def readFunctionFilter(fns, fileName):
    try:
        f = open(fileName, "r")
    except:
        print("ERROR: Can't open filter file: %s" % fileName)
        return 0

    count = 0
    while f:

            

Reported by Pylint.

                      decl_args = []
        for (i, t) in enumerate(fn['params']):
            decl_args.append(getTypeWithParam(t, 'p%d' % (i+1)))
        decl_args_str = '(' + (', '.join(decl_args)) + ')'
        print(commentStr + ('CL_RUNTIME_EXPORT %s%s (%s *%s_pfn)(%s) = %s;' % \
            ((' '.join(fn['modifiers'] + ' ') if len(fn['modifiers']) > 0 else ''),
             ' '.join(fn['ret']), ' '.join(fn['calling']), fn['name'], ', '.join(fn['params']), \
             fn['name'])))


            

Reported by Pylint.

                  print('// generated by %s' % os.path.basename(sys.argv[0]))
    print('static const struct DynamicFnEntry* %s[] = {' % (name))
    for fn in fns:
        commentStr = '' if 'enabled' in fn else '//'
        if 'enabled' in fn:
            print('    &%s_definition,' % (fn['name']))
        else:
            print('    NULL/*&%s_definition*/,' % (fn['name']))
        first = False

            

Reported by Pylint.

                          print('    &%s_definition,' % (fn['name']))
        else:
            print('    NULL/*&%s_definition*/,' % (fn['name']))
        first = False
    print('};')

@outputToString
def generateEnums(fns, prefix='OPENCL_FN'):
    print('// generated by %s' % os.path.basename(sys.argv[0]))

            

Reported by Pylint.

              from __future__ import print_function
import sys, os, re

#
# Parser helpers
#

def remove_comments(s):
    def replacer(match):

            

Reported by Pylint.

              from __future__ import print_function
import sys, os, re

#
# Parser helpers
#

def remove_comments(s):
    def replacer(match):

            

Reported by Pylint.

              # Parser helpers
#

def remove_comments(s):
    def replacer(match):
        s = match.group(0)
        if s.startswith('/'):
            return ""
        else:

            

Reported by Pylint.

              # Parser helpers
#

def remove_comments(s):
    def replacer(match):
        s = match.group(0)
        if s.startswith('/'):
            return ""
        else:

            

Reported by Pylint.

              
def remove_comments(s):
    def replacer(match):
        s = match.group(0)
        if s.startswith('/'):
            return ""
        else:
            return s
    pattern = re.compile(

            

Reported by Pylint.

              def remove_comments(s):
    def replacer(match):
        s = match.group(0)
        if s.startswith('/'):
            return ""
        else:
            return s
    pattern = re.compile(
        r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"',

            

Reported by Pylint.

              import json
from fnmatch import fnmatch
from math import pi
import cv2 as cv
import argparse
import os
import sys
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

            

Reported by Pylint.

              import argparse
import os
import sys
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

parser = argparse.ArgumentParser(
        description='Evaluate OpenCV face detection algorithms '
                    'using COCO evaluation tool, http://cocodataset.org/#detections-eval')

            

Reported by Pylint.

              import os
import sys
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

parser = argparse.ArgumentParser(
        description='Evaluate OpenCV face detection algorithms '
                    'using COCO evaluation tool, http://cocodataset.org/#detections-eval')
parser.add_argument('--proto', help='Path to .prototxt of Caffe model or .pbtxt of TensorFlow graph')

            

Reported by Pylint.

              from math import pi
import cv2 as cv
import argparse
import os
import sys
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

parser = argparse.ArgumentParser(

            

Reported by Pylint.

              
def addImage(imagePath):
    assert('images' in  dataset)
    imageId = len(dataset['images'])
    dataset['images'].append({
        'id': int(imageId),
        'file_name': imagePath
    })
    return imageId

            

Reported by Pylint.

                  })
    return imageId

def addBBox(imageId, left, top, width, height):
    assert('annotations' in  dataset)
    dataset['annotations'].append({
        'id': len(dataset['annotations']),
        'image_id': int(imageId),
        'category_id': 0,  # Face

            

Reported by Pylint.

