# loop over the image and mask paths
for imagen_entrenamiento in imagenes_entrenamiento:
# load the image and mask
image = cv2.imread(imagen_entrenamiento)
target.append(imagen_entrenamiento.split("_")[-2])
#print imagen_entrenamiento.split("_")[-2]
#print "."
le = LabelEncoder()
target = le.fit_transform(target)
#################################################################################
# initialize the HOG descriptor
hog = HOG(orientations=18,
pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1),
normalize=True)
"""
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# blur the image, find edges, and then find contours along
# the edged regions
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
# extract features from the image and classify it
hist = hog.describe(blurred)
direccion = le.inverse_transform(model.predict(hist))[0]
#le.inverse_transform(model.predict(features))[0]
print " Por favor: %s" % (direccion)
import mahotas
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required = True,
help = "path to where the model will be stored")
ap.add_argument("-i", "--image", required = True,
help = "path to the image file")
args = vars(ap.parse_args())
# load the model
model = joblib.load(args["model"])
# initialize the HOG descriptor
hog = HOG(orientations = 18, pixelsPerCell = (10, 10),
cellsPerBlock = (1, 1), transform = True, block_norm="L2-Hys")
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# blur the image, find edges, and then find contours along
# the edged regions
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 30, 150)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# sort the contours by their x-axis position, ensuring
# that we read the numbers from left to right
cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnts], key = lambda x: x[1])
import mahotas
import cv2
ap = argparse.ArgumentParser()
ap.add_argument('-m',
'--model',
required=True,
help='path to where the model will be stored')
ap.add_argument('-i', '--image', required=True, help='path to the image file')
args = vars(ap.parse_args())
model = joblib.load(args['model'])
hog = HOG(orientation=18,
pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1),
transform=True)
image = cv2.imread(args['image'])
# image=imutils.resize(image,width=28,height=28)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 30, 150)
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnts], key=lambda x: x[1])
for (c, _) in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w >= 7 and h >= 20:
roi = gray[y:y + h, x:x + w]
