public MatOfDMatch(DMatch...ap) {
        super();
        fromArray(ap);
    }

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

            

Reported by PMD.

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

import org.opencv.core.DMatch;

public class MatOfDMatch extends Mat {
    // 32FC4
    private static final int _depth = CvType.CV_32F;
    private static final int _channels = 4;

            

Reported by PMD.

                          return;
        int num = a.length;
        alloc(num);
        float buff[] = new float[num * _channels];
        for(int i=0; i<num; i++) {
            DMatch m = a[i];
            buff[_channels*i+0] = m.queryIdx;
            buff[_channels*i+1] = m.trainIdx;
            buff[_channels*i+2] = m.imgIdx;

            

Reported by PMD.

                      float buff[] = new float[num * _channels];
        for(int i=0; i<num; i++) {
            DMatch m = a[i];
            buff[_channels*i+0] = m.queryIdx;
            buff[_channels*i+1] = m.trainIdx;
            buff[_channels*i+2] = m.imgIdx;
            buff[_channels*i+3] = m.distance;
        }
        put(0, 0, buff); //TODO: check ret val!

            

Reported by PMD.

                      for(int i=0; i<num; i++) {
            DMatch m = a[i];
            buff[_channels*i+0] = m.queryIdx;
            buff[_channels*i+1] = m.trainIdx;
            buff[_channels*i+2] = m.imgIdx;
            buff[_channels*i+3] = m.distance;
        }
        put(0, 0, buff); //TODO: check ret val!
    }

            

Reported by PMD.

                          DMatch m = a[i];
            buff[_channels*i+0] = m.queryIdx;
            buff[_channels*i+1] = m.trainIdx;
            buff[_channels*i+2] = m.imgIdx;
            buff[_channels*i+3] = m.distance;
        }
        put(0, 0, buff); //TODO: check ret val!
    }


            

Reported by PMD.

                          buff[_channels*i+0] = m.queryIdx;
            buff[_channels*i+1] = m.trainIdx;
            buff[_channels*i+2] = m.imgIdx;
            buff[_channels*i+3] = m.distance;
        }
        put(0, 0, buff); //TODO: check ret val!
    }

    public DMatch[] toArray() {

            

Reported by PMD.

              
    public DMatch[] toArray() {
        int num = (int) total();
        DMatch[] a = new DMatch[num];
        if(num == 0)
            return a;
        float buff[] = new float[num * _channels];
        get(0, 0, buff); //TODO: check ret val!
        for(int i=0; i<num; i++)

            

Reported by PMD.

                      float buff[] = new float[num * _channels];
        get(0, 0, buff); //TODO: check ret val!
        for(int i=0; i<num; i++)
            a[i] = new DMatch((int) buff[_channels*i+0], (int) buff[_channels*i+1], (int) buff[_channels*i+2], buff[_channels*i+3]);
        return a;
    }

    public void fromList(List<DMatch> ldm) {
        DMatch adm[] = ldm.toArray(new DMatch[0]);

            

Reported by PMD.

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

parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()

            

Reported by Pylint.

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

parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()

            

Reported by Pylint.

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

parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()

            

Reported by Pylint.

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

parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()

            

Reported by Pylint.

              img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
    print('Could not open or find the images!')
    exit(0)

#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)

            

Reported by Pylint.

                  exit(0)

#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)

#-- Step 2: Matching descriptor vectors with a FLANN based matcher

            

Reported by Pylint.

              knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)

#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.7
good_matches = []
for m,n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)


            

Reported by Pylint.

                      good_matches.append(m)

#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

#-- Show detected matches
cv.imshow('Good Matches', img_matches)


            

Reported by Pylint.

              
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

#-- Show detected matches
cv.imshow('Good Matches', img_matches)

cv.waitKey()

            

Reported by Pylint.

              package org.opencv.core;

import org.opencv.core.Point;

//javadoc: KeyPoint
public class KeyPoint {

    /**
     * Coordinates of the keypoint.

            

Reported by PMD.

              import org.opencv.core.Point;

//javadoc: KeyPoint
public class KeyPoint {

    /**
     * Coordinates of the keypoint.
     */
    public Point pt;

            

Reported by PMD.