                      'area': float(width * height)
    })

def addDetection(detections, imageId, left, top, width, height, score):
    detections.append({
      'image_id': int(imageId),
      'category_id': 0,  # Face
      'bbox': [int(left), int(top), int(width), int(height)],
      'score': float(score)

            

Reported by Pylint.

                      'area': float(width * height)
    })

def addDetection(detections, imageId, left, top, width, height, score):
    detections.append({
      'image_id': int(imageId),
      'category_id': 0,  # Face
      'bbox': [int(left), int(top), int(width), int(height)],
      'score': float(score)

            

Reported by Pylint.

              def fddb_dataset(annotations, images):
    for d in os.listdir(annotations):
        if fnmatch(d, 'FDDB-fold-*-ellipseList.txt'):
            with open(os.path.join(annotations, d), 'rt') as f:
                lines = [line.rstrip('\n') for line in f]
                lineId = 0
                while lineId < len(lines):
                    # Image
                    imgPath = lines[lineId]

            

Reported by Pylint.

                                  # Image
                    imgPath = lines[lineId]
                    lineId += 1
                    imageId = addImage(os.path.join(images, imgPath) + '.jpg')

                    img = cv.imread(os.path.join(images, imgPath) + '.jpg')

                    # Faces
                    numFaces = int(lines[lineId])

            

Reported by Pylint.

                  xrange = range

import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

def rnd_warp(a):
    h, w = a.shape[:2]

            

Reported by Pylint.

                      self.H[...,1] *= -1

class App:
    def __init__(self, video_src, paused = False):
        self.cap = video.create_capture(video_src)
        _, self.frame = self.cap.read()
        cv.imshow('frame', self.frame)
        self.rect_sel = RectSelector('frame', self.onrect)
        self.trackers = []

            

Reported by Pylint.

              
if __name__ == '__main__':
    print (__doc__)
    import sys, getopt
    opts, args = getopt.getopt(sys.argv[1:], '', ['pause'])
    opts = dict(opts)
    try:
        video_src = args[0]
    except:

            

Reported by Pylint.

                  opts = dict(opts)
    try:
        video_src = args[0]
    except:
        video_src = '0'

    App(video_src, paused = '--pause' in opts).run()

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

if PY3:
    xrange = range

import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

            

Reported by Pylint.

              if PY3:
    xrange = range

import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

def rnd_warp(a):

            

Reported by Pylint.

                  xrange = range

import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

def rnd_warp(a):
    h, w = a.shape[:2]

            

Reported by Pylint.

              
import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

def rnd_warp(a):
    h, w = a.shape[:2]
    T = np.zeros((2, 3))

            

Reported by Pylint.

              import numpy as np
import cv2 as cv
from common import draw_str, RectSelector
import video

def rnd_warp(a):
    h, w = a.shape[:2]
    T = np.zeros((2, 3))
    coef = 0.2

            

Reported by Pylint.

              from common import draw_str, RectSelector
import video

def rnd_warp(a):
    h, w = a.shape[:2]
    T = np.zeros((2, 3))
    coef = 0.2
    ang = (np.random.rand()-0.5)*coef
    c, s = np.cos(ang), np.sin(ang)

            

Reported by Pylint.

                                          if isinstance(v, str) and not v.startswith('DT_'):
                                try:
                                    float(v)
                                except:
                                    isString = True

                            if isinstance(v, bool):
                                printed = 'true' if v else 'false'
                            elif v == 'true' or v == 'false':

            

Reported by Pylint.

                      import cv2 as cv

        cv.dnn.writeTextGraph(modelPath, outputPath)
    except:
        import tensorflow as tf
        from tensorflow.tools.graph_transforms import TransformGraph

        with tf.gfile.FastGFile(modelPath, 'rb') as f:
            graph_def = tf.GraphDef()

            

Reported by Pylint.

              def tokenize(s):
    tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

            

Reported by Pylint.

              def tokenize(s):
    tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

            

Reported by Pylint.

              def tokenize(s):
    tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

            

Reported by Pylint.

              def tokenize(s):
    tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

            

Reported by Bandit.

              def tokenize(s):
    tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

            

