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

rng.seed(12345)

def thresh_callback(val):

            

Reported by Pylint.

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

rng.seed(12345)

def thresh_callback(val):

            

Reported by Pylint.

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

rng.seed(12345)

def thresh_callback(val):

            

Reported by Pylint.

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

rng.seed(12345)

def thresh_callback(val):

            

Reported by Pylint.

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

rng.seed(12345)

def thresh_callback(val):
    threshold = val

            

Reported by Pylint.

              
rng.seed(12345)

def thresh_callback(val):
    threshold = val

    ## [Canny]
    # Detect edges using Canny
    canny_output = cv.Canny(src_gray, threshold, threshold * 2)

            

Reported by Pylint.

                  ## [findContours]

    # Find the rotated rectangles and ellipses for each contour
    minRect = [None]*len(contours)
    minEllipse = [None]*len(contours)
    for i, c in enumerate(contours):
        minRect[i] = cv.minAreaRect(c)
        if c.shape[0] > 5:
            minEllipse[i] = cv.fitEllipse(c)

            

Reported by Pylint.

              
    # Find the rotated rectangles and ellipses for each contour
    minRect = [None]*len(contours)
    minEllipse = [None]*len(contours)
    for i, c in enumerate(contours):
        minRect[i] = cv.minAreaRect(c)
        if c.shape[0] > 5:
            minEllipse[i] = cv.fitEllipse(c)


            

Reported by Pylint.

                  # Find the rotated rectangles and ellipses for each contour
    minRect = [None]*len(contours)
    minEllipse = [None]*len(contours)
    for i, c in enumerate(contours):
        minRect[i] = cv.minAreaRect(c)
        if c.shape[0] > 5:
            minEllipse[i] = cv.fitEllipse(c)

    # Draw contours + rotated rects + ellipses

            

Reported by Pylint.

                  drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
    ## [zeroMat]
    ## [forContour]
    for i, c in enumerate(contours):
        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
        # contour
        cv.drawContours(drawing, contours, i, color)
        # ellipse
        if c.shape[0] > 5:

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):


            

Reported by Pylint.

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

import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):

            

Reported by Pylint.

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

import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):

            

Reported by Pylint.

              PY3 = sys.version_info[0] == 3

import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):


            

Reported by Pylint.

              import numpy as np
import cv2 as cv

from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):

    def test_morphology(self):


            

Reported by Pylint.

              
from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):

    def test_morphology(self):

        fn = 'samples/data/rubberwhale1.png'
        img = self.get_sample(fn)

            

Reported by Pylint.

              
from tests_common import NewOpenCVTests

class morphology_test(NewOpenCVTests):

    def test_morphology(self):

        fn = 'samples/data/rubberwhale1.png'
        img = self.get_sample(fn)

            

Reported by Pylint.

              
class morphology_test(NewOpenCVTests):

    def test_morphology(self):

        fn = 'samples/data/rubberwhale1.png'
        img = self.get_sample(fn)

        modes = ['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient']

            

Reported by Pylint.

              
    def test_morphology(self):

        fn = 'samples/data/rubberwhale1.png'
        img = self.get_sample(fn)

        modes = ['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient']
        str_modes = ['ellipse', 'rect', 'cross']


            

Reported by Pylint.

                      modes = ['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient']
        str_modes = ['ellipse', 'rect', 'cross']

        referenceHashes = { modes[0]: '071a526425b79e45b4d0d71ef51b0562', modes[1] : '071a526425b79e45b4d0d71ef51b0562',
            modes[2] : '427e89f581b7df1b60a831b1ed4c8618', modes[3] : '0dd8ad251088a63d0dd022bcdc57361c'}

        def update(cur_mode):
            cur_str_mode = str_modes[0]
            sz = 10

            

Reported by Pylint.

                      Mat src = Imgcodecs.imread(filename, img_codec);

        if (src.empty()) {
            System.out.println("Can't open image [" + filename + "]");
            System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
            System.exit(-1);
        }

        HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);

            

Reported by PMD.

              
        if (src.empty()) {
            System.out.println("Can't open image [" + filename + "]");
            System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
            System.exit(-1);
        }

        HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);
        HighGui.namedWindow("Output", HighGui.WINDOW_AUTOSIZE);

            

Reported by PMD.

                      Mat dst0 = sharpen(src, new Mat());

        t = ((double) System.currentTimeMillis() - t) / 1000;
        System.out.println("Hand written function time passed in seconds: " + t);

        HighGui.imshow( "Output", dst0 );
        HighGui.moveWindow("Output", 400, 400);
        HighGui.waitKey();


            

Reported by PMD.