                  /**
     * Coordinates of the keypoint.
     */
    public Point pt;
    /**
     * Diameter of the useful keypoint adjacent area.
     */
    public float size;
    /**

            

Reported by PMD.

                  /**
     * Diameter of the useful keypoint adjacent area.
     */
    public float size;
    /**
     * Computed orientation of the keypoint (-1 if not applicable).
     */
    public float angle;
    /**

            

Reported by PMD.

                  /**
     * Computed orientation of the keypoint (-1 if not applicable).
     */
    public float angle;
    /**
     * The response, by which the strongest keypoints have been selected. Can
     * be used for further sorting or subsampling.
     */
    public float response;

            

Reported by PMD.

                   * The response, by which the strongest keypoints have been selected. Can
     * be used for further sorting or subsampling.
     */
    public float response;
    /**
     * Octave (pyramid layer), from which the keypoint has been extracted.
     */
    public int octave;
    /**

            

Reported by PMD.

                  /**
     * Octave (pyramid layer), from which the keypoint has been extracted.
     */
    public int octave;
    /**
     * Object ID, that can be used to cluster keypoints by an object they
     * belong to.
     */
    public int class_id;

            

Reported by PMD.

                   * Object ID, that can be used to cluster keypoints by an object they
     * belong to.
     */
    public int class_id;

    // javadoc:KeyPoint::KeyPoint(x,y,_size,_angle,_response,_octave,_class_id)
    public KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) {
        pt = new Point(x, y);
        size = _size;

            

Reported by PMD.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt

## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt

## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt

## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')

            

Reported by Pylint.

              from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt

## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')

            

Reported by Pylint.

              import cv2 as cv
import numpy as np
import argparse
from math import sqrt

## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')
parser.add_argument('--input2', help='Path to input image 2.', default='graf3.png')

            

Reported by Pylint.

              img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
    print('Could not open or find the images!')
    exit(0)

fs = cv.FileStorage(cv.samples.findFile(args.homography), cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()
## [load]


            

Reported by Pylint.

              ## [ratio test filtering]
matched1 = []
matched2 = []
nn_match_ratio = 0.8 # Nearest neighbor matching ratio
for m, n in nn_matches:
    if m.distance < nn_match_ratio * n.distance:
        matched1.append(kpts1[m.queryIdx])
        matched2.append(kpts2[m.trainIdx])
## [ratio test filtering]

            

Reported by Pylint.

              inliers1 = []
inliers2 = []
good_matches = []
inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
for i, m in enumerate(matched1):
    col = np.ones((3,1), dtype=np.float64)
    col[0:2,0] = m.pt

    col = np.dot(homography, col)

            

Reported by Pylint.

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


def show_wait_destroy(winname, img):
    cv.imshow(winname, img)
    cv.moveWindow(winname, 500, 0)

            

Reported by Pylint.

                  vertical = cv.bitwise_not(vertical)
    show_wait_destroy("vertical_bit", vertical)

    '''
    Extract edges and smooth image according to the logic
    1. extract edges
    2. dilate(edges)
    3. src.copyTo(smooth)
    4. blur smooth img

            

Reported by Pylint.

              @brief Use morphology transformations for extracting horizontal and vertical lines sample code
"""
import numpy as np
import sys
import cv2 as cv


def show_wait_destroy(winname, img):
    cv.imshow(winname, img)

            

Reported by Pylint.

              import cv2 as cv


def show_wait_destroy(winname, img):
    cv.imshow(winname, img)
    cv.moveWindow(winname, 500, 0)
    cv.waitKey(0)
    cv.destroyWindow(winname)


            

Reported by Pylint.

                  cv.destroyWindow(winname)


def main(argv):
    # [load_image]
    # Check number of arguments
    if len(argv) < 1:
        print ('Not enough parameters')
        print ('Usage:\nmorph_lines_detection.py < path_to_image >')

            

Reported by Pylint.

