PY3 = sys.version_info[0] == 3

import numpy as np
import cv2 as cv


def main():
    import sys
    from itertools import cycle

            

Reported by Pylint.

                      cur_mode = next(modes)
        cur_str_mode = next(str_modes)
    else:
        cur_mode = modes.next()
        cur_str_mode = str_modes.next()

    def update(dummy=None):
        try: # do not get trackbar position while trackbar is not created
            sz = cv.getTrackbarPos('op/size', 'morphology')

            

Reported by Pylint.

                      cur_str_mode = next(str_modes)
    else:
        cur_mode = modes.next()
        cur_str_mode = str_modes.next()

    def update(dummy=None):
        try: # do not get trackbar position while trackbar is not created
            sz = cv.getTrackbarPos('op/size', 'morphology')
            iters = cv.getTrackbarPos('iters', 'morphology')

            

Reported by Pylint.

                          if PY3:
                cur_mode = next(modes)
            else:
                cur_mode = modes.next()
        if ch == ord('2'):
            if PY3:
                cur_str_mode = next(str_modes)
            else:
                cur_str_mode = str_modes.next()

            

Reported by Pylint.

                          if PY3:
                cur_str_mode = next(str_modes)
            else:
                cur_str_mode = str_modes.next()
        update()

    print('Done')



            

Reported by Pylint.

              import sys
PY3 = sys.version_info[0] == 3

import numpy as np
import cv2 as cv


def main():
    import sys

            

Reported by Pylint.

              

def main():
    import sys
    from itertools import cycle
    from common import draw_str

    try:
        fn = sys.argv[1]

            

Reported by Pylint.

              

def main():
    import sys
    from itertools import cycle
    from common import draw_str

    try:
        fn = sys.argv[1]

            

Reported by Pylint.

              
    try:
        fn = sys.argv[1]
    except:
        fn = 'baboon.jpg'

    img = cv.imread(cv.samples.findFile(fn))

    if img is None:

            

Reported by Pylint.

                      cur_mode = modes.next()
        cur_str_mode = str_modes.next()

    def update(dummy=None):
        try: # do not get trackbar position while trackbar is not created
            sz = cv.getTrackbarPos('op/size', 'morphology')
            iters = cv.getTrackbarPos('iters', 'morphology')
        except:
            return

            

Reported by Pylint.

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

        /// Convert image to gray and blur it
        Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;

            

Reported by PMD.

              
class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

            

Reported by PMD.

              
class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

            

Reported by PMD.

              class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);


            

Reported by PMD.

              class GeneralContours1 {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);


            

Reported by PMD.

                  private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

    public GeneralContours1(String[] args) {

            

Reported by PMD.

                  private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

    public GeneralContours1(String[] args) {
        //! [setup]
        /// Load source image

            

Reported by PMD.

                  private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

    public GeneralContours1(String[] args) {
        //! [setup]
        /// Load source image
        String filename = args.length > 0 ? args[0] : "../data/stuff.jpg";

            

Reported by PMD.

              
    public static void imshow(String winname, Mat img) {
        if (img.empty()) {
            System.err.println("Error: Empty image in imshow");
            System.exit(-1);
        } else {
            ImageWindow tmpWindow = windows.get(winname);
            if (tmpWindow == null) {
                ImageWindow newWin = new ImageWindow(winname, img);

            

Reported by PMD.

              
        // If there are no windows to be shown return
        if (windows.isEmpty()) {
            System.err.println("Error: waitKey must be used after an imshow");
            System.exit(-1);
        }

        // Remove the unused windows
        Iterator<Map.Entry<String,

            

Reported by PMD.

                                  win.lbl.setIcon(icon);
                }
            } else {
                System.err.println("Error: no imshow associated with" + " namedWindow: \"" + win.name + "\"");
                System.exit(-1);
            }
        }

        try {

            

Reported by PMD.

               * This class was designed for use in Java applications
 * to recreate the OpenCV HighGui functionalities.
 */
public final class HighGui {

    // Constants for namedWindow
    public final static int WINDOW_NORMAL = ImageWindow.WINDOW_NORMAL;
    public final static int WINDOW_AUTOSIZE = ImageWindow.WINDOW_AUTOSIZE;


            

Reported by PMD.

               * This class was designed for use in Java applications
 * to recreate the OpenCV HighGui functionalities.
 */
public final class HighGui {

    // Constants for namedWindow
    public final static int WINDOW_NORMAL = ImageWindow.WINDOW_NORMAL;
    public final static int WINDOW_AUTOSIZE = ImageWindow.WINDOW_AUTOSIZE;


            

Reported by PMD.

