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Merge pull request #14393 from mehlukas:3.4-meanshift
Extend meanshift tutorial (#14393) * copy original tutorial and python code * add cpp code, fix python code * add camshift cpp code, fix bug in meanshift code * add description to ToC page * fix shadowing previous local declaration * fix grammar: with -> within * docs: remove content of old py_meanshift tutorial, add link * docs: replace meanshift tutorial subpage in Python tutorials * switch to ref to fix wrong breadcrumb navigation * switch to cmdline for path as in #14314 * Apply suggestions from code review * order programming languages alphabetically
This commit is contained in:
mehlukas
committed by
Alexander Alekhin
Alexander Alekhin
parent
9fabc3406f
commit
47c45e5bd3
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#include <iostream>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/video.hpp>
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using namespace cv;
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using namespace std;
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int main(int argc, char **argv)
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{
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const string about =
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"This sample demonstrates the camshift algorithm.\n"
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"The example file can be downloaded from:\n"
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" https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4";
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const string keys =
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"{ h help | | print this help message }"
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"{ @image |<none>| path to image file }";
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CommandLineParser parser(argc, argv, keys);
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parser.about(about);
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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string filename = parser.get<string>("@image");
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if (!parser.check())
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{
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parser.printErrors();
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return 0;
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}
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VideoCapture capture(filename);
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if (!capture.isOpened()){
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//error in opening the video input
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cerr << "Unable to open file!" << endl;
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return 0;
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}
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Mat frame, roi, hsv_roi, mask;
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// take first frame of the video
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capture >> frame;
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// setup initial location of window
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Rect track_window(300, 200, 100, 50); // simply hardcoded the values
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// set up the ROI for tracking
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roi = frame(track_window);
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cvtColor(roi, hsv_roi, COLOR_BGR2HSV);
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inRange(hsv_roi, Scalar(0, 60, 32), Scalar(180, 255, 255), mask);
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float range_[] = {0, 180};
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const float* range[] = {range_};
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Mat roi_hist;
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int histSize[] = {180};
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int channels[] = {0};
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calcHist(&hsv_roi, 1, channels, mask, roi_hist, 1, histSize, range);
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normalize(roi_hist, roi_hist, 0, 255, NORM_MINMAX);
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// Setup the termination criteria, either 10 iteration or move by atleast 1 pt
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TermCriteria term_crit(TermCriteria::EPS | TermCriteria::COUNT, 10, 1);
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while(true){
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Mat hsv, dst;
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capture >> frame;
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if (frame.empty())
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break;
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cvtColor(frame, hsv, COLOR_BGR2HSV);
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calcBackProject(&hsv, 1, channels, roi_hist, dst, range);
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// apply camshift to get the new location
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RotatedRect rot_rect = CamShift(dst, track_window, term_crit);
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// Draw it on image
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Point2f points[4];
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rot_rect.points(points);
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for (int i = 0; i < 4; i++)
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line(frame, points[i], points[(i+1)%4], 255, 2);
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imshow("img2", frame);
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int keyboard = waitKey(30);
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if (keyboard == 'q' || keyboard == 27)
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break;
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}
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}
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#include <iostream>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/video.hpp>
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using namespace cv;
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using namespace std;
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int main(int argc, char **argv)
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{
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const string about =
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"This sample demonstrates the meanshift algorithm.\n"
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"The example file can be downloaded from:\n"
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" https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4";
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const string keys =
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"{ h help | | print this help message }"
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"{ @image |<none>| path to image file }";
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CommandLineParser parser(argc, argv, keys);
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parser.about(about);
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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string filename = parser.get<string>("@image");
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if (!parser.check())
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{
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parser.printErrors();
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return 0;
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}
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VideoCapture capture(filename);
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if (!capture.isOpened()){
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//error in opening the video input
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cerr << "Unable to open file!" << endl;
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return 0;
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}
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Mat frame, roi, hsv_roi, mask;
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// take first frame of the video
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capture >> frame;
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// setup initial location of window
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Rect track_window(300, 200, 100, 50); // simply hardcoded the values
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// set up the ROI for tracking
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roi = frame(track_window);
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cvtColor(roi, hsv_roi, COLOR_BGR2HSV);
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inRange(hsv_roi, Scalar(0, 60, 32), Scalar(180, 255, 255), mask);
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float range_[] = {0, 180};
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const float* range[] = {range_};
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Mat roi_hist;
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int histSize[] = {180};
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int channels[] = {0};
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calcHist(&hsv_roi, 1, channels, mask, roi_hist, 1, histSize, range);
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normalize(roi_hist, roi_hist, 0, 255, NORM_MINMAX);
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// Setup the termination criteria, either 10 iteration or move by atleast 1 pt
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TermCriteria term_crit(TermCriteria::EPS | TermCriteria::COUNT, 10, 1);
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while(true){
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Mat hsv, dst;
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capture >> frame;
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if (frame.empty())
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break;
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cvtColor(frame, hsv, COLOR_BGR2HSV);
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calcBackProject(&hsv, 1, channels, roi_hist, dst, range);
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// apply meanshift to get the new location
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meanShift(dst, track_window, term_crit);
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// Draw it on image
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rectangle(frame, track_window, 255, 2);
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imshow("img2", frame);
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int keyboard = waitKey(30);
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if (keyboard == 'q' || keyboard == 27)
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break;
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}
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}
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@@ -0,0 +1,50 @@
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import numpy as np
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import cv2 as cv
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import argparse
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parser = argparse.ArgumentParser(description='This sample demonstrates the camshift algorithm. \
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The example file can be downloaded from: \
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https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
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parser.add_argument('image', type=str, help='path to image file')
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args = parser.parse_args()
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cap = cv.VideoCapture(args.image)
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# take first frame of the video
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ret,frame = cap.read()
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# setup initial location of window
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x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
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track_window = (x, y, w, h)
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# set up the ROI for tracking
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roi = frame[y:y+h, x:x+w]
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hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
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mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
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roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
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cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
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# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
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term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
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while(1):
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ret, frame = cap.read()
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if ret == True:
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hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
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dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
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# apply camshift to get the new location
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ret, track_window = cv.CamShift(dst, track_window, term_crit)
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# Draw it on image
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pts = cv.boxPoints(ret)
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pts = np.int0(pts)
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img2 = cv.polylines(frame,[pts],True, 255,2)
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cv.imshow('img2',img2)
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k = cv.waitKey(30) & 0xff
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if k == 27:
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break
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else:
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break
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@@ -0,0 +1,49 @@
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import numpy as np
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import cv2 as cv
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import argparse
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parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
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The example file can be downloaded from: \
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https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
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parser.add_argument('image', type=str, help='path to image file')
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args = parser.parse_args()
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cap = cv.VideoCapture(args.image)
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# take first frame of the video
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ret,frame = cap.read()
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# setup initial location of window
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x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
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track_window = (x, y, w, h)
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# set up the ROI for tracking
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roi = frame[y:y+h, x:x+w]
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hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
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mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
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roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
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cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
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# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
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term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
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while(1):
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ret, frame = cap.read()
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if ret == True:
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hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
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dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
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# apply meanshift to get the new location
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ret, track_window = cv.meanShift(dst, track_window, term_crit)
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# Draw it on image
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x,y,w,h = track_window
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img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
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cv.imshow('img2',img2)
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k = cv.waitKey(30) & 0xff
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if k == 27:
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break
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else:
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break
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