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;
        Mat matCorners = new Mat(corners.rows(), 2, CvType.CV_32F);
        float[] matCornersData = new float[(int) (matCorners.total() * matCorners.channels())];

            

Reported by PMD.

                      /// Write them down
        matCorners.get(0, 0, matCornersData);
        for (int i = 0; i < corners.rows(); i++) {
            System.out.println(
                    " -- Refined Corner [" + i + "]  (" + matCornersData[i * 2] + "," + matCornersData[i * 2 + 1] + ")");
        }
    }
}


            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;

            

Reported by PMD.

              
class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;
    private Random rng = new Random(12345);

            

Reported by PMD.

              
class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;
    private Random rng = new Random(12345);

            

Reported by PMD.

              class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;
    private Random rng = new Random(12345);


            

Reported by PMD.

              class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;
    private Random rng = new Random(12345);


            

Reported by PMD.

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

source_window = 'Image'
maxTrackbar = 25
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 = 25
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 = 25
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 = 25
rng.seed(12345)

            

Reported by Pylint.

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

source_window = 'Image'
maxTrackbar = 25
rng.seed(12345)


            

Reported by Pylint.

              import argparse
import random as rng

source_window = 'Image'
maxTrackbar = 25
rng.seed(12345)

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

            

Reported by Pylint.

              import random as rng

source_window = 'Image'
maxTrackbar = 25
rng.seed(12345)

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


            

Reported by Pylint.

              maxTrackbar = 25
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 = 25
rng.seed(12345)

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

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

            

Reported by Pylint.

              # usage:
#     cat clFFT.h | $0
from __future__ import print_function
import sys, re;

from common import remove_comments, getTokens, getParameters, postProcessParameters


try:

            

Reported by Pylint.

                      f = open(sys.argv[1], "r")
    else:
        f = sys.stdin
except:
    sys.exit("ERROR. Can't open input file")

fns = []

while True:

            

Reported by Pylint.

                          nl = nl.strip()
            nl = re.sub(r'\n', r'', nl)
            if len(nl) == 0:
                break;
            line += ' ' + nl

        line = remove_comments(line)

        parts = getTokens(line)

            

Reported by Pylint.

                      fn['ret'] = ret
        fn['calling'] = calling

        name = parts[i]; i += 1;
        fn['name'] = name
        print('name=' + name)

        params = getParameters(i, parts)


            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              from pprint import pprint
pprint(fns)

from common import *

filterFileName='./filter/opencl_clfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)

functionsFilter = generateFilterNames(fns)

            

Reported by Pylint.

              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, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,

            

Reported by Pylint.

                         cv.dnn.DNN_TARGET_HDDL, cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)


def preprocess(image):
    """
    Create 4-dimensional blob from image and flip image
    :param image: input image
    """
    image_rev = np.flip(image, axis=1)

            

Reported by Pylint.

                  :param image: input image
    """
    image_rev = np.flip(image, axis=1)
    input = cv.dnn.blobFromImages([image, image_rev], mean=(104.00698793, 116.66876762, 122.67891434))
    return input


def run_net(input, model_path, backend, target):
    """

            

Reported by Pylint.

                  return input


def run_net(input, model_path, backend, target):
    """
    Read network and infer model
    :param model_path: path to JPPNet model
    :param backend: computation backend
    :param target: computation device

            

Reported by Pylint.

                  # 17 RightLeg
    # 18 LeftShoe
    # 19 RightShoe
    head_output, tail_output = np.split(out, indices_or_sections=[1], axis=0)
    head_output = head_output.squeeze(0)
    tail_output = tail_output.squeeze(0)

    head_output = np.stack([cv.resize(img, dsize=input_shape) for img in head_output[:, ...]])
    tail_output = np.stack([cv.resize(img, dsize=input_shape) for img in tail_output[:, ...]])

            

Reported by Pylint.

                  return segm


def parse_human(image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
    """
    Prepare input for execution, run net and postprocess output to parse human.
    :param image: input image
    :param model_path: path to JPPNet model
    :param backend: name of computation backend

            

Reported by Pylint.

