import mahotas
import cv2
import imutils
# 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 = open(args["model"], "rb").read()
model = pickle.loads(model)
# initialize the HOG descriptor
hog = HOG(orientations = 18, pixelsPerCell = (10, 10),
cellsPerBlock = (1, 1), normalize = True, block_norm = "L1")
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
# blur the image and find edges and then find the 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])
required=True,
help="path to the configuration file")
ap.add_argument("-i",
"--image",
required=True,
help="path to the image to be classified")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
# load the classifier, then initialize the Histogram of Oriented Gradients descriptor
# and the object detector
model = cPickle.loads(open(conf["classifier_path"]).read())
hog = HOG(orientations=conf["orientations"],
pixelsPerCell=tuple(conf["pixels_per_cell"]),
cellsPerBlock=tuple(conf["cells_per_block"]),
normalize=conf["normalize"])
od = ObjectDetector(model, hog)
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=min(260, image.shape[1]))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect objects in the image
(boxes, probs) = od.detect(gray,
conf["window_dim"],
winStep=conf["window_step"],
pyramidScale=conf["pyramid_scale"],
minProb=conf["min_probability"])
"--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)
# train the model
model = LinearSVC(random_state=42)
model.fit(data, target)
ap.add_argument("-t",
"--type",
required=True,
help="type of annotation file (xml or mat)")
ap.add_argument("-l",
"--name",
required=False,
help="label's name. (for xml files)")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
# initialize the HOG descriptor along with the list of data and labels
hog = HOG(orientations=conf["orientations"],
pixelsPerCell=tuple(conf["pixels_per_cell"]),
cellsPerBlock=tuple(conf["cells_per_block"]),
normalize=conf["normalize"])
data = []
labels = []
# grab the set of ground-truth images and select a percentage of them for training
trnPaths = list(paths.list_images(conf["image_dataset"]))
trnPaths = random.sample(trnPaths,
int(len(trnPaths) * conf["percent_gt_images"]))
print("[INFO] describing training ROIs...")
# setup the progress bar
widgets = [
"Extracting: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",