*/
public class DetectFaceDemo {
    public void run() {
        System.out.println("\nRunning DetectFaceDemo");

        // Create a face detector from the cascade file in the resources
        // directory.
        CascadeClassifier faceDetector = new CascadeClassifier(getClass()
                .getResource("/lbpcascade_frontalface.xml").getPath());

            

Reported by PMD.

                      MatOfRect faceDetections = new MatOfRect();
        faceDetector.detectMultiScale(image, faceDetections);

        System.out.println(String.format("Detected %s faces",
                faceDetections.toArray().length));

        // Draw a bounding box around each face.
        for (Rect rect : faceDetections.toArray()) {
            Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x

            

Reported by PMD.

              
        // Save the visualized detection.
        String filename = "faceDetection.png";
        System.out.println(String.format("Writing %s", filename));
        Imgcodecs.imwrite(filename, image);
    }
}

            

Reported by PMD.

              
        // Create a face detector from the cascade file in the resources
        // directory.
        CascadeClassifier faceDetector = new CascadeClassifier(getClass()
                .getResource("/lbpcascade_frontalface.xml").getPath());
        Mat image = Imgcodecs.imread(getClass().getResource(
                "/AverageMaleFace.jpg").getPath());

        // Detect faces in the image.

            

Reported by PMD.

              
        // Create a face detector from the cascade file in the resources
        // directory.
        CascadeClassifier faceDetector = new CascadeClassifier(getClass()
                .getResource("/lbpcascade_frontalface.xml").getPath());
        Mat image = Imgcodecs.imread(getClass().getResource(
                "/AverageMaleFace.jpg").getPath());

        // Detect faces in the image.

            

Reported by PMD.

                      // directory.
        CascadeClassifier faceDetector = new CascadeClassifier(getClass()
                .getResource("/lbpcascade_frontalface.xml").getPath());
        Mat image = Imgcodecs.imread(getClass().getResource(
                "/AverageMaleFace.jpg").getPath());

        // Detect faces in the image.
        // MatOfRect is a special container class for Rect.
        MatOfRect faceDetections = new MatOfRect();

            

Reported by PMD.

                      // directory.
        CascadeClassifier faceDetector = new CascadeClassifier(getClass()
                .getResource("/lbpcascade_frontalface.xml").getPath());
        Mat image = Imgcodecs.imread(getClass().getResource(
                "/AverageMaleFace.jpg").getPath());

        // Detect faces in the image.
        // MatOfRect is a special container class for Rect.
        MatOfRect faceDetections = new MatOfRect();

            

Reported by PMD.

                      faceDetector.detectMultiScale(image, faceDetections);

        System.out.println(String.format("Detected %s faces",
                faceDetections.toArray().length));

        // Draw a bounding box around each face.
        for (Rect rect : faceDetections.toArray()) {
            Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
                    + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));

            

Reported by PMD.

              
        // Draw a bounding box around each face.
        for (Rect rect : faceDetections.toArray()) {
            Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
                    + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
        }

        // Save the visualized detection.
        String filename = "faceDetection.png";

            

Reported by PMD.

              
        // Draw a bounding box around each face.
        for (Rect rect : faceDetections.toArray()) {
            Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
                    + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
        }

        // Save the visualized detection.
        String filename = "faceDetection.png";

            

Reported by PMD.

                  long = int

import numpy as np
import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():

            

Reported by Pylint.

              import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500
    kalman = cv.KalmanFilter(2, 1, 0)

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

if PY3:
    long = int

import numpy as np
import cv2 as cv

from math import cos, sin, sqrt

            

Reported by Pylint.

              if PY3:
    long = int

import numpy as np
import cv2 as cv

from math import cos, sin, sqrt
import numpy as np


            

Reported by Pylint.

                  long = int

import numpy as np
import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500

            

Reported by Pylint.

              import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500
    kalman = cv.KalmanFilter(2, 1, 0)

            

Reported by Pylint.

              import cv2 as cv

from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500
    kalman = cv.KalmanFilter(2, 1, 0)

