public final static int WINDOW_NORMAL = 0;
    public final static int WINDOW_AUTOSIZE = 1;

    public String name;
    public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;

            

Reported by PMD.

                  public final static int WINDOW_AUTOSIZE = 1;

    public String name;
    public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;

            

Reported by PMD.

                  public final static int WINDOW_AUTOSIZE = 1;

    public String name;
    public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;

            

Reported by PMD.

              
    public String name;
    public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;

            

Reported by PMD.

                  public String name;
    public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;

            

Reported by PMD.

                  public Mat img = null;
    public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;
    public int x = -1;

            

Reported by PMD.

                  public Boolean alreadyUsed = false;
    public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;
    public int x = -1;
    public int y = -1;

            

Reported by PMD.

                  public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;
    public int x = -1;
    public int y = -1;
    public int width = -1;

            

Reported by PMD.

                  public Boolean imgToBeResized = false;
    public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;
    public int x = -1;
    public int y = -1;
    public int width = -1;

            

Reported by PMD.

                  public Boolean windowToBeResized = false;
    public Boolean positionToBeChanged = false;
    public JFrame frame = null;
    public JLabel lbl = null;
    public int flag;
    public int x = -1;
    public int y = -1;
    public int width = -1;
    public int height = -1;

            

Reported by PMD.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')


            

Reported by Pylint.

              
parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py

            

Reported by Pylint.

                                               formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):

            

Reported by Pylint.

              parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):
# -     out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# -     zero_pads = torch.Tensor(out.size(0), planes - out.size(1),

            

Reported by Pylint.

              
# To ONNX export use torch.onnx.export(model, inputs, model_name)

def get_class_names(path):
    class_names = []
    with open(path) as f:
        for row in f:
            class_names.append(row[:-1])
    return class_names

            

Reported by Pylint.

              
def get_class_names(path):
    class_names = []
    with open(path) as f:
        for row in f:
            class_names.append(row[:-1])
    return class_names

def classify_video(video_path, net_path):

            

Reported by Pylint.

              import cv2 as cv
import argparse

parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)

            

Reported by Pylint.

              
#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):
        self.xstart = 0
        self.xend = 0
        self.ystart = 0
        self.yend = 0


            

Reported by Pylint.

              
#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):
        self.xstart = 0
        self.xend = 0
        self.ystart = 0
        self.yend = 0


            

Reported by Pylint.

              import cv2 as cv
import argparse

parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)

            

Reported by Pylint.

              import cv2 as cv
import argparse

parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)

            

Reported by Pylint.

              
parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to hed_pretrained_bsds.caffemodel', required=True)
parser.add_argument('--width', help='Resize input image to a specific width', default=500, type=int)

            

Reported by Pylint.

              parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to hed_pretrained_bsds.caffemodel', required=True)
parser.add_argument('--width', help='Resize input image to a specific width', default=500, type=int)
parser.add_argument('--height', help='Resize input image to a specific height', default=500, type=int)

            

Reported by Pylint.

              parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to hed_pretrained_bsds.caffemodel', required=True)
parser.add_argument('--width', help='Resize input image to a specific width', default=500, type=int)
parser.add_argument('--height', help='Resize input image to a specific height', default=500, type=int)
args = parser.parse_args()

#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):

            

Reported by Pylint.

              args = parser.parse_args()

#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):
        self.xstart = 0
        self.xend = 0
        self.ystart = 0
        self.yend = 0

            

Reported by Pylint.

              args = parser.parse_args()

#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):
        self.xstart = 0
        self.xend = 0
        self.ystart = 0
        self.yend = 0

            

Reported by Pylint.

              
        // Check if image is loaded fine
        if( src.empty() ) {
            System.out.println("Error opening image!");
            System.out.println("Program Arguments: [image_name -- default "
                    + default_file +"] \n");
            System.exit(-1);
        }
        //! [load]

            

Reported by PMD.

                      // Check if image is loaded fine
        if( src.empty() ) {
            System.out.println("Error opening image!");
            System.out.println("Program Arguments: [image_name -- default "
                    + default_file +"] \n");
            System.exit(-1);
        }
        //! [load]


            

Reported by PMD.

