzotti/Documents/repos/OpenCvDigits/data/digits.csv")
data = []
# initialize the HOG descriptor
hog = HOG(orientations = 18, pixelsPerCell = (10, 10),
cellsPerBlock = (1, 1), normalize = True)
image = digits[0]
image = dataset.deskew(image, 20)
image = dataset.center_extent(image, (20, 20))
# 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))
