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

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

            

Reported by Pylint.

              rng.seed(12345)

def goodFeaturesToTrack_Demo(val):
    maxCorners = max(val, 1)

    # Parameters for Shi-Tomasi algorithm
    qualityLevel = 0.01
    minDistance = 10
    blockSize = 3

            

Reported by Pylint.

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

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

            

Reported by Pylint.

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

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

            

Reported by Pylint.

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

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

            

Reported by Pylint.

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

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)


            

Reported by Pylint.

              import argparse
import random as rng

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

def goodFeaturesToTrack_Demo(val):
    maxCorners = max(val, 1)

            

Reported by Pylint.

              import random as rng

source_window = 'Image'
maxTrackbar = 100
rng.seed(12345)

def goodFeaturesToTrack_Demo(val):
    maxCorners = max(val, 1)


            

Reported by Pylint.

              maxTrackbar = 100
rng.seed(12345)

def goodFeaturesToTrack_Demo(val):
    maxCorners = max(val, 1)

    # Parameters for Shi-Tomasi algorithm
    qualityLevel = 0.01
    minDistance = 10

            

Reported by Pylint.

              maxTrackbar = 100
rng.seed(12345)

def goodFeaturesToTrack_Demo(val):
    maxCorners = max(val, 1)

    # Parameters for Shi-Tomasi algorithm
    qualityLevel = 0.01
    minDistance = 10

            

Reported by Pylint.

              """

from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder

            

Reported by Pylint.

              """

from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder

            

Reported by Pylint.

              
from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder


            

Reported by Pylint.

              
from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder


            

Reported by Pylint.

              
from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder


            

Reported by Pylint.

              
from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder


            

Reported by Pylint.

              
# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder

class OSXDocBuilder(DocBuilder):

    def getToolchain(self):
        return None

            

Reported by Pylint.

              """

from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder

            

Reported by Pylint.

              
from __future__ import print_function
import os, sys, multiprocessing, argparse, traceback
from subprocess import check_call, check_output, CalledProcessError, Popen

# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder


            

Reported by Bandit.

              
# import common code
sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios'))
from build_docs import DocBuilder

class OSXDocBuilder(DocBuilder):

    def getToolchain(self):
        return None

            

Reported by Pylint.

              import org.opencv.test.OpenCVTestCase;
import org.opencv.test.OpenCVTestRunner;

public class CascadeClassifierTest extends OpenCVTestCase {

    private CascadeClassifier cc;

    @Override
    protected void setUp() throws Exception {

            

Reported by PMD.

              
public class CascadeClassifierTest extends OpenCVTestCase {

    private CascadeClassifier cc;

    @Override
    protected void setUp() throws Exception {
        super.setUp();


            

Reported by PMD.

              
    private CascadeClassifier cc;

    @Override
    protected void setUp() throws Exception {
        super.setUp();

        cc = null;
    }

            

Reported by PMD.

                  protected void setUp() throws Exception {
        super.setUp();

        cc = null;
    }

    public void testCascadeClassifier() {
        cc = new CascadeClassifier();
        assertNotNull(cc);

            

Reported by PMD.

                      cc = null;
    }

    public void testCascadeClassifier() {
        cc = new CascadeClassifier();
        assertNotNull(cc);
    }

    public void testCascadeClassifierString() {

            

Reported by PMD.

                      assertNotNull(cc);
    }

    public void testCascadeClassifierString() {
        cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
        assertNotNull(cc);
    }

    public void testDetectMultiScaleMatListOfRect() {

            

Reported by PMD.

                      assertNotNull(cc);
    }

    public void testDetectMultiScaleMatListOfRect() {
        CascadeClassifier cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
        MatOfRect faces = new MatOfRect();

        Mat greyLena = new Mat();
        Imgproc.cvtColor(rgbLena, greyLena, Imgproc.COLOR_RGB2GRAY);

            

Reported by PMD.

                      assertEquals(1, faces.total());
    }

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

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

            

Reported by PMD.