                      Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);

        // Check if image is loaded fine
        if( src.empty() ) {
            System.out.println("Error opening image!");
            System.out.println("Program Arguments: [image_name -- default "
                    + default_file +"] \n");
            System.exit(-1);
        }

            

Reported by PMD.

                          System.out.println("Error opening image!");
            System.out.println("Program Arguments: [image_name -- default "
                    + default_file +"] \n");
            System.exit(-1);
        }
        //! [load]

        //! [edge_detection]
        // Edge detection

            

Reported by PMD.

              
            double a = Math.cos(theta), b = Math.sin(theta);
            double x0 = a*rho, y0 = b*rho;
            Point pt1 = new Point(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));
            Point pt2 = new Point(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));
            Imgproc.line(cdst, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines]


            

Reported by PMD.

                          double a = Math.cos(theta), b = Math.sin(theta);
            double x0 = a*rho, y0 = b*rho;
            Point pt1 = new Point(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));
            Point pt2 = new Point(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));
            Imgproc.line(cdst, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines]

        //! [hough_lines_p]

            

Reported by PMD.

                          double x0 = a*rho, y0 = b*rho;
            Point pt1 = new Point(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));
            Point pt2 = new Point(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));
            Imgproc.line(cdst, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines]

        //! [hough_lines_p]
        // Probabilistic Line Transform

            

Reported by PMD.

                      // Draw the lines
        for (int x = 0; x < linesP.rows(); x++) {
            double[] l = linesP.get(x, 0);
            Imgproc.line(cdstP, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines_p]

        //! [imshow]
        // Show results

            

Reported by PMD.

                      // Draw the lines
        for (int x = 0; x < linesP.rows(); x++) {
            double[] l = linesP.get(x, 0);
            Imgproc.line(cdstP, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines_p]

        //! [imshow]
        // Show results

            

Reported by PMD.

                      // Draw the lines
        for (int x = 0; x < linesP.rows(); x++) {
            double[] l = linesP.get(x, 0);
            Imgproc.line(cdstP, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
        }
        //! [draw_lines_p]

        //! [imshow]
        // Show results

            

Reported by PMD.

              
import numpy as np
from numpy import random
import cv2 as cv

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    for _ in xrange(cluster_n):

            

Reported by Pylint.

              #!/usr/bin/env python

# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3

if PY3:
    xrange = range

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

if PY3:
    xrange = range

import numpy as np
from numpy import random
import cv2 as cv


            

Reported by Pylint.

              if PY3:
    xrange = range

import numpy as np
from numpy import random
import cv2 as cv

def make_gaussians(cluster_n, img_size):
    points = []

            

Reported by Pylint.

                  xrange = range

import numpy as np
from numpy import random
import cv2 as cv

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []

            

Reported by Pylint.

              
import numpy as np
from numpy import random
import cv2 as cv

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    for _ in xrange(cluster_n):

            

Reported by Pylint.

              from numpy import random
import cv2 as cv

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    for _ in xrange(cluster_n):
        mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1

            

Reported by Pylint.

                  ref_distrs = []
    for _ in xrange(cluster_n):
        mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1
        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
        n = 100 + random.randint(900)
        pts = random.multivariate_normal(mean, cov, n)
        points.append( pts )
        ref_distrs.append( (mean, cov) )

            

Reported by Pylint.

                      mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1
        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
        n = 100 + random.randint(900)
        pts = random.multivariate_normal(mean, cov, n)
        points.append( pts )
        ref_distrs.append( (mean, cov) )
    points = np.float32( np.vstack(points) )
    return points, ref_distrs

            

Reported by Pylint.

                  points = np.float32( np.vstack(points) )
    return points, ref_distrs

from tests_common import NewOpenCVTests

class gaussian_mix_test(NewOpenCVTests):

    def test_gaussian_mix(self):


            

Reported by Pylint.