               * This class was designed for use in Java applications
 * to recreate the OpenCV HighGui functionalities.
 */
public final class HighGui {

    // Constants for namedWindow
    public final static int WINDOW_NORMAL = ImageWindow.WINDOW_NORMAL;
    public final static int WINDOW_AUTOSIZE = ImageWindow.WINDOW_AUTOSIZE;


            

Reported by PMD.

               * This class was designed for use in Java applications
 * to recreate the OpenCV HighGui functionalities.
 */
public final class HighGui {

    // Constants for namedWindow
    public final static int WINDOW_NORMAL = ImageWindow.WINDOW_NORMAL;
    public final static int WINDOW_AUTOSIZE = ImageWindow.WINDOW_AUTOSIZE;


            

Reported by PMD.

                  public final static int WINDOW_AUTOSIZE = ImageWindow.WINDOW_AUTOSIZE;

    // Control Variables
    public static int n_closed_windows = 0;
    public static int pressedKey = -1;
    public static CountDownLatch latch = new CountDownLatch(1);

    // Windows Map
    public static Map<String, ImageWindow> windows = new HashMap<String, ImageWindow>();

            

Reported by PMD.

                  public static void imshow(String winname, Mat img) {
        if (img.empty()) {
            System.err.println("Error: Empty image in imshow");
            System.exit(-1);
        } else {
            ImageWindow tmpWindow = windows.get(winname);
            if (tmpWindow == null) {
                ImageWindow newWin = new ImageWindow(winname, img);
                windows.put(winname, newWin);

            

Reported by PMD.

                              ImageWindow newWin = new ImageWindow(winname, img);
                windows.put(winname, newWin);
            } else {
                tmpWindow.setMat(img);
            }
        }
    }

    public static Image toBufferedImage(Mat m) {

            

Reported by PMD.

              # To use Inference Engine backend, specify location of plugins:
# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse

parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '

            

Reported by Pylint.

              # To use Inference Engine backend, specify location of plugins:
# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse

parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '

            

Reported by Pylint.

              # To use Inference Engine backend, specify location of plugins:
# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse

parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '

            

Reported by Pylint.

              # source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse

parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '
                    'The sample and model are simplified and could be used for a single person on the frame.')

            

Reported by Pylint.

              
parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '
                    'The sample and model are simplified and could be used for a single person on the frame.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--proto', help='Path to .prototxt')
parser.add_argument('--model', help='Path to .caffemodel')
parser.add_argument('--dataset', help='Specify what kind of model was trained. '

            

Reported by Pylint.

              parser = argparse.ArgumentParser(
        description='This script is used to demonstrate OpenPose human pose estimation network '
                    'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '
                    'The sample and model are simplified and could be used for a single person on the frame.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--proto', help='Path to .prototxt')
parser.add_argument('--model', help='Path to .caffemodel')
parser.add_argument('--dataset', help='Specify what kind of model was trained. '
                                      'It could be (COCO, MPI, HAND) depends on dataset.')

            

Reported by Pylint.

              parser.add_argument('--model', help='Path to .caffemodel')
parser.add_argument('--dataset', help='Specify what kind of model was trained. '
                                      'It could be (COCO, MPI, HAND) depends on dataset.')
parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
parser.add_argument('--scale', default=0.003922, type=float, help='Scale for blob.')

args = parser.parse_args()

            

Reported by Pylint.

              elif args.dataset == 'HAND':
    BODY_PARTS = { "Wrist": 0,
                   "ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
                   "IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
                   "MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
                   "RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
                   "LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
                 }


            

Reported by Pylint.

                  BODY_PARTS = { "Wrist": 0,
                   "ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
                   "IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
                   "MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
                   "RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
                   "LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
                 }

    POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],

            

Reported by Pylint.

                                 "ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
                   "IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
                   "MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
                   "RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
                   "LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
                 }

    POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],
                   ["ThumbProximal", "ThumbMiddle"], ["ThumbMiddle", "ThumbDistal"],

            

Reported by Pylint.

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

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21

            

Reported by Pylint.

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

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21

            

Reported by Pylint.

              
## [main]
def main(image):
    global src
    src = cv.imread(cv.samples.findFile(image))
    if src is None:
        print('Could not open or find the image: ', image)
        exit(0)


            

Reported by Pylint.

