cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnts], key = lambda x: x[1])
# loop over the contours
for (c, _) in cnts:
(x,y,w,h) = cv2.boundingRect(c)
# if the width is atleast 7 pixels and the height is atleast 20 pixels, the ocntour is likely a digit
if w >= 7 and h >= 20:
roi = gray[y:y+h, x:x+w]
thresh = roi.copy()
T = mahotas.thresholding.otsu(roi)
thresh[thresh > T] = 255
thresh = cv2.bitwise_not(thresh)
# deskwe the image center its extent
thresh = dataset.deskew(thresh, 20)
thresh = dataset.center_extent(thresh, (20, 20))
cv2.imshow("thresh", thresh)
# extract features from the image and reshape the array
hist = hog.describe(thresh)
hist = hist.reshape(1, -1)
# classify
digit = model.predict(hist)[0]
print("I think that number is: {}".format(digit))
# draw a rectangle around the digit, the show what the
# digit was classified as
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 1)
cv2.putText(image, str(digit), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2)
cv2.imshow("image", image)
"--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)
# train the model
model = LinearSVC(random_state=42)
model.fit(data, target)
# dump the model to file
joblib.dump(model, args["model"])