                  # [bin]
    # Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
    gray = cv.bitwise_not(gray)
    bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
                                cv.THRESH_BINARY, 15, -2)
    # Show binary image
    show_wait_destroy("binary", bw)
    # [bin]


            

Reported by Pylint.

                  horizontal_size = cols // 30

    # Create structure element for extracting horizontal lines through morphology operations
    horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))

    # Apply morphology operations
    horizontal = cv.erode(horizontal, horizontalStructure)
    horizontal = cv.dilate(horizontal, horizontalStructure)


            

Reported by Pylint.

                  verticalsize = rows // 30

    # Create structure element for extracting vertical lines through morphology operations
    verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))

    # Apply morphology operations
    vertical = cv.erode(vertical, verticalStructure)
    vertical = cv.dilate(vertical, verticalStructure)


            

Reported by Pylint.

              from abc import ABC, ABCMeta, abstractmethod


class AbstractModel(ABC):

    @abstractmethod
    def get_prepared_models(self):
        pass


            

Reported by Pylint.

              from abc import ABC, ABCMeta, abstractmethod


class AbstractModel(ABC):

    @abstractmethod
    def get_prepared_models(self):
        pass


            

Reported by Pylint.

              from abc import ABC, ABCMeta, abstractmethod


class AbstractModel(ABC):

    @abstractmethod
    def get_prepared_models(self):
        pass


            

Reported by Pylint.

              class AbstractModel(ABC):

    @abstractmethod
    def get_prepared_models(self):
        pass


class Framework(object):
    in_blob_name = ''

            

Reported by Pylint.

                      pass


class Framework(object):
    in_blob_name = ''
    out_blob_name = ''

    __metaclass__ = ABCMeta


            

Reported by Pylint.

                      pass


class Framework(object):
    in_blob_name = ''
    out_blob_name = ''

    __metaclass__ = ABCMeta


            

Reported by Pylint.

                  __metaclass__ = ABCMeta

    @abstractmethod
    def get_name(self):
        pass

    @abstractmethod
    def get_output(self, input_blob):
        pass

            

Reported by Pylint.

                      pass

    @abstractmethod
    def get_output(self, input_blob):
        pass

            

Reported by Pylint.

              
public class Tutorial4Activity extends Activity {

    private MyGLSurfaceView mView;
    private TextView mProcMode;

    @Override
    public void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);

            

Reported by PMD.

              public class Tutorial4Activity extends Activity {

    private MyGLSurfaceView mView;
    private TextView mProcMode;

    @Override
    public void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        requestWindowFeature(Window.FEATURE_NO_TITLE);

            

Reported by PMD.

                  public void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        requestWindowFeature(Window.FEATURE_NO_TITLE);
        getWindow().setFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN,
                WindowManager.LayoutParams.FLAG_FULLSCREEN);
        getWindow().setFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON,
                WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
        setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_LANDSCAPE);


            

Reported by PMD.

                      requestWindowFeature(Window.FEATURE_NO_TITLE);
        getWindow().setFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN,
                WindowManager.LayoutParams.FLAG_FULLSCREEN);
        getWindow().setFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON,
                WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
        setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_LANDSCAPE);

        //mView = new MyGLSurfaceView(this, null);
        //setContentView(mView);

            

Reported by PMD.

                      setContentView(R.layout.activity);
        mView = (MyGLSurfaceView) findViewById(R.id.my_gl_surface_view);
        mView.setCameraTextureListener(mView);
        TextView tv = (TextView)findViewById(R.id.fps_text_view);
        mProcMode = (TextView)findViewById(R.id.proc_mode_text_view);
        runOnUiThread(new Runnable() {
            public void run() {
                mProcMode.setText("Processing mode: No processing");
            }

            

Reported by PMD.

                  @Override
    public boolean onCreateOptionsMenu(Menu menu) {
        MenuInflater inflater = getMenuInflater();
        inflater.inflate(R.menu.menu, menu);
        return super.onCreateOptionsMenu(menu);
    }

    @Override
    public boolean onOptionsItemSelected(MenuItem item) {

            

Reported by PMD.

                      setContentView(R.layout.activity);
        mView = (MyGLSurfaceView) findViewById(R.id.my_gl_surface_view);
        mView.setCameraTextureListener(mView);
        TextView tv = (TextView)findViewById(R.id.fps_text_view);
        mProcMode = (TextView)findViewById(R.id.proc_mode_text_view);
        runOnUiThread(new Runnable() {
            public void run() {
                mProcMode.setText("Processing mode: No processing");
            }

            

Reported by PMD.