              

## [erosion]
def erosion(val):
    erosion_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_erosion_window)
    erosion_shape = morph_shape(cv.getTrackbarPos(title_trackbar_element_shape, title_erosion_window))

    ## [kernel]
    element = cv.getStructuringElement(erosion_shape, (2 * erosion_size + 1, 2 * erosion_size + 1),

            

Reported by Pylint.

              
## [erosion]
def erosion(val):
    erosion_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_erosion_window)
    erosion_shape = morph_shape(cv.getTrackbarPos(title_trackbar_element_shape, title_erosion_window))

    ## [kernel]
    element = cv.getStructuringElement(erosion_shape, (2 * erosion_size + 1, 2 * erosion_size + 1),
                                       (erosion_size, erosion_size))

            

Reported by Pylint.

              

## [dilation]
def dilatation(val):
    dilatation_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_dilation_window)
    dilation_shape = morph_shape(cv.getTrackbarPos(title_trackbar_element_shape, title_dilation_window))

    element = cv.getStructuringElement(dilation_shape, (2 * dilatation_size + 1, 2 * dilatation_size + 1),
                                       (dilatation_size, dilatation_size))

            

Reported by Pylint.

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

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21

            

Reported by Pylint.

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

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21

            

Reported by Pylint.

              import numpy as np
import argparse

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21
title_trackbar_element_shape = 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse'
title_trackbar_kernel_size = 'Kernel size:\n 2n +1'

            

Reported by Pylint.

              import argparse

src = None
erosion_size = 0
max_elem = 2
max_kernel_size = 21
title_trackbar_element_shape = 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse'
title_trackbar_kernel_size = 'Kernel size:\n 2n +1'
title_erosion_window = 'Erosion Demo'

            

Reported by Pylint.

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


class OpticalFlow(object):
    def __init__(self, proto, model, height, width):
        self.net = cv.dnn.readNetFromCaffe(proto, model)

            

Reported by Pylint.

              

class OpticalFlow(object):
    def __init__(self, proto, model, height, width):
        self.net = cv.dnn.readNetFromCaffe(proto, model)
        self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        self.height = height
        self.width = width


            

Reported by Pylint.

                      inp1 = cv.dnn.blobFromImage(second_img, size=(self.width, self.height))
        self.net.setInput(inp0, "img0")
        self.net.setInput(inp1, "img1")
        flow = self.net.forward()
        output = self.motion_to_color(flow)
        return output

    def motion_to_color(self, flow):
        arr = np.arange(0, 255, dtype=np.uint8)

            

Reported by Pylint.

                      output = self.motion_to_color(flow)
        return output

    def motion_to_color(self, flow):
        arr = np.arange(0, 255, dtype=np.uint8)
        colormap = cv.applyColorMap(arr, cv.COLORMAP_HSV)
        colormap = colormap.squeeze(1)

        flow = flow.squeeze(0)

            

Reported by Pylint.

              Original paper: https://arxiv.org/abs/1612.01925.
Original repo:  https://github.com/lmb-freiburg/flownet2.

Download the converted .caffemodel model from https://drive.google.com/open?id=16qvE9VNmU39NttpZwZs81Ga8VYQJDaWZ
and .prototxt from https://drive.google.com/file/d/1RyNIUsan1ZOh2hpYIH36A-jofAvJlT6a/view?usp=sharing.
Otherwise download original model from https://lmb.informatik.uni-freiburg.de/resources/binaries/flownet2/flownet2-models.tar.gz,
convert .h5 model to .caffemodel and modify original .prototxt using .prototxt from link above.
'''


            

Reported by Pylint.

              Original repo:  https://github.com/lmb-freiburg/flownet2.

Download the converted .caffemodel model from https://drive.google.com/open?id=16qvE9VNmU39NttpZwZs81Ga8VYQJDaWZ
and .prototxt from https://drive.google.com/file/d/1RyNIUsan1ZOh2hpYIH36A-jofAvJlT6a/view?usp=sharing.
Otherwise download original model from https://lmb.informatik.uni-freiburg.de/resources/binaries/flownet2/flownet2-models.tar.gz,
convert .h5 model to .caffemodel and modify original .prototxt using .prototxt from link above.
'''

import argparse

            

Reported by Pylint.