Reported by Pylint.

                  tokens = []
    token = ""
    isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue


            

Reported by Pylint.

                  isString = False
    isComment = False
    for symbol in s:
        isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
        if isComment:
            continue

        if symbol == ' ' or symbol == '\t' or symbol == '\r' or symbol == '\'' or \
           symbol == '\n' or symbol == ':' or symbol == '\"' or symbol == ';' or \

            

Reported by Pylint.

                      if isComment:
            continue

        if symbol == ' ' or symbol == '\t' or symbol == '\r' or symbol == '\'' or \
           symbol == '\n' or symbol == ':' or symbol == '\"' or symbol == ';' or \
           symbol == ',':

            if (symbol == '\"' or symbol == '\'') and isString:
                tokens.append(token)

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

              import argparse
import re
from math import sqrt
from tf_text_graph_common import *

class SSDAnchorGenerator:
    def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
                 reduce_boxes_in_lowest_layer, image_width, image_height):
        self.min_scale = min_scale

            

Reported by Pylint.

                      input_shape = graph_def.node[0].attr['shape']['shape'][0]['dim']
        input_shape[1]['size'] = image_height
        input_shape[2]['size'] = image_width
    except:
        print("Input shapes are undefined")
    # assert(graph_def.node[1].op == 'Conv2D')
    weights = graph_def.node[1].input[-1]
    for i in range(len(graph_def.node[1].input)):
        graph_def.node[1].input.pop()

            

Reported by Pylint.

              
    num_matched_layers = 0
    for node in graph_def.node:
        if re.match('BoxPredictor_\d/BoxEncodingPredictor/convolution', node.name) or \
           re.match('BoxPredictor_\d/BoxEncodingPredictor/Conv2D', node.name) or \
           re.match('WeightSharedConvolutionalBoxPredictor(_\d)*/BoxPredictor/Conv2D', node.name):
            node.addAttr('loc_pred_transposed', True)
            num_matched_layers += 1
    assert(num_matched_layers == num_layers)

            

Reported by Pylint.

                  num_matched_layers = 0
    for node in graph_def.node:
        if re.match('BoxPredictor_\d/BoxEncodingPredictor/convolution', node.name) or \
           re.match('BoxPredictor_\d/BoxEncodingPredictor/Conv2D', node.name) or \
           re.match('WeightSharedConvolutionalBoxPredictor(_\d)*/BoxPredictor/Conv2D', node.name):
            node.addAttr('loc_pred_transposed', True)
            num_matched_layers += 1
    assert(num_matched_layers == num_layers)


            

Reported by Pylint.

              #!/usr/bin/env python

import numpy as np
import cv2 as cv
import os
import sys
import unittest

from tests_common import NewOpenCVTests

            

Reported by Pylint.

              import sys
import unittest

from tests_common import NewOpenCVTests


try:

    if sys.version_info[:2] < (3, 0):

            

Reported by Pylint.

                                                        cv.NORM_INF))


        def test_person_detection_retail_0013(self):
            # NB: Check IE
            if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
                return

            root_path    = '/omz_intel_models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013'

            

Reported by Pylint.

                      def setUp(self):
            self.skipTest('Skip tests: ' + message)

        def test_skip():
            pass

    pass



            

Reported by Pylint.

                              h, w = size
                bboxes = []
                detections = detections.reshape(-1, 7)
                for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                    if confidence >= 0.5:
                        x      = int(xmin * w)
                        y      = int(ymin * h)
                        width  = int(xmax * w - x)
                        height = int(ymax * h - y)

            

Reported by Pylint.

                              h, w = size
                bboxes = []
                detections = detections.reshape(-1, 7)
                for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                    if confidence >= 0.5:
                        x      = int(xmin * w)
                        y      = int(ymin * h)
                        width  = int(xmax * w - x)
                        height = int(ymax * h - y)

            

Reported by Pylint.

                              h, w = size
                bboxes = []
                detections = detections.reshape(-1, 7)
                for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                    if confidence >= 0.5:
                        x      = int(xmin * w)
                        y      = int(ymin * h)
                        width  = int(xmax * w - x)
                        height = int(ymax * h - y)

            

Reported by Pylint.