                  }

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

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

            

Reported by PMD.

                      fail("Not yet implemented");
    }

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

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

            

Reported by PMD.

              
        String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);

        if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");

        File testDataPath = new File(envTestDataPath);

        File f = new File(testDataPath, "dnn/grace_hopper_227.png");
        sourceImageFile = f.toString();

            

Reported by PMD.

              
        File f = new File(testDataPath, "dnn/grace_hopper_227.png");
        sourceImageFile = f.toString();
        if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);

        net = Dnn.readNetFromTensorflow(modelFileName);

        Mat image = Imgcodecs.imread(sourceImageFile);
        assertNotNull("Loading image from file failed!", image);

            

Reported by PMD.

              
    private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";

    String modelFileName = "";
    String sourceImageFile = "";

    Net net;

    @Override

            

Reported by PMD.

                  private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";

    String modelFileName = "";
    String sourceImageFile = "";

    Net net;

    @Override
    protected void setUp() throws Exception {

            

Reported by PMD.

                  String modelFileName = "";
    String sourceImageFile = "";

    Net net;

    @Override
    protected void setUp() throws Exception {
        super.setUp();


            

Reported by PMD.

              
    Net net;

    @Override
    protected void setUp() throws Exception {
        super.setUp();

        String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);


            

Reported by PMD.

                      net.setInput(inputBlob, "input");
    }

    public void testSetInputsNames() {
        List<String> inputs = new ArrayList();
        inputs.add("input");
        try {
            net.setInputsNames(inputs);
        } catch(Exception e) {

            

Reported by PMD.

                      inputs.add("input");
        try {
            net.setInputsNames(inputs);
        } catch(Exception e) {
            fail("Net setInputsNames failed: " + e.getMessage());
        }
    }

    public void testForward() {

            

Reported by PMD.

                      }
    }

    public void testForward() {
        List<Mat> outs = new ArrayList();
        List<String> outNames = new ArrayList();
        outNames.add("softmax2");
        try {
            net.forward(outs,outNames);

            

Reported by PMD.

                      outNames.add("softmax2");
        try {
            net.forward(outs,outNames);
        } catch(Exception e) {
            fail("Net forward failed: " + e.getMessage());
        }
    }

    public void testGetMemoryConsumption() {

            

Reported by PMD.

                          File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);

            FileOutputStream os = new FileOutputStream(resFile);

            byte[] buffer = new byte[4096];
            int bytesRead;
            while ((bytesRead = is.read(buffer)) != -1) {
                os.write(buffer, 0, bytesRead);

            

Reported by PMD.

              import java.io.IOException;
import java.io.InputStream;

public class Utils {

    public static String exportResource(Context context, int resourceId) {
        return exportResource(context, resourceId, "OpenCV_data");
    }


            

Reported by PMD.

                  }

    public static String exportResource(Context context, int resourceId, String dirname) {
        String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);

            

Reported by PMD.

              
    public static String exportResource(Context context, int resourceId, String dirname) {
        String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);


            

Reported by PMD.

              
    public static String exportResource(Context context, int resourceId, String dirname) {
        String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);


            

Reported by PMD.

              
    public static String exportResource(Context context, int resourceId, String dirname) {
        String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);


            

Reported by PMD.

                      String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);

            FileOutputStream os = new FileOutputStream(resFile);


            

Reported by PMD.

                      String fullname = context.getResources().getString(resourceId);
        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
        try {
            InputStream is = context.getResources().openRawResource(resourceId);
            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);

            FileOutputStream os = new FileOutputStream(resFile);


            

Reported by PMD.

                          File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
            File resFile = new File(resDir, resName);

            FileOutputStream os = new FileOutputStream(resFile);

            byte[] buffer = new byte[4096];
            int bytesRead;
            while ((bytesRead = is.read(buffer)) != -1) {
                os.write(buffer, 0, bytesRead);

            

Reported by PMD.