              
Download the converted .caffemodel model from https://drive.google.com/open?id=16qvE9VNmU39NttpZwZs81Ga8VYQJDaWZ
and .prototxt from https://drive.google.com/file/d/1RyNIUsan1ZOh2hpYIH36A-jofAvJlT6a/view?usp=sharing.
Otherwise download original model from https://lmb.informatik.uni-freiburg.de/resources/binaries/flownet2/flownet2-models.tar.gz,
convert .h5 model to .caffemodel and modify original .prototxt using .prototxt from link above.
'''

import argparse
import os.path

            

Reported by Pylint.

              import cv2 as cv


class OpticalFlow(object):
    def __init__(self, proto, model, height, width):
        self.net = cv.dnn.readNetFromCaffe(proto, model)
        self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        self.height = height
        self.width = width

            

Reported by Pylint.

              import cv2 as cv


class OpticalFlow(object):
    def __init__(self, proto, model, height, width):
        self.net = cv.dnn.readNetFromCaffe(proto, model)
        self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        self.height = height
        self.width = width

            

Reported by Pylint.

                      self.height = height
        self.width = width

    def compute_flow(self, first_img, second_img):
        inp0 = cv.dnn.blobFromImage(first_img, size=(self.width, self.height))
        inp1 = cv.dnn.blobFromImage(second_img, size=(self.width, self.height))
        self.net.setInput(inp0, "img0")
        self.net.setInput(inp1, "img1")
        flow = self.net.forward()

            

Reported by Pylint.

              from __future__ import print_function

import numpy as np
import cv2 as cv

import sys

class App():


            

Reported by Pylint.

              
class App():

    def update(self, dummy=None):
        if self.seed_pt is None:
            cv.imshow('floodfill', self.img)
            return
        flooded = self.img.copy()
        self.mask[:] = 0

            

Reported by Pylint.

                      cv.circle(flooded, self.seed_pt, 2, (0, 0, 255), -1)
        cv.imshow('floodfill', flooded)

    def onmouse(self, event, x, y, flags, param):
        if flags & cv.EVENT_FLAG_LBUTTON:
            self.seed_pt = x, y
            self.update()

    def run(self):

            

Reported by Pylint.

                      cv.circle(flooded, self.seed_pt, 2, (0, 0, 255), -1)
        cv.imshow('floodfill', flooded)

    def onmouse(self, event, x, y, flags, param):
        if flags & cv.EVENT_FLAG_LBUTTON:
            self.seed_pt = x, y
            self.update()

    def run(self):

            

Reported by Pylint.

              
    def onmouse(self, event, x, y, flags, param):
        if flags & cv.EVENT_FLAG_LBUTTON:
            self.seed_pt = x, y
            self.update()

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

            

Reported by Pylint.

                  def run(self):
        try:
            fn = sys.argv[1]
        except:
            fn = 'fruits.jpg'

        self.img = cv.imread(cv.samples.findFile(fn))
        if self.img is None:
            print('Failed to load image file:', fn)

            

Reported by Pylint.

                      except:
            fn = 'fruits.jpg'

        self.img = cv.imread(cv.samples.findFile(fn))
        if self.img is None:
            print('Failed to load image file:', fn)
            sys.exit(1)

        h, w = self.img.shape[:2]

            

Reported by Pylint.

                          sys.exit(1)

        h, w = self.img.shape[:2]
        self.mask = np.zeros((h+2, w+2), np.uint8)
        self.seed_pt = None
        self.fixed_range = True
        self.connectivity = 4

        self.update()

            

Reported by Pylint.

              
        h, w = self.img.shape[:2]
        self.mask = np.zeros((h+2, w+2), np.uint8)
        self.seed_pt = None
        self.fixed_range = True
        self.connectivity = 4

        self.update()
        cv.setMouseCallback('floodfill', self.onmouse)

            

Reported by Pylint.

                      h, w = self.img.shape[:2]
        self.mask = np.zeros((h+2, w+2), np.uint8)
        self.seed_pt = None
        self.fixed_range = True
        self.connectivity = 4

        self.update()
        cv.setMouseCallback('floodfill', self.onmouse)
        cv.createTrackbar('lo', 'floodfill', 20, 255, self.update)

            

Reported by Pylint.

              import android.text.TextUtils;
import android.util.Log;

public class HardwareDetector {
    private static String TAG = "OpenCVEngine/HardwareDetector";

    public static final int ARCH_UNKNOWN = -1;

    public static final int ARCH_X86 = 0x01000000;

            

Reported by PMD.