                              h, w = size
                bboxes = []
                detections = detections.reshape(-1, 7)
                for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                    if confidence >= 0.5:
                        x      = int(xmin * w)
                        y      = int(ymin * h)
                        width  = int(xmax * w - x)
                        height = int(ymax * h - y)

            

Reported by Pylint.

                      def test_skip():
            pass

    pass


if __name__ == '__main__':
    NewOpenCVTests.bootstrap()

            

Reported by Pylint.

              #!/usr/bin/env python

import numpy as np
import cv2 as cv
import os
import sys
import unittest

from tests_common import NewOpenCVTests

            

Reported by Pylint.

              package org.opencv.utils;

import java.util.ArrayList;
import java.util.List;

import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfDMatch;

            

Reported by PMD.

              import org.opencv.core.DMatch;
import org.opencv.core.KeyPoint;

public class Converters {

    public static Mat vector_Point_to_Mat(List<Point> pts) {
        return vector_Point_to_Mat(pts, CvType.CV_32S);
    }


            

Reported by PMD.

              import org.opencv.core.DMatch;
import org.opencv.core.KeyPoint;

public class Converters {

    public static Mat vector_Point_to_Mat(List<Point> pts) {
        return vector_Point_to_Mat(pts, CvType.CV_32S);
    }


            

Reported by PMD.

              import org.opencv.core.DMatch;
import org.opencv.core.KeyPoint;

public class Converters {

    public static Mat vector_Point_to_Mat(List<Point> pts) {
        return vector_Point_to_Mat(pts, CvType.CV_32S);
    }


            

Reported by PMD.

              import org.opencv.core.DMatch;
import org.opencv.core.KeyPoint;

public class Converters {

    public static Mat vector_Point_to_Mat(List<Point> pts) {
        return vector_Point_to_Mat(pts, CvType.CV_32S);
    }


            

Reported by PMD.

                      return vector_Point_to_Mat(pts, CvType.CV_64F);
    }

    public static Mat vector_Point_to_Mat(List<Point> pts, int typeDepth) {
        Mat res;
        int count = (pts != null) ? pts.size() : 0;
        if (count > 0) {
            switch (typeDepth) {
            case CvType.CV_32S: {

            

Reported by PMD.

                      return vector_Point3_to_Mat(pts, CvType.CV_64F);
    }

    public static Mat vector_Point3_to_Mat(List<Point3> pts, int typeDepth) {
        Mat res;
        int count = (pts != null) ? pts.size() : 0;
        if (count > 0) {
            switch (typeDepth) {
            case CvType.CV_32S: {

            

Reported by PMD.

                      Mat_to_vector_Point(m, pts);
    }

    public static void Mat_to_vector_Point(Mat m, List<Point> pts) {
        if (pts == null)
            throw new IllegalArgumentException("Output List can't be null");
        int count = m.rows();
        int type = m.type();
        if (m.cols() != 1)

            

Reported by PMD.

              
    public static void Mat_to_vector_Point(Mat m, List<Point> pts) {
        if (pts == null)
            throw new IllegalArgumentException("Output List can't be null");
        int count = m.rows();
        int type = m.type();
        if (m.cols() != 1)
            throw new IllegalArgumentException("Input Mat should have one column\n" + m);


            

Reported by PMD.

                          throw new IllegalArgumentException("Output List can't be null");
        int count = m.rows();
        int type = m.type();
        if (m.cols() != 1)
            throw new IllegalArgumentException("Input Mat should have one column\n" + m);

        pts.clear();
        if (type == CvType.CV_32SC2) {
            int[] buff = new int[2 * count];

            

Reported by PMD.

              import android.os.RemoteException;
import android.util.Log;

class AsyncServiceHelper
{
    public static boolean initOpenCV(String Version, final Context AppContext,
            final LoaderCallbackInterface Callback)
    {
        AsyncServiceHelper helper = new AsyncServiceHelper(Version, AppContext, Callback);

            

Reported by PMD.