            

Reported by Pylint.

              from math import cos, sin, sqrt
import numpy as np

def main():
    img_height = 500
    img_width = 500
    kalman = cv.KalmanFilter(2, 1, 0)

    code = long(-1)

            

Reported by Pylint.

              
public class SIMPLEBLOBFeatureDetectorTest extends OpenCVTestCase {

    Feature2D detector;
    int matSize;
    KeyPoint[] truth;

    private Mat getMaskImg() {
        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));

            

Reported by PMD.

              public class SIMPLEBLOBFeatureDetectorTest extends OpenCVTestCase {

    Feature2D detector;
    int matSize;
    KeyPoint[] truth;

    private Mat getMaskImg() {
        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
        Mat right = mask.submat(0, matSize, matSize / 2, matSize);

            

Reported by PMD.

              
    Feature2D detector;
    int matSize;
    KeyPoint[] truth;

    private Mat getMaskImg() {
        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
        Mat right = mask.submat(0, matSize, matSize / 2, matSize);
        right.setTo(new Scalar(0));

            

Reported by PMD.

                      return img;
    }

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        detector = SimpleBlobDetector.create();
        matSize = 200;
        truth = new KeyPoint[] {

            

Reported by PMD.

                          };
    }

    public void testCreate() {
        assertNotNull(detector);
    }

    public void testDetectListOfMatListOfListOfKeyPoint() {
        fail("Not yet implemented");

            

Reported by PMD.

                      assertNotNull(detector);
    }

    public void testDetectListOfMatListOfListOfKeyPoint() {
        fail("Not yet implemented");
    }

    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
        fail("Not yet implemented");

            

Reported by PMD.

                      fail("Not yet implemented");
    }

    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
        fail("Not yet implemented");
    }

    public void testDetectMatListOfKeyPoint() {
        Mat img = getTestImg();

            

Reported by PMD.

                      fail("Not yet implemented");
    }

    public void testDetectMatListOfKeyPoint() {
        Mat img = getTestImg();
        MatOfKeyPoint keypoints = new MatOfKeyPoint();

        detector.detect(img, keypoints);


            

Reported by PMD.

                      assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
    }

    public void testDetectMatListOfKeyPointMat() {
        Mat img = getTestImg();
        Mat mask = getMaskImg();
        MatOfKeyPoint keypoints = new MatOfKeyPoint();

        detector.detect(img, keypoints, mask);

            

Reported by PMD.

                      assertListKeyPointEquals(Arrays.asList(truth[1]), keypoints.toList(), EPS);
    }

    public void testEmpty() {
//        assertFalse(detector.empty());
        fail("Not yet implemented");
    }

    public void testRead() {

            

Reported by PMD.

              from __future__ import print_function
import cv2 as cv
import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

            

Reported by Pylint.

              import cv2 as cv
import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3


            

Reported by Pylint.

              import argparse

max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

def CannyThreshold(val):

            

Reported by Pylint.

              
max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

def CannyThreshold(val):
    low_threshold = val

            

Reported by Pylint.

              max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

def CannyThreshold(val):
    low_threshold = val
    img_blur = cv.blur(src_gray, (3,3))

            

Reported by Pylint.

              window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3

def CannyThreshold(val):
    low_threshold = val
    img_blur = cv.blur(src_gray, (3,3))
    detected_edges = cv.Canny(img_blur, low_threshold, low_threshold*ratio, kernel_size)

            

Reported by Pylint.

              ratio = 3
kernel_size = 3

def CannyThreshold(val):
    low_threshold = val
    img_blur = cv.blur(src_gray, (3,3))
    detected_edges = cv.Canny(img_blur, low_threshold, low_threshold*ratio, kernel_size)
    mask = detected_edges != 0
    dst = src * (mask[:,:,None].astype(src.dtype))

            

Reported by Pylint.

              # To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/tree/caffe/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
    parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
    parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)

            

Reported by Pylint.

                  parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
    parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)

    args = parser.parse_args()
    return args

if __name__ == '__main__':
    W_in = 224
    H_in = 224

            

Reported by Pylint.

              # Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/caffe/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/tree/caffe/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')

            

Reported by Pylint.

              # Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/caffe/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/tree/caffe/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')

            

Reported by Pylint.

              # Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/caffe/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/tree/caffe/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')

            

Reported by Pylint.

              # To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/caffe/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/tree/caffe/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
    parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')

            

Reported by Pylint.

              import argparse
import cv2 as cv

def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
    parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
    parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)
    parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
    parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)

            

Reported by Pylint.

              
def parse_args():
    parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
    parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
    parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)
    parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
    parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)

    args = parser.parse_args()

            

Reported by Pylint.

                  parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
    parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
    parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)
    parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
    parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)

    args = parser.parse_args()
    return args


            

Reported by Pylint.

                  return args

if __name__ == '__main__':
    W_in = 224
    H_in = 224
    imshowSize = (640, 480)

    args = parse_args()


            

Reported by Pylint.

              from __future__ import print_function

import numpy as np
import cv2 as cv
import os
import datetime

from tests_common import NewOpenCVTests


            

Reported by Pylint.

              import numpy as np
import cv2 as cv
import os
import datetime

from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):
    def test_get_cache_dir(self):

            

Reported by Pylint.

              # Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv
import os
import datetime

from tests_common import NewOpenCVTests

            

Reported by Pylint.

              
import numpy as np
import cv2 as cv
import os
import datetime

from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):

            

Reported by Pylint.

              import numpy as np
import cv2 as cv
import os
import datetime

from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):
    def test_get_cache_dir(self):

            

Reported by Pylint.

              
from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):
    def test_get_cache_dir(self):
        #New binding
        path = cv.utils.fs.getCacheDirectoryForDownloads()
        self.assertTrue(os.path.exists(path))
        self.assertTrue(os.path.isdir(path))

            

Reported by Pylint.

              
from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):
    def test_get_cache_dir(self):
        #New binding
        path = cv.utils.fs.getCacheDirectoryForDownloads()
        self.assertTrue(os.path.exists(path))
        self.assertTrue(os.path.isdir(path))

            

Reported by Pylint.

              from tests_common import NewOpenCVTests

class get_cache_dir_test(NewOpenCVTests):
    def test_get_cache_dir(self):
        #New binding
        path = cv.utils.fs.getCacheDirectoryForDownloads()
        self.assertTrue(os.path.exists(path))
        self.assertTrue(os.path.isdir(path))


            

Reported by Pylint.

                      self.assertTrue(os.path.exists(path))
        self.assertTrue(os.path.isdir(path))

    def get_cache_dir_imread_interop(self, ext):
        path = cv.utils.fs.getCacheDirectoryForDownloads()
        gold_image = np.ones((16, 16, 3), np.uint8)
        read_from_file = np.zeros((16, 16, 3), np.uint8)
        test_file_name = os.path.join(path, "test." + ext)
        try:

            

Reported by Pylint.

              
        self.assertEqual(cv.norm(gold_image, read_from_file), 0)

    def test_get_cache_dir_imread_interop_png(self):
        self.get_cache_dir_imread_interop("png")

    def test_get_cache_dir_imread_interop_jpeg(self):
        self.get_cache_dir_imread_interop("jpg")


            

Reported by Pylint.

              
    public MatOfKeyPoint(KeyPoint...a) {
        super();
        fromArray(a);
    }

    public void alloc(int elemNumber) {
        if(elemNumber>0)
            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));

            

Reported by PMD.

              import java.util.Arrays;
import java.util.List;

import org.opencv.core.KeyPoint;

public class MatOfKeyPoint extends Mat {
    // 32FC7
    private static final int _depth = CvType.CV_32F;
    private static final int _channels = 7;

            

Reported by PMD.