              
            byte[] buffer = new byte[4096];
            int bytesRead;
            while ((bytesRead = is.read(buffer)) != -1) {
                os.write(buffer, 0, bytesRead);
            }
            is.close();
            os.close();


            

Reported by PMD.

              sys.dont_write_bytecode = True  # Don't generate .pyc files / __pycache__ directories

import os
from pprint import pprint
import re
import logging
import json

import html_functions

            

Reported by Pylint.

              
import os
from pprint import pprint
import re
import logging
import json

import html_functions
import doxygen_scan

            

Reported by Pylint.

                  print("Loaded Python signatures: %d" % len(python_signatures))

import xml.etree.ElementTree as ET
root = ET.parse(ROOT_DIR + 'opencv.tag')
files_dict = {}

# constants and function from opencv.tag
namespaces = root.findall("./compound[@kind='namespace']")
#print("Found {} namespaces".format(len(namespaces)))

            

Reported by Bandit.

              
    active_anchors_dict = {a.anchor: a for a in active_anchors}
    if len(active_anchors_dict) != len(active_anchors):
        logging.info('Duplicate entries detected: %s -> %s (%s)' % (len(active_anchors), len(active_anchors_dict), file))

    files_processed = files_processed + 1

    #pprint(active_anchors)
    symbols_processed = symbols_processed + len(active_anchors_dict)

            

Reported by Pylint.

                  #pprint(active_anchors)
    symbols_processed = symbols_processed + len(active_anchors_dict)

    logging.info('File: %r' % file)
    html_functions.insert_python_signatures(python_signatures, active_anchors_dict, ROOT_DIR + file)

print('Done (processed files %d, symbols %d, skipped %d files)' % (files_processed, symbols_processed, files_skipped))

            

Reported by Pylint.

              import sys
sys.dont_write_bytecode = True  # Don't generate .pyc files / __pycache__ directories

import os
from pprint import pprint
import re
import logging
import json


            

Reported by Pylint.

              sys.dont_write_bytecode = True  # Don't generate .pyc files / __pycache__ directories

import os
from pprint import pprint
import re
import logging
import json

import html_functions

            

Reported by Pylint.

              
import os
from pprint import pprint
import re
import logging
import json

import html_functions
import doxygen_scan

            

Reported by Pylint.

              import os
from pprint import pprint
import re
import logging
import json

import html_functions
import doxygen_scan


            

Reported by Pylint.

              from pprint import pprint
import re
import logging
import json

import html_functions
import doxygen_scan

loglevel=os.environ.get("LOGLEVEL", None)

            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)

            

Reported by Pylint.

                  # Construct a buffer used by the pca analysis
    sz = len(pts)
    data_pts = np.empty((sz, 2), dtype=np.float64)
    for i in range(data_pts.shape[0]):
        data_pts[i,0] = pts[i,0,0]
        data_pts[i,1] = pts[i,0,1]

    # Perform PCA analysis
    mean = np.empty((0))

            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)

            

Reported by Pylint.

              from __future__ import division
import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)

            

Reported by Pylint.

              import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]

            

Reported by Pylint.

              import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))

            

Reported by Pylint.

              import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))

            

Reported by Pylint.

              import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))

            

Reported by Pylint.

              import argparse
from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))

            

Reported by Pylint.

              from math import atan2, cos, sin, sqrt, pi

def drawAxis(img, p_, q_, colour, scale):
    p = list(p_)
    q = list(q_)
    ## [visualization1]
    angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
    hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))


            

Reported by Pylint.

                      //! [find-corners]

        if (!found1 || !found2) {
            System.out.println("Error, cannot find the chessboard corners in both images.");
            System.exit(-1);
        }

        //! [estimate-homography]
        Mat H = new Mat();

            

Reported by PMD.