              import android.os.RemoteException;
import android.util.Log;

class AsyncServiceHelper
{
    public static boolean initOpenCV(String Version, final Context AppContext,
            final LoaderCallbackInterface Callback)
    {
        AsyncServiceHelper helper = new AsyncServiceHelper(Version, AppContext, Callback);

            

Reported by PMD.

              
    protected static final String TAG = "OpenCVManager/Helper";
    protected static final int MINIMUM_ENGINE_VERSION = 2;
    protected OpenCVEngineInterface mEngineService;
    protected LoaderCallbackInterface mUserAppCallback;
    protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;

            

Reported by PMD.

                  protected static final String TAG = "OpenCVManager/Helper";
    protected static final int MINIMUM_ENGINE_VERSION = 2;
    protected OpenCVEngineInterface mEngineService;
    protected LoaderCallbackInterface mUserAppCallback;
    protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;


            

Reported by PMD.

                  protected static final int MINIMUM_ENGINE_VERSION = 2;
    protected OpenCVEngineInterface mEngineService;
    protected LoaderCallbackInterface mUserAppCallback;
    protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;

    protected static boolean InstallServiceQuiet(Context context)

            

Reported by PMD.

                  protected OpenCVEngineInterface mEngineService;
    protected LoaderCallbackInterface mUserAppCallback;
    protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;

    protected static boolean InstallServiceQuiet(Context context)
    {

            

Reported by PMD.

                  protected LoaderCallbackInterface mUserAppCallback;
    protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;

    protected static boolean InstallServiceQuiet(Context context)
    {
        boolean result = true;

            

Reported by PMD.

                  protected String mOpenCVersion;
    protected Context mAppContext;
    protected static boolean mServiceInstallationProgress = false;
    protected static boolean mLibraryInstallationProgress = false;

    protected static boolean InstallServiceQuiet(Context context)
    {
        boolean result = true;
        try

            

Reported by PMD.

                          intent.addFlags(Intent.FLAG_ACTIVITY_NEW_TASK);
            context.startActivity(intent);
        }
        catch(Exception e)
        {
            result = false;
        }

        return result;

            

Reported by PMD.

                      return result;
    }

    protected static void InstallService(final Context AppContext, final LoaderCallbackInterface Callback)
    {
        if (!mServiceInstallationProgress)
        {
                Log.d(TAG, "Request new service installation");
                InstallCallbackInterface InstallQuery = new InstallCallbackInterface() {

            

Reported by PMD.

                      // Load the native OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        System.out.println("\n--------------------------------------------------------------------------");
        System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
        System.out.println("--------------------------------------------------------------------------\n");

        // Data for visual representation
        int width = 512, height = 512;

            

Reported by PMD.

                      System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        System.out.println("\n--------------------------------------------------------------------------");
        System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
        System.out.println("--------------------------------------------------------------------------\n");

        // Data for visual representation
        int width = 512, height = 512;
        Mat I = Mat.zeros(height, width, CvType.CV_8UC3);

            

Reported by PMD.

              
        System.out.println("\n--------------------------------------------------------------------------");
        System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
        System.out.println("--------------------------------------------------------------------------\n");

        // Data for visual representation
        int width = 512, height = 512;
        Mat I = Mat.zeros(height, width, CvType.CV_8UC3);


            

Reported by PMD.

                      labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(new Scalar(2)); // Class 2

        // ------------------------ 2. Set up the support vector machines parameters--------------------
        System.out.println("Starting training process");
        //! [init]
        SVM svm = SVM.create();
        svm.setType(SVM.C_SVC);
        svm.setC(0.1);
        svm.setKernel(SVM.LINEAR);

            

Reported by PMD.