                      //! [capture]
        VideoCapture capture = new VideoCapture(input);
        if (!capture.isOpened()) {
            System.err.println("Unable to open: " + input);
            System.exit(0);
        }
        //! [capture]

        Mat frame = new Mat(), fgMask = new Mat();

            

Reported by PMD.

              class BackgroundSubtraction {
    public void run(String[] args) {
        String input = args.length > 0 ? args[0] : "../data/vtest.avi";
        boolean useMOG2 = args.length > 1 ? args[1] == "MOG2" : true;

        //! [create]
        BackgroundSubtractor backSub;
        if (useMOG2) {
            backSub = Video.createBackgroundSubtractorMOG2();

            

Reported by PMD.

              class BackgroundSubtraction {
    public void run(String[] args) {
        String input = args.length > 0 ? args[0] : "../data/vtest.avi";
        boolean useMOG2 = args.length > 1 ? args[1] == "MOG2" : true;

        //! [create]
        BackgroundSubtractor backSub;
        if (useMOG2) {
            backSub = Video.createBackgroundSubtractorMOG2();

            

Reported by PMD.

              class BackgroundSubtraction {
    public void run(String[] args) {
        String input = args.length > 0 ? args[0] : "../data/vtest.avi";
        boolean useMOG2 = args.length > 1 ? args[1] == "MOG2" : true;

        //! [create]
        BackgroundSubtractor backSub;
        if (useMOG2) {
            backSub = Video.createBackgroundSubtractorMOG2();

            

Reported by PMD.

                      VideoCapture capture = new VideoCapture(input);
        if (!capture.isOpened()) {
            System.err.println("Unable to open: " + input);
            System.exit(0);
        }
        //! [capture]

        Mat frame = new Mat(), fgMask = new Mat();
        while (true) {

            

Reported by PMD.

              
            //! [display_frame_number]
            // get the frame number and write it on the current frame
            Imgproc.rectangle(frame, new Point(10, 2), new Point(100, 20), new Scalar(255, 255, 255), -1);
            String frameNumberString = String.format("%d", (int)capture.get(Videoio.CAP_PROP_POS_FRAMES));
            Imgproc.putText(frame, frameNumberString, new Point(15, 15), Core.FONT_HERSHEY_SIMPLEX, 0.5,
                    new Scalar(0, 0, 0));
            //! [display_frame_number]


            

Reported by PMD.

              
            //! [display_frame_number]
            // get the frame number and write it on the current frame
            Imgproc.rectangle(frame, new Point(10, 2), new Point(100, 20), new Scalar(255, 255, 255), -1);
            String frameNumberString = String.format("%d", (int)capture.get(Videoio.CAP_PROP_POS_FRAMES));
            Imgproc.putText(frame, frameNumberString, new Point(15, 15), Core.FONT_HERSHEY_SIMPLEX, 0.5,
                    new Scalar(0, 0, 0));
            //! [display_frame_number]


            

Reported by PMD.

              
            //! [display_frame_number]
            // get the frame number and write it on the current frame
            Imgproc.rectangle(frame, new Point(10, 2), new Point(100, 20), new Scalar(255, 255, 255), -1);
            String frameNumberString = String.format("%d", (int)capture.get(Videoio.CAP_PROP_POS_FRAMES));
            Imgproc.putText(frame, frameNumberString, new Point(15, 15), Core.FONT_HERSHEY_SIMPLEX, 0.5,
                    new Scalar(0, 0, 0));
            //! [display_frame_number]


            

Reported by PMD.

                          // get the frame number and write it on the current frame
            Imgproc.rectangle(frame, new Point(10, 2), new Point(100, 20), new Scalar(255, 255, 255), -1);
            String frameNumberString = String.format("%d", (int)capture.get(Videoio.CAP_PROP_POS_FRAMES));
            Imgproc.putText(frame, frameNumberString, new Point(15, 15), Core.FONT_HERSHEY_SIMPLEX, 0.5,
                    new Scalar(0, 0, 0));
            //! [display_frame_number]

            //! [show]
            // show the current frame and the fg masks

            

Reported by PMD.

