import org.opencv.imgproc.Imgproc;
import org.opencv.features2d.Feature2D;

public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {

    DescriptorMatcher matcher;
    int matSize;
    DMatch[] truth;


            

Reported by PMD.

              
public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {

    DescriptorMatcher matcher;
    int matSize;
    DMatch[] truth;

    private Mat getMaskImg() {
        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {

            

Reported by PMD.

              public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {

    DescriptorMatcher matcher;
    int matSize;
    DMatch[] truth;

    private Mat getMaskImg() {
        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
            {

            

Reported by PMD.

              
    DescriptorMatcher matcher;
    int matSize;
    DMatch[] truth;

    private Mat getMaskImg() {
        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
            {
                put(0, 0, 1, 1, 1, 1);

            

Reported by PMD.

              
    private Mat getMaskImg() {
        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
            {
                put(0, 0, 1, 1, 1, 1);
            }
        };
    }


            

Reported by PMD.

                      return cross;
    }

    protected void setUp() throws Exception {
        super.setUp();
        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
        matSize = 100;

        truth = new DMatch[] {

            

Reported by PMD.

                      return cross;
    }

    protected void setUp() throws Exception {
        super.setUp();
        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
        matSize = 100;

        truth = new DMatch[] {

            

Reported by PMD.

                              };
    }

    public void testAdd() {
        matcher.add(Arrays.asList(new Mat()));
        assertFalse(matcher.empty());
    }

    public void testClear() {

            

Reported by PMD.

                      assertFalse(matcher.empty());
    }

    public void testClear() {
        matcher.add(Arrays.asList(new Mat()));

        matcher.clear();

        assertTrue(matcher.empty());

            

Reported by PMD.

                      assertTrue(matcher.empty());
    }

    public void testClone() {
        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
        Mat truth = train.clone();
        matcher.add(Arrays.asList(train));

        DescriptorMatcher cloned = matcher.clone();

            

Reported by PMD.

                          }
            if(mCameraID != null) {
                if (!mCameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) {
                    throw new RuntimeException(
                            "Time out waiting to lock camera opening.");
                }
                Log.i(LOGTAG, "Opening camera: " + mCameraID);
                manager.openCamera(mCameraID, mStateCallback, mBackgroundHandler);
            }

            

Reported by PMD.

                              mCameraDevice = null;
            }
        } catch (InterruptedException e) {
            throw new RuntimeException("Interrupted while trying to lock camera closing.", e);
        } finally {
            mCameraOpenCloseLock.release();
        }
    }


            

Reported by PMD.

                      } catch (CameraAccessException e) {
            Log.e(LOGTAG, "createCameraPreviewSession");
        } catch (InterruptedException e) {
            throw new RuntimeException(
                    "Interrupted while createCameraPreviewSession", e);
        }
        finally {
            //mCameraOpenCloseLock.release();
        }

            

Reported by PMD.

                          createCameraPreviewSession();
        } catch (InterruptedException e) {
            mCameraOpenCloseLock.release();
            throw new RuntimeException("Interrupted while setCameraPreviewSize.", e);
        }
    }
}

            

Reported by PMD.

                  }

    @Override
    protected void setCameraPreviewSize(int width, int height) {
        Log.i(LOGTAG, "setCameraPreviewSize("+width+"x"+height+")");
        if(mMaxCameraWidth  > 0 && mMaxCameraWidth  < width)  width  = mMaxCameraWidth;
        if(mMaxCameraHeight > 0 && mMaxCameraHeight < height) height = mMaxCameraHeight;
        try {
            mCameraOpenCloseLock.acquire();

            

Reported by PMD.

                  }

    @Override
    protected void setCameraPreviewSize(int width, int height) {
        Log.i(LOGTAG, "setCameraPreviewSize("+width+"x"+height+")");
        if(mMaxCameraWidth  > 0 && mMaxCameraWidth  < width)  width  = mMaxCameraWidth;
        if(mMaxCameraHeight > 0 && mMaxCameraHeight < height) height = mMaxCameraHeight;
        try {
            mCameraOpenCloseLock.acquire();

            

Reported by PMD.

              import android.view.Surface;

@TargetApi(21)
public class Camera2Renderer extends CameraGLRendererBase {

    protected final String LOGTAG = "Camera2Renderer";
    private CameraDevice mCameraDevice;
    private CameraCaptureSession mCaptureSession;
    private CaptureRequest.Builder mPreviewRequestBuilder;

            

Reported by PMD.

              import android.view.Surface;

@TargetApi(21)
public class Camera2Renderer extends CameraGLRendererBase {

    protected final String LOGTAG = "Camera2Renderer";
    private CameraDevice mCameraDevice;
    private CameraCaptureSession mCaptureSession;
    private CaptureRequest.Builder mPreviewRequestBuilder;

            

Reported by PMD.