                  :param backend: name of computation backend
    :param target: name of computation target
    """
    input = preprocess(image)
    input_h, input_w = input.shape[2:]
    output = run_net(input, model_path, backend, target)
    grayscale_out = postprocess(output, (input_w, input_h))
    segmentation = decode_labels(grayscale_out)
    return segmentation

            

Reported by Pylint.

                  """
    input = preprocess(image)
    input_h, input_w = input.shape[2:]
    output = run_net(input, model_path, backend, target)
    grayscale_out = postprocess(output, (input_w, input_h))
    segmentation = decode_labels(grayscale_out)
    return segmentation



            

Reported by Pylint.

              #!/usr/bin/env python
'''
You can download the converted pb model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
or convert the model yourself.

Follow these steps if you want to convert the original model yourself:
    To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
    For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
    Change script evaluate_parsing_JPPNet-s2.py for human parsing

            

Reported by Pylint.

              or convert the model yourself.

Follow these steps if you want to convert the original model yourself:
    To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
    For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
    Change script evaluate_parsing_JPPNet-s2.py for human parsing
    1. Remove preprocessing to create image_batch_origin:
        with tf.name_scope("create_inputs"):
        ...

            

Reported by Pylint.

                  public CalcBackProject1(String[] args) {
        //! [Read the image]
        if (args.length != 1) {
            System.err.println("You must supply one argument that corresponds to the path to the image.");
            System.exit(0);
        }

        Mat src = Imgcodecs.imread(args[0]);
        if (src.empty()) {

            

Reported by PMD.

              
        Mat src = Imgcodecs.imread(args[0]);
        if (src.empty()) {
            System.err.println("Empty image: " + args[0]);
            System.exit(0);
        }
        //! [Read the image]

        //! [Transform it to HSV]

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;

            

Reported by PMD.

              
class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;

            

Reported by PMD.

              
class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;

            

Reported by PMD.

              class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;
    private int bins = 25;

            

Reported by PMD.

              class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;
    private int bins = 25;

            

Reported by PMD.

                  private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;
    private int bins = 25;


            

Reported by PMD.

                  private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;
    private int bins = 25;


            

Reported by PMD.

              from __future__ import print_function

import numpy as np
import cv2 as cv

def basicPanoramaStitching(img1Path, img2Path):
    img1 = cv.imread(cv.samples.findFile(img1Path))
    img2 = cv.imread(cv.samples.findFile(img2Path))


            

Reported by Pylint.

              #!/usr/bin/env python
# -*- coding: utf-8 -*-

# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv


            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def basicPanoramaStitching(img1Path, img2Path):
    img1 = cv.imread(cv.samples.findFile(img1Path))
    img2 = cv.imread(cv.samples.findFile(img2Path))

    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def basicPanoramaStitching(img1Path, img2Path):
    img1 = cv.imread(cv.samples.findFile(img1Path))
    img2 = cv.imread(cv.samples.findFile(img2Path))

    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def basicPanoramaStitching(img1Path, img2Path):
    img1 = cv.imread(cv.samples.findFile(img1Path))
    img2 = cv.imread(cv.samples.findFile(img2Path))

    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def basicPanoramaStitching(img1Path, img2Path):
    img1 = cv.imread(cv.samples.findFile(img1Path))
    img2 = cv.imread(cv.samples.findFile(img2Path))

    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],

            

Reported by Pylint.

                  img2 = cv.imread(cv.samples.findFile(img2Path))

    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],
                     [ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
                     [-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
                     [0, 0, 0, 1]],dtype=np.float64)
    # [camera-pose-from-Blender-at-location-1]


            

Reported by Pylint.

              
    # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],
                     [ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
                     [-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
                     [0, 0, 0, 1]],dtype=np.float64)
    # [camera-pose-from-Blender-at-location-1]

    # [camera-pose-from-Blender-at-location-2]

            

Reported by Pylint.

                  # [camera-pose-from-Blender-at-location-1]
    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],
                     [ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
                     [-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
                     [0, 0, 0, 1]],dtype=np.float64)
    # [camera-pose-from-Blender-at-location-1]

    # [camera-pose-from-Blender-at-location-2]
    c2Mo = np.array([[0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112],

            

Reported by Pylint.