                      setContentView(R.layout.activity);
        mView = (MyGLSurfaceView) findViewById(R.id.my_gl_surface_view);
        mView.setCameraTextureListener(mView);
        TextView tv = (TextView)findViewById(R.id.fps_text_view);
        mProcMode = (TextView)findViewById(R.id.proc_mode_text_view);
        runOnUiThread(new Runnable() {
            public void run() {
                mProcMode.setText("Processing mode: No processing");
            }

            

Reported by PMD.

              import android.content.SharedPreferences;
import android.util.Log;

public abstract class CalibrationResult {
    private static final String TAG = "OCV::CalibrationResult";

    private static final int CAMERA_MATRIX_ROWS = 3;
    private static final int CAMERA_MATRIX_COLS = 3;
    private static final int DISTORTION_COEFFICIENTS_SIZE = 5;

            

Reported by PMD.

              
    public static void save(Activity activity, Mat cameraMatrix, Mat distortionCoefficients) {
        SharedPreferences sharedPref = activity.getPreferences(Context.MODE_PRIVATE);
        SharedPreferences.Editor editor = sharedPref.edit();

        double[] cameraMatrixArray = new double[CAMERA_MATRIX_ROWS * CAMERA_MATRIX_COLS];
        cameraMatrix.get(0,  0, cameraMatrixArray);
        for (int i = 0; i < CAMERA_MATRIX_ROWS; i++) {
            for (int j = 0; j < CAMERA_MATRIX_COLS; j++) {

            

Reported by PMD.

                          editor.putFloat(Integer.toString(i), (float)distortionCoefficientsArray[i-shift]);
        }

        editor.apply();
        Log.i(TAG, "Saved camera matrix: " + cameraMatrix.dump());
        Log.i(TAG, "Saved distortion coefficients: " + distortionCoefficients.dump());
    }

    public static boolean tryLoad(Activity activity, Mat cameraMatrix, Mat distortionCoefficients) {

            

Reported by PMD.

              
    public static boolean tryLoad(Activity activity, Mat cameraMatrix, Mat distortionCoefficients) {
        SharedPreferences sharedPref = activity.getPreferences(Context.MODE_PRIVATE);
        if (sharedPref.getFloat("0", -1) == -1) {
            Log.i(TAG, "No previous calibration results found");
            return false;
        }

        double[] cameraMatrixArray = new double[CAMERA_MATRIX_ROWS * CAMERA_MATRIX_COLS];

            

Reported by PMD.

                          return false;
        }

        double[] cameraMatrixArray = new double[CAMERA_MATRIX_ROWS * CAMERA_MATRIX_COLS];
        for (int i = 0; i < CAMERA_MATRIX_ROWS; i++) {
            for (int j = 0; j < CAMERA_MATRIX_COLS; j++) {
                int id = i * CAMERA_MATRIX_ROWS + j;
                cameraMatrixArray[id] = sharedPref.getFloat(Integer.toString(id), -1);
            }

            

Reported by PMD.

                      for (int i = 0; i < CAMERA_MATRIX_ROWS; i++) {
            for (int j = 0; j < CAMERA_MATRIX_COLS; j++) {
                int id = i * CAMERA_MATRIX_ROWS + j;
                cameraMatrixArray[id] = sharedPref.getFloat(Integer.toString(id), -1);
            }
        }
        cameraMatrix.put(0, 0, cameraMatrixArray);
        Log.i(TAG, "Loaded camera matrix: " + cameraMatrix.dump());


            

Reported by PMD.

                      cameraMatrix.put(0, 0, cameraMatrixArray);
        Log.i(TAG, "Loaded camera matrix: " + cameraMatrix.dump());

        double[] distortionCoefficientsArray = new double[DISTORTION_COEFFICIENTS_SIZE];
        int shift = CAMERA_MATRIX_ROWS * CAMERA_MATRIX_COLS;
        for (int i = shift; i < DISTORTION_COEFFICIENTS_SIZE + shift; i++) {
            distortionCoefficientsArray[i - shift] = sharedPref.getFloat(Integer.toString(i), -1);
        }
        distortionCoefficients.put(0, 0, distortionCoefficientsArray);

            

Reported by PMD.

                      double[] distortionCoefficientsArray = new double[DISTORTION_COEFFICIENTS_SIZE];
        int shift = CAMERA_MATRIX_ROWS * CAMERA_MATRIX_COLS;
        for (int i = shift; i < DISTORTION_COEFFICIENTS_SIZE + shift; i++) {
            distortionCoefficientsArray[i - shift] = sharedPref.getFloat(Integer.toString(i), -1);
        }
        distortionCoefficients.put(0, 0, distortionCoefficientsArray);
        Log.i(TAG, "Loaded distortion coefficients: " + distortionCoefficients.dump());

        return true;

            

Reported by PMD.