              @TargetApi(21)
public class Camera2Renderer extends CameraGLRendererBase {

    protected final String LOGTAG = "Camera2Renderer";
    private CameraDevice mCameraDevice;
    private CameraCaptureSession mCaptureSession;
    private CaptureRequest.Builder mPreviewRequestBuilder;
    private String mCameraID;
    private Size mPreviewSize = new Size(-1, -1);

            

Reported by PMD.

              @TargetApi(21)
public class Camera2Renderer extends CameraGLRendererBase {

    protected final String LOGTAG = "Camera2Renderer";
    private CameraDevice mCameraDevice;
    private CameraCaptureSession mCaptureSession;
    private CaptureRequest.Builder mPreviewRequestBuilder;
    private String mCameraID;
    private Size mPreviewSize = new Size(-1, -1);

            

Reported by PMD.

              from __future__ import print_function

import numpy as np
import cv2 as cv

def load_base(fn):
    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
    samples, responses = a[:,1:], a[:,0]
    return samples, responses

            

Reported by Pylint.

                  train_ratio = 0.5

    def load(self, fn):
        self.model.load(fn)
    def save(self, fn):
        self.model.save(fn)

    def unroll_samples(self, samples):
        sample_n, var_n = samples.shape

            

Reported by Pylint.

                  def load(self, fn):
        self.model.load(fn)
    def save(self, fn):
        self.model.save(fn)

    def unroll_samples(self, samples):
        sample_n, var_n = samples.shape
        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)

            

Reported by Pylint.

                      _ret, resp = self.model.predict(samples)
        return resp.argmax(-1)

from tests_common import NewOpenCVTests

class letter_recog_test(NewOpenCVTests):

    def test_letter_recog(self):


            

Reported by Pylint.

              
'''
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.

We use the sample database letter-recognition.data
from UCI Repository, here is the link:

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def load_base(fn):
    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
    samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def load_base(fn):
    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
    samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):

            

Reported by Pylint.

              import cv2 as cv

def load_base(fn):
    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
    samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):
    class_n = 26

            

Reported by Pylint.

                  samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):
    class_n = 26
    train_ratio = 0.5

    def load(self, fn):
        self.model.load(fn)

            

Reported by Pylint.

                  samples, responses = a[:,1:], a[:,0]
    return samples, responses

class LetterStatModel(object):
    class_n = 26
    train_ratio = 0.5

    def load(self, fn):
        self.model.load(fn)

            

Reported by Pylint.

                      String filename = args.length > 0 ? args[0] : "../data/building.jpg";
        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.

              import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();

            

Reported by PMD.

              import org.opencv.imgproc.Imgproc;

class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();

            

Reported by PMD.

              
class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();

            

Reported by PMD.

              
class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();

            

Reported by PMD.

              class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();
    private JFrame frame;

            

Reported by PMD.

              class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();
    private JFrame frame;

            

Reported by PMD.

                  private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();
    private JFrame frame;
    private JLabel harrisImgLabel;

            

Reported by PMD.

              from __future__ import print_function

import numpy as np
import cv2 as cv

#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect


            

Reported by Pylint.

              import cv2 as cv

#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect

lk_params = dict( winSize  = (15, 15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))

            

Reported by Pylint.

              
#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect

lk_params = dict( winSize  = (15, 15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))


            

Reported by Pylint.

                          frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)

            if len(self.tracks) > 0:
                img0, img1 = self.prev_gray, frame_gray
                p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
                p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                d = abs(p0-p0r).reshape(-1, 2).max(-1)
                good = d < 1

            

Reported by Pylint.

              
            if len(self.tracks) > 0:
                img0, img1 = self.prev_gray, frame_gray
                p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
                p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                d = abs(p0-p0r).reshape(-1, 2).max(-1)
                good = d < 1
                new_tracks = []

            

Reported by Pylint.