                  # [camera-pose-from-Blender-at-location-1]

    # [camera-pose-from-Blender-at-location-2]
    c2Mo = np.array([[0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112],
                     [-0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
                     [0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
                     [0, 0, 0, 1]],dtype=np.float64)
    # [camera-pose-from-Blender-at-location-2]


            

Reported by Pylint.

              from __future__ import print_function

import numpy as np
import cv2 as cv

import os

from tests_common import NewOpenCVTests


            

Reported by Pylint.

              #!/usr/bin/env python
from __future__ import print_function

import numpy as np
import cv2 as cv

import os

from tests_common import NewOpenCVTests

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

import os

from tests_common import NewOpenCVTests


def load_exposure_seq(path):

            

Reported by Pylint.

              from tests_common import NewOpenCVTests


def load_exposure_seq(path):
    images = []
    times = []
    with open(os.path.join(path, 'list.txt'), 'r') as list_file:
        for line in list_file.readlines():
            name, time = line.split()

            

Reported by Pylint.

                  return images, times


class UMat(NewOpenCVTests):

    def test_umat_construct(self):
        data = np.random.random([512, 512])
        # UMat constructors
        data_um = cv.UMat(data)  # from ndarray

            

Reported by Pylint.

              
class UMat(NewOpenCVTests):

    def test_umat_construct(self):
        data = np.random.random([512, 512])
        # UMat constructors
        data_um = cv.UMat(data)  # from ndarray
        data_sub_um = cv.UMat(data_um, (128, 256), (128, 256))  # from UMat
        data_dst_um = cv.UMat(128, 128, cv.CV_64F)  # from size/type

            

Reported by Pylint.

              
class UMat(NewOpenCVTests):

    def test_umat_construct(self):
        data = np.random.random([512, 512])
        # UMat constructors
        data_um = cv.UMat(data)  # from ndarray
        data_sub_um = cv.UMat(data_um, (128, 256), (128, 256))  # from UMat
        data_dst_um = cv.UMat(128, 128, cv.CV_64F)  # from size/type

            

Reported by Pylint.

                      data_sub_um = cv.UMat(data_um, (128, 256), (128, 256))  # from UMat
        data_dst_um = cv.UMat(128, 128, cv.CV_64F)  # from size/type
        # test continuous and submatrix flags
        assert data_um.isContinuous() and not data_um.isSubmatrix()
        assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
        # test operation on submatrix
        cv.multiply(data_sub_um, 2., dst=data_dst_um)
        assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())


            

Reported by Bandit.

                      data_dst_um = cv.UMat(128, 128, cv.CV_64F)  # from size/type
        # test continuous and submatrix flags
        assert data_um.isContinuous() and not data_um.isSubmatrix()
        assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
        # test operation on submatrix
        cv.multiply(data_sub_um, 2., dst=data_dst_um)
        assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())

    def test_umat_handle(self):

            

Reported by Bandit.

                      assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
        # test operation on submatrix
        cv.multiply(data_sub_um, 2., dst=data_dst_um)
        assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())

    def test_umat_handle(self):
        a_um = cv.UMat(256, 256, cv.CV_32F)
        _ctx_handle = cv.UMat.context()  # obtain context handle
        _queue_handle = cv.UMat.queue()  # obtain queue handle

            

Reported by Bandit.

                  xrange = range

import numpy as np
import cv2 as cv

from numpy import random

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

            

Reported by Pylint.

              #!/usr/bin/env python

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

if PY3:
    xrange = range

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

if PY3:
    xrange = range

import numpy as np
import cv2 as cv

from numpy import random

            

Reported by Pylint.

              if PY3:
    xrange = range

import numpy as np
import cv2 as cv

from numpy import random

def make_gaussians(cluster_n, img_size):

            

Reported by Pylint.

                  xrange = range

import numpy as np
import cv2 as cv

from numpy import random

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

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

from numpy import random

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

            

Reported by Pylint.

              
from numpy import random

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

            

Reported by Pylint.

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

            

Reported by Pylint.

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

            

Reported by Pylint.

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

def draw_gaussain(img, mean, cov, color):
    x, y = mean
    w, u, _vt = cv.SVDecomp(cov)
    ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
    s1, s2 = np.sqrt(w)*3.0
    cv.ellipse(img, (int(x), int(y)), (int(s1), int(s2)), ang, 0, 360, color, 1, cv.LINE_AA)

            

Reported by Pylint.