              # Python 2/3 compatibility
from __future__ import print_function

import cv2 as cv
import numpy as np

# relative module
import video


            

Reported by Pylint.

              def main():
    try:
        fn = sys.argv[1]
    except:
        fn = 0

    def nothing(*arg):
        pass


            

Reported by Pylint.

                  except:
        fn = 0

    def nothing(*arg):
        pass

    cv.namedWindow('edge')
    cv.createTrackbar('thrs1', 'edge', 2000, 5000, nothing)
    cv.createTrackbar('thrs2', 'edge', 4000, 5000, nothing)

            

Reported by Pylint.

              import video

# built-in module
import sys


def main():
    try:
        fn = sys.argv[1]

            

Reported by Pylint.

              import sys


def main():
    try:
        fn = sys.argv[1]
    except:
        fn = 0


            

Reported by Pylint.

              
def main():
    try:
        fn = sys.argv[1]
    except:
        fn = 0

    def nothing(*arg):
        pass

            

Reported by Pylint.

                  try:
        fn = sys.argv[1]
    except:
        fn = 0

    def nothing(*arg):
        pass

    cv.namedWindow('edge')

            

Reported by Pylint.

                      vis = np.uint8(vis/2.)
        vis[edge != 0] = (0, 255, 0)
        cv.imshow('edge', vis)
        ch = cv.waitKey(5)
        if ch == 27:
            break

    print('Done')


            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse

## [Load three images with different environment settings]
parser = argparse.ArgumentParser(description='Code for Histogram Comparison tutorial.')
parser.add_argument('--input1', help='Path to input image 1.')

            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse

## [Load three images with different environment settings]
parser = argparse.ArgumentParser(description='Code for Histogram Comparison tutorial.')
parser.add_argument('--input1', help='Path to input image 1.')

            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse

## [Load three images with different environment settings]
parser = argparse.ArgumentParser(description='Code for Histogram Comparison tutorial.')
parser.add_argument('--input1', help='Path to input image 1.')

            

Reported by Pylint.

              from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse

## [Load three images with different environment settings]
parser = argparse.ArgumentParser(description='Code for Histogram Comparison tutorial.')
parser.add_argument('--input1', help='Path to input image 1.')

            

Reported by Pylint.

              from __future__ import division
import cv2 as cv
import numpy as np
import argparse

## [Load three images with different environment settings]
parser = argparse.ArgumentParser(description='Code for Histogram Comparison tutorial.')
parser.add_argument('--input1', help='Path to input image 1.')
parser.add_argument('--input2', help='Path to input image 2.')

            

Reported by Pylint.

              src_test2 = cv.imread(args.input3)
if src_base is None or src_test1 is None or src_test2 is None:
    print('Could not open or find the images!')
    exit(0)
## [Load three images with different environment settings]

## [Convert to HSV]
hsv_base = cv.cvtColor(src_base, cv.COLOR_BGR2HSV)
hsv_test1 = cv.cvtColor(src_test1, cv.COLOR_BGR2HSV)

            

Reported by Pylint.

              ## [Convert to HSV half]

## [Using 50 bins for hue and 60 for saturation]
h_bins = 50
s_bins = 60
histSize = [h_bins, s_bins]

# hue varies from 0 to 179, saturation from 0 to 255
h_ranges = [0, 180]

            

Reported by Pylint.

              
## [Using 50 bins for hue and 60 for saturation]
h_bins = 50
s_bins = 60
histSize = [h_bins, s_bins]

# hue varies from 0 to 179, saturation from 0 to 255
h_ranges = [0, 180]
s_ranges = [0, 256]

            

Reported by Pylint.