示例#1
0
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)

# train the model
model = LinearSVC(random_state=42)
model.fit(data, target)

# dump the model to file
joblib.dump(model, models_path)
print "create model done..."
示例#2
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                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
    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"])
示例#3
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	# 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 = dataset.deskew(thresh, 20)
		thresh = dataset.center_extent(thresh, (20, 20))

		cv2.imshow("thresh", thresh)

		# extract features from the image and classify it
		hist = hog.describe(thresh)
		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)
    # 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 >= 28:
        
        # crop the ROI and then threshold the grayscale
        # ROI to reveal the digit
        # apply filer
        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 = dataset.deskew(thresh, 20)
        thresh = dataset.center_extent(thresh, (20, 20))
                
        cv2.imshow("thresh", thresh)
        
        # extract features from the image and classify it
        hist = hog.describe(thresh)
        digit = model.predict(hist)[0]
        print "Creo que el numero es: %d" % (digit)
        numeros.append(digit)
        
        # draw a rectangle around the digit, the show what the
        # digit was classified as
        cv2.rectangle(image, (x, y), (x + w, y + h), 255, 1)
        cv2.putText(image, str(digit), (x - 5, y - 5),
            cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)