def __init__(self):
model_path = "model/digitals/svm.cpickle"
self.model = joblib.load(model_path)
self.hog = HOG(orientations=18, pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1), normalize=True)
class DigitalRegnition:
def __init__(self):
model_path = "model/digitals/svm.cpickle"
self.model = joblib.load(model_path)
self.hog = HOG(orientations=18, pixelsPerCell=(10, 10),
cellsPerBlock=(1, 1), normalize=True)
def recognize(self, image):
# load the image and convert it to grayscale
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)
# 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])
# loop over the contours
length = 50
dx, dy = 0, 0
for (c, _) in cnts:
# compute the bounding box for the rectangle
(x, y, w, h) = cv2.boundingRect(c)
# if the width is at least 7 pixels and the height
# is at least 20 pixels, the contour is likely a digit
if w >= 7 and h >= 20:
# crop the ROI and then threshold the grayscale
# ROI to reveal the digit
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)
# deskew the image center its extent
thresh = deskew(thresh, 20)
thresh = center_extent(thresh, (20, 20))
#cv2.imshow("thresh", thresh)
# extract features from the image and classify it
hist = self.hog.describe(thresh)
hist = hist.reshape(1, -1)
digit = self.model.predict(hist)[0]
# draw a rectangle around the digit, the show what the
# digit was classified as
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 1)
cv2.putText(image, str(digit), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
cv2.rectangle(image, (dx, dy),
(dx + length, dy + length), (0, 0, 255), 2)
cv2.putText(image, str(digit), (dx + 15, dy + 35),
cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
dx = dx + length
return image