                      //! [estimate-homography]
        Mat H = new Mat();
        H = Calib3d.findHomography(corners1, corners2);
        System.out.println(H.dump());
        //! [estimate-homography]

        //! [warp-chessboard]
        Mat img1_warp = new Mat();
        Imgproc.warpPerspective(img1, img1_warp, H, img1.size());

            

Reported by PMD.

              
        if (!found1 || !found2) {
            System.out.println("Error, cannot find the chessboard corners in both images.");
            System.exit(-1);
        }

        //! [estimate-homography]
        Mat H = new Mat();
        H = Calib3d.findHomography(corners1, corners2);

            

Reported by PMD.

                      }

        //! [estimate-homography]
        Mat H = new Mat();
        H = Calib3d.findHomography(corners1, corners2);
        System.out.println(H.dump());
        //! [estimate-homography]

        //! [warp-chessboard]

            

Reported by PMD.

                      //! [estimate-homography]
        Mat H = new Mat();
        H = Calib3d.findHomography(corners1, corners2);
        System.out.println(H.dump());
        //! [estimate-homography]

        //! [warp-chessboard]
        Mat img1_warp = new Mat();
        Imgproc.warpPerspective(img1, img1_warp, H, img1.size());

            

Reported by PMD.

              
        //! [warp-chessboard]
        Mat img1_warp = new Mat();
        Imgproc.warpPerspective(img1, img1_warp, H, img1.size());
        //! [warp-chessboard]

        Mat img_draw_warp = new Mat();
        List<Mat> list1 = new ArrayList<>(), list2 = new ArrayList<>() ;
        list1.add(img2);

            

Reported by PMD.

                      Core.hconcat(list2, img_draw_matches);
        Point []corners1Arr = corners1.toArray();

        for (int i = 0 ; i < corners1Arr.length; i++) {
            Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat();
            pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 );

            Core.gemm(H, pt1, 1, new Mat(), 0, pt2);
            double[] data = pt2.get(2, 0);

            

Reported by PMD.

                      Point []corners1Arr = corners1.toArray();

        for (int i = 0 ; i < corners1Arr.length; i++) {
            Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat();
            pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 );

            Core.gemm(H, pt1, 1, new Mat(), 0, pt2);
            double[] data = pt2.get(2, 0);
            Core.divide(pt2, new Scalar(data[0]), pt2);

            

Reported by PMD.

                      Point []corners1Arr = corners1.toArray();

        for (int i = 0 ; i < corners1Arr.length; i++) {
            Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat();
            pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 );

            Core.gemm(H, pt1, 1, new Mat(), 0, pt2);
            double[] data = pt2.get(2, 0);
            Core.divide(pt2, new Scalar(data[0]), pt2);

            

Reported by PMD.

                          Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat();
            pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 );

            Core.gemm(H, pt1, 1, new Mat(), 0, pt2);
            double[] data = pt2.get(2, 0);
            Core.divide(pt2, new Scalar(data[0]), pt2);

            double[] data1 =pt2.get(0, 0);
            double[] data2 = pt2.get(1, 0);

            

Reported by PMD.

                      String filename = args.length > 0 ? args[0] : "../data/pic3.png";
        src = Imgcodecs.imread(filename);
        if (src.empty()) {
            System.err.println("Cannot read image: " + filename);
            System.exit(0);
        }

        Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);


            

Reported by PMD.

                              blockSize, gradientSize, useHarrisDetector, k);

        /// Draw corners detected
        System.out.println("** Number of corners detected: " + corners.rows());
        int[] cornersData = new int[(int) (corners.total() * corners.channels())];
        corners.get(0, 0, cornersData);
        int radius = 4;
        for (int i = 0; i < corners.rows(); i++) {
            Imgproc.circle(copy, new Point(cornersData[i * 2], cornersData[i * 2 + 1]), radius,

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;

            

Reported by PMD.

              
class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;
    private Random rng = new Random(12345);

            

Reported by PMD.

              
class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;
    private Random rng = new Random(12345);

            

Reported by PMD.

              class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;
    private Random rng = new Random(12345);


            

Reported by PMD.