                  // Return CPU flags list
    public static List<String> getFlags() {
        Map<String, String> raw = getRawCpuInfo();
        String f = raw.get("flags");
        if (f == null)
            f = raw.get("Features");
        if (f == null)
            return Arrays.asList();
        return Arrays.asList(TextUtils.split(f, " "));

            

Reported by PMD.

                      Map<String, String> raw = getRawCpuInfo();
        String f = raw.get("flags");
        if (f == null)
            f = raw.get("Features");
        if (f == null)
            return Arrays.asList();
        return Arrays.asList(TextUtils.split(f, " "));
    }


            

Reported by PMD.

                  // Return CPU arch
    public static int getAbi() {
        List<String> abis = Arrays.asList(Build.CPU_ABI, Build.CPU_ABI2);
        Log.i(TAG, "ABIs: " + abis.toString());
        if (abis.contains("x86_64")) {
            return ARCH_X86_64;
        } else if (abis.contains("x86")) {
            return ARCH_X86;
        } else if (abis.contains("arm64-v8a")) {

            

Reported by PMD.

                  public static int getAbi() {
        List<String> abis = Arrays.asList(Build.CPU_ABI, Build.CPU_ABI2);
        Log.i(TAG, "ABIs: " + abis.toString());
        if (abis.contains("x86_64")) {
            return ARCH_X86_64;
        } else if (abis.contains("x86")) {
            return ARCH_X86;
        } else if (abis.contains("arm64-v8a")) {
            return ARCH_ARMv8;

            

Reported by PMD.

                      Log.i(TAG, "ABIs: " + abis.toString());
        if (abis.contains("x86_64")) {
            return ARCH_X86_64;
        } else if (abis.contains("x86")) {
            return ARCH_X86;
        } else if (abis.contains("arm64-v8a")) {
            return ARCH_ARMv8;
        } else if (abis.contains("armeabi-v7a")
                || abis.contains("armeabi-v7a-hard")) {

            

Reported by PMD.

                          return ARCH_X86_64;
        } else if (abis.contains("x86")) {
            return ARCH_X86;
        } else if (abis.contains("arm64-v8a")) {
            return ARCH_ARMv8;
        } else if (abis.contains("armeabi-v7a")
                || abis.contains("armeabi-v7a-hard")) {
            return ARCH_ARMv7;
        } else if (abis.contains("armeabi")) {

            

Reported by PMD.

                          return ARCH_X86;
        } else if (abis.contains("arm64-v8a")) {
            return ARCH_ARMv8;
        } else if (abis.contains("armeabi-v7a")
                || abis.contains("armeabi-v7a-hard")) {
            return ARCH_ARMv7;
        } else if (abis.contains("armeabi")) {
            return ARCH_ARM;
        } else if (abis.contains("mips64")) {

            

Reported by PMD.

                      } else if (abis.contains("arm64-v8a")) {
            return ARCH_ARMv8;
        } else if (abis.contains("armeabi-v7a")
                || abis.contains("armeabi-v7a-hard")) {
            return ARCH_ARMv7;
        } else if (abis.contains("armeabi")) {
            return ARCH_ARM;
        } else if (abis.contains("mips64")) {
            return ARCH_MIPS_64;

            

Reported by PMD.

                      } else if (abis.contains("armeabi-v7a")
                || abis.contains("armeabi-v7a-hard")) {
            return ARCH_ARMv7;
        } else if (abis.contains("armeabi")) {
            return ARCH_ARM;
        } else if (abis.contains("mips64")) {
            return ARCH_MIPS_64;
        } else if (abis.contains("mips")) {
            return ARCH_MIPS;

            

Reported by PMD.

              
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import torch

from .test.configs.test_config import CommonConfig

SEED_VAL = 42

            

Reported by Pylint.

              import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import torch

from .test.configs.test_config import CommonConfig

SEED_VAL = 42
DNN_LIB = "DNN"

            

Reported by Pylint.

              import tensorflow as tf
import torch

from .test.configs.test_config import CommonConfig

SEED_VAL = 42
DNN_LIB = "DNN"
# common path for model savings
MODEL_PATH_ROOT = os.path.join(CommonConfig().output_data_root_dir, "{}/models")

            

Reported by Pylint.