                          return;
        int num = a.length;
        alloc(num);
        float buff[] = new float[num * _channels];
        for(int i=0; i<num; i++) {
            KeyPoint kp = a[i];
            buff[_channels*i+0] = (float) kp.pt.x;
            buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;

            

Reported by PMD.

                      float buff[] = new float[num * _channels];
        for(int i=0; i<num; i++) {
            KeyPoint kp = a[i];
            buff[_channels*i+0] = (float) kp.pt.x;
            buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;

            

Reported by PMD.

                      for(int i=0; i<num; i++) {
            KeyPoint kp = a[i];
            buff[_channels*i+0] = (float) kp.pt.x;
            buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;

            

Reported by PMD.

                          KeyPoint kp = a[i];
            buff[_channels*i+0] = (float) kp.pt.x;
            buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;
        }

            

Reported by PMD.

                          buff[_channels*i+0] = (float) kp.pt.x;
            buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;
        }
        put(0, 0, buff); //TODO: check ret val!

            

Reported by PMD.

                          buff[_channels*i+1] = (float) kp.pt.y;
            buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;
        }
        put(0, 0, buff); //TODO: check ret val!
    }

            

Reported by PMD.

                          buff[_channels*i+2] = kp.size;
            buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;
        }
        put(0, 0, buff); //TODO: check ret val!
    }


            

Reported by PMD.

                          buff[_channels*i+3] = kp.angle;
            buff[_channels*i+4] = kp.response;
            buff[_channels*i+5] = kp.octave;
            buff[_channels*i+6] = kp.class_id;
        }
        put(0, 0, buff); //TODO: check ret val!
    }

    public KeyPoint[] toArray() {

            

Reported by PMD.

                      Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
        Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
        if (img1.empty() || img2.empty()) {
            System.err.println("Cannot read images!");
            System.exit(0);
        }

        //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
        double hessianThreshold = 400;

            

Reported by PMD.

                      String filename2 = args.length > 1 ? args[1] : "../data/box_in_scene.png";
        Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
        Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
        if (img1.empty() || img2.empty()) {
            System.err.println("Cannot read images!");
            System.exit(0);
        }

        //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors

            

Reported by PMD.

                      String filename2 = args.length > 1 ? args[1] : "../data/box_in_scene.png";
        Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
        Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
        if (img1.empty() || img2.empty()) {
            System.err.println("Cannot read images!");
            System.exit(0);
        }

        //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors

            

Reported by PMD.

                      Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
        if (img1.empty() || img2.empty()) {
            System.err.println("Cannot read images!");
            System.exit(0);
        }

        //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
        double hessianThreshold = 400;
        int nOctaves = 4, nOctaveLayers = 3;

            

Reported by PMD.

                      // Since SURF is a floating-point descriptor NORM_L2 is used
        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
        MatOfDMatch matches = new MatOfDMatch();
        matcher.match(descriptors1, descriptors2, matches);

        //-- Draw matches
        Mat imgMatches = new Mat();
        Features2d.drawMatches(img1, keypoints1, img2, keypoints2, matches, imgMatches);


            

Reported by PMD.

                      HighGui.imshow("Matches", imgMatches);
        HighGui.waitKey(0);

        System.exit(0);
    }
}

public class SURFMatchingDemo {
    public static void main(String[] args) {

            

Reported by PMD.

                  }
}

public class SURFMatchingDemo {
    public static void main(String[] args) {
        // Load the native OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        new SURFMatching().run(args);

            

Reported by PMD.

              import org.opencv.xfeatures2d.SURF;

class SURFMatching {
    public void run(String[] args) {
        String filename1 = args.length > 1 ? args[0] : "../data/box.png";
        String filename2 = args.length > 1 ? args[1] : "../data/box_in_scene.png";
        Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
        Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
        if (img1.empty() || img2.empty()) {

            

Reported by PMD.

              
        //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
        double hessianThreshold = 400;
        int nOctaves = 4, nOctaveLayers = 3;
        boolean extended = false, upright = false;
        SURF detector = SURF.create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
        MatOfKeyPoint keypoints1 = new MatOfKeyPoint(), keypoints2 = new MatOfKeyPoint();
        Mat descriptors1 = new Mat(), descriptors2 = new Mat();
        detector.detectAndCompute(img1, new Mat(), keypoints1, descriptors1);

            

Reported by PMD.