                      //! [train]
        svm.train(trainData, Ml.ROW_SAMPLE, labels);
        //! [train]
        System.out.println("Finished training process");

        // ------------------------ 4. Show the decision regions----------------------------------------
        //! [show]
        byte[] IData = new byte[(int) (I.total() * I.channels())];
        Mat sampleMat = new Mat(1, 2, CvType.CV_32F);

            

Reported by PMD.

              import org.opencv.ml.Ml;
import org.opencv.ml.SVM;

public class NonLinearSVMsDemo {
    public static final int NTRAINING_SAMPLES = 100;
    public static final float FRAC_LINEAR_SEP = 0.9f;

    public static void main(String[] args) {
        // Load the native OpenCV library

            

Reported by PMD.

              import org.opencv.ml.Ml;
import org.opencv.ml.SVM;

public class NonLinearSVMsDemo {
    public static final int NTRAINING_SAMPLES = 100;
    public static final float FRAC_LINEAR_SEP = 0.9f;

    public static void main(String[] args) {
        // Load the native OpenCV library

            

Reported by PMD.

              import org.opencv.ml.Ml;
import org.opencv.ml.SVM;

public class NonLinearSVMsDemo {
    public static final int NTRAINING_SAMPLES = 100;
    public static final float FRAC_LINEAR_SEP = 0.9f;

    public static void main(String[] args) {
        // Load the native OpenCV library

            

Reported by PMD.

                  public static final int NTRAINING_SAMPLES = 100;
    public static final float FRAC_LINEAR_SEP = 0.9f;

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

        System.out.println("\n--------------------------------------------------------------------------");
        System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");

            

Reported by PMD.

                  public static final int NTRAINING_SAMPLES = 100;
    public static final float FRAC_LINEAR_SEP = 0.9f;

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

        System.out.println("\n--------------------------------------------------------------------------");
        System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");

            

Reported by PMD.

                      if wv[0]:
            return "Windows" + wv[0]
        else:
            lv = platform.linux_distribution()
            if lv[0]:
                return lv[0] + lv[1]
    return None



            

Reported by Pylint.

                      if not self.tests_dir:
            self.tests_dir = path
        else:
            rel = os.path.relpath(self.tests_dir, self.opencv_build)
            self.tests_dir = os.path.join(path, rel)
        self.tests_dir = os.path.normpath(self.tests_dir)

        # fix VS test binary path (add Debug or Release)
        if "Visual Studio" in self.cmake_generator:

            

Reported by Pylint.

                      self.tests_dir = os.path.normpath(self.tests_dir)

        # fix VS test binary path (add Debug or Release)
        if "Visual Studio" in self.cmake_generator:
            self.tests_dir = os.path.join(self.tests_dir, self.build_type)

        for module, path in module_paths.items():
            rel = os.path.relpath(path, self.opencv_home)
            if ".." not in rel:

            

Reported by Pylint.

                          self.tests_dir = os.path.join(self.tests_dir, self.build_type)

        for module, path in module_paths.items():
            rel = os.path.relpath(path, self.opencv_home)
            if ".." not in rel:
                self.main_modules.append(module)

    def setDefaultAttrs(self):
        for p in parse_patterns:

            

Reported by Pylint.

                      return name in self.main_modules + ['python2', 'python3']

    def withJava(self):
        return self.ant_executable and self.java_test_dir and os.path.exists(self.java_test_dir)

    def withPython2(self):
        return self.python2 == 'ON'

    def withPython3(self):

            

Reported by Pylint.

                      return name in self.main_modules + ['python2', 'python3']

    def withJava(self):
        return self.ant_executable and self.java_test_dir and os.path.exists(self.java_test_dir)

    def withPython2(self):
        return self.python2 == 'ON'

    def withPython3(self):

            

Reported by Pylint.

                      return name in self.main_modules + ['python2', 'python3']

    def withJava(self):
        return self.ant_executable and self.java_test_dir and os.path.exists(self.java_test_dir)

    def withPython2(self):
        return self.python2 == 'ON'

    def withPython3(self):

            

Reported by Pylint.

                      return self.ant_executable and self.java_test_dir and os.path.exists(self.java_test_dir)

    def withPython2(self):
        return self.python2 == 'ON'

    def withPython3(self):
        return self.python3 == 'ON'

    def getOS(self):

            

Reported by Pylint.

                      return self.python2 == 'ON'

    def withPython3(self):
        return self.python3 == 'ON'

    def getOS(self):
        if self.android_executable:
            return "android"
        else:

            

Reported by Pylint.

                      return self.python3 == 'ON'

    def getOS(self):
        if self.android_executable:
            return "android"
        else:
            return hostos



            

Reported by Pylint.