                      Imgproc.filter2D(src, dst1, src.depth(), kern);
        //![filter2D]
        t = ((double) System.currentTimeMillis() - t) / 1000;
        System.out.println("Built-in filter2D time passed in seconds:     " + t);

        HighGui.imshow( "Output", dst1 );

        HighGui.waitKey();
        System.exit(0);

            

Reported by PMD.

                      int img_codec = Imgcodecs.IMREAD_COLOR;
        if (args.length != 0) {
            filename = args[0];
            if (args.length >= 2 && args[1].equals("G"))
                img_codec = Imgcodecs.IMREAD_GRAYSCALE;
        }

        Mat src = Imgcodecs.imread(filename, img_codec);


            

Reported by PMD.

              
        Mat src = Imgcodecs.imread(filename, img_codec);

        if (src.empty()) {
            System.out.println("Can't open image [" + filename + "]");
            System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
            System.exit(-1);
        }


            

Reported by PMD.

                      if (src.empty()) {
            System.out.println("Can't open image [" + filename + "]");
            System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
            System.exit(-1);
        }

        HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);
        HighGui.namedWindow("Output", HighGui.WINDOW_AUTOSIZE);


            

Reported by PMD.

                      }

        HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);
        HighGui.namedWindow("Output", HighGui.WINDOW_AUTOSIZE);

        HighGui.imshow( "Input", src );
        double t = System.currentTimeMillis();

        Mat dst0 = sharpen(src, new Mat());

            

Reported by PMD.

              
        Mat dst1 = new Mat();
        //![filter2D]
        Imgproc.filter2D(src, dst1, src.depth(), kern);
        //![filter2D]
        t = ((double) System.currentTimeMillis() - t) / 1000;
        System.out.println("Built-in filter2D time passed in seconds:     " + t);

        HighGui.imshow( "Output", dst1 );

            

Reported by PMD.

                      HighGui.imshow( "Output", dst1 );

        HighGui.waitKey();
        System.exit(0);
    }

    //! [basic_method]
    public static double saturate(double x) {
        return x > 255.0 ? 255.0 : (x < 0.0 ? 0.0 : x);

            

Reported by PMD.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')
try:
    import cv2 as cv

            

Reported by Pylint.

              try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')
try:
    import cv2 as cv
except ImportError:
    raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

                  raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')
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)')

if __name__ == "__main__":

            

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)')

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")

            

Reported by Pylint.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
    import caffe
except ImportError:
    raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '

            

Reported by Pylint.

              
public class PointTest extends OpenCVTestCase {

    private Point p1;
    private Point p2;

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

            

Reported by PMD.

              public class PointTest extends OpenCVTestCase {

    private Point p1;
    private Point p2;

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


            

Reported by PMD.

                  private Point p1;
    private Point p2;

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

        p1 = new Point(2, 2);
        p2 = new Point(1, 1);

            

Reported by PMD.

                      p2 = new Point(1, 1);
    }

    public void testClone() {
        Point truth = new Point(1, 1);
        Point dstPoint = truth.clone();
        assertEquals(truth, dstPoint);
    }


            

Reported by PMD.

                      assertEquals(truth, dstPoint);
    }

    public void testDot() {
        double result = p1.dot(p2);
        assertEquals(4.0, result);
    }

    public void testEqualsObject() {

            

Reported by PMD.

                      assertEquals(4.0, result);
    }

    public void testEqualsObject() {
        boolean flag = p1.equals(p1);
        assertTrue(flag);

        flag = p1.equals(p2);
        assertFalse(flag);

            

Reported by PMD.

                      assertEquals(4.0, result);
    }

    public void testEqualsObject() {
        boolean flag = p1.equals(p1);
        assertTrue(flag);

        flag = p1.equals(p2);
        assertFalse(flag);

            

Reported by PMD.

                      assertFalse(flag);
    }

    public void testHashCode() {
        assertEquals(p1.hashCode(), p1.hashCode());
    }

    public void testInside() {
        Rect rect = new Rect(0, 0, 5, 3);

            

Reported by PMD.

                      assertEquals(p1.hashCode(), p1.hashCode());
    }

    public void testInside() {
        Rect rect = new Rect(0, 0, 5, 3);
        assertTrue(p1.inside(rect));

        Point p2 = new Point(3, 3);
        assertFalse(p2.inside(rect));

            

Reported by PMD.

                      assertEquals(p1.hashCode(), p1.hashCode());
    }

    public void testInside() {
        Rect rect = new Rect(0, 0, 5, 3);
        assertTrue(p1.inside(rect));