                  "\t* mean {} for the original model: {}\t"
    "\t* mean time (min) for the original model inferences: {}\n"
    "\t* mean {} for the DNN model: {}\t"
    "\t* mean time (min) for the DNN model inferences: {}\n".format(
        metric_name, np.mean(general_quality_metric[:, 0]),
        np.mean(general_inference_time[:, 0]) / 60000,
        metric_name, np.mean(general_quality_metric[:, 1]),
        np.mean(general_inference_time[:, 1]) / 60000,
    )

            

Reported by Pylint.

                  general_quality_metric = np.array(general_quality_metric)
    general_inference_time = np.array(general_inference_time)
    summary_line = "===== End of processing. General results:\n"
    "\t* mean {} for the original model: {}\t"
    "\t* mean time (min) for the original model inferences: {}\n"
    "\t* mean {} for the DNN model: {}\t"
    "\t* mean time (min) for the DNN model inferences: {}\n".format(
        metric_name, np.mean(general_quality_metric[:, 0]),
        np.mean(general_inference_time[:, 0]) / 60000,

            

Reported by Pylint.

                  general_inference_time = np.array(general_inference_time)
    summary_line = "===== End of processing. General results:\n"
    "\t* mean {} for the original model: {}\t"
    "\t* mean time (min) for the original model inferences: {}\n"
    "\t* mean {} for the DNN model: {}\t"
    "\t* mean time (min) for the DNN model inferences: {}\n".format(
        metric_name, np.mean(general_quality_metric[:, 0]),
        np.mean(general_inference_time[:, 0]) / 60000,
        metric_name, np.mean(general_quality_metric[:, 1]),

            

Reported by Pylint.

                  summary_line = "===== End of processing. General results:\n"
    "\t* mean {} for the original model: {}\t"
    "\t* mean time (min) for the original model inferences: {}\n"
    "\t* mean {} for the DNN model: {}\t"
    "\t* mean time (min) for the DNN model inferences: {}\n".format(
        metric_name, np.mean(general_quality_metric[:, 0]),
        np.mean(general_inference_time[:, 0]) / 60000,
        metric_name, np.mean(general_quality_metric[:, 1]),
        np.mean(general_inference_time[:, 1]) / 60000,

            

Reported by Pylint.

                  "\t* mean {} for the original model: {}\t"
    "\t* mean time (min) for the original model inferences: {}\n"
    "\t* mean {} for the DNN model: {}\t"
    "\t* mean time (min) for the DNN model inferences: {}\n".format(
        metric_name, np.mean(general_quality_metric[:, 0]),
        np.mean(general_inference_time[:, 0]) / 60000,
        metric_name, np.mean(general_quality_metric[:, 1]),
        np.mean(general_inference_time[:, 1]) / 60000,
    )

            

Reported by Pylint.

              
def create_parser():
    parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
    parser.add_argument(
        "--test",
        type=str_bool,
        help="Define whether you'd like to run the model with OpenCV for testing.",
        default=False
    ),

            

Reported by Pylint.

                      help="Define whether you'd like to run the model with OpenCV for testing.",
        default=False
    ),
    parser.add_argument(
        "--default_img_preprocess",
        type=str_bool,
        help="Define whether you'd like to preprocess the input image with defined"
             " PyTorch or TF functions for model test with OpenCV.",
        default=False

            

Reported by Pylint.

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

        /// Convert image to gray and blur it
        Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);

            

Reported by PMD.

                      //! [showDrawings]

        /// Calculate the area with the moments 00 and compare with the result of the OpenCV function
        System.out.println("\t Info: Area and Contour Length \n");
        for (int i = 0; i < contours.size(); i++) {
            System.out.format(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f\n", i,
                    mu.get(i).m00, Imgproc.contourArea(contours.get(i)),
                    Imgproc.arcLength(new MatOfPoint2f(contours.get(i).toArray()), true));
        }

            

Reported by PMD.

              import org.opencv.imgproc.Moments;

class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;

            

Reported by PMD.

              import org.opencv.imgproc.Moments;

class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;

            

Reported by PMD.

              
class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

            

Reported by PMD.

              
class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

            

Reported by PMD.

              class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);


            

Reported by PMD.

              class MomentsClass {
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);


            

Reported by PMD.

                  private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

    public MomentsClass(String[] args) {

            

Reported by PMD.

                  private JLabel imgSrcLabel;
    private JLabel imgContoursLabel;
    private static final int MAX_THRESHOLD = 255;
    private int threshold = 100;
    private Random rng = new Random(12345);

    public MomentsClass(String[] args) {
        //! [setup]
        /// Load source image

            

Reported by PMD.