              class GoodFeaturesToTrack {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;
    private Random rng = new Random(12345);


            

Reported by PMD.

                  private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_THRESHOLD = 100;
    private int maxCorners = 23;
    private Random rng = new Random(12345);

    public GoodFeaturesToTrack(String[] args) {

            

Reported by PMD.

              import argparse
import os.path
import numpy as np
import cv2 as cv

backends = (cv.dnn.DNN_BACKEND_DEFAULT,
    cv.dnn.DNN_BACKEND_INFERENCE_ENGINE,
    cv.dnn.DNN_BACKEND_OPENCV,
    cv.dnn.DNN_BACKEND_VKCOM,

            

Reported by Pylint.

                  images = np.array(img_list)
    images = (images / 255.0 - MEAN) / STD

    input = cv.dnn.blobFromImages(images.astype(np.float32), ddepth = cv.CV_32F)
    return input

def extract_feature(img_dir, model_path, batch_size = 32, resize_h = 384, resize_w = 128, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
    """
    Extract features from images in a target directory

            

Reported by Pylint.

                  dist = np.matmul(array1, array2.T)
    return dist

def topk(query_feat, gallery_feat, topk = 5):
    """
    Return the index of top K gallery images most similar to the query images
    :param query_feat: array of feature vectors of query images
    :param gallery_feat: array of feature vectors of gallery images
    :param topk: number of gallery images to return

            

Reported by Pylint.

                  dist = np.matmul(array1, array2.T)
    return dist

def topk(query_feat, gallery_feat, topk = 5):
    """
    Return the index of top K gallery images most similar to the query images
    :param query_feat: array of feature vectors of query images
    :param gallery_feat: array of feature vectors of gallery images
    :param topk: number of gallery images to return

            

Reported by Pylint.

                  dist = np.matmul(array1, array2.T)
    return dist

def topk(query_feat, gallery_feat, topk = 5):
    """
    Return the index of top K gallery images most similar to the query images
    :param query_feat: array of feature vectors of query images
    :param gallery_feat: array of feature vectors of gallery images
    :param topk: number of gallery images to return

            

Reported by Pylint.

                  return ret


def visualization(topk_idx, query_names, gallery_names, output_dir = 'vis'):
    """
    Visualize the retrieval results with the person ReID model
    :param topk_idx: the index of ranked gallery images for each query image
    :param query_names: the list of paths of query images
    :param gallery_names: the list of paths of gallery images

            

Reported by Pylint.

                  return ret


def visualization(topk_idx, query_names, gallery_names, output_dir = 'vis'):
    """
    Visualize the retrieval results with the person ReID model
    :param topk_idx: the index of ranked gallery images for each query image
    :param query_names: the list of paths of query images
    :param gallery_names: the list of paths of gallery images

            

Reported by Pylint.

                  return ret


def visualization(topk_idx, query_names, gallery_names, output_dir = 'vis'):
    """
    Visualize the retrieval results with the person ReID model
    :param topk_idx: the index of ranked gallery images for each query image
    :param query_names: the list of paths of query images
    :param gallery_names: the list of paths of gallery images

            

Reported by Pylint.

                  input = cv.dnn.blobFromImages(images.astype(np.float32), ddepth = cv.CV_32F)
    return input

def extract_feature(img_dir, model_path, batch_size = 32, resize_h = 384, resize_w = 128, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
    """
    Extract features from images in a target directory
    :param img_dir: the input image directory
    :param model_path: path to ReID model
    :param batch_size: the batch size for each network inference iteration

            

Reported by Pylint.

                  input = cv.dnn.blobFromImages(images.astype(np.float32), ddepth = cv.CV_32F)
    return input

def extract_feature(img_dir, model_path, batch_size = 32, resize_h = 384, resize_w = 128, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
    """
    Extract features from images in a target directory
    :param img_dir: the input image directory
    :param model_path: path to ReID model
    :param batch_size: the batch size for each network inference iteration

            

Reported by Pylint.