                          Imgproc.rectangle(frame, new Point(10, 2), new Point(100, 20), new Scalar(255, 255, 255), -1);
            String frameNumberString = String.format("%d", (int)capture.get(Videoio.CAP_PROP_POS_FRAMES));
            Imgproc.putText(frame, frameNumberString, new Point(15, 15), Core.FONT_HERSHEY_SIMPLEX, 0.5,
                    new Scalar(0, 0, 0));
            //! [display_frame_number]

            //! [show]
            // show the current frame and the fg masks
            HighGui.imshow("Frame", frame);

            

Reported by PMD.

                      matImgSrc2 = Imgcodecs.imread(imagePath2);
        //! [load]
        if (matImgSrc1.empty()) {
            System.out.println("Empty image: " + imagePath1);
            System.exit(0);
        }
        if (matImgSrc2.empty()) {
            System.out.println("Empty image: " + imagePath2);
            System.exit(0);

            

Reported by PMD.

                          System.exit(0);
        }
        if (matImgSrc2.empty()) {
            System.out.println("Empty image: " + imagePath2);
            System.exit(0);
        }

        //! [window]
        // Create and set up the window.

            

Reported by PMD.

              
public class AddingImagesTrackbar {
    private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

            

Reported by PMD.

              
public class AddingImagesTrackbar {
    private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

            

Reported by PMD.

              public class AddingImagesTrackbar {
    private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;


            

Reported by PMD.

              public class AddingImagesTrackbar {
    private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;


            

Reported by PMD.

                  private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public AddingImagesTrackbar(String[] args) {

            

Reported by PMD.

                  private static final int ALPHA_SLIDER_MAX = 100;
    private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public AddingImagesTrackbar(String[] args) {

            

Reported by PMD.

                  private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public AddingImagesTrackbar(String[] args) {
        //! [load]

            

Reported by PMD.

                  private int alphaVal = 0;
    private Mat matImgSrc1;
    private Mat matImgSrc2;
    private Mat matImgDst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public AddingImagesTrackbar(String[] args) {
        //! [load]

            

Reported by PMD.

                      String filename = args.length > 0 ? args[0] : "../data/lena.jpg";
        Mat src = Imgcodecs.imread(filename);
        if (src.empty()) {
            System.err.println("Cannot read image: " + filename);
            System.exit(0);
        }
        //! [Load image]

        //! [Separate the image in 3 places ( B, G and R )]

            

Reported by PMD.

                      //! [Load image]
        String filename = args.length > 0 ? args[0] : "../data/lena.jpg";
        Mat src = Imgcodecs.imread(filename);
        if (src.empty()) {
            System.err.println("Cannot read image: " + filename);
            System.exit(0);
        }
        //! [Load image]


            

Reported by PMD.

                      Mat src = Imgcodecs.imread(filename);
        if (src.empty()) {
            System.err.println("Cannot read image: " + filename);
            System.exit(0);
        }
        //! [Load image]

        //! [Separate the image in 3 places ( B, G and R )]
        List<Mat> bgrPlanes = new ArrayList<>();

            

Reported by PMD.

                      rHist.get(0, 0, rHistData);

        for( int i = 1; i < histSize; i++ ) {
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);

            

Reported by PMD.

              
        for( int i = 1; i < histSize; i++ ) {
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }

            

Reported by PMD.

              
        for( int i = 1; i < histSize; i++ ) {
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }

            

Reported by PMD.

                      for( int i = 1; i < histSize; i++ ) {
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }
        //! [Draw for each channel]

            

Reported by PMD.

                          Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }
        //! [Draw for each channel]


            

Reported by PMD.

                          Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(bHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }
        //! [Draw for each channel]


            

Reported by PMD.

                                  new Point(binW * (i), histH - Math.round(bHistData[i])), new Scalar(255, 0, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(gHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(gHistData[i])), new Scalar(0, 255, 0), 2);
            Imgproc.line(histImage, new Point(binW * (i - 1), histH - Math.round(rHistData[i - 1])),
                    new Point(binW * (i), histH - Math.round(rHistData[i])), new Scalar(0, 0, 255), 2);
        }
        //! [Draw for each channel]

        //! [Display]

            

Reported by PMD.