                                  cv.circle(mask, (x, y), 5, 0, -1)
                p = cv.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
                if p is not None:
                    for x, y in np.float32(p).reshape(-1, 2):
                        self.tracks.append([[(x, y), self.frame_idx]])

            self.frame_idx += 1
            self.prev_gray = frame_gray


            

Reported by Pylint.

              
#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect

lk_params = dict( winSize  = (15, 15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))


            

Reported by Pylint.

                                      self.tracks.append([[(x, y), self.frame_idx]])

            self.frame_idx += 1
            self.prev_gray = frame_gray

            if self.frame_idx > 300:
                break



            

Reported by Pylint.

                                     minDistance = 7,
                       blockSize = 7 )

def getRectFromPoints(points):

    distances = []
    for point in points:
        distances.append(cv.norm(point, cv.NORM_L2))


            

Reported by Pylint.

                                     minDistance = 7,
                       blockSize = 7 )

def getRectFromPoints(points):

    distances = []
    for point in points:
        distances.append(cv.norm(point, cv.NORM_L2))


            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              #!/usr/bin/env python

import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - default)", metavar="FMT", default="auto")

            

Reported by Pylint.

              from __future__ import print_function

import numpy as np
import cv2 as cv

import sys

class App():
    BLUE = [255,0,0]        # rectangle color

            

Reported by Pylint.

                  value = DRAW_FG         # drawing initialized to FG
    thickness = 3           # brush thickness

    def onmouse(self, event, x, y, flags, param):
        # Draw Rectangle
        if event == cv.EVENT_RBUTTONDOWN:
            self.rectangle = True
            self.ix, self.iy = x,y


            

Reported by Pylint.

                  value = DRAW_FG         # drawing initialized to FG
    thickness = 3           # brush thickness

    def onmouse(self, event, x, y, flags, param):
        # Draw Rectangle
        if event == cv.EVENT_RBUTTONDOWN:
            self.rectangle = True
            self.ix, self.iy = x,y


            

Reported by Pylint.

                      # Draw Rectangle
        if event == cv.EVENT_RBUTTONDOWN:
            self.rectangle = True
            self.ix, self.iy = x,y

        elif event == cv.EVENT_MOUSEMOVE:
            if self.rectangle == True:
                self.img = self.img2.copy()
                cv.rectangle(self.img, (self.ix, self.iy), (x, y), self.BLUE, 2)

            

Reported by Pylint.

                      # Draw Rectangle
        if event == cv.EVENT_RBUTTONDOWN:
            self.rectangle = True
            self.ix, self.iy = x,y

        elif event == cv.EVENT_MOUSEMOVE:
            if self.rectangle == True:
                self.img = self.img2.copy()
                cv.rectangle(self.img, (self.ix, self.iy), (x, y), self.BLUE, 2)

            

Reported by Pylint.

              
        elif event == cv.EVENT_MOUSEMOVE:
            if self.rectangle == True:
                self.img = self.img2.copy()
                cv.rectangle(self.img, (self.ix, self.iy), (x, y), self.BLUE, 2)
                self.rect = (min(self.ix, x), min(self.iy, y), abs(self.ix - x), abs(self.iy - y))
                self.rect_or_mask = 0

        elif event == cv.EVENT_RBUTTONUP:

            

Reported by Pylint.

                          print("Correct Usage: python grabcut.py <filename> \n")
            filename = 'lena.jpg'

        self.img = cv.imread(cv.samples.findFile(filename))
        self.img2 = self.img.copy()                               # a copy of original image
        self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG
        self.output = np.zeros(self.img.shape, np.uint8)           # output image to be shown

        # input and output windows

            

Reported by Pylint.

                          filename = 'lena.jpg'

        self.img = cv.imread(cv.samples.findFile(filename))
        self.img2 = self.img.copy()                               # a copy of original image
        self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG
        self.output = np.zeros(self.img.shape, np.uint8)           # output image to be shown

        # input and output windows
        cv.namedWindow('output')

            

Reported by Pylint.

              
        self.img = cv.imread(cv.samples.findFile(filename))
        self.img2 = self.img.copy()                               # a copy of original image
        self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG
        self.output = np.zeros(self.img.shape, np.uint8)           # output image to be shown

        # input and output windows
        cv.namedWindow('output')
        cv.namedWindow('input')

            

Reported by Pylint.