                      //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
        double hessianThreshold = 400;
        int nOctaves = 4, nOctaveLayers = 3;
        boolean extended = false, upright = false;
        SURF detector = SURF.create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
        MatOfKeyPoint keypoints1 = new MatOfKeyPoint(), keypoints2 = new MatOfKeyPoint();
        Mat descriptors1 = new Mat(), descriptors2 = new Mat();
        detector.detectAndCompute(img1, new Mat(), keypoints1, descriptors1);
        detector.detectAndCompute(img2, new Mat(), keypoints2, descriptors2);

            

Reported by PMD.

              """
import sys
import math
import cv2 as cv
import numpy as np


def main(argv):
    ## [load]

            

Reported by Pylint.

              import numpy as np


def main(argv):
    ## [load]
    default_file = 'sudoku.png'
    filename = argv[0] if len(argv) > 0 else default_file

    # Loads an image

            

Reported by Pylint.

              import numpy as np


def main(argv):
    ## [load]
    default_file = 'sudoku.png'
    filename = argv[0] if len(argv) > 0 else default_file

    # Loads an image

            

Reported by Pylint.

              
    # Copy edges to the images that will display the results in BGR
    cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
    cdstP = np.copy(cdst)

    ## [hough_lines]
    #  Standard Hough Line Transform
    lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
    ## [hough_lines]

            

Reported by Pylint.

                  ## [draw_lines]
    # Draw the lines
    if lines is not None:
        for i in range(0, len(lines)):
            rho = lines[i][0][0]
            theta = lines[i][0][1]
            a = math.cos(theta)
            b = math.sin(theta)
            x0 = a * rho

            

Reported by Pylint.

                      for i in range(0, len(lines)):
            rho = lines[i][0][0]
            theta = lines[i][0][1]
            a = math.cos(theta)
            b = math.sin(theta)
            x0 = a * rho
            y0 = b * rho
            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))

            

Reported by Pylint.

                          rho = lines[i][0][0]
            theta = lines[i][0][1]
            a = math.cos(theta)
            b = math.sin(theta)
            x0 = a * rho
            y0 = b * rho
            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))


            

Reported by Pylint.

                          theta = lines[i][0][1]
            a = math.cos(theta)
            b = math.sin(theta)
            x0 = a * rho
            y0 = b * rho
            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))

            cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)

            

Reported by Pylint.

                          a = math.cos(theta)
            b = math.sin(theta)
            x0 = a * rho
            y0 = b * rho
            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))

            cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
    ## [draw_lines]

            

Reported by Pylint.

              
    ## [hough_lines_p]
    # Probabilistic Line Transform
    linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
    ## [hough_lines_p]
    ## [draw_lines_p]
    # Draw the lines
    if linesP is not None:
        for i in range(0, len(linesP)):

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

## [load_image]

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

## [load_image]

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

## [load_image]

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

## [load_image]

            

Reported by Pylint.

              import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

## [load_image]
# Load the image

            

Reported by Pylint.

              
## [load_image]
# Load the image
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
    Sample code showing how to segment overlapping objects using Laplacian filtering, \
    in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()


            

Reported by Pylint.

              src = cv.imread(cv.samples.findFile(args.input))
if src is None:
    print('Could not open or find the image:', args.input)
    exit(0)

# Show source image
cv.imshow('Source Image', src)
## [load_image]


            

Reported by Pylint.

              # Generate random colors
colors = []
for contour in contours:
    colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))

# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)

# Fill labeled objects with random colors

            

Reported by Bandit.

              # Generate random colors
colors = []
for contour in contours:
    colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))

# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)

# Fill labeled objects with random colors

            

Reported by Bandit.

              # Generate random colors
colors = []
for contour in contours:
    colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))

# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)

# Fill labeled objects with random colors

            

Reported by Bandit.