              #
# Tested on COCO 2017 object detection dataset, http://cocodataset.org/#download
import os
import cv2 as cv
import json
import argparse

parser = argparse.ArgumentParser(
    description='Evaluate MobileNet-SSD model using both TensorFlow and OpenCV. '

            

Reported by Pylint.

                  json.dump(detections, f)

### Get TensorFlow predictions #################################################
import tensorflow as tf

with tf.gfile.FastGFile(args.weights) as f:
    # Load the model
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())

            

Reported by Pylint.

              
# %matplotlib inline
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)

            

Reported by Pylint.

              # %matplotlib inline
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)


            

Reported by Pylint.

              from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)

annType = ['segm','bbox','keypoints']
annType = annType[1]      #specify type here

            

Reported by Pylint.

              ### Evaluation part ############################################################

# %matplotlib inline
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab

            

Reported by Pylint.

              import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)

annType = ['segm','bbox','keypoints']

            

Reported by Pylint.

              from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)

annType = ['segm','bbox','keypoints']
annType = annType[1]      #specify type here

            

Reported by Pylint.

              from __future__ import print_function
# Script to evaluate MobileNet-SSD object detection model trained in TensorFlow
# using both TensorFlow and OpenCV. Example:
#
# python mobilenet_ssd_accuracy.py \
#   --weights=frozen_inference_graph.pb \
#   --prototxt=ssd_mobilenet_v1_coco.pbtxt \
#   --images=val2017 \
#   --annotations=annotations/instances_val2017.json

            

Reported by Pylint.

              # Tested on COCO 2017 object detection dataset, http://cocodataset.org/#download
import os
import cv2 as cv
import json
import argparse

parser = argparse.ArgumentParser(
    description='Evaluate MobileNet-SSD model using both TensorFlow and OpenCV. '
                'COCO evaluation framework is required: http://cocodataset.org')

            

Reported by Pylint.

              import org.opencv.core.Scalar;
import org.opencv.test.OpenCVTestCase;

public class ScalarTest extends OpenCVTestCase {

    private Scalar dstScalar;
    private Scalar s1;
    private Scalar s2;


            

Reported by PMD.

              
public class ScalarTest extends OpenCVTestCase {

    private Scalar dstScalar;
    private Scalar s1;
    private Scalar s2;

    @Override
    protected void setUp() throws Exception {

            

Reported by PMD.

              public class ScalarTest extends OpenCVTestCase {

    private Scalar dstScalar;
    private Scalar s1;
    private Scalar s2;

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

            

Reported by PMD.

              
    private Scalar dstScalar;
    private Scalar s1;
    private Scalar s2;

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


            

Reported by PMD.

                  private Scalar s1;
    private Scalar s2;

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

        s1 = new Scalar(1.0);
        s2 = Scalar.all(1.0);

            

Reported by PMD.

              
        s1 = new Scalar(1.0);
        s2 = Scalar.all(1.0);
        dstScalar = null;
    }

    public void testAll() {
        dstScalar = Scalar.all(2.0);
        Scalar truth = new Scalar(2.0, 2.0, 2.0, 2.0);

            

Reported by PMD.

                      dstScalar = null;
    }

    public void testAll() {
        dstScalar = Scalar.all(2.0);
        Scalar truth = new Scalar(2.0, 2.0, 2.0, 2.0);
        assertEquals(truth, dstScalar);
    }


            

Reported by PMD.

                      assertEquals(truth, dstScalar);
    }

    public void testClone() {
        dstScalar = s2.clone();
        assertEquals(s2, dstScalar);
    }

    public void testConj() {

            

Reported by PMD.

                      assertEquals(s2, dstScalar);
    }

    public void testConj() {
        dstScalar = s2.conj();
        Scalar truth = new Scalar(1, -1, -1, -1);
        assertEquals(truth, dstScalar);
    }


            

Reported by PMD.

                      assertEquals(truth, dstScalar);
    }

    public void testEqualsObject() {
        dstScalar = s2.clone();
        assertTrue(s2.equals(dstScalar));

        assertFalse(s2.equals(s1));
    }

            

Reported by PMD.