        Point p2 = new Point(3, 3);
        assertFalse(p2.inside(rect));

            

Reported by PMD.

              import java.util.Collections;
import java.util.List;

public class CameraCalibrationActivity extends CameraActivity implements CvCameraViewListener2, OnTouchListener {
    private static final String TAG = "OCVSample::Activity";

    private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;

            

Reported by PMD.

              import java.util.Collections;
import java.util.List;

public class CameraCalibrationActivity extends CameraActivity implements CvCameraViewListener2, OnTouchListener {
    private static final String TAG = "OCVSample::Activity";

    private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;

            

Reported by PMD.

              public class CameraCalibrationActivity extends CameraActivity implements CvCameraViewListener2, OnTouchListener {
    private static final String TAG = "OCVSample::Activity";

    private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;

            

Reported by PMD.

                  private static final String TAG = "OCVSample::Activity";

    private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;


            

Reported by PMD.

              
    private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {

            

Reported by PMD.

                  private CameraBridgeViewBase mOpenCvCameraView;
    private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override

            

Reported by PMD.

                  private CameraCalibrator mCalibrator;
    private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {

            

Reported by PMD.

                  private OnCameraFrameRender mOnCameraFrameRender;
    private Menu mMenu;
    private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {
            switch (status) {

            

Reported by PMD.

                  private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {
            switch (status) {
            case LoaderCallbackInterface.SUCCESS:
            {

            

Reported by PMD.

                  private int mWidth;
    private int mHeight;

    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {
            switch (status) {
            case LoaderCallbackInterface.SUCCESS:
            {

            

Reported by PMD.

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

public class CvTypeTest extends OpenCVTestCase {

    public void testMakeType() {
        assertEquals(CvType.CV_8UC4, CvType.makeType(CvType.CV_8U, 4));
    }


            

Reported by PMD.

              
public class CvTypeTest extends OpenCVTestCase {

    public void testMakeType() {
        assertEquals(CvType.CV_8UC4, CvType.makeType(CvType.CV_8U, 4));
    }

    public void testCV_8UC() {
        assertEquals(CvType.CV_8UC4, CvType.CV_8UC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_8UC4, CvType.makeType(CvType.CV_8U, 4));
    }

    public void testCV_8UC() {
        assertEquals(CvType.CV_8UC4, CvType.CV_8UC(4));
    }

    public void testCV_8SC() {
        assertEquals(CvType.CV_8SC4, CvType.CV_8SC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_8UC4, CvType.CV_8UC(4));
    }

    public void testCV_8SC() {
        assertEquals(CvType.CV_8SC4, CvType.CV_8SC(4));
    }

    public void testCV_16UC() {
        assertEquals(CvType.CV_16UC4, CvType.CV_16UC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_8SC4, CvType.CV_8SC(4));
    }

    public void testCV_16UC() {
        assertEquals(CvType.CV_16UC4, CvType.CV_16UC(4));
    }

    public void testCV_16SC() {
        assertEquals(CvType.CV_16SC4, CvType.CV_16SC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_16UC4, CvType.CV_16UC(4));
    }

    public void testCV_16SC() {
        assertEquals(CvType.CV_16SC4, CvType.CV_16SC(4));
    }

    public void testCV_32SC() {
        assertEquals(CvType.CV_32SC4, CvType.CV_32SC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_16SC4, CvType.CV_16SC(4));
    }

    public void testCV_32SC() {
        assertEquals(CvType.CV_32SC4, CvType.CV_32SC(4));
    }

    public void testCV_32FC() {
        assertEquals(CvType.CV_32FC4, CvType.CV_32FC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_32SC4, CvType.CV_32SC(4));
    }

    public void testCV_32FC() {
        assertEquals(CvType.CV_32FC4, CvType.CV_32FC(4));
    }

    public void testCV_64FC() {
        assertEquals(CvType.CV_64FC4, CvType.CV_64FC(4));

            

Reported by PMD.

                      assertEquals(CvType.CV_32FC4, CvType.CV_32FC(4));
    }

    public void testCV_64FC() {
        assertEquals(CvType.CV_64FC4, CvType.CV_64FC(4));
    }

    public void testCV_16FC() {
        assertEquals(CvType.CV_16FC1, CvType.CV_16FC(1));

            

Reported by PMD.

                      assertEquals(CvType.CV_64FC4, CvType.CV_64FC(4));
    }

    public void testCV_16FC() {
        assertEquals(CvType.CV_16FC1, CvType.CV_16FC(1));
        assertEquals(CvType.CV_16FC2, CvType.CV_16FC(2));
        assertEquals(CvType.CV_16FC3, CvType.CV_16FC(3));
        assertEquals(CvType.CV_16FC4, CvType.CV_16FC(4));
    }

            

Reported by PMD.

              from __future__ import print_function

import numpy as np
import cv2 as cv

def detect(img, cascade):
    rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
                                     flags=cv.CASCADE_SCALE_IMAGE)
    if len(rects) == 0:

            

Reported by Pylint.

                  rects[:,2:] += rects[:,:2]
    return rects

from tests_common import NewOpenCVTests, intersectionRate

class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'

            

Reported by Pylint.

              # Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv

def detect(img, cascade):
    rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
                                     flags=cv.CASCADE_SCALE_IMAGE)

            

Reported by Pylint.

              import numpy as np
import cv2 as cv

def detect(img, cascade):
    rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
                                     flags=cv.CASCADE_SCALE_IMAGE)
    if len(rects) == 0:
        return []
    rects[:,2:] += rects[:,:2]

            

Reported by Pylint.

                  rects[:,2:] += rects[:,:2]
    return rects

from tests_common import NewOpenCVTests, intersectionRate

class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'

            

Reported by Pylint.

              
from tests_common import NewOpenCVTests, intersectionRate

class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'


            

Reported by Pylint.

              
from tests_common import NewOpenCVTests, intersectionRate

class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'


            

Reported by Pylint.

              
from tests_common import NewOpenCVTests, intersectionRate

class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'


            

Reported by Pylint.

              
class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'

        cascade = cv.CascadeClassifier(cascade_fn)
        nested = cv.CascadeClassifier(nested_fn)

            

Reported by Pylint.

              
class facedetect_test(NewOpenCVTests):

    def test_facedetect(self):
        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'

        cascade = cv.CascadeClassifier(cascade_fn)
        nested = cv.CascadeClassifier(nested_fn)

            

Reported by Pylint.

              import cv2
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"

            

Reported by Pylint.

              import cv2
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"

            

Reported by Pylint.

              import cv2
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"

            

Reported by Pylint.

              import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"


            

Reported by Pylint.

              from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"



            

Reported by Pylint.

                  def _set_dnn_model(self):
        if not self._is_ready_to_transfer_graph:
            # get model TF graph
            tf_model_graph = tf.function(lambda x: self._original_model(x))

            tf_model_graph = tf_model_graph.get_concrete_function(
                tf.TensorSpec(self._original_model.inputs[0].shape, self._original_model.inputs[0].dtype))

            # obtain frozen concrete function

            

Reported by Pylint.

              import cv2
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

from ..common.abstract_model import AbstractModel, Framework
from ..common.utils import DNN_LIB, get_full_model_path

CURRENT_LIB = "TF"
MODEL_FORMAT = ".pb"

            

Reported by Pylint.

              MODEL_FORMAT = ".pb"


class TFModelPreparer(AbstractModel):
    """ Class for the preparation of the TF models: original and converted OpenCV Net.

    Args:
        model_name: TF model name
        original_model: TF configured model object or session

            

Reported by Pylint.

                          tf_model_graph = tf.function(lambda x: self._original_model(x))

            tf_model_graph = tf_model_graph.get_concrete_function(
                tf.TensorSpec(self._original_model.inputs[0].shape, self._original_model.inputs[0].dtype))

            # obtain frozen concrete function
            frozen_tf_func = convert_variables_to_constants_v2(tf_model_graph)
            frozen_tf_func.graph.as_graph_def()


            

Reported by Pylint.

              
        return model_paths_dict

    def get_prepared_models(self):
        original_lib_name = CURRENT_LIB + " " + self._model_name
        configured_model_dict = {
            original_lib_name: self._original_model,
            DNN_LIB + " " + self._model_name: self._dnn_model
        }

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')

            

Reported by Pylint.

              import argparse
from common import findFile

parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')


            

Reported by Pylint.

              
parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py

            

Reported by Pylint.

                                               formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):

            

Reported by Pylint.

              parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')

# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):
# -     out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# -     zero_pads = torch.Tensor(out.size(0), planes - out.size(1),

            

Reported by Pylint.

              
# To ONNX export use torch.onnx.export(model, inputs, model_name)

def get_class_names(path):
    class_names = []
    with open(path) as f:
        for row in f:
            class_names.append(row[:-1])
    return class_names

            

Reported by Pylint.

              
def get_class_names(path):
    class_names = []
    with open(path) as f:
        for row in f:
            class_names.append(row[:-1])
    return class_names

def classify_video(video_path, net_path):

            

Reported by Pylint.