                      self.img = cv.imread(cv.samples.findFile(filename))
        self.img2 = self.img.copy()                               # a copy of original image
        self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG
        self.output = np.zeros(self.img.shape, np.uint8)           # output image to be shown

        # input and output windows
        cv.namedWindow('output')
        cv.namedWindow('input')
        cv.setMouseCallback('input', self.onmouse)

            

Reported by Pylint.

              from __future__ import print_function

import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class solvepnp_test(NewOpenCVTests):


            

Reported by Pylint.

              import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class solvepnp_test(NewOpenCVTests):

    def test_regression_16040(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16040_2(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16040_2(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16040_2(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16049(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16049(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                      )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        r = np.array([], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs, reprojectionError=r
        )

    def test_regression_16049(self):
        obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)

            

Reported by Pylint.

                          [[712.0634, 0, 800], [0, 712.540, 500], [0, 0, 1]], dtype=np.float32
        )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs
        )
        if e is None:
            # noArray() is supported, see https://github.com/opencv/opencv/issues/16049
            pass

            

Reported by Pylint.

                          [[712.0634, 0, 800], [0, 712.540, 500], [0, 0, 1]], dtype=np.float32
        )
        distCoeffs = np.array([[0, 0, 0, 0]], dtype=np.float32)
        x, r, t, e = cv.solvePnPGeneric(
            obj_points, img_points, cameraMatrix, distCoeffs
        )
        if e is None:
            # noArray() is supported, see https://github.com/opencv/opencv/issues/16049
            pass

            

Reported by Pylint.

              except ImportError:
    raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel


class NormalizePreproc:

            

Reported by Pylint.

                          result.append(DatasetImageFetch.pix_to_c(c))
        return result

    def __iter__(self):
        return self

    def next(self):
        if self.i < len(self.segm_files):
            segm_file = self.segm_files[self.i]

            

Reported by Pylint.

              import numpy as np
import sys
import os
import fnmatch
import argparse

try:
    import cv2 as cv
except ImportError:

            

Reported by Pylint.

              try:
    import cv2 as cv
except ImportError:
    raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
try:
    import torch
except ImportError:
    raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

            

Reported by Pylint.

              try:
    import torch
except ImportError:
    raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel


            

Reported by Pylint.

                  raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel


class NormalizePreproc:
    def __init__(self):

            

Reported by Pylint.

                  raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel


class NormalizePreproc:
    def __init__(self):

            

Reported by Pylint.

                  raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')

from torch.utils.serialization import load_lua
from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
from imagenet_cls_test_alexnet import Framework, DnnCaffeModel


class NormalizePreproc:
    def __init__(self):

            

Reported by Pylint.

              
    @staticmethod
    def locate(pattern, root_path):
        for path, dirs, files in os.walk(os.path.abspath(root_path)):
            for filename in fnmatch.filter(files, pattern):
                yield os.path.join(path, filename)

    @staticmethod
    def rreplace(s, old, new, occurrence=1):

            

Reported by Pylint.

              class DnnTorchModel(DnnCaffeModel):
    net = cv.dnn.Net()

    def __init__(self, model_file):
        self.net = cv.dnn.readNetFromTorch(model_file)

    def get_output(self, input_blob):
        self.net.setBlob("", input_blob)
        self.net.forward()

            

Reported by Pylint.

              import cv2 as cv
import argparse
import numpy as np

parser = argparse.ArgumentParser(description=
        'Use this script to run Mask-RCNN object detection and semantic '
        'segmentation network from TensorFlow Object Detection API.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to a .pb file with weights.')

            

Reported by Pylint.

                      colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]

legend = None
def showLegend(classes):
    global legend
    if not classes is None and legend is None:
        blockHeight = 30
        assert(len(classes) == len(colors))


            

Reported by Pylint.

              
legend = None
def showLegend(classes):
    global legend
    if not classes is None and legend is None:
        blockHeight = 30
        assert(len(classes) == len(colors))

        legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)

            

Reported by Pylint.

                      assert(len(classes) == len(colors))

        legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
        for i in range(len(classes)):
            block = legend[i * blockHeight:(i + 1) * blockHeight]
            block[:,:] = colors[i]
            cv.putText(block, classes[i], (0, blockHeight//2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))

        cv.namedWindow('Legend', cv.WINDOW_NORMAL)

            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.

                      classes = None


def drawBox(frame, classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))

    label = '%.2f' % conf


            

Reported by Pylint.