                      // Load an image
        src = Imgcodecs.imread(imagePath);
        if (src.empty()) {
            System.out.println("Empty image: " + imagePath);
            System.exit(0);
        }
        // Convert the image to Gray
        Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);
        //! [load]

            

Reported by PMD.

                          + "1: Binary Inverted <br> 2: Truncate <br> "
            + "3: To Zero <br> 4: To Zero Inverted</body></html>";
    private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;

            

Reported by PMD.

                          + "1: Binary Inverted <br> 2: Truncate <br> "
            + "3: To Zero <br> 4: To Zero Inverted</body></html>";
    private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;

            

Reported by PMD.

                          + "3: To Zero <br> 4: To Zero Inverted</body></html>";
    private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

            

Reported by PMD.

                  private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;


            

Reported by PMD.

                  private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;


            

Reported by PMD.

                  private static final String TRACKBAR_VALUE = "Value";
    private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;


            

Reported by PMD.

                  private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public Threshold(String[] args) {

            

Reported by PMD.

                  private int thresholdValue = 0;
    private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public Threshold(String[] args) {

            

Reported by PMD.

                  private int thresholdType = 3;
    private Mat src;
    private Mat srcGray = new Mat();
    private Mat dst = new Mat();
    private JFrame frame;
    private JLabel imgLabel;

    public Threshold(String[] args) {
        //! [load]

            

Reported by PMD.

              from __future__ import print_function

import numpy as np
import cv2 as cv
from numpy import random
import sys
PY3 = sys.version_info[0] == 3
if PY3:
    xrange = range

            

Reported by Pylint.

              import numpy as np
import cv2 as cv
from numpy import random
import sys
PY3 = sys.version_info[0] == 3
if PY3:
    xrange = range

from tests_common import NewOpenCVTests

            

Reported by Pylint.

              import sys
PY3 = sys.version_info[0] == 3
if PY3:
    xrange = range

from tests_common import NewOpenCVTests

def make_gaussians(cluster_n, img_size):
    points = []

            

Reported by Pylint.

              if PY3:
    xrange = range

from tests_common import NewOpenCVTests

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    sizes = []

            

Reported by Pylint.

              
from tests_common import NewOpenCVTests

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    sizes = []
    for _ in xrange(cluster_n):
        mean = (0.1 + 0.8*random.rand(2)) * img_size

            

Reported by Pylint.

                  sizes = []
    for _ in xrange(cluster_n):
        mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1
        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
        n = 100 + random.randint(900)
        pts = random.multivariate_normal(mean, cov, n)
        points.append( pts )
        ref_distrs.append( (mean, cov) )

            

Reported by Pylint.

                      mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1
        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
        n = 100 + random.randint(900)
        pts = random.multivariate_normal(mean, cov, n)
        points.append( pts )
        ref_distrs.append( (mean, cov) )
        sizes.append(n)
    points = np.float32( np.vstack(points) )

            

Reported by Pylint.

                  points = np.float32( np.vstack(points) )
    return points, ref_distrs, sizes

def getMainLabelConfidence(labels, nLabels):

    n = len(labels)
    labelsDict = dict.fromkeys(range(nLabels), 0)
    labelsConfDict = dict.fromkeys(range(nLabels))


            

Reported by Pylint.

                  points = np.float32( np.vstack(points) )
    return points, ref_distrs, sizes

def getMainLabelConfidence(labels, nLabels):

    n = len(labels)
    labelsDict = dict.fromkeys(range(nLabels), 0)
    labelsConfDict = dict.fromkeys(range(nLabels))


            

Reported by Pylint.

                  points = np.float32( np.vstack(points) )
    return points, ref_distrs, sizes

def getMainLabelConfidence(labels, nLabels):

    n = len(labels)
    labelsDict = dict.fromkeys(range(nLabels), 0)
    labelsConfDict = dict.fromkeys(range(nLabels))


            

Reported by Pylint.